From edbfa6befa3609be2b97308af30c7f5dc77dfce7 Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Wed, 13 Sep 2023 04:21:21 +0200
Subject: [PATCH 01/13] ordinal model with cumulative link notebook

---
 docs/notebooks/ordinal_regression.ipynb | 559 ++++++++++++++++++++++++
 1 file changed, 559 insertions(+)
 create mode 100644 docs/notebooks/ordinal_regression.ipynb

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
new file mode 100644
index 000000000..61e8d53ba
--- /dev/null
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -0,0 +1,559 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import bambi as bmb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ordinal Regression\n",
+    "\n",
+    "- section on changing the default prior of cutpoints."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cumulative link model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trolly = pd.read_csv(\"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/Trolley.csv\", sep=\";\")\n",
+    "cols = [\"response\", \"action\", \"intention\", \"contact\"]\n",
+    "trolly = trolly[cols]\n",
+    "trolly[\"action\"] = pd.Categorical(trolly[\"action\"], ordered=False)\n",
+    "trolly[\"intention\"] = pd.Categorical(trolly[\"intention\"], ordered=False)\n",
+    "trolly[\"contact\"] = pd.Categorical(trolly[\"contact\"], ordered=False)\n",
+    "trolly[\"response\"] = pd.Categorical(trolly[\"response\"], ordered=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 3, 5, 2, 1, 7, 6]\n",
+       "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trolly.response.unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Intercept only model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = bmb.Model(\"response ~ 1\", data=trolly, family=\"cumulative\")\n",
+    "idata = model.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>response_threshold[0]</th>\n",
+       "      <td>-1.923</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>-1.978</td>\n",
+       "      <td>-1.868</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3964.0</td>\n",
+       "      <td>2831.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>response_threshold[1]</th>\n",
+       "      <td>-1.269</td>\n",
+       "      <td>0.024</td>\n",
+       "      <td>-1.313</td>\n",
+       "      <td>-1.226</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4710.0</td>\n",
+       "      <td>3680.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>response_threshold[2]</th>\n",
+       "      <td>-0.719</td>\n",
+       "      <td>0.021</td>\n",
+       "      <td>-0.759</td>\n",
+       "      <td>-0.679</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4719.0</td>\n",
+       "      <td>2947.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>response_threshold[3]</th>\n",
+       "      <td>0.249</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>0.213</td>\n",
+       "      <td>0.288</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4610.0</td>\n",
+       "      <td>3557.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>response_threshold[4]</th>\n",
+       "      <td>0.893</td>\n",
+       "      <td>0.022</td>\n",
+       "      <td>0.852</td>\n",
+       "      <td>0.934</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5110.0</td>\n",
+       "      <td>3338.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>response_threshold[5]</th>\n",
+       "      <td>1.776</td>\n",
+       "      <td>0.028</td>\n",
+       "      <td>1.721</td>\n",
+       "      <td>1.826</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4841.0</td>\n",
+       "      <td>3760.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
+       "response_threshold[0] -1.923  0.030  -1.978   -1.868        0.0      0.0  \\\n",
+       "response_threshold[1] -1.269  0.024  -1.313   -1.226        0.0      0.0   \n",
+       "response_threshold[2] -0.719  0.021  -0.759   -0.679        0.0      0.0   \n",
+       "response_threshold[3]  0.249  0.020   0.213    0.288        0.0      0.0   \n",
+       "response_threshold[4]  0.893  0.022   0.852    0.934        0.0      0.0   \n",
+       "response_threshold[5]  1.776  0.028   1.721    1.826        0.0      0.0   \n",
+       "\n",
+       "                       ess_bulk  ess_tail  r_hat  \n",
+       "response_threshold[0]    3964.0    2831.0    1.0  \n",
+       "response_threshold[1]    4710.0    3680.0    1.0  \n",
+       "response_threshold[2]    4719.0    2947.0    1.0  \n",
+       "response_threshold[3]    4610.0    3557.0    1.0  \n",
+       "response_threshold[4]    5110.0    3338.0    1.0  \n",
+       "response_threshold[5]    4841.0    3760.0    1.0  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(idata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the the cumalative link is used, we need to apply the logistic sigmoid function to transform back to cumulative probabilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expit_func = lambda x: 1 / (1 + np.exp(-x))\n",
+    "cumprobs = expit_func(idata.posterior.response_threshold).mean((\"chain\", \"draw\"))\n",
+    "cumprobs = np.append(cumprobs, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 3))\n",
+    "plt.plot(sorted(trolly.response.unique()), cumprobs, marker='o')\n",
+    "plt.ylabel(\"Cumulative probability\")\n",
+    "plt.xlabel(\"Response category\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The intercept only model above..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding predictors\n",
+    "\n",
+    "\n",
+    "To include predictor variables, we define the log-cumulative-odds of each response $k$ as a sum of its intercept $\\alpha_k$ and a linear model $\\phi$ where $\\phi_i = \\beta x_i$\n",
+    "\n",
+    "$$\\text{log} \\frac{Pr(y_i \\le k)}{1 - Pr(y_i \\le k)} = \\alpha_k - \\phi_i$$\n",
+    "\n",
+    "The linear model $\\phi$ is subtracted from each intercept because if we decrease the log-cumulative- odds of every outcome value $k$ below the maximum, this shifts probability mass upwards towards higher outcome values. Thus, positive $\\beta$ values correspond to increasing $x$, which is associated with an increase in the mean $y$.\n",
+    "\n",
+    "To add predictors for ordinal models in Bambi, we continue to use the familiar syntax."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
+      "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [response_threshold, action, intention, contact, action:intention, contact:intention]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 01:19&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 79 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = bmb.Model(\n",
+    "    \"response ~ action + intention + contact + action:intention + contact:intention\", \n",
+    "    data=trolly, \n",
+    "    family=\"cumulative\"\n",
+    ")\n",
+    "idata = model.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>action[1]</th>\n",
+       "      <td>-0.465</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>-0.567</td>\n",
+       "      <td>-0.363</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>2617.0</td>\n",
+       "      <td>2808.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>intention[1]</th>\n",
+       "      <td>-0.274</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>-0.385</td>\n",
+       "      <td>-0.168</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>2436.0</td>\n",
+       "      <td>2892.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>contact[1]</th>\n",
+       "      <td>-0.323</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>-0.448</td>\n",
+       "      <td>-0.192</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>2633.0</td>\n",
+       "      <td>3029.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>action:intention[1, 1]</th>\n",
+       "      <td>-0.456</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>-0.604</td>\n",
+       "      <td>-0.301</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>2666.0</td>\n",
+       "      <td>2890.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>contact:intention[1, 1]</th>\n",
+       "      <td>-1.286</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>-1.479</td>\n",
+       "      <td>-1.107</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>2676.0</td>\n",
+       "      <td>3062.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
+       "action[1]               -0.465  0.055  -0.567   -0.363      0.001    0.001  \\\n",
+       "intention[1]            -0.274  0.058  -0.385   -0.168      0.001    0.001   \n",
+       "contact[1]              -0.323  0.069  -0.448   -0.192      0.001    0.001   \n",
+       "action:intention[1, 1]  -0.456  0.082  -0.604   -0.301      0.002    0.001   \n",
+       "contact:intention[1, 1] -1.286  0.100  -1.479   -1.107      0.002    0.001   \n",
+       "\n",
+       "                         ess_bulk  ess_tail  r_hat  \n",
+       "action[1]                  2617.0    2808.0    1.0  \n",
+       "intention[1]               2436.0    2892.0    1.0  \n",
+       "contact[1]                 2633.0    3029.0    1.0  \n",
+       "action:intention[1, 1]     2666.0    2890.0    1.0  \n",
+       "contact:intention[1, 1]    2676.0    3062.0    1.0  "
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(\n",
+    "    idata, \n",
+    "    var_names=[\"action\", \"intention\", \"contact\", \n",
+    "               \"action:intention\", \"contact:intention\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_forest(\n",
+    "    idata,\n",
+    "    combined=True,\n",
+    "    var_names=[\"action\", \"intention\", \"contact\", \n",
+    "               \"action:intention\", \"contact:intention\"],\n",
+    "    figsize=(7, 3),\n",
+    "    textsize=11\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bambinos",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 6d89eb4f883d60647241f3d73d4f6006a7dbd82d Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Wed, 13 Sep 2023 18:39:32 +0200
Subject: [PATCH 02/13] ordinal model with cumulative link function

ordinal models (cumulative and sratio)
---
 docs/notebooks/gallery.yml                    |   4 +
 docs/notebooks/ordinal_regression.ipynb       | 497 ++++++++++++++----
 .../thumbnails/ordinal_regression.png         | Bin 0 -> 26915 bytes
 3 files changed, 393 insertions(+), 108 deletions(-)
 create mode 100644 docs/notebooks/thumbnails/ordinal_regression.png

diff --git a/docs/notebooks/gallery.yml b/docs/notebooks/gallery.yml
index 3d4c5a36b..ae157ebb5 100644
--- a/docs/notebooks/gallery.yml
+++ b/docs/notebooks/gallery.yml
@@ -88,6 +88,10 @@
       subtitle: When the outcome is mostly zeros and or is overdispersed
       href: zero_inflated_regression.ipynb
       thumbnail: thumbnails/zero_inflated_pps.png
+    - title: Ordinal regression
+      subtitle: Model ordered category outcomes
+      href: ordinal_regression.ipynb
+      thumbnail: thumbnails/ordinal_regression.png
 - category: More advanced models
   description: ""
   tiles:
diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index 61e8d53ba..9913f843a 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,7 +11,10 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
-    "import bambi as bmb"
+    "import bambi as bmb\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
    ]
   },
   {
@@ -20,25 +23,57 @@
    "source": [
     "# Ordinal Regression\n",
     "\n",
-    "- section on changing the default prior of cutpoints."
+    "In some scenarios, the response variable is discrete, like a count, and ordered. For example, a rating from 1 to 5. The result is a set of **ordered categories**. Ordered category data presents three challenges when modelling:\n",
+    "\n",
+    "1. Unlike a count, the differences in the values are not necessarily equidistant. For example, it may be much harder for a restuarant to go from 4 to 5 stars than from 2 to 3 stars. \n",
+    "2. The distribution of ordinal responses may be nonnormal, particularly if very low or high values are infrequently chosen.\n",
+    "3. The variances of the unobserved variables that underlie the observed ordered category may differ between the category, time points, etc. \n",
+    "\n",
+    "Thus, treating ordered categories as continuous is not appropriate. To this extent, Bambi supports two classes of ordinal regression models: (1) cumulative link, and (2) sequential link. Below, it is demonstrated how to fit these two models using Bambi to overcome the challenges of ordered category response data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cumulative link model\n",
+    "\n",
+    "In principle, an ordered categorical response is a multinomial prediction problem. However, the constraint that the categories are *ordered* requires a different approach. Ideally, what we would like is for any predictor variable, as it increases, predictions are moved progressively (increased) through the categories in sequence. \n",
+    "\n",
+    "To achieve this, a cumulative link function is used. A cumulative model assumes that the observed ordinal category $Y$ is generated from an underlying latent continuous variable $\\hat{Y}$, which is then mapped to the observed category $Y$ via a set of cutpoints $\\tau$. For example, the model assumes $K$ cutpoints $\\tau_{k}$ that partition $\\hat{Y}$ into $K+1$ observable, ordered categories.\n",
+    "\n",
+    "By linking a linear model to a cumulative probability, it is possible to guarantee the ordering of outcomes. The cumulative probability of an ordered category is the probability of that value or *any smaller value*. Building off of the rating example above, the probability of a 4 is the probability of 4, 3, 2, and 1.\n",
+    "\n",
+    "The log-cumulative-odds that a response value $y_i$ is equal to or less than some possible outcome value $k$ is given by:\n",
+    "\n",
+    "$$\\text{log} \\frac{Pr(y_i \\le k)}{1 - Pr(y_i \\le k)} = \\alpha_k$$\n",
+    "\n",
+    "where $\\alpha_k$ is the intercept of outcome $k$. Each intercept $\\alpha_k$ implies a cumulative probability for each $k$. Since the largest response value always has a cumulative probability of 1, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 5$ possible response values, we only need $K − 1 = 4$ intercepts."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Cumulative link model"
+    "### The moral intuition dataset\n",
+    "\n",
+    "To illustrate an ordinal model with a cumulative link function, we will model data from a series of experiments conducted by philsophers (this example comes from Richard McElreath's [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)). The experiments aim to collect empirical evidence relevant to debates about moral intuition, the forms of reasoning through which people develop judgments about the moral goodness and badness of actions. \n",
+    "\n",
+    "In the dataset there are 12 columns and 9930 rows, comprising data for 331 unique individuals. The response we are interested in `response`, is an integer from 1 to 7 indicating how morally permissible the participant found the action to be taken (or not) in the story. The predictors are as follows:\n",
+    "\n",
+    "- `action`: a factor with levels 0 and 1 where 1 indicates that the story contained \"harm caused by action is morally worse than equivalent harm caused by omission\".\n",
+    "- `intention`: a factor with levels 0 and 1 where 1 indicates that the story contained \"harm intended as the means to a goal is morally worse than equivalent harm foreseen as the side effect of a goal\".\n",
+    "- `contact`: a factor with levels 0 and 1 where 1 indicates that the story contained \"using physical contact to cause harm to a victim is morally worse than causing equivalent harm to a victim without using physical contact\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "trolly = pd.read_csv(\"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/Trolley.csv\", sep=\";\")\n",
-    "cols = [\"response\", \"action\", \"intention\", \"contact\"]\n",
-    "trolly = trolly[cols]\n",
+    "trolly = trolly[[\"response\", \"action\", \"intention\", \"contact\"]]\n",
     "trolly[\"action\"] = pd.Categorical(trolly[\"action\"], ordered=False)\n",
     "trolly[\"intention\"] = pd.Categorical(trolly[\"intention\"], ordered=False)\n",
     "trolly[\"contact\"] = pd.Categorical(trolly[\"contact\"], ordered=False)\n",
@@ -47,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -57,12 +92,13 @@
        "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# 7 ordered categories from 1-7\n",
     "trolly.response.unique()"
    ]
   },
@@ -70,14 +106,123 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Intercept only model"
+    "### Intercept only model\n",
+    "\n",
+    "Before we fit a model with predictors, let's attempt to recover the parameters of an ordered distribution with an intercepts only model to get a feel for the cumulative link function. First, to compare the intercepts only model, we compute the empirical log-cumulative-odds of the categories directly from the data below. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_32825/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "  logit_func = lambda x: np.log(x / (1 - x))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.91609116, -1.26660559, -0.718634  ,  0.24778573,  0.88986365,\n",
+       "        1.76938091,         nan])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pr_k = trolly.response.value_counts().sort_index().values / trolly.shape[0]\n",
+    "cum_pr_k = np.cumsum(pr_k)\n",
+    "logit_func = lambda x: np.log(x / (1 - x))\n",
+    "cum_logit = logit_func(cum_pr_k)\n",
+    "cum_logit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
+      "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [response_threshold]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
+     ]
+    }
+   ],
    "source": [
     "model = bmb.Model(\"response ~ 1\", data=trolly, family=\"cumulative\")\n",
     "idata = model.fit()"
@@ -85,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -125,72 +270,72 @@
        "      <th>response_threshold[0]</th>\n",
        "      <td>-1.923</td>\n",
        "      <td>0.030</td>\n",
-       "      <td>-1.978</td>\n",
-       "      <td>-1.868</td>\n",
+       "      <td>-1.981</td>\n",
+       "      <td>-1.869</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3964.0</td>\n",
-       "      <td>2831.0</td>\n",
+       "      <td>4206.0</td>\n",
+       "      <td>3065.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[1]</th>\n",
-       "      <td>-1.269</td>\n",
+       "      <td>-1.270</td>\n",
        "      <td>0.024</td>\n",
-       "      <td>-1.313</td>\n",
-       "      <td>-1.226</td>\n",
+       "      <td>-1.314</td>\n",
+       "      <td>-1.225</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4710.0</td>\n",
-       "      <td>3680.0</td>\n",
+       "      <td>5095.0</td>\n",
+       "      <td>3329.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[2]</th>\n",
        "      <td>-0.719</td>\n",
        "      <td>0.021</td>\n",
-       "      <td>-0.759</td>\n",
-       "      <td>-0.679</td>\n",
+       "      <td>-0.760</td>\n",
+       "      <td>-0.681</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4719.0</td>\n",
-       "      <td>2947.0</td>\n",
+       "      <td>5042.0</td>\n",
+       "      <td>3302.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[3]</th>\n",
-       "      <td>0.249</td>\n",
+       "      <td>0.248</td>\n",
        "      <td>0.020</td>\n",
-       "      <td>0.213</td>\n",
-       "      <td>0.288</td>\n",
+       "      <td>0.212</td>\n",
+       "      <td>0.286</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4610.0</td>\n",
-       "      <td>3557.0</td>\n",
+       "      <td>4812.0</td>\n",
+       "      <td>3272.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[4]</th>\n",
-       "      <td>0.893</td>\n",
+       "      <td>0.892</td>\n",
        "      <td>0.022</td>\n",
-       "      <td>0.852</td>\n",
-       "      <td>0.934</td>\n",
+       "      <td>0.851</td>\n",
+       "      <td>0.933</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5110.0</td>\n",
-       "      <td>3338.0</td>\n",
+       "      <td>4978.0</td>\n",
+       "      <td>3397.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[5]</th>\n",
-       "      <td>1.776</td>\n",
+       "      <td>1.775</td>\n",
        "      <td>0.028</td>\n",
        "      <td>1.721</td>\n",
-       "      <td>1.826</td>\n",
+       "      <td>1.827</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4841.0</td>\n",
-       "      <td>3760.0</td>\n",
+       "      <td>5575.0</td>\n",
+       "      <td>3372.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -199,23 +344,23 @@
       ],
       "text/plain": [
        "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "response_threshold[0] -1.923  0.030  -1.978   -1.868        0.0      0.0  \\\n",
-       "response_threshold[1] -1.269  0.024  -1.313   -1.226        0.0      0.0   \n",
-       "response_threshold[2] -0.719  0.021  -0.759   -0.679        0.0      0.0   \n",
-       "response_threshold[3]  0.249  0.020   0.213    0.288        0.0      0.0   \n",
-       "response_threshold[4]  0.893  0.022   0.852    0.934        0.0      0.0   \n",
-       "response_threshold[5]  1.776  0.028   1.721    1.826        0.0      0.0   \n",
+       "response_threshold[0] -1.923  0.030  -1.981   -1.869        0.0      0.0  \\\n",
+       "response_threshold[1] -1.270  0.024  -1.314   -1.225        0.0      0.0   \n",
+       "response_threshold[2] -0.719  0.021  -0.760   -0.681        0.0      0.0   \n",
+       "response_threshold[3]  0.248  0.020   0.212    0.286        0.0      0.0   \n",
+       "response_threshold[4]  0.892  0.022   0.851    0.933        0.0      0.0   \n",
+       "response_threshold[5]  1.775  0.028   1.721    1.827        0.0      0.0   \n",
        "\n",
        "                       ess_bulk  ess_tail  r_hat  \n",
-       "response_threshold[0]    3964.0    2831.0    1.0  \n",
-       "response_threshold[1]    4710.0    3680.0    1.0  \n",
-       "response_threshold[2]    4719.0    2947.0    1.0  \n",
-       "response_threshold[3]    4610.0    3557.0    1.0  \n",
-       "response_threshold[4]    5110.0    3338.0    1.0  \n",
-       "response_threshold[5]    4841.0    3760.0    1.0  "
+       "response_threshold[0]    4206.0    3065.0    1.0  \n",
+       "response_threshold[1]    5095.0    3329.0    1.0  \n",
+       "response_threshold[2]    5042.0    3302.0    1.0  \n",
+       "response_threshold[3]    4812.0    3272.0    1.0  \n",
+       "response_threshold[4]    4978.0    3397.0    1.0  \n",
+       "response_threshold[5]    5575.0    3372.0    1.0  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -228,28 +373,46 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As the the cumalative link is used, we need to apply the logistic sigmoid function to transform back to cumulative probabilities."
+    "Viewing the summary dataframe, we see a total of six cutpoints (`response_threshold`) coefficients. Why six? Remember, we get the last parameter for free, so we only need $K-1$ cutpoints. The index (using zero based indexing) of the `response_threshold` indicates the category that the cutpoint is associated with. Comparing to the empirical log-cumulative-odds computation above, the mean of the posterior distribution for each category is close to the empirical value.\n",
+    "\n",
+    "As the the log cumalative link is used, we need to apply the inverse of the logit function to transform back to cumulative probabilities. Below, we plot the cumulative probabilities for each category. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 103,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "expit_func = lambda x: 1 / (1 + np.exp(-x))\n",
     "cumprobs = expit_func(idata.posterior.response_threshold).mean((\"chain\", \"draw\"))\n",
-    "cumprobs = np.append(cumprobs, 1)"
+    "cumprobs = np.append(cumprobs, 1)\n",
+    "\n",
+    "plt.figure(figsize=(7, 3))\n",
+    "plt.plot(sorted(trolly.response.unique()), cumprobs, marker='o')\n",
+    "plt.ylabel(\"Cumulative probability\")\n",
+    "plt.xlabel(\"Response category\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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WbgHXi4yMxNq1azFmzBhUV1ejrq4OI0aMuGXT2ri4OMybN8+g2YmIqHVSqQV2pRdgVWIWEjP+Xc/cycMesZEBGNXDG7ZWnKKkO2P0/3NuvKUrhLjpbd60tDRMnz4db731FoYOHQqFQoFXX30VkyZNwooVKxq9Zs6cOZg1a5bmdXl5OXx9fQ33DRARUYt3+UoN1h/OxbcHs5F3+SoAQCoBhnT1RGxkAPq2d+YUJd01nYqyWbNm4Z133oGdnZ1WgdOYTz75RKcv7OrqCplM1uCuWGFhYYO7Z9fExcUhKioKr776KgAgNDQUdnZ26N+/PxYsWAAvr4bbS1hbW8Pa2lqnTERERNdLV5RjVWIWNh/Pg7Kuvnl6W1tLPHmvH57p6wcfJ05RkuHoVJQdP34ctbW1mt/fjD4/JVhZWSE8PBwJCQl49NFHNeMJCQkYOXJko9dcuXIFFhbaka9t7aTD8wpERES3VadSY2daAVYmZuFQZolmvKuXAyZEBmBEd2/ILbmtIBmeTkXZ7t27G/393Zo1axbGjRuHXr16ISIiAsuWLUNOTg4mTZoEoH7qMS8vD6tXrwYADB8+HC+88AKWLl2qmb586aWX0Lt3b3h7exssFxERtT7FlUqsP5yLNUnZms2+ZVIJhoV4YkJkAHr5O3GKkprUXa0py83NhUQigY+Pzx1dP2bMGBQXF2P+/PlQKBQICQnBtm3bNPtsKhQK5OTkaM6fMGECKioq8MUXX+Dll19G27ZtMXDgQHzwwQd3820QEVErdiqvDCsTs7DlxEXU/DNF6WJnhbG9/fB0Xz94OdoYOSG1Fjr1KbteXV0d5s2bh8WLF6Oysn5X+zZt2mDatGmYO3cuLC1Ne+8u9ikjIqJalRq/ncrHqsQsHM0u1YyH+jgiNiIAD4d6cYqSDMagfcquN3XqVGzevBkLFy5EREQEAODgwYN4++23UVRUhK+++urOUxMRETWhSxVKrDuUgzVJ2SisqO9haSGV4OFQL8RGBqCHb1tOUZLR6H2nzNHREevXr0d0dLTW+G+//YYnn3wSZWVlBg1oaLxTRkTU+iTnXsaqxCz8mqJAjap+itLN3hpP9fbD03384O4gN3JCasma7E6ZXC5HQEBAg/GAgABYWVnp+3ZERERNQlmnwraTCqxMzMaJ3Mua8R5+bTEhMgDRIV6wspAaLyDRDfQuyqZMmYJ33nkH8fHxmv5fSqUS7777LqZOnWrwgERERPooKK/G2qRsfHcoF0WV9VOUVjIpHgnzwoTIAIT6tDVuQKKb0Kkoe+yxx7Re79q1Cz4+PggLCwMAnDhxAjU1NRg0aJDhExIREd2GEALHckqxMjEbv51UoE5dvzLHw8Eaz/Txx9g+fnBtw0biZNp0KsocHR21Xj/++ONar7ltERERGUN1rQpbT1zEqoNZOJVXrhm/N8AJsZEBGBrsCUsZpyjJPOhUlMXHxzd1DiIiIp1dvHwVa5Kysf5wLkqqagAAVhZSjOrujfERAQhp53ibdyAyPUbfkJyIiEgXQggcyizBqoNZ2JFaANU/U5TejnI8E+GPJ+/1g7MdHzgj83VHRdmmTZvw/fffIycnBzU1NVrHjh07ZpBgREREAHC1RoWfk/OwMjELf+dXaMb7tnfGhMgAPNjFAxacoqQWQO+ibPHixfjvf/+L2NhY/Pzzz3j22WeRkZGBw4cPY8qUKU2RkYiIWqELpVfwbVI2NhzOxeUrtQAAuaUUj/bwQWykPzp7stcktSx6F2VLlizBsmXLMHbsWKxatQqvvfYa2rdvj7feegslJSVNkZGIiFoIlbp+CrKwohru9nL0DnSGTPpvB30hBA5mFGNlYhZ2pRfgnxlK+DjZYHyEP2J6+aKtLacoqWXSuyjLyclBZGQkAMDGxgYVFfW3kseNG4e+ffviiy++MGxCIiJqEbafUmDe1jQoyqo1Y16Ocswd3hX33eOGH4/lYfXBLJwpqNQc7xfkitjIAAzs7K5VvBG1RHoXZZ6eniguLoa/vz/8/f2RlJSEsLAwZGZmQs8dm4iIqJXYfkqByWuO4cZ/JRRl1Zi05hjkllJU19Zvf2RrJcPjPeunKIPc7Zs/LJGR6F2UDRw4EFu3bkXPnj3x3HPPYebMmdi0aROOHDnSoMksERGRSi0wb2tag4LsetW1avg52yA2MhCje/nAQW7ZbPmITIXeRdmyZcugVtf/NDNp0iQ4Oztj//79GD58OCZNmmTwgEREZN4OZZZoTVnezPuPhSIyyLUZEhGZJr2LMqlUCqn030ePY2JiEBMTY9BQRETUcvytKL/9SQAu/bNPJVFrdUd9ykpLS7FixQqkp6dDIpGgS5cuePbZZ+Hs7GzofEREZIaEEDh4vhjxB7KQkFag0zXu9vImTkVk2vTutrd3714EBgZi8eLFKC0tRUlJCRYvXozAwEDs3bu3KTISEZGZqK5VYf2hHER/tg9PLf9LU5BZWdz8nxsJ6p/C7B3IH+ypddP7TtmUKVMQExODpUuXQiaTAQBUKhVefPFFTJkyBadOnTJ4SCIiMm2Ksqv49mA21h3KQek/jV5tLGV4PLwdJkQG4FxhJSavqd/x5foF/9eaXMwd3pUtL6jVkwg9+1jY2NggOTkZnTp10ho/ffo0unfvjqtXrxo0oKGVl5fD0dERZWVlcHBgN2giojslhMDR7FLEJ2Zh+6l8zV6UPk42iI0IQEwvXzja/vsU5a36lA0L8Wr2/ETNRdfaQ+87ZT179kR6enqDoiw9PR3du3fXOygREZkXZZ0Kv6YoEH8gCyfzyjTj9XtRBmJwV49G73oNC/HC4K6et+zoT9Sa6VSUpaSkaH4/ffp0zJgxA+fOnUPfvn0BAElJSfjyyy/x/vvvN01KIiIyusKKaqxNysHav3JQ9M+TklYWUjzavR1iIwPQ1fv2sw8yqQQRHVyaOiqRWdJp+lIqlUIikdy2Y79EIoFKpTJYuKbA6UsiIv2kXLiM+ANZ+CXlImpV9f8OeDrIMS7CH2N7+8HZjntREt2KQacvMzMzDRaMiIhMX61Kje2n8rEyMQtHs0s14z392uLZqEAMC/GEpUzvB/iJ6BZ0Ksr8/f2bOgcREZmAkqoarDuUg28PZiO/vH5BvqVMgkdCvTEhMgBhvm2NG5CoBbuj5rEZGRlYtGiRVvPYGTNmoEOHDobOR0REzSBdUY6VB7LwU3IelHX1W+m5trHC03388XQfP7g7sLErUVPTuyjbsWMHRowYge7duyMqKgpCCCQmJiI4OBhbt27F4MGDmyInEREZmEotsCu9APEHMpF0vkQzHtLOAc9GBuKRMC9YW8iMmJCoddG7T1mPHj0wdOjQBk9avv7669i5cyeOHTtm0ICGxoX+RNTalV2txfeHc7HqYBYulNb3lpRJJRgW7IlnowIQ7u8EiYRtKogMRdfaQ++iTC6X4+TJk+jYsaPW+JkzZxAaGorq6uqbXGkaWJQRUWt1rrASKxMz8cPRPFytrX9Svq2tJcb29sO4vv7wbmtj5IRELVOTNY91c3NDcnJyg6IsOTkZ7u7u+iclIqImo1YL7D1zCfGJWfjzzCXNeCcPezwbFYBRPdpBbskpSiJToHdR9sILL+A///kPzp8/j8jISEgkEuzfvx8ffPABXn755abISEREeqpU1mHTkVysOpiNzKIqAIBEAjzYxQPPRgYgooMLpyiJTIze05dCCCxatAgff/wxLl68CADw9vbGq6++iunTp5v8H3JOXxJRS5ZdXIVVidnYeCQXFco6AIC93AJjevlifEQA/FxsjZyQqPVpkunLuro6rF27FmPHjsXMmTNRUVEBALC3t7+7tEREdMeEEEjMKEb8gUz8/nchrv2o3d7VDhOiAvB4Tx/YWd9RByQiakZ6/Sm1sLDA5MmTkZ6eDoDFGBGRMV2tUWHz8TysTMzEmYJKzfiAe9zwbFQA7uvoBik3+yYyG3r/6NSnTx8cP36cXf6JiIwk7/JVrD6YhfWHclF2tRYAYGslwxPhPoiNDEAHtzZGTkhEd0LvouzFF1/Eyy+/jAsXLiA8PBx2dnZax0NDQw0WjoiI6gkhcDirFCsTM7EjtQAqdf0cpa+zDWIjAhBzry8c5JZGTklEd0Pvhf5SacMNaCUSCYQQkEgkUKlUBgvXFLjQn4jMSXWtCr+kKBB/IBOpF8s145EdXDAhMgCDunhAxilKIpPWZH3KMjMz7yrYjZYsWYIPP/wQCoUCwcHBWLRoEfr373/T85VKJebPn481a9YgPz8fPj4++O9//4uJEycaNBcRkTEVlldjTVI21v6Vg+KqGgCAtYUUj/Vsh9jIAHT25A+VRC2N3kWZIdeSbdiwAS+99BKWLFmCqKgofP3114iOjkZaWhr8/PwavSYmJgYFBQVYsWIFgoKCUFhYiLq6OoNlIiIypuTcy4g/kIlfUxSo+2eK0stRjnER/hh7rx+c7KyMnJCImore05cAcPr0aXz++edIT0+HRCJB586dMW3aNHTq1Emv9+nTpw969uyJpUuXasa6dOmCUaNGIS4ursH527dvx5NPPonz58/D2dlZ39gAOH1JRKanVqXGtpMKrEzMwvGcy5rxXv5OeDYqEEODPWAha7h0hIjMg661h95/yjdt2oSQkBAcPXoUYWFhCA0NxbFjxxASEoKNGzfq/D41NTU4evQohgwZojU+ZMgQJCYmNnrNli1b0KtXLyxcuBDt2rXDPffcg1deeQVXr17V99sgIjK64kolPv/9LPp98AdmrE/G8ZzLsJLVT1FundoPmyZH4uFQLxZkRK2E3tOXr732GubMmYP58+drjc+dOxezZ8/G6NGjdXqfoqIiqFQqeHh4aI17eHggPz+/0WvOnz+P/fv3Qy6XY/PmzSgqKsKLL76IkpISfPPNN41eo1QqoVQqNa/Ly8sbPY+IqLmkXizDygNZ+PnERdTUqQEAbvbWeKaPP57q4wc3e2sjJyQiY9C7KMvPz8f48eMbjD/zzDP48MMP9Q5w47ZM157ibIxarYZEIsHatWvh6OgIAPjkk0/wxBNP4Msvv4SNjU2Da+Li4jBv3jy9cxERGVKdSo1d6QX45kAWDmWWaMZDfRzxbFQAHu7mDSsL3hEjas30Lsruv/9+7Nu3D0FBQVrj+/fvv+VTkzdydXWFTCZrcFessLCwwd2za7y8vNCuXTtNQQbUr0ETQuDChQvo2LFjg2vmzJmDWbNmaV6Xl5fD19dX55xERHej7Eot1h/OweqD2ci7XL/UwkIqQXQ3L0yIDEBPv7Ymv2cwETUPvYuyESNGYPbs2Th69Cj69u0LAEhKSsLGjRsxb948bNmyRevcm7GyskJ4eDgSEhLw6KOPasYTEhIwcuTIRq+JiorCxo0bUVlZiTZt6jtWnzlzBlKpFD4+Po1eY21tDWtrTgUQUfM6W1CB+MQsbD6Wh6u19f0bnWwt8VQfP4zrGwBPR7mRExKRqTFI89hG31iHRrIbNmzAuHHj8NVXXyEiIgLLli3D8uXLkZqaCn9/f8yZMwd5eXlYvXo1AKCyshJdunRB3759MW/ePBQVFeH555/HgAEDsHz5cp1y8elLImoqarXA7tOFWJmYhX1nizTjnT3tMTEqECO6e0NuKTNiQiIyhiZrHqtWq+8q2PXGjBmD4uJizJ8/HwqFAiEhIdi2bZumF5pCoUBOTo7m/DZt2iAhIQHTpk1Dr1694OLigpiYGCxYsMBgmYiIGqNSCxzKLEFhRTXc7eXoHeis6aRfUV2LTUcvYFViFrKKrwAApBJgcFcPPBsViD6BzpyiJKLbuqM+ZeaMd8qISF/bTykwb2saFGXVmjEvRzkmDeiAzKIqbDp6AZXK+ibWDnILPNnbD+P6+sPX2dZYkYnIhDTZnTIiotZk+ykFJq85hht/elWUVWPullTN6w5udpgQFYjHe7aDrRX/aiUi/fFvDiKim1CpBeZtTWtQkF3P2kKKr58Jx4BObpyiJKK7wqY4REQ3cSizRGvKsjHKOjWsLWUsyIjorrEoIyK6iYLyWxdk1xRW6HYeEdGt3FFRlpGRgf/9738YO3YsCgsLAdRvFp6amnqbK4mIzMOF0iv4+s8Mnc51t2fPMSK6e3oXZXv37kW3bt3w119/4ccff0RlZSUAICUlBXPnzjV4QCKi5iSEwPdHcjFs0T6kKypuea4E9U9h9g50bp5wRNSi6V2Uvf7661iwYAESEhJgZWWlGX/ggQdw8OBBg4YjImpOlyqUeGH1Uby2KQWVyjqE+zth/ohgSFBfgF3v2uu5w7tq+pUREd0NvZ++PHnyJL777rsG425ubiguLjZIKCKi5rb9lAJvbD6FkqoaWMmkmDXkHrzQvz1kUgncHawb9CnzdJRj7vCuGBbiZcTURNSS6F2UtW3bFgqFAoGBgVrjx48fR7t27QwWjIioOZRdrcW8Lan48XgegPotkT4d0x1dvP5t8DgsxAuDu3retKM/EZEh6F2UPfXUU5g9ezY2btwIiUQCtVqNAwcO4JVXXsH48eObIiMRUZPYd/YSXtuUAkVZNaQSYPL9HTBj0D2wsmi4skMmlSCig4sRUhJRa6H3Nku1tbWYMGEC1q9fDyEELCwsoFKp8NRTT2HlypWQyUx7s11us0REV2rq8P5vf2P1wWwAQICLLT6O6Y5wfycjJyOilkjX2uOO977MyMjA8ePHoVar0aNHD3Ts2PGOwzYnFmVErduxnFK8/P0JZBZVAQDG9fXHnIc6c2skImoyTbb35d69ezFgwAB06NABHTp0uKuQRETNpaZOjcW/n8WSPeegFoCngxwLnwjFffe4GTsaERGAOyjKBg8eDE9PTzz11FN45plnEBIS0hS5iIgM5u/8cszacAJpinIAwKju3pg3IgSOtpZGTkZE9C+9+5RdvHgRr732Gvbt24fQ0FCEhoZi4cKFuHDhQlPkIyK6Yyq1wNd7MzDi8wNIU5TDydYSS57uiUVP9mBBRkQm547XlAFAZmYmvvvuO6xbtw5///037rvvPvzxxx+GzGdwXFNG1DpkF1fhlY0ncDirFAAwqLM74h7vxi2RiKjZNflC/2tUKhV+++03vPnmm0hJSYFKpbqbt2tyLMqIWjYhBNYdysWCX9NwpUYFOysZ5g4PxuhePpBI2FeMiJpfky30v+bAgQNYu3YtNm3ahOrqaowYMQLvvffenb4dEdFdKyivxuwfUrDn9CUAQJ9AZ3w0Ogy+zrZGTkZEdHt6F2VvvPEG1q1bh4sXL+LBBx/EokWLMGrUKNja8i89IjKeLScu4s2fTqHsai2sLKR4bWgnTIwKhJRd94nITOhdlO3ZswevvPIKxowZA1dX16bIRESks9KqGrz58yn8kqIAAHRr54hPYsLQ0cPeyMmIiPSjd1GWmJjYFDmIiPS2+3QhZm9KQWGFEjKpBFMfCMLUgUGwlOn9YDkRkdHpVJRt2bIF0dHRsLS0xJYtW2557ogRIwwSjIjoZqqUdVjwazrWHcoBAHRws8MnMd0R5tvWuMGIiO6CTk9fSqVS5Ofnw93dHVLpzX8ClUgkfPqSiJrU4awSvPz9CeSUXAEATIwKxGvDOkFuadr77hJR62XQpy/VanWjvyciai7VtSp8mnAGy/adhxBAu7Y2+HB0KCI7cG0rEbUMei+8WL16NZRKZYPxmpoarF692iChiIiul3qxDCO/OICv/6wvyEaH++C3l/qzICOiFkXv5rEymQwKhQLu7u5a48XFxXB3d+f0JREZTJ1Kja/2ZmDRrrOoUwu4trHCe492w5BgT2NHIyLSWZM1jxVCNNoV+8KFC3B0dNT37YiIGnX+UiVmfX8CybmXAQBDgz3w3qPd4NLG2rjBiIiaiM5FWY8ePSCRSCCRSDBo0CBYWPx7qUqlQmZmJoYNG9YkIYmo9VCrBb5Nykbcb+morlXD3toC80YG49Ee7bhNEhG1aDoXZaNGjQIAJCcnY+jQoWjTpo3mmJWVFQICAvD4448bPCARtR4XL1/Fa5tSsP9cEQCgX5ArFj4RCu+2NkZORkTU9HQuyubOnQsACAgIwJgxYyCXy5ssFBG1LkIIbD6eh7lbUlFRXQe5pRRvPNQFz/Tx5zZJRNRq6L2mLDY2tilyEFErVVypxBubT2JHagEAoLtvW3wSE4b2bm1ucyURUcuid1GmUqnw6aef4vvvv0dOTg5qamq0jpeUlBgsHBG1bDtT8/HG5pMoqqyBhVSClx7siEkDOsCC2yQRUSuk99988+bNwyeffIKYmBiUlZVh1qxZeOyxxyCVSvH22283QUQiamnKq2vxysYT+M+3R1FUWYNOHvb4aUoUpg7syIKMiFotvfuUdejQAYsXL8bDDz8Me3t7JCcna8aSkpLw3XffNVVWg2CfMiLjSswowqsbU5B3+SokEuA//dtj5uB7uE0SEbVYTdanLD8/H926dQMAtGnTBmVlZQCARx55BG+++eYdxiWilq66VoWF20/jmwOZAABfZxt8PLo7egc6GzkZEZFp0HuewMfHBwqFAgAQFBSEnTt3AgAOHz4Ma2s2dSSihk7kXsbDi/dpCrKxvf3w24z7WJAREV1H7ztljz76KH7//Xf06dMHM2bMwNixY7FixQrk5ORg5syZTZGRiMxUrUqNL/44hy92n4NKLeBub40PHg/FA53db38xEVEro/eashslJSUhMTERQUFBGDFihN7XL1myBB9++CEUCgWCg4OxaNEi9O/f/7bXHThwAAMGDEBISAiSk5N1/npcU0bUPM4WVGDW9ydwMu+fJQ6hXnhnZAic7KyMnIyIqHnpWnvcdVF2NzZs2IBx48ZhyZIliIqKwtdff43/+7//Q1paGvz8/G56XVlZGXr27ImgoCAUFBSwKCMyIWq1wDcHMrFwx2nU1KnhaGOJd0aFYESYt7GjEREZhUGLsi1btuj8hfW5W9anTx/07NkTS5cu1Yx16dIFo0aNQlxc3E2ve/LJJ9GxY0fIZDL89NNPLMqITERuyRW8svEE/sqs71c44B43LHwiFB4O3AGEiFovgz59eW3fy9uRSCRQqVQ6nVtTU4OjR4/i9ddf1xofMmQIEhMTb3pdfHw8MjIysGbNGixYsECnr0VETUsIge+P5GL+1jRU1ahgayXDfx/ugqd6+3ETcSIiHelUlKnVaoN/4aKiIqhUKnh4eGiNe3h4ID8/v9Frzp49i9dffx379u2DhYVuzygolUoolUrN6/Ly8jsPTUQNFFZUY84PJ/H734UAgHsDnPDR6DD4u9gZORkRkXnR++lLQ7vxp2ghRKM/WatUKjz11FOYN28e7rnnHp3fPy4uDvPmzbvrnETU0LaTCvx380mUXqmFlUyKl4fcg+f7t4eMm4gTEelN74X+8+fPv+Xxt956S6f3qampga2tLTZu3IhHH31UMz5jxgwkJydj7969WudfvnwZTk5OkMn+7fqtVqshhIBMJsPOnTsxcODABl+nsTtlvr6+XFNGdBfKrtRi7pZT+Cn5IgCgq5cDPhkThs6e/DNFRHSjJuvov3nzZq3XtbW1yMzMhIWFBTp06KBzUWZlZYXw8HAkJCRoFWUJCQkYOXJkg/MdHBxw8uRJrbElS5bgjz/+wKZNmxAYGNjo17G2tmZTWyID+vPMJby2KQX55dWQSoAX7w/C9EEdYWXBPSuJiO6G3kXZ8ePHG4yVl5djwoQJWsWVLmbNmoVx48ahV69eiIiIwLJly5CTk4NJkyYBAObMmYO8vDysXr0aUqkUISEhWte7u7tDLpc3GCciw7tSU4e4bX/j26RsAECgqx0+jglDTz8nIycjImoZDLKmzMHBAfPnz8cjjzyCcePG6XzdmDFjUFxcjPnz50OhUCAkJATbtm2Dv78/AEChUCAnJ8cQEYnoLhzNLsXL3ycjq/gKACA2wh+zozvD1sroy1KJiFoMgzWP3b9/P4YPH47S0lJDvF2TYZ8yIt3V1KmxaNcZfLU3A2oBeDrI8eHoUPTv6GbsaEREZqPJ1pQtXrxY67UQAgqFAt9++y2GDRumf1IiMknpinLM+v4E0hX1bWQe69EOc0cEw9HG0sjJiIhaJr2Lsk8//VTrtVQqhZubG2JjYzFnzhyDBSMi41CpBZb9eR6fJJxGrUrAydYS7z3aDdHdvIwdjYioRdO7KMvMzGyKHERkArKKqvDyxhM4ml2/DOHBLu6IeywUbvZ8gpmIqKlxlS4RQQiBtX/l4N1f03G1VoU21hZ4a3hXjA734TZJRETNRO+irLq6Gp9//jl2796NwsLCBlswHTt2zGDhiKjp5ZdV47UfUvDnmUsAgL7tnfHR6DD4ONkaORkRUeuid1E2ceJEJCQk4IknnkDv3r35UzSRmRJCYMuJi3jzp1Mor66DtYUUrw3rjGcjAyDlNklERM1O76Ls119/xbZt2xAVFdUUeYjIgFRqgUOZJSisqIa7vRy9A50hk0pQWlWD//18Cr+mKAAAoT6O+CQmDEHu9kZOTETUeuldlLVr1w729vyLm8jUbT+lwLytaVCUVWvGvBzleKxnO3x/5AIuVSghk0owbWAQpjwQBEsZt0kiIjImvf8W/vjjjzF79mxkZ2c3RR4iMoDtpxSYvOaYVkEGAIqyany5OwOXKpQIcm+DzS9G4qUH72FBRkRkAvS+U9arVy9UV1ejffv2sLW1haWldiPJkpISg4UjIv2p1ALztqbhVlt12FnJ8POUKNhZ8wFsIiJTofffyGPHjkVeXh7ee+89eHh4cKE/kYk5lFnS4A7ZjapqVEi5UIaIDi7NlIqIiG5H76IsMTERBw8eRFhYWFPkIaK7kFlUhXWHcnQ6t7Di1oUbERE1L72Lss6dO+Pq1atNkYWI9CSEQOrFcuxMzceO1AKcLqjQ+Vp3e3kTJiMiIn3pXZS9//77ePnll/Huu++iW7duDdaU3Wr3cyK6eyq1wNHsUmw/lY+dafm4UPrvD0kWUgn6tndGyoUylFfXNXq9BICnY317DCIiMh16F2XDhg0DAAwaNEhrXAgBiUQClUplmGREpKGsUyHxXDF2pOYjIa0AxVU1mmNySykG3OOGocGeGNTZA462lpqnLwFoLfi/tgJ07vCukLFBLBGRSdG7KNu9e3dT5CCiG1Qq67D770LsSM3HntOXUKn8986Xo40lBnVxx9BgT9zX0Q02VjKta4eFeGHpMz0b9CnzdJRj7vCuGBbi1WzfBxER6UYihLjVk/MtTnl5ORwdHVFWVsapVjI5xZVK7EovwI7UAuw/W4Qa1b97y3o4WGNIV08MC/FE70BnnXqL3ayjPxERNR9daw+975T9+eeftzx+33336fuWRK3ahdIr2JFagB2p+TiSVQL1dT8mtXe1w5BgTwwN9kCYT1u996SUSSVse0FEZCb0Lsruv//+BmPX9yrjmjKiWxNC4GxhJbafyseO1HykXizXOh7SzgFD/7kjFuTehr0AiYhaCb2LstLSUq3XtbW1OH78ON588028++67BgtG1JKo1QLJFy5jR2o+dqYWILOoSnNMKgHuDXDG0GBPDAn2gI+TrRGTEhGRsehdlDk6OjYYGzx4MKytrTFz5kwcPXrUIMGIzF2tSo2/zpdge6oCCWkFKChXao5ZyaTo19EVw4I9MaiLO1zaWBsxKRERmQKDbXzn5uaG06dPG+rtiMzS1RoV9p65hJ2p+fj970KUXa3VHGtjbYEHOrtjaLAH7u/kjjbcd5KIiK6j978KKSkpWq+FEFAoFHj//fe59RK1SmVXav95YjIff569hOraf5+YdLGzwuCuHhga4onIDi6wtpDd4p2IiKg107so6969OyQSCW7spNG3b1988803BgtGZMoKyqs1WxslnS9G3XWPTPo42WBosCeGBnsi3N+JLSiIiEgnehdlmZmZWq+lUinc3Nwgl3MfPWrZzl+q1LSuSM69rHWsk4c9hgbX3xHr6uXAJyaJiEhvehdl/v7+TZGDyORc2+x7R2p964ozBZVax3v6tdXcEQtwtTNSSiIiail0Lsr++OMPTJ06FUlJSQ260ZaVlSEyMhJfffUV+vfvb/CQRM1FpRY4nFWiaV2Rd1l7s++IDi71rSu6esDdgXeHiYjIcHQuyhYtWoQXXnih0e0BHB0d8f/+3//DJ598wqKMzE51rQqJGUXYcaoACekFKLlus28bS1n9Zt8hHhjYqX6zbyIioqagc1F24sQJfPDBBzc9PmTIEHz00UcGCUXU1Cqqa7H79KX6zb7/LkRVzb87UTjaWOLBLh4YGuyB/o1s9k1ERNQUdC7KCgoKYGl587sEFhYWuHTpkkFCETWFokoldqUVYHtqPhLPFWtt9u3pIMeQYA8MDdZ9s28iIiJD0rkoa9euHU6ePImgoKBGj6ekpMDLy8tgwYgMIbfkimZ92JHsGzb7drPTLNQPbeeo92bfREREhqRzUfbQQw/hrbfeQnR0dIP2F1evXsXcuXPxyCOPGDwgkT6EEDhTUIkdqfnYfiofaQrtzb67tXPE0GCPfzb7tjdSSiIiooYk4sYusDdRUFCAnj17QiaTYerUqejUqRMkEgnS09Px5ZdfQqVS4dixY/Dw8GjqzHelvLwcjo6OKCsra/ShBTINKrXAocwSFFZUw91ejt6BzjdtwqpWCxzPvfxPM9d8ZBVf0RyTSoDegdc2+/ZEu7Y2zfUtEBERAdC99tD5TpmHhwcSExMxefJkzJkzR9PRXyKRYOjQoViyZInJF2RkHrafUmDe1jQoyqo1Y16Ocswd3hXDQuqnyGtVaiSdL8b2U/lISCtAYcV1m31bSNE/yBVDudk3ERGZEZ3vlF2vtLQU586dgxACHTt2hJOTU1NkaxK8U2batp9SYPKaY7jxf8pr98j+c18gCitq8Ht6Acqr6zTH7TWbfXtiQCc3bvZNREQmQ9fa446KMnPGosx0qdQC/T74Q+sO2a24tqnf7HtIMDf7JiIi02Xw6UuipnYos0SnguyhEE882y8QPf242TcREbUcLMrIqIQQyCq+gv1nL+H7I7k6XTM0xBP3Bjg3cTIiIqLmZfQOmUuWLEFgYCDkcjnCw8Oxb9++m577448/YvDgwXBzc4ODgwMiIiKwY8eOZkxLhlBSVYNfUi7i9R9S0O+D3Xjgoz148+dUnMwrv/3FANztueckERG1PEa9U7Zhwwa89NJLWLJkCaKiovD1118jOjoaaWlp8PPza3D+n3/+icGDB+O9995D27ZtER8fj+HDh+Ovv/5Cjx49jPAdkC6qa1U4ml2KfWeLsP/cJaReLMf1KxktZRKE+zshsoMLViVmo6SqpsFCf6B+sb+nY317DCIiopbGqAv9+/Tpg549e2Lp0qWasS5dumDUqFGIi4vT6T2Cg4MxZswYvPXWWzqdz4X+TU+tFkjPL8f+s0XYf64IhzJLoKxTa53TycMe/Tq6ol9HV/QJdIatVf3PB9eevgSgVZhdWzm29JmemrYYRERE5sDkF/rX1NTg6NGjeP3117XGhwwZgsTERJ3eQ61Wo6KiAs7ON79zolQqoVT+28OqvFy3KTLSj6Lsav2dsLNFOHCuCMVVNVrH3e2t0a+jK/p3dEVUB1e4OzQ+BTksxAtLn+nZoE+Z5w19yoiIiFoaoxVlRUVFUKlUDRrOenh4ID8/X6f3+Pjjj1FVVYWYmJibnhMXF4d58+bdVVZqqKK6FknnS7D/7CXsO1eE85eqtI7bWsnQJ9AZ/Tq6oX9HV3R0bwOJRLcnJYeFeGFwV0+dO/oTERG1BEZ/+vLGf6iFEDr9471u3Tq8/fbb+Pnnn+Hu7n7T8+bMmYNZs2ZpXpeXl8PX1/fOA7dStSo1Ui5c1twNO557GarrdveWSoBQn7bo39EV/YJc0cPPCVYWd/4ciUwqQUQHF0NEJyIiMgtGK8pcXV0hk8ka3BUrLCy87XZNGzZswHPPPYeNGzfiwQcfvOW51tbWsLbmNjv6EkLgfFEV9p8twr6zRUg6X4xKZZ3WOQEutogKqp+SjGjvCkdbSyOlJSIiMn9GK8qsrKwQHh6OhIQEPProo5rxhIQEjBw58qbXrVu3DhMnTsS6devw8MMPN0fUVqO4Uon95+rXhO0/W4SLNzRybWtriagO9Yvz+wW5wtfZ1khJiYiIWh6jTl/OmjUL48aNQ69evRAREYFly5YhJycHkyZNAlA/9ZiXl4fVq1cDqC/Ixo8fj88++wx9+/bV3GWzsbGBo6Oj0b4Pc1Vdq8LhrBLN3bA0hfZDEFYyKXoFOGmKsGBvR67rIiIiaiJGLcrGjBmD4uJizJ8/HwqFAiEhIdi2bRv8/f0BAAqFAjk5OZrzv/76a9TV1WHKlCmYMmWKZjw2NhYrV65s7vhmR60WSFOUa/qFHc4qRc0NrSo6e9rXrwvr6IbeAc6wseJ+kkRERM2BG5K3cHmXr9Y/IXm2CIkZxSi5oVWFp4Nc06oisoMr3Oy5/o6IiMiQTL5PGTWN8upaHMwo1jRuzSzSblVhZyVDRAcXzQL9Dm66t6ogIiKipsOizMzVqtQ4nnMZ+89ewv5zRThxoUyrVYVMKkGYj6OmX1h337awlBl9y1MiIiK6AYsyMyOEwLnCSuz/5wnJpPPFqKpRaZ3T3tUO/Tq6IirIFREdXOAgZ6sKIiIiU8eizAxcqlDiwLn6JyQPnCtCfrl2qwpnOytEdnDRLNBv19bGSEmJiIjoTrEoM0FXa1T4K7NYU4j9nV+hddzKQoreAc6aVhVdvRwgZasKIiIis8aizMBUaqH3no0qtUDqxTLNFkZHs0tRo9JuVRHs7YB+QfWNW+8NcIbckq0qiIiIWhIWZQa0/ZQC87amQXFdJ3wvRznmDu+KYSFeWufmllzR9AtLzCjG5Su1Wse9HetbVfTr6IaoDi5wacNWFURERC0ZizID2X5KgclrjuHGpm/5ZdWYvOYYPhodBjtr2T+FWBGyi69onWdvbYG+/6wLiwpyRXtXO7aqICIiakVYlBmASi0wb2tag4IMgGbs5Y0ntMZlUgl6+LbVNG4N82kLC7aqICIiarVYlBnAocwSrSnLm/F2lGNIsCf6BbmiT3tn2LNVBREREf2DRZkBFFbcviADgNnRnTGye7smTkNERETmiPNlBuBuLzfoeURERNT6sCgzgN6BzvBylONmy/IlqH8Ks3egc3PGIiIiIjPCoswAZFIJ5g7vCgANCrNrr+cO73rbfmVERETUerEoM5BhIV5Y+kxPeDpqT1F6Osqx9JmeDfqUEREREV2PC/0NaFiIFwZ39dS7oz8RERERizIDk0kliOjgYuwYREREZGY4fUlERERkAliUEREREZkAFmVEREREJqDVrSkTon43yvLyciMnISIiotbgWs1xrQa5mVZXlFVUVAAAfH19jZyEiIiIWpOKigo4Ojre9LhE3K5sa2HUajUuXrwIe3t7SCRN06qivLwcvr6+yM3NhYODQ5N8jdaAn6Nh8HM0HH6WhsHP0TD4ORpGc3yOQghUVFTA29sbUunNV461ujtlUqkUPj4+zfK1HBwc+AfFAPg5GgY/R8PhZ2kY/BwNg5+jYTT153irO2TXcKE/ERERkQlgUUZERERkAliUNQFra2vMnTsX1tbWxo5i1vg5GgY/R8PhZ2kY/BwNg5+jYZjS59jqFvoTERERmSLeKSMiIiIyASzKiIiIiEwAizIiIiIiE8CijIiIiMgEsCgzoD///BPDhw+Ht7c3JBIJfvrpJ2NHMktxcXG49957YW9vD3d3d4waNQqnT582diyzs3TpUoSGhmoaIkZEROC3334zdiyzFxcXB4lEgpdeesnYUczK22+/DYlEovXL09PT2LHMVl5eHp555hm4uLjA1tYW3bt3x9GjR40dy6wEBAQ0+H9SIpFgypQpRsvEosyAqqqqEBYWhi+++MLYUcza3r17MWXKFCQlJSEhIQF1dXUYMmQIqqqqjB3NrPj4+OD999/HkSNHcOTIEQwcOBAjR45EamqqsaOZrcOHD2PZsmUIDQ01dhSzFBwcDIVCofl18uRJY0cyS6WlpYiKioKlpSV+++03pKWl4eOPP0bbtm2NHc2sHD58WOv/x4SEBADA6NGjjZap1W2z1JSio6MRHR1t7Bhmb/v27Vqv4+Pj4e7ujqNHj+K+++4zUirzM3z4cK3X7777LpYuXYqkpCQEBwcbKZX5qqysxNNPP43ly5djwYIFxo5jliwsLHh3zAA++OAD+Pr6Ij4+XjMWEBBgvEBmys3NTev1+++/jw4dOmDAgAFGSsQ7ZWQGysrKAADOzs5GTmK+VCoV1q9fj6qqKkRERBg7jlmaMmUKHn74YTz44IPGjmK2zp49C29vbwQGBuLJJ5/E+fPnjR3JLG3ZsgW9evXC6NGj4e7ujh49emD58uXGjmXWampqsGbNGkycOBESicRoOViUkUkTQmDWrFno168fQkJCjB3H7Jw8eRJt2rSBtbU1Jk2ahM2bN6Nr167GjmV21q9fj2PHjiEuLs7YUcxWnz59sHr1auzYsQPLly9Hfn4+IiMjUVxcbOxoZuf8+fNYunQpOnbsiB07dmDSpEmYPn06Vq9ebexoZuunn37C5cuXMWHCBKPm4PQlmbSpU6ciJSUF+/fvN3YUs9SpUyckJyfj8uXL+OGHHxAbG4u9e/eyMNNDbm4uZsyYgZ07d0Iulxs7jtm6fmlHt27dEBERgQ4dOmDVqlWYNWuWEZOZH7VajV69euG9994DAPTo0QOpqalYunQpxo8fb+R05mnFihWIjo6Gt7e3UXPwThmZrGnTpmHLli3YvXs3fHx8jB3HLFlZWSEoKAi9evVCXFwcwsLC8Nlnnxk7llk5evQoCgsLER4eDgsLC1hYWGDv3r1YvHgxLCwsoFKpjB3RLNnZ2aFbt244e/assaOYHS8vrwY/WHXp0gU5OTlGSmTesrOzsWvXLjz//PPGjsI7ZWR6hBCYNm0aNm/ejD179iAwMNDYkVoMIQSUSqWxY5iVQYMGNXhK8Nlnn0Xnzp0xe/ZsyGQyIyUzb0qlEunp6ejfv7+xo5idqKioBm2Czpw5A39/fyMlMm/XHiZ7+OGHjR2FRZkhVVZW4ty5c5rXmZmZSE5OhrOzM/z8/IyYzLxMmTIF3333HX7++WfY29sjPz8fAODo6AgbGxsjpzMfb7zxBqKjo+Hr64uKigqsX78ee/bsafB0K92avb19g/WMdnZ2cHFx4TpHPbzyyisYPnw4/Pz8UFhYiAULFqC8vByxsbHGjmZ2Zs6cicjISLz33nuIiYnBoUOHsGzZMixbtszY0cyOWq1GfHw8YmNjYWFhAiWRIIPZvXu3ANDgV2xsrLGjmZXGPkMAIj4+3tjRzMrEiROFv7+/sLKyEm5ubmLQoEFi586dxo7VIgwYMEDMmDHD2DHMypgxY4SXl5ewtLQU3t7e4rHHHhOpqanGjmW2tm7dKkJCQoS1tbXo3LmzWLZsmbEjmaUdO3YIAOL06dPGjiKEEEIihBDGKQeJiIiI6Bou9CciIiIyASzKiIiIiEwAizIiIiIiE8CijIiIiMgEsCgjIiIiMgEsyoiIiIhMAIsyIiIiIhPAooyIiIjIBLAoIyKdTJgwARKJBBKJBBYWFvDz88PkyZNRWlpq7Ghmb8KECRg1apSxYxCRkbEoIyKdDRs2DAqFAllZWfi///s/bN26FS+++KKxY5EBCSFQV1dn7BhErRKLMiLSmbW1NTw9PeHj44MhQ4ZgzJgx2Llzp9Y58fHx6NKlC+RyOTp37owlS5ZojtXU1GDq1Knw8vKCXC5HQEAA4uLiNMclEgmWLl2K6Oho2NjYIDAwEBs3btR6/5MnT2LgwIGwsbGBi4sL/vOf/6CyslJz/Npdp48++gheXl5wcXHBlClTUFtbqzlnyZIl6NixI+RyOTw8PPDEE09ojgkhsHDhQrRv3x42NjYICwvDpk2bbvm5KJVKvPbaa/D19YW1tTU6duyIFStWAABUKhWee+45BAYGwsbGBp06dcJnn32mufbtt9/GqlWr8PPPP2vuRO7ZswcAkJeXhzFjxsDJyQkuLi4YOXIksrKyNNfW1dVh+vTpaNu2LVxcXDB79mzExsZq3XVTKpWYPn063N3dIZfL0a9fPxw+fFhzfM+ePZBIJNixYwd69eoFa2trfPvtt5BKpThy5IjW9/n555/D398f3J2PqIkYdedNIjIbsbGxYuTIkZrXGRkZomvXrsLDw0MztmzZMuHl5SV++OEHcf78efHDDz8IZ2dnsXLlSiGEEB9++KHw9fUVf/75p8jKyhL79u0T3333neZ6AMLFxUUsX75cnD59Wvzvf/8TMplMpKWlCSGEqKqq0mxmffLkSfH777+LwMBAERsbq5XTwcFBTJo0SaSnp4utW7cKW1tbzYbNhw8fFjKZTHz33XciKytLHDt2THz22Wea69944w3RuXNnsX37dpGRkSHi4+OFtbW12LNnz00/m5iYGOHr6yt+/PFHkZGRIXbt2iXWr18vhBCipqZGvPXWW+LQoUPi/PnzYs2aNcLW1lZs2LBBCCFERUWFiImJEcOGDRMKhUIoFAqhVCpFVVWV6Nixo5g4caJISUkRaWlp4qmnnhKdOnUSSqVSCCHEggULhLOzs/jxxx9Fenq6mDRpknBwcND67zR9+nTh7e0ttm3bJlJTU0VsbKxwcnISxcXFQgghdu/eLQCI0NBQsXPnTnHu3DlRVFQkBg8eLF588UWt77NHjx7irbfeuvX/KER0x1iUEZFOYmNjhUwmE3Z2dkIulwsAAoD45JNPNOf4+vpqFVlCCPHOO++IiIgIIYQQ06ZNEwMHDhRqtbrRrwFATJo0SWusT58+YvLkyUKI+qLPyclJVFZWao7/+uuvQiqVivz8fE1Of39/UVdXpzln9OjRYsyYMUIIIX744Qfh4OAgysvLG3z9yspKIZfLRWJiotb4c889J8aOHdto5tOnTwsAIiEhodHjjXnxxRfF448/rnl9Y8ErhBArVqwQnTp10vqslEqlsLGxETt27BBCCOHh4SE+/PBDzfG6ujrh5+enea/KykphaWkp1q5dqzmnpqZGeHt7i4ULFwoh/i3KfvrpJ62vv2HDBuHk5CSqq6uFEEIkJycLiUQiMjMzdf4+iUg/nL4kIp098MADSE5Oxl9//YVp06Zh6NChmDZtGgDg0qVLyM3NxXPPPYc2bdpofi1YsAAZGRkA6qcWk5OT0alTJ0yfPr3B1CcARERENHidnp4OAEhPT0dYWBjs7Ow0x6OioqBWq3H69GnNWHBwMGQymea1l5cXCgsLAQCDBw+Gv78/2rdvj3HjxmHt2rW4cuUKACAtLQ3V1dUYPHiw1vewevVqzfdwo+TkZMhkMgwYMOCmn9tXX32FXr16wc3NDW3atMHy5cuRk5Nz8w8awNGjR3Hu3DnY29trcjg7O6O6uhoZGRkoKytDQUEBevfurblGJpMhPDxc8zojIwO1tbWIiorSjFlaWqJ3796az/SaXr16ab0eNWoULCwssHnzZgDAN998gwceeAABAQG3zE1Ed87C2AGIyHzY2dkhKCgIALB48WI88MADmDdvHt555x2o1WoAwPLly9GnTx+t664VSD179kRmZiZ+++037Nq1CzExMXjwwQdvu2ZLIpEAqF/vde33NzsHqC88bjx2LZ+9vT2OHTuGPXv2YOfOnXjrrbfw9ttv4/Dhw5pzfv31V7Rr107rPaytrRv9ujY2NrfM/v3332PmzJn4+OOPERERAXt7e3z44Yf466+/bnmdWq1GeHg41q5d2+CYm5ub1vd2PXHdeq9rv2/snBvHri90AcDKygrjxo1DfHw8HnvsMXz33XdYtGjRLTMT0d3hnTIiumNz587FRx99hIsXL8LDwwPt2rXD+fPnERQUpPUrMDBQc42DgwPGjBmD5cuXY8OGDfjhhx9QUlKiOZ6UlKT1NZKSktC5c2cAQNeuXZGcnIyqqirN8QMHDkAqleKee+7RObeFhQUefPBBLFy4ECkpKcjKysIff/yBrl27wtraGjk5OQ2+B19f30bfq1u3blCr1di7d2+jx/ft24fIyEi8+OKL6NGjB4KCghrcdbOysoJKpdIa69mzJ86ePQt3d/cGWRwdHeHo6AgPDw8cOnRIc41KpcLx48c1r4OCgmBlZYX9+/drxmpra3HkyBF06dLltp/T888/j127dmHJkiWora3FY489dttriOjO8U4ZEd2x+++/H8HBwXjvvffwxRdf4O2338b06dPh4OCA6OhoKJVKHDlyBKWlpZg1axY+/fRTeHl5oXv37pBKpdi4cSM8PT3Rtm1bzXtu3LgRvXr1Qr9+/bB27VocOnRI8yTj008/jblz5yI2NhZvv/02Ll26hGnTpmHcuHHw8PDQKfMvv/yC8+fP47777oOTkxO2bdsGtVqNTp06wd7eHq+88gpmzpwJtVqNfv36oby8HImJiWjTpg1iY2MbvF9AQABiY2MxceJELF68GGFhYcjOzkZhYSFiYmIQFBSE1atXY8eOHQgMDMS3336Lw4cPaxWqAQEB2LFjB06fPg0XFxc4Ojri6aefxocffoiRI0di/vz58PHxQU5ODn788Ue8+uqr8PHxwbRp0xAXF4egoCB07twZn3/+OUpLSzV3wezs7DB58mS8+uqrcHZ2hp+fHxYuXIgrV67gueeeu+1n1aVLF/Tt2xezZ8/GxIkTb3tXkIjuknGXtBGRuWhsMboQQqxdu1ZYWVmJnJwczevu3bsLKysr4eTkJO677z7x448/CiHqF+p3795d2NnZCQcHBzFo0CBx7NgxzXsBEF9++aUYPHiwsLa2Fv7+/mLdunVaXy8lJUU88MADQi6XC2dnZ/HCCy+IioqKW+acMWOGGDBggBBCiH379okBAwYIJycnYWNjI0JDQzVPQgohhFqtFp999pno1KmTsLS0FG5ubmLo0KFi7969N/1srl69KmbOnCm8vLyElZWVCAoKEt98840QQojq6moxYcIE4ejoKNq2bSsmT54sXn/9dREWFqa5vrCwUAwePFi0adNGABC7d+8WQgihUCjE+PHjhaurq7C2thbt27cXL7zwgigrKxNCCFFbWyumTp0qHBwchJOTk5g9e7YYPXq0ePLJJ7WyTZs2TfMeUVFR4tChQ5rj1xb6l5aWNvq9rVixQgDQuoaImoZECDacISLTIJFIsHnzZna3v0NqtRpdunRBTEwM3nnnHYO857vvvov169fj5MmTBnk/Iro5Tl8SEZmp7Oxs7Ny5EwMGDIBSqcQXX3yBzMxMPPXUU3f93pWVlUhPT8fnn39usAKPiG6NC/2JiMyUVCrFypUrce+99yIqKgonT57Erl27dFrEfztTp05Fv379MGDAAEycONEAaYnodjh9SURERGQCeKeMiIiIyASwKCMiIiIyASzKiIiIiEwAizIiIiIiE8CijIiIiMgEsCgjIiIiMgEsyoiIiIhMAIsyIiIiIhPAooyIiIjIBPx/+2XKs7yWAxQAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -259,17 +422,21 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(7, 3))\n",
-    "plt.plot(sorted(trolly.response.unique()), cumprobs, marker='o')\n",
-    "plt.ylabel(\"Cumulative probability\")\n",
-    "plt.xlabel(\"Response category\");"
+    "fig, ax = plt.subplots(figsize=(7, 3))\n",
+    "for i in range(6):\n",
+    "    outcome = expit_func(idata.posterior.response_threshold).sel(response_threshold_dim=i).to_numpy().flatten()\n",
+    "    ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n",
+    "ax.set_xlabel(\"Probability\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Cumulative Probability by Category\")\n",
+    "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The intercept only model above..."
+    "Notice in both plots above, the jump in cumulative probability from category 3 to 4. Additionally, the estimates of the coefficients is precise for each category. Now that we have an understanding how the cumulative link function is applied to produce ordered cumulative outcomes, we will add predictors to the model. "
    ]
   },
   {
@@ -283,14 +450,14 @@
     "\n",
     "$$\\text{log} \\frac{Pr(y_i \\le k)}{1 - Pr(y_i \\le k)} = \\alpha_k - \\phi_i$$\n",
     "\n",
-    "The linear model $\\phi$ is subtracted from each intercept because if we decrease the log-cumulative- odds of every outcome value $k$ below the maximum, this shifts probability mass upwards towards higher outcome values. Thus, positive $\\beta$ values correspond to increasing $x$, which is associated with an increase in the mean $y$.\n",
+    "The linear model $\\phi$ is subtracted from each intercept because if we decrease the log-cumulative-odds of every outcome value $k$ below the maximum, this shifts probability mass upwards towards higher outcome values. Thus, positive $\\beta$ values correspond to increasing $x$, which is associated with an increase in the mean $y$.\n",
     "\n",
-    "To add predictors for ordinal models in Bambi, we continue to use the familiar syntax."
+    "However, to add predictors for ordinal models in Bambi, we continue to use the familiar syntax."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -353,6 +520,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
       "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 79 seconds.\n"
@@ -368,9 +537,16 @@
     "idata = model.fit()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the summary dataframe below, we only select the predictor variables as the cutpoints are not of interest at the moment."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -408,62 +584,62 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>action[1]</th>\n",
-       "      <td>-0.465</td>\n",
+       "      <td>-0.466</td>\n",
        "      <td>0.055</td>\n",
        "      <td>-0.567</td>\n",
-       "      <td>-0.363</td>\n",
+       "      <td>-0.361</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2617.0</td>\n",
-       "      <td>2808.0</td>\n",
+       "      <td>2387.0</td>\n",
+       "      <td>2733.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>intention[1]</th>\n",
-       "      <td>-0.274</td>\n",
+       "      <td>-0.276</td>\n",
        "      <td>0.058</td>\n",
-       "      <td>-0.385</td>\n",
-       "      <td>-0.168</td>\n",
+       "      <td>-0.392</td>\n",
+       "      <td>-0.174</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2436.0</td>\n",
-       "      <td>2892.0</td>\n",
+       "      <td>2376.0</td>\n",
+       "      <td>2645.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact[1]</th>\n",
-       "      <td>-0.323</td>\n",
-       "      <td>0.069</td>\n",
-       "      <td>-0.448</td>\n",
-       "      <td>-0.192</td>\n",
+       "      <td>-0.325</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>-0.459</td>\n",
+       "      <td>-0.195</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2633.0</td>\n",
-       "      <td>3029.0</td>\n",
+       "      <td>2709.0</td>\n",
+       "      <td>2889.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>action:intention[1, 1]</th>\n",
-       "      <td>-0.456</td>\n",
-       "      <td>0.082</td>\n",
-       "      <td>-0.604</td>\n",
-       "      <td>-0.301</td>\n",
+       "      <td>-0.455</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>-0.600</td>\n",
+       "      <td>-0.299</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2666.0</td>\n",
-       "      <td>2890.0</td>\n",
+       "      <td>2483.0</td>\n",
+       "      <td>2882.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact:intention[1, 1]</th>\n",
-       "      <td>-1.286</td>\n",
-       "      <td>0.100</td>\n",
-       "      <td>-1.479</td>\n",
-       "      <td>-1.107</td>\n",
+       "      <td>-1.284</td>\n",
+       "      <td>0.102</td>\n",
+       "      <td>-1.461</td>\n",
+       "      <td>-1.078</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2676.0</td>\n",
-       "      <td>3062.0</td>\n",
+       "      <td>2759.0</td>\n",
+       "      <td>2926.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -472,21 +648,21 @@
       ],
       "text/plain": [
        "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "action[1]               -0.465  0.055  -0.567   -0.363      0.001    0.001  \\\n",
-       "intention[1]            -0.274  0.058  -0.385   -0.168      0.001    0.001   \n",
-       "contact[1]              -0.323  0.069  -0.448   -0.192      0.001    0.001   \n",
-       "action:intention[1, 1]  -0.456  0.082  -0.604   -0.301      0.002    0.001   \n",
-       "contact:intention[1, 1] -1.286  0.100  -1.479   -1.107      0.002    0.001   \n",
+       "action[1]               -0.466  0.055  -0.567   -0.361      0.001    0.001  \\\n",
+       "intention[1]            -0.276  0.058  -0.392   -0.174      0.001    0.001   \n",
+       "contact[1]              -0.325  0.071  -0.459   -0.195      0.001    0.001   \n",
+       "action:intention[1, 1]  -0.455  0.080  -0.600   -0.299      0.002    0.001   \n",
+       "contact:intention[1, 1] -1.284  0.102  -1.461   -1.078      0.002    0.001   \n",
        "\n",
        "                         ess_bulk  ess_tail  r_hat  \n",
-       "action[1]                  2617.0    2808.0    1.0  \n",
-       "intention[1]               2436.0    2892.0    1.0  \n",
-       "contact[1]                 2633.0    3029.0    1.0  \n",
-       "action:intention[1, 1]     2666.0    2890.0    1.0  \n",
-       "contact:intention[1, 1]    2676.0    3062.0    1.0  "
+       "action[1]                  2387.0    2733.0    1.0  \n",
+       "intention[1]               2376.0    2645.0    1.0  \n",
+       "contact[1]                 2709.0    2889.0    1.0  \n",
+       "action:intention[1, 1]     2483.0    2882.0    1.0  \n",
+       "contact:intention[1, 1]    2759.0    2926.0    1.0  "
       ]
      },
-     "execution_count": 73,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -499,14 +675,21 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The posterior distribution of the slopes are all negative indicating that each of these story features reduces the rating—the acceptability of the story. Below, a forest plot is used to make this insight more clear."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -526,6 +709,104 @@
     ");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we can plot the cumulative probability of each category. Compared to the same plot above, notice how most of the category probabilities have been shifted to the left. Additionally, there is more uncertainty for category 3, 4, and 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3))\n",
+    "for i in range(6):\n",
+    "    outcome = expit_func(idata.posterior.response_threshold).sel(response_threshold_dim=i).to_numpy().flatten()\n",
+    "    ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n",
+    "ax.set_xlabel(\"Probability\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Cumulative Probability by Category\")\n",
+    "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Posterior predictive distribution\n",
+    "\n",
+    "To get a sense of how well the ordinal model fits the data, we can plot samples from the posterior predictive distribution. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idata_pps = model.predict(idata=idata, kind=\"pps\", inplace=False)\n",
+    "\n",
+    "ax = az.plot_ppc(idata_pps, figsize=(7, 3), textsize=11)\n",
+    "ax.set_xticks(np.linspace(0.2, 7, 7))\n",
+    "ax.set_xticklabels(model.response_component.response_term.levels);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the observed and posterior predictive mean, the model captures the observed frequencies of the categories well. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# does not work because predictions are a vector of probabilities\n",
+    "bmb.interpret.predictions(\n",
+    "    model=model,\n",
+    "    idata=idata,\n",
+    "    covariates=\"action\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Changing the default prior of cutpoints\n",
+    "\n",
+    "\n",
+    "**TO DO**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/docs/notebooks/thumbnails/ordinal_regression.png b/docs/notebooks/thumbnails/ordinal_regression.png
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HcmV?d00001


From a2167bc80670f36bce42b31b0144694cd9a761fe Mon Sep 17 00:00:00 2001
From: Gabriel Stechschulte <gabriel.stechschulte@schindler.com>
Date: Thu, 14 Sep 2023 14:23:44 +0200
Subject: [PATCH 03/13] unified explanation for cumulative and sequential
 models

---
 docs/notebooks/ordinal_regression.ipynb | 210 +++++++++++++++---------
 1 file changed, 129 insertions(+), 81 deletions(-)

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index 9913f843a..cb1cfed0e 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -2,9 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING (pytensor.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`\n",
+      "WARNING (pytensor.configdefaults): g++ not detected!  PyTensor will be unable to compile C-implementations and will default to Python. Performance may be severely degraded. To remove this warning, set PyTensor flags cxx to an empty string.\n",
+      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    }
+   ],
    "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
@@ -36,19 +46,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Cumulative link model\n",
+    "## Cumulative model\n",
     "\n",
-    "In principle, an ordered categorical response is a multinomial prediction problem. However, the constraint that the categories are *ordered* requires a different approach. Ideally, what we would like is for any predictor variable, as it increases, predictions are moved progressively (increased) through the categories in sequence. \n",
+    "A cumulative model assumes that the observed ordinal variable $Y$ originates from the \"categorization\" of a latent continuous variable $\\hat{Y}$. To model the categorization process, the model assumes that there are $K$ thresholds (cutpoints) $\\tau_k$ that partition $\\hat{Y}$ into $K+1$ observable, ordered categories. Additionally, if we assume $\\hat{Y}$ to have a certain distribution (e.g., Normal) with a cumulative distribution function $F$, the probability of $Y$ being equal to category $k$ is\n",
     "\n",
-    "To achieve this, a cumulative link function is used. A cumulative model assumes that the observed ordinal category $Y$ is generated from an underlying latent continuous variable $\\hat{Y}$, which is then mapped to the observed category $Y$ via a set of cutpoints $\\tau$. For example, the model assumes $K$ cutpoints $\\tau_{k}$ that partition $\\hat{Y}$ into $K+1$ observable, ordered categories.\n",
+    "$$Pr(Y = k) = F(\\tau_k) - F(\\tau_{k-1})$$\n",
     "\n",
-    "By linking a linear model to a cumulative probability, it is possible to guarantee the ordering of outcomes. The cumulative probability of an ordered category is the probability of that value or *any smaller value*. Building off of the rating example above, the probability of a 4 is the probability of 4, 3, 2, and 1.\n",
+    "where each $F(\\tau)$ is a cumulative probability. For example, suppose we have 3 categories and we are interested in the probability of $k=3$, and have thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$. Additionally, if we assume $\\hat{Y}$ to be normally distributed with $\\sigma = 1$ then\n",
     "\n",
-    "The log-cumulative-odds that a response value $y_i$ is equal to or less than some possible outcome value $k$ is given by:\n",
+    "$$Pr(Y = 3) = \\Phi(\\tau_2) - \\Phi(\\tau_1) - \\Phi(\\tau_0)$$\n",
     "\n",
-    "$$\\text{log} \\frac{Pr(y_i \\le k)}{1 - Pr(y_i \\le k)} = \\alpha_k$$\n",
+    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$'s that depends on the number of $k$ as the prior for the thresholds. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
     "\n",
-    "where $\\alpha_k$ is the intercept of outcome $k$. Each intercept $\\alpha_k$ implies a cumulative probability for each $k$. Since the largest response value always has a cumulative probability of 1, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 5$ possible response values, we only need $K − 1 = 4$ intercepts."
+    "Lastly, as each $F(\\tau)$ implies a cumulative probability for each $k$, the largest response value always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 3$ response values, we only need $K − 1 = 2$ intercepts."
    ]
   },
   {
@@ -68,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +102,7 @@
        "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -113,14 +123,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_32825/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "C:\\Users\\stechsga\\AppData\\Local\\Temp\\ipykernel_9160\\1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
       "  logit_func = lambda x: np.log(x / (1 - x))\n"
      ]
     },
@@ -131,7 +141,7 @@
        "        1.76938091,         nan])"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,15 +156,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
+      "c:\\Users\\stechsga\\Miniconda3\\envs\\bambi\\Lib\\site-packages\\bambi\\formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
       "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
+      "c:\\Users\\stechsga\\Miniconda3\\envs\\bambi\\Lib\\site-packages\\formulae\\terms\\variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
@@ -194,7 +206,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 19:14&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -209,17 +221,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1174 seconds.\n"
      ]
     }
    ],
@@ -230,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -270,11 +272,11 @@
        "      <th>response_threshold[0]</th>\n",
        "      <td>-1.923</td>\n",
        "      <td>0.030</td>\n",
-       "      <td>-1.981</td>\n",
-       "      <td>-1.869</td>\n",
+       "      <td>-1.979</td>\n",
+       "      <td>-1.868</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4206.0</td>\n",
+       "      <td>4152.0</td>\n",
        "      <td>3065.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
@@ -282,60 +284,60 @@
        "      <th>response_threshold[1]</th>\n",
        "      <td>-1.270</td>\n",
        "      <td>0.024</td>\n",
-       "      <td>-1.314</td>\n",
-       "      <td>-1.225</td>\n",
+       "      <td>-1.315</td>\n",
+       "      <td>-1.223</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5095.0</td>\n",
-       "      <td>3329.0</td>\n",
+       "      <td>5031.0</td>\n",
+       "      <td>3389.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[2]</th>\n",
        "      <td>-0.719</td>\n",
        "      <td>0.021</td>\n",
-       "      <td>-0.760</td>\n",
-       "      <td>-0.681</td>\n",
+       "      <td>-0.758</td>\n",
+       "      <td>-0.678</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5042.0</td>\n",
-       "      <td>3302.0</td>\n",
+       "      <td>5272.0</td>\n",
+       "      <td>3462.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[3]</th>\n",
        "      <td>0.248</td>\n",
        "      <td>0.020</td>\n",
-       "      <td>0.212</td>\n",
-       "      <td>0.286</td>\n",
+       "      <td>0.208</td>\n",
+       "      <td>0.285</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4812.0</td>\n",
-       "      <td>3272.0</td>\n",
+       "      <td>4636.0</td>\n",
+       "      <td>3102.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[4]</th>\n",
        "      <td>0.892</td>\n",
        "      <td>0.022</td>\n",
-       "      <td>0.851</td>\n",
-       "      <td>0.933</td>\n",
+       "      <td>0.852</td>\n",
+       "      <td>0.935</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4978.0</td>\n",
-       "      <td>3397.0</td>\n",
+       "      <td>4861.0</td>\n",
+       "      <td>3639.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[5]</th>\n",
-       "      <td>1.775</td>\n",
-       "      <td>0.028</td>\n",
+       "      <td>1.774</td>\n",
+       "      <td>0.029</td>\n",
        "      <td>1.721</td>\n",
-       "      <td>1.827</td>\n",
+       "      <td>1.829</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5575.0</td>\n",
-       "      <td>3372.0</td>\n",
+       "      <td>5602.0</td>\n",
+       "      <td>3655.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -343,24 +345,24 @@
        "</div>"
       ],
       "text/plain": [
-       "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "response_threshold[0] -1.923  0.030  -1.981   -1.869        0.0      0.0  \\\n",
-       "response_threshold[1] -1.270  0.024  -1.314   -1.225        0.0      0.0   \n",
-       "response_threshold[2] -0.719  0.021  -0.760   -0.681        0.0      0.0   \n",
-       "response_threshold[3]  0.248  0.020   0.212    0.286        0.0      0.0   \n",
-       "response_threshold[4]  0.892  0.022   0.851    0.933        0.0      0.0   \n",
-       "response_threshold[5]  1.775  0.028   1.721    1.827        0.0      0.0   \n",
+       "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "response_threshold[0] -1.923  0.030  -1.979   -1.868        0.0      0.0   \n",
+       "response_threshold[1] -1.270  0.024  -1.315   -1.223        0.0      0.0   \n",
+       "response_threshold[2] -0.719  0.021  -0.758   -0.678        0.0      0.0   \n",
+       "response_threshold[3]  0.248  0.020   0.208    0.285        0.0      0.0   \n",
+       "response_threshold[4]  0.892  0.022   0.852    0.935        0.0      0.0   \n",
+       "response_threshold[5]  1.774  0.029   1.721    1.829        0.0      0.0   \n",
        "\n",
        "                       ess_bulk  ess_tail  r_hat  \n",
-       "response_threshold[0]    4206.0    3065.0    1.0  \n",
-       "response_threshold[1]    5095.0    3329.0    1.0  \n",
-       "response_threshold[2]    5042.0    3302.0    1.0  \n",
-       "response_threshold[3]    4812.0    3272.0    1.0  \n",
-       "response_threshold[4]    4978.0    3397.0    1.0  \n",
-       "response_threshold[5]    5575.0    3372.0    1.0  "
+       "response_threshold[0]    4152.0    3065.0    1.0  \n",
+       "response_threshold[1]    5031.0    3389.0    1.0  \n",
+       "response_threshold[2]    5272.0    3462.0    1.0  \n",
+       "response_threshold[3]    4636.0    3102.0    1.0  \n",
+       "response_threshold[4]    4861.0    3639.0    1.0  \n",
+       "response_threshold[5]    5602.0    3655.0    1.0  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,17 +382,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 700x300 with 1 Axes>"
+       "<Figure size 504x216 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -407,17 +411,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 700x300 with 1 Axes>"
+       "<Figure size 504x216 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -445,14 +451,17 @@
    "source": [
     "### Adding predictors\n",
     "\n",
+    "In the cumulative model described above, adding predictors was explicitly left out. In this section, it is described how predictors are added to ordinal cumulative models. When adding predictor variables, what we would like is for any predictor, as it increases, predictions are moved progressively (increased) through the categories in sequence. A predictor term $\\eta$ is defined as\n",
+    "\n",
+    "$$\\eta = \\beta_1 x_1 + \\beta_2 x_2 +, . . ., \\beta_n x_n + \\epsilon$$\n",
     "\n",
-    "To include predictor variables, we define the log-cumulative-odds of each response $k$ as a sum of its intercept $\\alpha_k$ and a linear model $\\phi$ where $\\phi_i = \\beta x_i$\n",
+    "Note how similar this looks to an ordinary linear model. However, there is no intercept term. This is because the intercept is replaced by the threshold $\\tau$. Putting the predictor term together with the thresholds and cumulative distribution function, we obtain the probability of $Y$ being equal to a category $k$ as\n",
     "\n",
-    "$$\\text{log} \\frac{Pr(y_i \\le k)}{1 - Pr(y_i \\le k)} = \\alpha_k - \\phi_i$$\n",
+    "$$Pr(Y = k | \\eta) = F(\\tau_k - \\eta) - F(\\tau_{k-1} - \\eta)$$\n",
     "\n",
-    "The linear model $\\phi$ is subtracted from each intercept because if we decrease the log-cumulative-odds of every outcome value $k$ below the maximum, this shifts probability mass upwards towards higher outcome values. Thus, positive $\\beta$ values correspond to increasing $x$, which is associated with an increase in the mean $y$.\n",
+    "The same predictor term $\\eta$ is subtracted from each threshold because if we decrease the log-cumulative-odds of every outcome value $k$ below the maximum, this shifts probability mass upwards towards higher outcome values. Thus, positive $\\beta$ values correspond to increasing $x$, which is associated with an increase in the mean response $Y$. The parameters to be estimated from the model are the thresholds $\\tau$ and the predictor terms $\\eta$ coefficients.  \n",
     "\n",
-    "However, to add predictors for ordinal models in Bambi, we continue to use the familiar syntax."
+    "To add predictors for ordinal models in Bambi, we continue to use the familiar syntax."
    ]
   },
   {
@@ -807,6 +816,45 @@
     "**TO DO**"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sequential Model\n",
+    "\n",
+    "For some ordinal variables, the assumption of a **single** underlying continuous variable (as in cumulative models) may not be appropriate. If the response can be understood as being the result of a sequential process, such that a higher response category is possible only after all lower categories are achieved, then a sequential model may be more appropriate than a cumulative model.\n",
+    "\n",
+    "Sequential models assume that for **every** category $k$ there is latent continuous variable $\\hat{Y_k}$ that determines the transition between categories $k$ and $k+1$. Now, a threshold $\\tau$ belongs to each latent process. If there are 3 categories, then there are 3 latent processes. If $\\hat{Y}_k$ is greater than the threshold $\\tau_k$, the sequential process continues, otherwise it stops at category $k$. As with the cumulative model, we still assume a distribution for $\\hat{Y}_k$ with a cumulative distribution function $F$.\n",
+    "\n",
+    "As an example, lets suppose we are interested in modeling the probability a boxer makes it to round 3. This implies that the particular boxer in question survived round 1 $\\hat{Y}_1 > \\tau_1$ , 2 $\\hat{Y}_2 > \\tau_2$, and 3 $\\hat{Y}_3 > \\tau_3$. This can be written as \n",
+    "\n",
+    "$$Pr(Y = 3) = (1 - Pr(\\hat{Y_1})(1 - Pr(\\hat{Y_2}))(1 - Pr(\\hat{Y_3})$$\n",
+    "\n",
+    "As in the cumulative model above, if we assume $Y$ to be normally distributed with the thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$ and cumulative distribution function $\\Phi$ then\n",
+    "\n",
+    "$$Pr(Y = 3) = (1 - \\Phi(\\tau_0))(1 - \\Phi(\\tau_1))(1 - \\Phi(\\tau_2))$$\n",
+    "\n",
+    "To add predictors to this sequential model, we follow the same specification in the _Adding Predictors_ section above. Thus, the sequential model with predictor terms becomes\n",
+    "\n",
+    "$$P(Y = k) = F(\\tau_k - \\eta) * \\prod_{j=1}^{k-1}{(1 - F(\\tau_j - \\eta))}$$\n",
+    "\n",
+    "Thus, the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$ rather than continuing past it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -831,7 +879,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.5"
   },
   "orig_nbformat": 4
  },

From 7f9b118e9734b4abec80437fa86ab46aed40301b Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Fri, 15 Sep 2023 06:34:15 +0200
Subject: [PATCH 04/13] sratio model and data

---
 .../data/hr_employee_attrition.tsv.txt        | 1471 +++++++++++++++++
 docs/notebooks/ordinal_regression.ipynb       | 1198 ++++++++++++--
 2 files changed, 2548 insertions(+), 121 deletions(-)
 create mode 100644 docs/notebooks/data/hr_employee_attrition.tsv.txt

diff --git a/docs/notebooks/data/hr_employee_attrition.tsv.txt b/docs/notebooks/data/hr_employee_attrition.tsv.txt
new file mode 100644
index 000000000..28ee7a020
--- /dev/null
+++ b/docs/notebooks/data/hr_employee_attrition.tsv.txt
@@ -0,0 +1,1471 @@
+Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
+41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
+49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
+37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
+33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
+27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2
+32	No	Travel_Frequently	1005	Research & Development	2	2	Life Sciences	1	8	4	Male	79	3	1	Laboratory Technician	4	Single	3068	11864	0	Y	No	13	3	3	80	0	8	2	2	7	7	3	6
+59	No	Travel_Rarely	1324	Research & Development	3	3	Medical	1	10	3	Female	81	4	1	Laboratory Technician	1	Married	2670	9964	4	Y	Yes	20	4	1	80	3	12	3	2	1	0	0	0
+30	No	Travel_Rarely	1358	Research & Development	24	1	Life Sciences	1	11	4	Male	67	3	1	Laboratory Technician	3	Divorced	2693	13335	1	Y	No	22	4	2	80	1	1	2	3	1	0	0	0
+38	No	Travel_Frequently	216	Research & Development	23	3	Life Sciences	1	12	4	Male	44	2	3	Manufacturing Director	3	Single	9526	8787	0	Y	No	21	4	2	80	0	10	2	3	9	7	1	8
+36	No	Travel_Rarely	1299	Research & Development	27	3	Medical	1	13	3	Male	94	3	2	Healthcare Representative	3	Married	5237	16577	6	Y	No	13	3	2	80	2	17	3	2	7	7	7	7
+35	No	Travel_Rarely	809	Research & Development	16	3	Medical	1	14	1	Male	84	4	1	Laboratory Technician	2	Married	2426	16479	0	Y	No	13	3	3	80	1	6	5	3	5	4	0	3
+29	No	Travel_Rarely	153	Research & Development	15	2	Life Sciences	1	15	4	Female	49	2	2	Laboratory Technician	3	Single	4193	12682	0	Y	Yes	12	3	4	80	0	10	3	3	9	5	0	8
+31	No	Travel_Rarely	670	Research & Development	26	1	Life Sciences	1	16	1	Male	31	3	1	Research Scientist	3	Divorced	2911	15170	1	Y	No	17	3	4	80	1	5	1	2	5	2	4	3
+34	No	Travel_Rarely	1346	Research & Development	19	2	Medical	1	18	2	Male	93	3	1	Laboratory Technician	4	Divorced	2661	8758	0	Y	No	11	3	3	80	1	3	2	3	2	2	1	2
+28	Yes	Travel_Rarely	103	Research & Development	24	3	Life Sciences	1	19	3	Male	50	2	1	Laboratory Technician	3	Single	2028	12947	5	Y	Yes	14	3	2	80	0	6	4	3	4	2	0	3
+29	No	Travel_Rarely	1389	Research & Development	21	4	Life Sciences	1	20	2	Female	51	4	3	Manufacturing Director	1	Divorced	9980	10195	1	Y	No	11	3	3	80	1	10	1	3	10	9	8	8
+32	No	Travel_Rarely	334	Research & Development	5	2	Life Sciences	1	21	1	Male	80	4	1	Research Scientist	2	Divorced	3298	15053	0	Y	Yes	12	3	4	80	2	7	5	2	6	2	0	5
+22	No	Non-Travel	1123	Research & Development	16	2	Medical	1	22	4	Male	96	4	1	Laboratory Technician	4	Divorced	2935	7324	1	Y	Yes	13	3	2	80	2	1	2	2	1	0	0	0
+53	No	Travel_Rarely	1219	Sales	2	4	Life Sciences	1	23	1	Female	78	2	4	Manager	4	Married	15427	22021	2	Y	No	16	3	3	80	0	31	3	3	25	8	3	7
+38	No	Travel_Rarely	371	Research & Development	2	3	Life Sciences	1	24	4	Male	45	3	1	Research Scientist	4	Single	3944	4306	5	Y	Yes	11	3	3	80	0	6	3	3	3	2	1	2
+24	No	Non-Travel	673	Research & Development	11	2	Other	1	26	1	Female	96	4	2	Manufacturing Director	3	Divorced	4011	8232	0	Y	No	18	3	4	80	1	5	5	2	4	2	1	3
+36	Yes	Travel_Rarely	1218	Sales	9	4	Life Sciences	1	27	3	Male	82	2	1	Sales Representative	1	Single	3407	6986	7	Y	No	23	4	2	80	0	10	4	3	5	3	0	3
+34	No	Travel_Rarely	419	Research & Development	7	4	Life Sciences	1	28	1	Female	53	3	3	Research Director	2	Single	11994	21293	0	Y	No	11	3	3	80	0	13	4	3	12	6	2	11
+21	No	Travel_Rarely	391	Research & Development	15	2	Life Sciences	1	30	3	Male	96	3	1	Research Scientist	4	Single	1232	19281	1	Y	No	14	3	4	80	0	0	6	3	0	0	0	0
+34	Yes	Travel_Rarely	699	Research & Development	6	1	Medical	1	31	2	Male	83	3	1	Research Scientist	1	Single	2960	17102	2	Y	No	11	3	3	80	0	8	2	3	4	2	1	3
+53	No	Travel_Rarely	1282	Research & Development	5	3	Other	1	32	3	Female	58	3	5	Manager	3	Divorced	19094	10735	4	Y	No	11	3	4	80	1	26	3	2	14	13	4	8
+32	Yes	Travel_Frequently	1125	Research & Development	16	1	Life Sciences	1	33	2	Female	72	1	1	Research Scientist	1	Single	3919	4681	1	Y	Yes	22	4	2	80	0	10	5	3	10	2	6	7
+42	No	Travel_Rarely	691	Sales	8	4	Marketing	1	35	3	Male	48	3	2	Sales Executive	2	Married	6825	21173	0	Y	No	11	3	4	80	1	10	2	3	9	7	4	2
+44	No	Travel_Rarely	477	Research & Development	7	4	Medical	1	36	1	Female	42	2	3	Healthcare Representative	4	Married	10248	2094	3	Y	No	14	3	4	80	1	24	4	3	22	6	5	17
+46	No	Travel_Rarely	705	Sales	2	4	Marketing	1	38	2	Female	83	3	5	Manager	1	Single	18947	22822	3	Y	No	12	3	4	80	0	22	2	2	2	2	2	1
+33	No	Travel_Rarely	924	Research & Development	2	3	Medical	1	39	3	Male	78	3	1	Laboratory Technician	4	Single	2496	6670	4	Y	No	11	3	4	80	0	7	3	3	1	1	0	0
+44	No	Travel_Rarely	1459	Research & Development	10	4	Other	1	40	4	Male	41	3	2	Healthcare Representative	4	Married	6465	19121	2	Y	Yes	13	3	4	80	0	9	5	4	4	2	1	3
+30	No	Travel_Rarely	125	Research & Development	9	2	Medical	1	41	4	Male	83	2	1	Laboratory Technician	3	Single	2206	16117	1	Y	No	13	3	1	80	0	10	5	3	10	0	1	8
+39	Yes	Travel_Rarely	895	Sales	5	3	Technical Degree	1	42	4	Male	56	3	2	Sales Representative	4	Married	2086	3335	3	Y	No	14	3	3	80	1	19	6	4	1	0	0	0
+24	Yes	Travel_Rarely	813	Research & Development	1	3	Medical	1	45	2	Male	61	3	1	Research Scientist	4	Married	2293	3020	2	Y	Yes	16	3	1	80	1	6	2	2	2	0	2	0
+43	No	Travel_Rarely	1273	Research & Development	2	2	Medical	1	46	4	Female	72	4	1	Research Scientist	3	Divorced	2645	21923	1	Y	No	12	3	4	80	2	6	3	2	5	3	1	4
+50	Yes	Travel_Rarely	869	Sales	3	2	Marketing	1	47	1	Male	86	2	1	Sales Representative	3	Married	2683	3810	1	Y	Yes	14	3	3	80	0	3	2	3	3	2	0	2
+35	No	Travel_Rarely	890	Sales	2	3	Marketing	1	49	4	Female	97	3	1	Sales Representative	4	Married	2014	9687	1	Y	No	13	3	1	80	0	2	3	3	2	2	2	2
+36	No	Travel_Rarely	852	Research & Development	5	4	Life Sciences	1	51	2	Female	82	2	1	Research Scientist	1	Married	3419	13072	9	Y	Yes	14	3	4	80	1	6	3	4	1	1	0	0
+33	No	Travel_Frequently	1141	Sales	1	3	Life Sciences	1	52	3	Female	42	4	2	Sales Executive	1	Married	5376	3193	2	Y	No	19	3	1	80	2	10	3	3	5	3	1	3
+35	No	Travel_Rarely	464	Research & Development	4	2	Other	1	53	3	Male	75	3	1	Laboratory Technician	4	Divorced	1951	10910	1	Y	No	12	3	3	80	1	1	3	3	1	0	0	0
+27	No	Travel_Rarely	1240	Research & Development	2	4	Life Sciences	1	54	4	Female	33	3	1	Laboratory Technician	1	Divorced	2341	19715	1	Y	No	13	3	4	80	1	1	6	3	1	0	0	0
+26	Yes	Travel_Rarely	1357	Research & Development	25	3	Life Sciences	1	55	1	Male	48	1	1	Laboratory Technician	3	Single	2293	10558	1	Y	No	12	3	3	80	0	1	2	2	1	0	0	1
+27	No	Travel_Frequently	994	Sales	8	3	Life Sciences	1	56	4	Male	37	3	3	Sales Executive	3	Single	8726	2975	1	Y	No	15	3	4	80	0	9	0	3	9	8	1	7
+30	No	Travel_Frequently	721	Research & Development	1	2	Medical	1	57	3	Female	58	3	2	Laboratory Technician	4	Single	4011	10781	1	Y	No	23	4	4	80	0	12	2	3	12	8	3	7
+41	Yes	Travel_Rarely	1360	Research & Development	12	3	Technical Degree	1	58	2	Female	49	3	5	Research Director	3	Married	19545	16280	1	Y	No	12	3	4	80	0	23	0	3	22	15	15	8
+34	No	Non-Travel	1065	Sales	23	4	Marketing	1	60	2	Male	72	3	2	Sales Executive	3	Single	4568	10034	0	Y	No	20	4	3	80	0	10	2	3	9	5	8	7
+37	No	Travel_Rarely	408	Research & Development	19	2	Life Sciences	1	61	2	Male	73	3	1	Research Scientist	2	Married	3022	10227	4	Y	No	21	4	1	80	0	8	1	3	1	0	0	0
+46	No	Travel_Frequently	1211	Sales	5	4	Marketing	1	62	1	Male	98	3	2	Sales Executive	4	Single	5772	20445	4	Y	Yes	21	4	3	80	0	14	4	3	9	6	0	8
+35	No	Travel_Rarely	1229	Research & Development	8	1	Life Sciences	1	63	4	Male	36	4	1	Laboratory Technician	4	Married	2269	4892	1	Y	No	19	3	4	80	0	1	2	3	1	0	0	1
+48	Yes	Travel_Rarely	626	Research & Development	1	2	Life Sciences	1	64	1	Male	98	2	3	Laboratory Technician	3	Single	5381	19294	9	Y	Yes	13	3	4	80	0	23	2	3	1	0	0	0
+28	Yes	Travel_Rarely	1434	Research & Development	5	4	Technical Degree	1	65	3	Male	50	3	1	Laboratory Technician	3	Single	3441	11179	1	Y	Yes	13	3	3	80	0	2	3	2	2	2	2	2
+44	No	Travel_Rarely	1488	Sales	1	5	Marketing	1	68	2	Female	75	3	2	Sales Executive	1	Divorced	5454	4009	5	Y	Yes	21	4	3	80	1	9	2	2	4	3	1	3
+35	No	Non-Travel	1097	Research & Development	11	2	Medical	1	70	3	Male	79	2	3	Healthcare Representative	1	Married	9884	8302	2	Y	Yes	13	3	3	80	1	10	3	3	4	0	2	3
+26	No	Travel_Rarely	1443	Sales	23	3	Marketing	1	72	3	Female	47	2	2	Sales Executive	4	Married	4157	21436	7	Y	Yes	19	3	3	80	1	5	2	2	2	2	0	0
+33	No	Travel_Frequently	515	Research & Development	1	2	Life Sciences	1	73	1	Female	98	3	3	Research Director	4	Single	13458	15146	1	Y	Yes	12	3	3	80	0	15	1	3	15	14	8	12
+35	No	Travel_Frequently	853	Sales	18	5	Life Sciences	1	74	2	Male	71	3	3	Sales Executive	1	Married	9069	11031	1	Y	No	22	4	4	80	1	9	3	2	9	8	1	8
+35	No	Travel_Rarely	1142	Research & Development	23	4	Medical	1	75	3	Female	30	3	1	Laboratory Technician	1	Married	4014	16002	3	Y	Yes	15	3	3	80	1	4	3	3	2	2	2	2
+31	No	Travel_Rarely	655	Research & Development	7	4	Life Sciences	1	76	4	Male	48	3	2	Laboratory Technician	4	Divorced	5915	9528	3	Y	No	22	4	4	80	1	10	3	2	7	7	1	7
+37	No	Travel_Rarely	1115	Research & Development	1	4	Life Sciences	1	77	1	Male	51	2	2	Manufacturing Director	3	Divorced	5993	2689	1	Y	No	18	3	3	80	1	7	2	4	7	5	0	7
+32	No	Travel_Rarely	427	Research & Development	1	3	Medical	1	78	1	Male	33	3	2	Manufacturing Director	4	Married	6162	10877	1	Y	Yes	22	4	2	80	1	9	3	3	9	8	7	8
+38	No	Travel_Frequently	653	Research & Development	29	5	Life Sciences	1	79	4	Female	50	3	2	Laboratory Technician	4	Single	2406	5456	1	Y	No	11	3	4	80	0	10	2	3	10	3	9	9
+50	No	Travel_Rarely	989	Research & Development	7	2	Medical	1	80	2	Female	43	2	5	Research Director	3	Divorced	18740	16701	5	Y	Yes	12	3	4	80	1	29	2	2	27	3	13	8
+59	No	Travel_Rarely	1435	Sales	25	3	Life Sciences	1	81	1	Female	99	3	3	Sales Executive	1	Single	7637	2354	7	Y	No	11	3	4	80	0	28	3	2	21	16	7	9
+36	No	Travel_Rarely	1223	Research & Development	8	3	Technical Degree	1	83	3	Female	59	3	3	Healthcare Representative	3	Divorced	10096	8202	1	Y	No	13	3	2	80	3	17	2	3	17	14	12	8
+55	No	Travel_Rarely	836	Research & Development	8	3	Medical	1	84	4	Female	33	3	4	Manager	3	Divorced	14756	19730	2	Y	Yes	14	3	3	80	3	21	2	3	5	0	0	2
+36	No	Travel_Frequently	1195	Research & Development	11	3	Life Sciences	1	85	2	Male	95	2	2	Manufacturing Director	2	Single	6499	22656	1	Y	No	13	3	3	80	0	6	3	3	6	5	0	3
+45	No	Travel_Rarely	1339	Research & Development	7	3	Life Sciences	1	86	2	Male	59	3	3	Research Scientist	1	Divorced	9724	18787	2	Y	No	17	3	3	80	1	25	2	3	1	0	0	0
+35	No	Travel_Frequently	664	Research & Development	1	3	Medical	1	88	2	Male	79	3	1	Research Scientist	1	Married	2194	5868	4	Y	No	13	3	4	80	1	5	2	2	3	2	1	2
+36	Yes	Travel_Rarely	318	Research & Development	9	3	Medical	1	90	4	Male	79	2	1	Research Scientist	3	Married	3388	21777	0	Y	Yes	17	3	1	80	1	2	0	2	1	0	0	0
+59	No	Travel_Frequently	1225	Sales	1	1	Life Sciences	1	91	1	Female	57	2	2	Sales Executive	3	Single	5473	24668	7	Y	No	11	3	4	80	0	20	2	2	4	3	1	3
+29	No	Travel_Rarely	1328	Research & Development	2	3	Life Sciences	1	94	3	Male	76	3	1	Research Scientist	2	Married	2703	4956	0	Y	No	23	4	4	80	1	6	3	3	5	4	0	4
+31	No	Travel_Rarely	1082	Research & Development	1	4	Medical	1	95	3	Male	87	3	1	Research Scientist	2	Single	2501	18775	1	Y	No	17	3	2	80	0	1	4	3	1	1	1	0
+32	No	Travel_Rarely	548	Research & Development	1	3	Life Sciences	1	96	2	Male	66	3	2	Research Scientist	2	Married	6220	7346	1	Y	No	17	3	2	80	2	10	3	3	10	4	0	9
+36	No	Travel_Rarely	132	Research & Development	6	3	Life Sciences	1	97	2	Female	55	4	1	Laboratory Technician	4	Married	3038	22002	3	Y	No	12	3	2	80	0	5	3	3	1	0	0	0
+31	No	Travel_Rarely	746	Research & Development	8	4	Life Sciences	1	98	3	Female	61	3	2	Manufacturing Director	4	Single	4424	20682	1	Y	No	23	4	4	80	0	11	2	3	11	7	1	8
+35	No	Travel_Rarely	776	Sales	1	4	Marketing	1	100	3	Male	32	2	2	Sales Executive	1	Single	4312	23016	0	Y	No	14	3	2	80	0	16	2	3	15	13	2	8
+45	No	Travel_Rarely	193	Research & Development	6	4	Other	1	101	4	Male	52	3	3	Research Director	1	Married	13245	15067	4	Y	Yes	14	3	2	80	0	17	3	4	0	0	0	0
+37	No	Travel_Rarely	397	Research & Development	7	4	Medical	1	102	1	Male	30	3	3	Research Director	3	Single	13664	25258	4	Y	No	13	3	1	80	0	16	3	4	5	2	0	2
+46	No	Travel_Rarely	945	Human Resources	5	2	Medical	1	103	2	Male	80	3	2	Human Resources	2	Divorced	5021	10425	8	Y	Yes	22	4	4	80	1	16	2	3	4	2	0	2
+30	No	Travel_Rarely	852	Research & Development	1	1	Life Sciences	1	104	4	Male	55	2	2	Laboratory Technician	4	Married	5126	15998	1	Y	Yes	12	3	3	80	2	10	1	2	10	8	3	0
+35	No	Travel_Rarely	1214	Research & Development	1	3	Medical	1	105	2	Male	30	2	1	Research Scientist	3	Single	2859	26278	1	Y	No	18	3	1	80	0	6	3	3	6	4	0	4
+55	No	Travel_Rarely	111	Sales	1	2	Life Sciences	1	106	1	Male	70	3	3	Sales Executive	4	Married	10239	18092	3	Y	No	14	3	4	80	1	24	4	3	1	0	1	0
+38	No	Non-Travel	573	Research & Development	6	3	Medical	1	107	2	Female	79	1	2	Research Scientist	4	Divorced	5329	15717	7	Y	Yes	12	3	4	80	3	17	3	3	13	11	1	9
+34	No	Travel_Rarely	1153	Research & Development	1	2	Medical	1	110	1	Male	94	3	2	Manufacturing Director	2	Married	4325	17736	1	Y	No	15	3	3	80	0	5	2	3	5	2	1	3
+56	No	Travel_Rarely	1400	Research & Development	7	3	Life Sciences	1	112	4	Male	49	1	3	Manufacturing Director	4	Single	7260	21698	4	Y	No	11	3	1	80	0	37	3	2	6	4	0	2
+23	No	Travel_Rarely	541	Sales	2	1	Technical Degree	1	113	3	Male	62	3	1	Sales Representative	1	Divorced	2322	9518	3	Y	No	13	3	3	80	1	3	3	3	0	0	0	0
+51	No	Travel_Rarely	432	Research & Development	9	4	Life Sciences	1	116	4	Male	96	3	1	Laboratory Technician	4	Married	2075	18725	3	Y	No	23	4	2	80	2	10	4	3	4	2	0	3
+30	No	Travel_Rarely	288	Research & Development	2	3	Life Sciences	1	117	3	Male	99	2	2	Healthcare Representative	4	Married	4152	15830	1	Y	No	19	3	1	80	3	11	3	3	11	10	10	8
+46	Yes	Travel_Rarely	669	Sales	9	2	Medical	1	118	3	Male	64	2	3	Sales Executive	4	Single	9619	13596	1	Y	No	16	3	4	80	0	9	3	3	9	8	4	7
+40	No	Travel_Frequently	530	Research & Development	1	4	Life Sciences	1	119	3	Male	78	2	4	Healthcare Representative	2	Married	13503	14115	1	Y	No	22	4	4	80	1	22	3	2	22	3	11	11
+51	No	Travel_Rarely	632	Sales	21	4	Marketing	1	120	3	Male	71	3	2	Sales Executive	4	Single	5441	8423	0	Y	Yes	22	4	4	80	0	11	2	1	10	7	1	0
+30	No	Travel_Rarely	1334	Sales	4	2	Medical	1	121	3	Female	63	2	2	Sales Executive	2	Divorced	5209	19760	1	Y	Yes	12	3	2	80	3	11	4	2	11	8	2	7
+46	No	Travel_Frequently	638	Research & Development	1	3	Medical	1	124	3	Male	40	2	3	Healthcare Representative	1	Married	10673	3142	2	Y	Yes	13	3	3	80	1	21	5	2	10	9	9	5
+32	No	Travel_Rarely	1093	Sales	6	4	Medical	1	125	2	Male	87	3	2	Sales Executive	3	Single	5010	24301	1	Y	No	16	3	1	80	0	12	0	3	11	8	5	7
+54	No	Travel_Rarely	1217	Research & Development	2	4	Technical Degree	1	126	1	Female	60	3	3	Research Director	3	Married	13549	24001	9	Y	No	12	3	1	80	1	16	5	1	4	3	0	3
+24	No	Travel_Rarely	1353	Sales	3	2	Other	1	128	1	Female	33	3	2	Sales Executive	3	Married	4999	17519	0	Y	No	21	4	1	80	1	4	2	2	3	2	0	2
+28	No	Non-Travel	120	Sales	4	3	Medical	1	129	2	Male	43	3	2	Sales Executive	3	Married	4221	8863	1	Y	No	15	3	2	80	0	5	3	4	5	4	0	4
+58	No	Travel_Rarely	682	Sales	10	4	Medical	1	131	4	Male	37	3	4	Sales Executive	3	Single	13872	24409	0	Y	No	13	3	3	80	0	38	1	2	37	10	1	8
+44	No	Non-Travel	489	Research & Development	23	3	Medical	1	132	2	Male	67	3	2	Laboratory Technician	2	Married	2042	25043	4	Y	No	12	3	3	80	1	17	3	4	3	2	1	2
+37	Yes	Travel_Rarely	807	Human Resources	6	4	Human Resources	1	133	3	Male	63	3	1	Human Resources	1	Divorced	2073	23648	4	Y	Yes	22	4	4	80	0	7	3	3	3	2	0	2
+32	No	Travel_Rarely	827	Research & Development	1	1	Life Sciences	1	134	4	Male	71	3	1	Research Scientist	1	Single	2956	15178	1	Y	No	13	3	4	80	0	1	2	3	1	0	0	0
+20	Yes	Travel_Frequently	871	Research & Development	6	3	Life Sciences	1	137	4	Female	66	2	1	Laboratory Technician	4	Single	2926	19783	1	Y	Yes	18	3	2	80	0	1	5	3	1	0	1	0
+34	No	Travel_Rarely	665	Research & Development	6	4	Other	1	138	1	Female	41	3	2	Research Scientist	3	Single	4809	12482	1	Y	No	14	3	3	80	0	16	3	3	16	13	2	10
+37	No	Non-Travel	1040	Research & Development	2	2	Life Sciences	1	139	3	Male	100	2	2	Healthcare Representative	4	Divorced	5163	15850	5	Y	No	14	3	4	80	1	17	2	4	1	0	0	0
+59	No	Non-Travel	1420	Human Resources	2	4	Human Resources	1	140	3	Female	32	2	5	Manager	4	Married	18844	21922	9	Y	No	21	4	4	80	1	30	3	3	3	2	2	2
+50	No	Travel_Frequently	1115	Research & Development	1	3	Life Sciences	1	141	1	Female	73	3	5	Research Director	2	Married	18172	9755	3	Y	Yes	19	3	1	80	0	28	1	2	8	3	0	7
+25	Yes	Travel_Rarely	240	Sales	5	3	Marketing	1	142	3	Male	46	2	2	Sales Executive	3	Single	5744	26959	1	Y	Yes	11	3	4	80	0	6	1	3	6	4	0	3
+25	No	Travel_Rarely	1280	Research & Development	7	1	Medical	1	143	4	Male	64	2	1	Research Scientist	4	Married	2889	26897	1	Y	No	11	3	3	80	2	2	2	3	2	2	2	1
+22	No	Travel_Rarely	534	Research & Development	15	3	Medical	1	144	2	Female	59	3	1	Laboratory Technician	4	Single	2871	23785	1	Y	No	15	3	3	80	0	1	5	3	0	0	0	0
+51	No	Travel_Frequently	1456	Research & Development	1	4	Medical	1	145	1	Female	30	2	3	Healthcare Representative	1	Single	7484	25796	3	Y	No	20	4	3	80	0	23	1	2	13	12	12	8
+34	Yes	Travel_Frequently	658	Research & Development	7	3	Life Sciences	1	147	1	Male	66	1	2	Laboratory Technician	3	Single	6074	22887	1	Y	Yes	24	4	4	80	0	9	3	3	9	7	0	6
+54	No	Non-Travel	142	Human Resources	26	3	Human Resources	1	148	4	Female	30	4	4	Manager	4	Single	17328	13871	2	Y	Yes	12	3	3	80	0	23	3	3	5	3	4	4
+24	No	Travel_Rarely	1127	Research & Development	18	1	Life Sciences	1	150	2	Male	52	3	1	Laboratory Technician	3	Married	2774	13257	0	Y	No	12	3	3	80	1	6	2	3	5	3	1	2
+34	No	Travel_Rarely	1031	Research & Development	6	4	Life Sciences	1	151	3	Female	45	2	2	Research Scientist	2	Divorced	4505	15000	6	Y	No	15	3	3	80	1	12	3	3	1	0	0	0
+37	No	Travel_Rarely	1189	Sales	3	3	Life Sciences	1	152	3	Male	87	3	3	Sales Executive	4	Single	7428	14506	2	Y	No	12	3	1	80	0	12	3	3	5	3	1	3
+34	No	Travel_Rarely	1354	Research & Development	5	3	Medical	1	153	3	Female	45	2	3	Manager	1	Single	11631	5615	2	Y	No	12	3	4	80	0	14	6	3	11	10	5	8
+36	No	Travel_Frequently	1467	Sales	11	2	Technical Degree	1	154	2	Female	92	3	3	Sales Executive	4	Married	9738	22952	0	Y	No	14	3	3	80	1	10	6	3	9	7	2	8
+36	No	Travel_Rarely	922	Research & Development	3	2	Life Sciences	1	155	1	Female	39	3	1	Laboratory Technician	4	Divorced	2835	2561	5	Y	No	22	4	1	80	1	7	2	3	1	0	0	0
+43	No	Travel_Frequently	394	Sales	26	2	Life Sciences	1	158	3	Male	92	3	4	Manager	4	Married	16959	19494	1	Y	Yes	12	3	4	80	2	25	3	4	25	12	4	12
+30	No	Travel_Frequently	1312	Research & Development	23	3	Life Sciences	1	159	1	Male	96	1	1	Research Scientist	3	Divorced	2613	22310	1	Y	No	25	4	3	80	3	10	2	2	10	7	0	9
+33	No	Non-Travel	750	Sales	22	2	Marketing	1	160	3	Male	95	3	2	Sales Executive	2	Married	6146	15480	0	Y	No	13	3	1	80	1	8	2	4	7	7	0	7
+56	Yes	Travel_Rarely	441	Research & Development	14	4	Life Sciences	1	161	2	Female	72	3	1	Research Scientist	2	Married	4963	4510	9	Y	Yes	18	3	1	80	3	7	2	3	5	4	4	3
+51	No	Travel_Rarely	684	Research & Development	6	3	Life Sciences	1	162	1	Male	51	3	5	Research Director	3	Single	19537	6462	7	Y	No	13	3	3	80	0	23	5	3	20	18	15	15
+31	Yes	Travel_Rarely	249	Sales	6	4	Life Sciences	1	163	2	Male	76	1	2	Sales Executive	3	Married	6172	20739	4	Y	Yes	18	3	2	80	0	12	3	2	7	7	7	7
+26	No	Travel_Rarely	841	Research & Development	6	3	Other	1	164	3	Female	46	2	1	Research Scientist	2	Married	2368	23300	1	Y	No	19	3	3	80	0	5	3	2	5	4	4	3
+58	Yes	Travel_Rarely	147	Research & Development	23	4	Medical	1	165	4	Female	94	3	3	Healthcare Representative	4	Married	10312	3465	1	Y	No	12	3	4	80	1	40	3	2	40	10	15	6
+19	Yes	Travel_Rarely	528	Sales	22	1	Marketing	1	167	4	Male	50	3	1	Sales Representative	3	Single	1675	26820	1	Y	Yes	19	3	4	80	0	0	2	2	0	0	0	0
+22	No	Travel_Rarely	594	Research & Development	2	1	Technical Degree	1	169	3	Male	100	3	1	Laboratory Technician	4	Married	2523	19299	0	Y	No	14	3	3	80	1	3	2	3	2	1	2	1
+49	No	Travel_Rarely	470	Research & Development	20	4	Medical	1	170	3	Female	96	3	2	Manufacturing Director	1	Married	6567	5549	1	Y	No	14	3	3	80	0	16	2	2	15	11	5	11
+43	No	Travel_Frequently	957	Research & Development	28	3	Medical	1	171	2	Female	72	4	1	Research Scientist	3	Single	4739	16090	4	Y	No	12	3	4	80	0	18	2	3	3	2	1	2
+50	No	Travel_Frequently	809	Sales	12	3	Marketing	1	174	3	Female	77	3	3	Sales Executive	4	Single	9208	6645	4	Y	No	11	3	4	80	0	16	3	3	2	2	2	1
+31	Yes	Travel_Rarely	542	Sales	20	3	Life Sciences	1	175	2	Female	71	1	2	Sales Executive	3	Married	4559	24788	3	Y	Yes	11	3	3	80	1	4	2	3	2	2	2	2
+41	No	Travel_Rarely	802	Sales	9	1	Life Sciences	1	176	3	Male	96	3	3	Sales Executive	3	Divorced	8189	21196	3	Y	Yes	13	3	3	80	1	12	2	3	9	7	0	7
+26	No	Travel_Rarely	1355	Human Resources	25	1	Life Sciences	1	177	3	Female	61	3	1	Human Resources	3	Married	2942	8916	1	Y	No	23	4	4	80	1	8	3	3	8	7	5	7
+36	No	Travel_Rarely	216	Research & Development	6	2	Medical	1	178	2	Male	84	3	2	Manufacturing Director	2	Divorced	4941	2819	6	Y	No	20	4	4	80	2	7	0	3	3	2	0	1
+51	Yes	Travel_Frequently	1150	Research & Development	8	4	Life Sciences	1	179	1	Male	53	1	3	Manufacturing Director	4	Single	10650	25150	2	Y	No	15	3	4	80	0	18	2	3	4	2	0	3
+39	No	Travel_Rarely	1329	Sales	4	4	Life Sciences	1	182	4	Female	47	2	2	Sales Executive	3	Married	5902	14590	4	Y	No	14	3	3	80	1	17	1	4	15	11	5	9
+25	No	Travel_Rarely	959	Sales	28	3	Life Sciences	1	183	1	Male	41	2	2	Sales Executive	3	Married	8639	24835	2	Y	No	18	3	4	80	0	6	3	3	2	2	2	2
+30	No	Travel_Rarely	1240	Human Resources	9	3	Human Resources	1	184	3	Male	48	3	2	Human Resources	4	Married	6347	13982	0	Y	Yes	19	3	4	80	0	12	2	1	11	9	4	7
+32	Yes	Travel_Rarely	1033	Research & Development	9	3	Medical	1	190	1	Female	41	3	1	Laboratory Technician	1	Single	4200	10224	7	Y	No	22	4	1	80	0	10	2	4	5	4	0	4
+45	No	Travel_Rarely	1316	Research & Development	29	3	Medical	1	192	3	Male	83	3	1	Research Scientist	4	Single	3452	9752	5	Y	No	13	3	2	80	0	9	2	2	6	5	0	3
+38	No	Travel_Rarely	364	Research & Development	3	5	Technical Degree	1	193	4	Female	32	3	2	Research Scientist	3	Single	4317	2302	3	Y	Yes	20	4	2	80	0	19	2	3	3	2	2	2
+30	No	Travel_Rarely	438	Research & Development	18	3	Life Sciences	1	194	1	Female	75	3	1	Research Scientist	3	Single	2632	23910	1	Y	No	14	3	3	80	0	5	4	2	5	4	0	4
+32	No	Travel_Frequently	689	Sales	9	2	Medical	1	195	4	Male	35	1	2	Sales Executive	4	Divorced	4668	22812	0	Y	No	17	3	4	80	3	9	2	4	8	7	0	7
+30	No	Travel_Rarely	201	Research & Development	5	3	Technical Degree	1	197	4	Female	84	3	1	Research Scientist	1	Divorced	3204	10415	5	Y	No	14	3	4	80	1	8	3	3	3	2	2	2
+30	No	Travel_Rarely	1427	Research & Development	2	1	Medical	1	198	2	Male	35	2	1	Laboratory Technician	4	Single	2720	11162	0	Y	No	13	3	4	80	0	6	3	3	5	3	1	2
+41	No	Travel_Frequently	857	Research & Development	10	3	Life Sciences	1	199	4	Male	91	2	4	Manager	1	Divorced	17181	12888	4	Y	No	13	3	2	80	1	21	2	2	7	6	7	7
+41	No	Travel_Rarely	933	Research & Development	9	4	Life Sciences	1	200	3	Male	94	3	1	Laboratory Technician	1	Married	2238	6961	2	Y	No	21	4	4	80	1	7	2	3	5	0	1	4
+19	No	Travel_Rarely	1181	Research & Development	3	1	Medical	1	201	2	Female	79	3	1	Laboratory Technician	2	Single	1483	16102	1	Y	No	14	3	4	80	0	1	3	3	1	0	0	0
+40	No	Travel_Frequently	1395	Research & Development	26	3	Medical	1	202	2	Female	54	3	2	Research Scientist	2	Divorced	5605	8504	1	Y	No	11	3	1	80	1	20	2	3	20	7	2	13
+35	No	Travel_Rarely	662	Sales	1	5	Marketing	1	204	3	Male	94	3	3	Sales Executive	2	Married	7295	11439	1	Y	No	13	3	1	80	2	10	3	3	10	8	0	6
+53	No	Travel_Rarely	1436	Sales	6	2	Marketing	1	205	2	Male	34	3	2	Sales Representative	3	Married	2306	16047	2	Y	Yes	20	4	4	80	1	13	3	1	7	7	4	5
+45	No	Travel_Rarely	194	Research & Development	9	3	Life Sciences	1	206	2	Male	60	3	2	Laboratory Technician	2	Divorced	2348	10901	8	Y	No	18	3	3	80	1	20	2	1	17	9	0	15
+32	No	Travel_Frequently	967	Sales	8	3	Marketing	1	207	2	Female	43	3	3	Sales Executive	4	Single	8998	15589	1	Y	No	14	3	4	80	0	9	2	3	9	8	3	7
+29	No	Non-Travel	1496	Research & Development	1	1	Technical Degree	1	208	4	Male	41	3	2	Manufacturing Director	3	Married	4319	26283	1	Y	No	13	3	1	80	1	10	1	3	10	7	0	9
+51	No	Travel_Rarely	1169	Research & Development	7	4	Medical	1	211	2	Male	34	2	2	Manufacturing Director	3	Married	6132	13983	2	Y	No	17	3	3	80	0	10	2	3	1	0	0	0
+58	No	Travel_Rarely	1145	Research & Development	9	3	Medical	1	214	2	Female	75	2	1	Research Scientist	2	Married	3346	11873	4	Y	Yes	20	4	2	80	1	9	3	2	1	0	0	0
+40	No	Travel_Rarely	630	Sales	4	4	Marketing	1	215	3	Male	67	2	3	Sales Executive	4	Married	10855	8552	7	Y	No	11	3	1	80	1	15	2	2	12	11	2	11
+34	No	Travel_Frequently	303	Sales	2	4	Marketing	1	216	3	Female	75	3	1	Sales Representative	3	Married	2231	11314	6	Y	No	18	3	4	80	1	6	3	3	4	3	1	2
+22	No	Travel_Rarely	1256	Research & Development	19	1	Medical	1	217	3	Male	80	3	1	Research Scientist	4	Married	2323	11992	1	Y	No	24	4	1	80	2	2	6	3	2	2	2	2
+27	No	Non-Travel	691	Research & Development	9	3	Medical	1	218	4	Male	57	3	1	Research Scientist	2	Divorced	2024	5970	6	Y	No	18	3	4	80	1	6	1	1	2	2	2	2
+28	No	Travel_Rarely	440	Research & Development	21	3	Medical	1	221	3	Male	42	3	1	Research Scientist	4	Married	2713	6672	1	Y	No	11	3	3	80	1	5	2	1	5	2	0	2
+57	No	Travel_Rarely	334	Research & Development	24	2	Life Sciences	1	223	3	Male	83	4	3	Healthcare Representative	4	Divorced	9439	23402	3	Y	Yes	16	3	2	80	1	12	2	1	5	3	1	4
+27	No	Non-Travel	1450	Research & Development	3	3	Medical	1	224	3	Male	79	2	1	Research Scientist	3	Divorced	2566	25326	1	Y	Yes	15	3	4	80	1	1	2	2	1	1	0	1
+50	No	Travel_Rarely	1452	Research & Development	11	3	Life Sciences	1	226	3	Female	53	3	5	Manager	2	Single	19926	17053	3	Y	No	15	3	2	80	0	21	5	3	5	4	4	4
+41	No	Travel_Rarely	465	Research & Development	14	3	Life Sciences	1	227	1	Male	56	3	1	Research Scientist	3	Divorced	2451	4609	4	Y	No	12	3	1	80	1	13	2	3	9	8	1	8
+30	No	Travel_Rarely	1339	Sales	5	3	Life Sciences	1	228	2	Female	41	3	3	Sales Executive	4	Married	9419	8053	2	Y	No	12	3	3	80	1	12	2	3	10	9	7	4
+38	No	Travel_Rarely	702	Sales	1	4	Life Sciences	1	230	1	Female	59	2	2	Sales Executive	4	Single	8686	12930	4	Y	No	22	4	3	80	0	12	2	4	8	3	0	7
+32	No	Travel_Rarely	120	Research & Development	6	5	Life Sciences	1	231	3	Male	43	3	1	Research Scientist	3	Single	3038	12430	3	Y	No	20	4	1	80	0	8	2	3	5	4	1	4
+27	No	Travel_Rarely	1157	Research & Development	17	3	Technical Degree	1	233	3	Male	51	3	1	Research Scientist	2	Married	3058	13364	0	Y	Yes	16	3	4	80	1	6	3	2	5	2	1	1
+19	Yes	Travel_Frequently	602	Sales	1	1	Technical Degree	1	235	3	Female	100	1	1	Sales Representative	1	Single	2325	20989	0	Y	No	21	4	1	80	0	1	5	4	0	0	0	0
+36	No	Travel_Frequently	1480	Research & Development	3	2	Medical	1	238	4	Male	30	3	1	Laboratory Technician	2	Single	2088	15062	4	Y	No	12	3	3	80	0	13	3	2	8	7	7	2
+30	No	Non-Travel	111	Research & Development	9	3	Medical	1	239	3	Male	66	3	2	Laboratory Technician	1	Divorced	3072	11012	1	Y	No	11	3	3	80	2	12	4	3	12	9	6	10
+45	No	Travel_Rarely	1268	Sales	4	2	Life Sciences	1	240	3	Female	30	3	2	Sales Executive	1	Divorced	5006	6319	4	Y	Yes	11	3	1	80	1	9	3	4	5	4	0	3
+56	No	Travel_Rarely	713	Research & Development	8	3	Life Sciences	1	241	3	Female	67	3	1	Research Scientist	1	Divorced	4257	13939	4	Y	Yes	18	3	3	80	1	19	3	3	2	2	2	2
+33	No	Travel_Rarely	134	Research & Development	2	3	Life Sciences	1	242	3	Male	90	3	1	Research Scientist	4	Single	2500	10515	0	Y	No	14	3	1	80	0	4	2	4	3	1	0	2
+19	Yes	Travel_Rarely	303	Research & Development	2	3	Life Sciences	1	243	2	Male	47	2	1	Laboratory Technician	4	Single	1102	9241	1	Y	No	22	4	3	80	0	1	3	2	1	0	1	0
+46	No	Travel_Rarely	526	Sales	1	2	Marketing	1	244	2	Female	92	3	3	Sales Executive	1	Divorced	10453	2137	1	Y	No	25	4	3	80	3	24	2	3	24	13	15	7
+38	No	Travel_Rarely	1380	Research & Development	9	2	Life Sciences	1	245	3	Female	75	3	1	Laboratory Technician	4	Single	2288	6319	1	Y	No	12	3	3	80	0	2	3	3	2	2	2	1
+31	No	Travel_Rarely	140	Research & Development	12	1	Medical	1	246	3	Female	95	3	1	Research Scientist	4	Married	3929	6984	8	Y	Yes	23	4	3	80	1	7	0	3	4	2	0	2
+34	No	Travel_Rarely	629	Research & Development	27	2	Medical	1	247	4	Female	95	3	1	Research Scientist	2	Single	2311	5711	2	Y	No	15	3	4	80	0	9	3	3	3	2	1	2
+41	Yes	Travel_Rarely	1356	Sales	20	2	Marketing	1	248	2	Female	70	3	1	Sales Representative	2	Single	3140	21728	1	Y	Yes	22	4	4	80	0	4	5	2	4	3	0	2
+50	No	Travel_Rarely	328	Research & Development	1	3	Medical	1	249	3	Male	86	2	1	Laboratory Technician	3	Married	3690	3425	2	Y	No	15	3	4	80	1	5	2	2	3	2	0	2
+53	No	Travel_Rarely	1084	Research & Development	13	2	Medical	1	250	4	Female	57	4	2	Manufacturing Director	1	Divorced	4450	26250	1	Y	No	11	3	3	80	2	5	3	3	4	2	1	3
+33	No	Travel_Rarely	931	Research & Development	14	3	Medical	1	252	4	Female	72	3	1	Research Scientist	2	Married	2756	4673	1	Y	No	13	3	4	80	1	8	5	3	8	7	1	6
+40	No	Travel_Rarely	989	Research & Development	4	1	Medical	1	253	4	Female	46	3	5	Manager	3	Married	19033	6499	1	Y	No	14	3	2	80	1	21	2	3	20	8	9	9
+55	No	Travel_Rarely	692	Research & Development	14	4	Medical	1	254	3	Male	61	4	5	Research Director	2	Single	18722	13339	8	Y	No	11	3	4	80	0	36	3	3	24	15	2	15
+34	No	Travel_Frequently	1069	Research & Development	2	1	Life Sciences	1	256	4	Male	45	2	2	Manufacturing Director	3	Married	9547	14074	1	Y	No	17	3	3	80	0	10	2	2	10	9	1	9
+51	No	Travel_Rarely	313	Research & Development	3	3	Medical	1	258	4	Female	98	3	4	Healthcare Representative	2	Single	13734	7192	3	Y	No	18	3	3	80	0	21	6	3	7	7	1	0
+52	No	Travel_Rarely	699	Research & Development	1	4	Life Sciences	1	259	3	Male	65	2	5	Manager	3	Married	19999	5678	0	Y	No	14	3	1	80	1	34	5	3	33	18	11	9
+27	No	Travel_Rarely	894	Research & Development	9	3	Medical	1	260	4	Female	99	3	1	Research Scientist	2	Single	2279	11781	1	Y	No	16	3	4	80	0	7	2	2	7	7	0	3
+35	Yes	Travel_Rarely	556	Research & Development	23	2	Life Sciences	1	261	2	Male	50	2	2	Manufacturing Director	3	Married	5916	15497	3	Y	Yes	13	3	1	80	0	8	1	3	1	0	0	1
+43	No	Non-Travel	1344	Research & Development	7	3	Medical	1	262	4	Male	37	4	1	Research Scientist	4	Divorced	2089	5228	4	Y	No	14	3	4	80	3	7	3	4	5	4	2	2
+45	No	Non-Travel	1195	Research & Development	2	2	Medical	1	264	1	Male	65	2	4	Manager	4	Married	16792	20462	9	Y	No	23	4	4	80	1	22	1	3	20	8	11	8
+37	No	Travel_Rarely	290	Research & Development	21	3	Life Sciences	1	267	2	Male	65	4	1	Research Scientist	1	Married	3564	22977	1	Y	Yes	12	3	1	80	1	8	3	2	8	7	1	7
+35	No	Travel_Frequently	138	Research & Development	2	3	Medical	1	269	2	Female	37	3	2	Laboratory Technician	2	Single	4425	15986	5	Y	No	11	3	4	80	0	10	5	3	6	2	1	2
+42	No	Non-Travel	926	Research & Development	21	2	Medical	1	270	3	Female	36	3	2	Manufacturing Director	3	Divorced	5265	16439	2	Y	No	16	3	2	80	1	11	5	3	5	3	0	2
+38	No	Travel_Rarely	1261	Research & Development	2	4	Life Sciences	1	271	4	Male	88	3	2	Manufacturing Director	3	Married	6553	7259	9	Y	No	14	3	2	80	0	14	3	3	1	0	0	0
+38	No	Travel_Rarely	1084	Research & Development	29	3	Technical Degree	1	273	4	Male	54	3	2	Manufacturing Director	4	Married	6261	4185	3	Y	No	18	3	1	80	1	9	3	1	7	7	1	7
+27	No	Travel_Frequently	472	Research & Development	1	1	Technical Degree	1	274	3	Male	60	2	2	Manufacturing Director	1	Married	4298	9679	5	Y	No	19	3	3	80	1	6	1	3	2	2	2	0
+49	No	Non-Travel	1002	Research & Development	18	4	Life Sciences	1	275	4	Male	92	3	2	Manufacturing Director	4	Divorced	6804	23793	1	Y	Yes	15	3	1	80	2	7	0	3	7	7	1	7
+34	No	Travel_Frequently	878	Research & Development	10	4	Medical	1	277	4	Male	43	3	1	Research Scientist	3	Divorced	3815	5972	1	Y	Yes	17	3	4	80	1	5	4	4	5	3	2	0
+40	No	Travel_Rarely	905	Research & Development	19	2	Medical	1	281	3	Male	99	3	2	Laboratory Technician	4	Married	2741	16523	8	Y	Yes	15	3	3	80	1	15	2	4	7	2	3	7
+38	Yes	Travel_Rarely	1180	Research & Development	29	1	Medical	1	282	2	Male	70	3	2	Healthcare Representative	1	Married	6673	11354	7	Y	Yes	19	3	2	80	0	17	2	3	1	0	0	0
+29	Yes	Travel_Rarely	121	Sales	27	3	Marketing	1	283	2	Female	35	3	3	Sales Executive	4	Married	7639	24525	1	Y	No	22	4	4	80	3	10	3	2	10	4	1	9
+22	No	Travel_Rarely	1136	Research & Development	5	3	Life Sciences	1	284	4	Male	60	4	1	Research Scientist	2	Divorced	2328	12392	1	Y	Yes	16	3	1	80	1	4	2	2	4	2	2	2
+36	No	Travel_Frequently	635	Research & Development	18	1	Medical	1	286	2	Female	73	3	1	Laboratory Technician	4	Single	2153	7703	1	Y	No	13	3	1	80	0	8	2	3	8	1	1	7
+40	No	Non-Travel	1151	Research & Development	9	5	Life Sciences	1	287	4	Male	63	2	2	Healthcare Representative	4	Married	4876	14242	9	Y	No	14	3	4	80	1	5	5	1	3	2	0	2
+46	No	Travel_Rarely	644	Research & Development	1	4	Medical	1	288	4	Male	97	3	3	Healthcare Representative	1	Divorced	9396	12368	7	Y	No	16	3	3	80	1	17	3	3	4	2	0	3
+32	Yes	Travel_Rarely	1045	Sales	4	4	Medical	1	291	4	Male	32	1	3	Sales Executive	4	Married	10400	25812	1	Y	No	11	3	3	80	0	14	2	2	14	8	9	8
+30	No	Non-Travel	829	Research & Development	1	1	Life Sciences	1	292	3	Male	88	2	3	Manufacturing Director	3	Single	8474	20925	1	Y	No	22	4	3	80	0	12	2	3	11	8	5	8
+27	No	Travel_Frequently	1242	Sales	20	3	Life Sciences	1	293	4	Female	90	3	2	Sales Executive	3	Single	9981	12916	1	Y	No	14	3	4	80	0	7	2	3	7	7	0	7
+51	No	Travel_Rarely	1469	Research & Development	8	4	Life Sciences	1	296	2	Male	81	2	3	Research Director	2	Married	12490	15736	5	Y	No	16	3	4	80	2	16	5	1	10	9	4	7
+30	Yes	Travel_Rarely	1005	Research & Development	3	3	Technical Degree	1	297	4	Female	88	3	1	Research Scientist	1	Single	2657	8556	5	Y	Yes	11	3	3	80	0	8	5	3	5	2	0	4
+41	No	Travel_Rarely	896	Sales	6	3	Life Sciences	1	298	4	Female	75	3	3	Manager	4	Single	13591	14674	3	Y	Yes	18	3	3	80	0	16	3	3	1	0	0	0
+30	Yes	Travel_Frequently	334	Sales	26	4	Marketing	1	299	3	Female	52	2	2	Sales Executive	1	Single	6696	22967	5	Y	No	15	3	3	80	0	9	5	2	6	3	0	1
+29	Yes	Travel_Rarely	992	Research & Development	1	3	Technical Degree	1	300	3	Male	85	3	1	Research Scientist	3	Single	2058	19757	0	Y	No	14	3	4	80	0	7	1	2	6	2	1	5
+45	No	Non-Travel	1052	Sales	6	3	Medical	1	302	4	Female	57	2	3	Sales Executive	4	Single	8865	16840	6	Y	No	12	3	4	80	0	23	2	3	19	7	12	8
+54	No	Travel_Rarely	1147	Sales	3	3	Marketing	1	303	4	Female	52	3	2	Sales Executive	1	Married	5940	17011	2	Y	No	14	3	4	80	1	16	4	3	6	2	0	5
+36	No	Travel_Rarely	1396	Research & Development	5	2	Life Sciences	1	304	4	Male	62	3	2	Laboratory Technician	2	Single	5914	9945	8	Y	No	16	3	4	80	0	16	3	4	13	11	3	7
+33	No	Travel_Rarely	147	Research & Development	4	4	Medical	1	305	3	Female	47	2	1	Research Scientist	2	Married	2622	13248	6	Y	No	21	4	4	80	0	7	3	3	3	2	1	1
+37	No	Travel_Frequently	663	Research & Development	11	3	Other	1	306	2	Male	47	3	3	Research Director	4	Divorced	12185	10056	1	Y	Yes	14	3	3	80	3	10	1	3	10	8	0	7
+38	No	Travel_Rarely	119	Sales	3	3	Life Sciences	1	307	1	Male	76	3	3	Sales Executive	3	Divorced	10609	9647	0	Y	No	12	3	3	80	2	17	6	2	16	10	5	13
+31	No	Non-Travel	979	Research & Development	1	4	Medical	1	308	3	Male	90	1	2	Manufacturing Director	3	Married	4345	4381	0	Y	No	12	3	4	80	1	6	2	3	5	4	1	4
+59	No	Travel_Rarely	142	Research & Development	3	3	Life Sciences	1	309	3	Male	70	2	1	Research Scientist	4	Married	2177	8456	3	Y	No	17	3	1	80	1	7	6	3	1	0	0	0
+37	No	Travel_Frequently	319	Sales	4	4	Marketing	1	311	1	Male	41	3	1	Sales Representative	4	Divorced	2793	2539	4	Y	No	17	3	3	80	1	13	2	3	9	8	5	8
+29	No	Travel_Frequently	1413	Sales	1	1	Medical	1	312	2	Female	42	3	3	Sales Executive	4	Married	7918	6599	1	Y	No	14	3	4	80	1	11	5	3	11	10	4	1
+35	No	Travel_Frequently	944	Sales	1	3	Marketing	1	314	3	Female	92	3	3	Sales Executive	3	Single	8789	9096	1	Y	No	14	3	1	80	0	10	3	4	10	7	0	8
+29	Yes	Travel_Rarely	896	Research & Development	18	1	Medical	1	315	3	Male	86	2	1	Research Scientist	4	Single	2389	14961	1	Y	Yes	13	3	3	80	0	4	3	2	4	3	0	1
+52	No	Travel_Rarely	1323	Research & Development	2	3	Life Sciences	1	316	3	Female	89	2	1	Laboratory Technician	4	Single	3212	3300	7	Y	No	15	3	2	80	0	6	3	2	2	2	2	2
+42	No	Travel_Rarely	532	Research & Development	4	2	Technical Degree	1	319	3	Male	58	3	5	Manager	4	Married	19232	4933	1	Y	No	11	3	4	80	0	22	3	3	22	17	11	15
+59	No	Travel_Rarely	818	Human Resources	6	2	Medical	1	321	2	Male	52	3	1	Human Resources	3	Married	2267	25657	8	Y	No	17	3	4	80	0	7	2	2	2	2	2	2
+50	No	Travel_Rarely	854	Sales	1	4	Medical	1	323	4	Female	68	3	5	Manager	4	Divorced	19517	24118	3	Y	No	11	3	3	80	1	32	3	2	7	0	0	6
+33	Yes	Travel_Rarely	813	Research & Development	14	3	Medical	1	325	3	Male	58	3	1	Laboratory Technician	4	Married	2436	22149	5	Y	Yes	13	3	3	80	1	8	2	1	5	4	0	4
+43	No	Travel_Rarely	1034	Sales	16	3	Marketing	1	327	4	Female	80	3	4	Manager	4	Married	16064	7744	5	Y	Yes	22	4	3	80	1	22	3	3	17	13	1	9
+33	Yes	Travel_Rarely	465	Research & Development	2	2	Life Sciences	1	328	1	Female	39	3	1	Laboratory Technician	1	Married	2707	21509	7	Y	No	20	4	1	80	0	13	3	4	9	7	1	7
+52	No	Non-Travel	771	Sales	2	4	Life Sciences	1	329	1	Male	79	2	5	Manager	3	Single	19068	21030	1	Y	Yes	18	3	4	80	0	33	2	4	33	7	15	12
+32	No	Travel_Rarely	1401	Sales	4	2	Life Sciences	1	330	3	Female	56	3	1	Sales Representative	2	Married	3931	20990	2	Y	No	11	3	1	80	1	6	5	3	4	3	1	2
+32	Yes	Travel_Rarely	515	Research & Development	1	3	Life Sciences	1	331	4	Male	62	2	1	Laboratory Technician	3	Single	3730	9571	0	Y	Yes	14	3	4	80	0	4	2	1	3	2	1	2
+39	No	Travel_Rarely	1431	Research & Development	1	4	Medical	1	332	3	Female	96	3	1	Laboratory Technician	3	Divorced	2232	15417	7	Y	No	14	3	3	80	3	7	1	3	3	2	1	2
+32	No	Non-Travel	976	Sales	26	4	Marketing	1	333	3	Male	100	3	2	Sales Executive	4	Married	4465	12069	0	Y	No	18	3	1	80	0	4	2	3	3	2	2	2
+41	No	Travel_Rarely	1411	Research & Development	19	2	Life Sciences	1	334	3	Male	36	3	2	Research Scientist	1	Divorced	3072	19877	2	Y	No	16	3	1	80	2	17	2	2	1	0	0	0
+40	No	Travel_Rarely	1300	Research & Development	24	2	Technical Degree	1	335	1	Male	62	3	2	Research Scientist	4	Divorced	3319	24447	1	Y	No	17	3	1	80	2	9	3	3	9	8	4	7
+45	No	Travel_Rarely	252	Research & Development	1	3	Other	1	336	3	Male	70	4	5	Manager	4	Married	19202	15970	0	Y	No	11	3	3	80	1	25	2	3	24	0	1	7
+31	No	Travel_Frequently	1327	Research & Development	3	4	Medical	1	337	2	Male	73	3	3	Research Director	3	Divorced	13675	13523	9	Y	No	12	3	1	80	1	9	3	3	2	2	2	2
+33	No	Travel_Rarely	832	Research & Development	5	4	Life Sciences	1	338	3	Female	63	2	1	Research Scientist	4	Married	2911	14776	1	Y	No	13	3	3	80	1	2	2	2	2	2	0	2
+34	No	Travel_Rarely	470	Research & Development	2	4	Life Sciences	1	339	4	Male	84	2	2	Manufacturing Director	1	Married	5957	23687	6	Y	No	13	3	2	80	1	13	3	3	11	9	5	9
+37	No	Travel_Rarely	1017	Research & Development	1	2	Medical	1	340	3	Female	83	2	1	Research Scientist	1	Married	3920	18697	2	Y	No	14	3	1	80	1	17	2	2	3	1	0	2
+45	No	Travel_Frequently	1199	Research & Development	7	4	Life Sciences	1	341	1	Male	77	4	2	Manufacturing Director	3	Married	6434	5118	4	Y	No	17	3	4	80	1	9	1	3	3	2	0	2
+37	Yes	Travel_Frequently	504	Research & Development	10	3	Medical	1	342	1	Male	61	3	3	Manufacturing Director	3	Divorced	10048	22573	6	Y	No	11	3	2	80	2	17	5	3	1	0	0	0
+39	No	Travel_Frequently	505	Research & Development	2	4	Technical Degree	1	343	3	Female	64	3	3	Healthcare Representative	3	Single	10938	6420	0	Y	No	25	4	4	80	0	20	1	3	19	6	11	8
+29	No	Travel_Rarely	665	Research & Development	15	3	Life Sciences	1	346	3	Male	60	3	1	Research Scientist	4	Single	2340	22673	1	Y	No	19	3	1	80	0	6	1	3	6	5	1	5
+42	No	Travel_Rarely	916	Research & Development	17	2	Life Sciences	1	347	4	Female	82	4	2	Research Scientist	1	Single	6545	23016	3	Y	Yes	13	3	3	80	0	10	1	3	3	2	0	2
+29	No	Travel_Rarely	1247	Sales	20	2	Marketing	1	349	4	Male	45	3	2	Sales Executive	4	Divorced	6931	10732	2	Y	No	14	3	4	80	1	10	2	3	3	2	0	2
+25	No	Travel_Rarely	685	Research & Development	1	3	Life Sciences	1	350	1	Female	62	3	2	Manufacturing Director	3	Married	4898	7505	0	Y	No	12	3	4	80	2	5	3	3	4	2	1	2
+42	No	Travel_Rarely	269	Research & Development	2	3	Medical	1	351	4	Female	56	2	1	Laboratory Technician	1	Divorced	2593	8007	0	Y	Yes	11	3	3	80	1	10	4	3	9	6	7	8
+40	No	Travel_Rarely	1416	Research & Development	2	2	Medical	1	352	1	Male	49	3	5	Research Director	3	Divorced	19436	5949	0	Y	No	19	3	4	80	1	22	5	3	21	7	3	9
+51	No	Travel_Rarely	833	Research & Development	1	3	Life Sciences	1	353	3	Male	96	3	1	Research Scientist	4	Married	2723	23231	1	Y	No	11	3	2	80	0	1	0	2	1	0	0	0
+31	Yes	Travel_Frequently	307	Research & Development	29	2	Medical	1	355	3	Male	71	2	1	Laboratory Technician	2	Single	3479	11652	0	Y	No	11	3	2	80	0	6	2	4	5	4	1	4
+32	No	Travel_Frequently	1311	Research & Development	7	3	Life Sciences	1	359	2	Male	100	4	1	Laboratory Technician	2	Married	2794	26062	1	Y	No	20	4	3	80	0	5	3	1	5	1	0	3
+38	No	Non-Travel	1327	Sales	2	2	Life Sciences	1	361	4	Male	39	2	2	Sales Executive	4	Married	5249	19682	3	Y	No	18	3	4	80	1	13	0	3	8	7	7	5
+32	No	Travel_Rarely	128	Research & Development	2	1	Technical Degree	1	362	4	Male	84	2	2	Laboratory Technician	1	Single	2176	19737	4	Y	No	13	3	4	80	0	9	5	3	6	2	0	4
+46	No	Travel_Rarely	488	Sales	2	3	Technical Degree	1	363	3	Female	75	1	4	Manager	2	Married	16872	14977	3	Y	Yes	12	3	2	80	1	28	2	2	7	7	7	7
+28	Yes	Travel_Rarely	529	Research & Development	2	4	Life Sciences	1	364	1	Male	79	3	1	Laboratory Technician	3	Single	3485	14935	2	Y	No	11	3	3	80	0	5	5	1	0	0	0	0
+29	No	Travel_Rarely	1210	Sales	2	3	Medical	1	366	1	Male	78	2	2	Sales Executive	2	Married	6644	3687	2	Y	No	19	3	2	80	2	10	2	3	0	0	0	0
+31	No	Travel_Rarely	1463	Research & Development	23	3	Medical	1	367	2	Male	64	2	2	Healthcare Representative	4	Married	5582	14408	0	Y	No	21	4	2	80	1	10	2	3	9	0	7	8
+25	No	Non-Travel	675	Research & Development	5	2	Life Sciences	1	369	2	Male	85	4	2	Healthcare Representative	1	Divorced	4000	18384	1	Y	No	12	3	4	80	2	6	2	3	6	3	1	5
+45	No	Travel_Rarely	1385	Research & Development	20	2	Medical	1	372	3	Male	79	3	4	Healthcare Representative	4	Married	13496	7501	0	Y	Yes	14	3	2	80	0	21	2	3	20	7	4	10
+36	No	Travel_Rarely	1403	Research & Development	6	3	Life Sciences	1	373	4	Male	47	3	1	Laboratory Technician	4	Married	3210	20251	0	Y	No	11	3	3	80	1	16	4	3	15	13	10	11
+55	No	Travel_Rarely	452	Research & Development	1	3	Medical	1	374	4	Male	81	3	5	Manager	1	Single	19045	18938	0	Y	Yes	14	3	3	80	0	37	2	3	36	10	4	13
+47	Yes	Non-Travel	666	Research & Development	29	4	Life Sciences	1	376	1	Male	88	3	3	Manager	2	Married	11849	10268	1	Y	Yes	12	3	4	80	1	10	2	2	10	7	9	9
+28	No	Travel_Rarely	1158	Research & Development	9	3	Medical	1	377	4	Male	94	3	1	Research Scientist	4	Married	2070	2613	1	Y	No	23	4	4	80	1	5	3	2	5	2	0	4
+37	No	Travel_Rarely	228	Sales	6	4	Medical	1	378	3	Male	98	3	2	Sales Executive	4	Married	6502	22825	4	Y	No	14	3	2	80	1	7	5	4	5	4	0	1
+21	No	Travel_Rarely	996	Research & Development	3	2	Medical	1	379	4	Male	100	2	1	Research Scientist	3	Single	3230	10531	1	Y	No	17	3	1	80	0	3	4	4	3	2	1	0
+37	No	Non-Travel	728	Research & Development	1	4	Medical	1	380	1	Female	80	3	3	Research Director	4	Divorced	13603	11677	2	Y	Yes	18	3	1	80	2	15	2	3	5	2	0	2
+35	No	Travel_Rarely	1315	Research & Development	22	3	Life Sciences	1	381	2	Female	71	4	3	Manager	2	Divorced	11996	19100	7	Y	No	18	3	2	80	1	10	6	2	7	7	6	2
+38	No	Travel_Rarely	322	Sales	7	2	Medical	1	382	1	Female	44	4	2	Sales Executive	1	Divorced	5605	19191	1	Y	Yes	24	4	3	80	1	8	3	3	8	0	7	7
+26	No	Travel_Frequently	1479	Research & Development	1	3	Life Sciences	1	384	3	Female	84	3	2	Manufacturing Director	2	Divorced	6397	26767	1	Y	No	20	4	1	80	1	6	6	1	6	5	1	4
+50	No	Travel_Rarely	797	Research & Development	4	1	Life Sciences	1	385	1	Male	96	3	5	Research Director	2	Divorced	19144	15815	3	Y	No	14	3	1	80	2	28	4	2	10	4	1	6
+53	No	Travel_Rarely	1070	Research & Development	3	4	Medical	1	386	3	Male	45	3	4	Research Director	3	Married	17584	21016	3	Y	Yes	16	3	4	80	3	21	5	2	5	3	1	3
+42	No	Travel_Rarely	635	Sales	1	1	Life Sciences	1	387	2	Male	99	3	2	Sales Executive	3	Married	4907	24532	1	Y	No	25	4	3	80	0	20	3	3	20	16	11	6
+29	No	Travel_Frequently	442	Sales	2	2	Life Sciences	1	388	2	Male	44	3	2	Sales Executive	4	Single	4554	20260	1	Y	No	18	3	1	80	0	10	3	2	10	7	0	9
+55	No	Travel_Rarely	147	Research & Development	20	2	Technical Degree	1	389	2	Male	37	3	2	Laboratory Technician	4	Married	5415	15972	3	Y	Yes	19	3	4	80	1	12	4	3	10	7	0	8
+26	No	Travel_Frequently	496	Research & Development	11	2	Medical	1	390	1	Male	60	3	2	Healthcare Representative	1	Married	4741	22722	1	Y	Yes	13	3	3	80	1	5	3	3	5	3	3	3
+37	No	Travel_Rarely	1372	Research & Development	1	3	Life Sciences	1	391	4	Female	42	3	1	Research Scientist	4	Single	2115	15881	1	Y	No	12	3	2	80	0	17	3	3	17	12	5	7
+44	Yes	Travel_Frequently	920	Research & Development	24	3	Life Sciences	1	392	4	Male	43	3	1	Laboratory Technician	3	Divorced	3161	19920	3	Y	Yes	22	4	4	80	1	19	0	1	1	0	0	0
+38	No	Travel_Rarely	688	Research & Development	23	4	Life Sciences	1	393	4	Male	82	3	2	Healthcare Representative	4	Divorced	5745	18899	9	Y	No	14	3	2	80	1	10	2	3	2	2	1	2
+26	Yes	Travel_Rarely	1449	Research & Development	16	4	Medical	1	394	1	Male	45	3	1	Laboratory Technician	2	Divorced	2373	14180	2	Y	Yes	13	3	4	80	1	5	2	3	3	2	0	2
+28	No	Travel_Rarely	1117	Research & Development	8	2	Life Sciences	1	395	4	Female	66	3	1	Research Scientist	4	Single	3310	4488	1	Y	No	21	4	4	80	0	5	3	3	5	3	0	2
+49	No	Travel_Frequently	636	Research & Development	10	4	Life Sciences	1	396	3	Female	35	3	5	Research Director	1	Single	18665	25594	9	Y	Yes	11	3	4	80	0	22	4	3	3	2	1	2
+36	No	Travel_Rarely	506	Research & Development	3	3	Technical Degree	1	397	3	Male	30	3	2	Research Scientist	2	Single	4485	26285	4	Y	No	12	3	4	80	0	10	2	3	8	0	7	7
+31	No	Travel_Frequently	444	Sales	5	3	Marketing	1	399	4	Female	84	3	1	Sales Representative	2	Divorced	2789	3909	1	Y	No	11	3	3	80	1	2	5	2	2	2	2	2
+26	Yes	Travel_Rarely	950	Sales	4	4	Marketing	1	401	4	Male	48	2	2	Sales Executive	4	Single	5828	8450	1	Y	Yes	12	3	2	80	0	8	0	3	8	7	7	4
+37	No	Travel_Frequently	889	Research & Development	9	3	Medical	1	403	2	Male	53	3	1	Research Scientist	4	Married	2326	11411	1	Y	Yes	12	3	3	80	3	4	3	2	4	2	1	2
+42	No	Travel_Frequently	555	Sales	26	3	Marketing	1	404	3	Female	77	3	4	Sales Executive	2	Married	13525	14864	5	Y	No	14	3	4	80	1	23	2	4	20	4	4	8
+18	Yes	Travel_Rarely	230	Research & Development	3	3	Life Sciences	1	405	3	Male	54	3	1	Laboratory Technician	3	Single	1420	25233	1	Y	No	13	3	3	80	0	0	2	3	0	0	0	0
+35	No	Travel_Rarely	1232	Sales	16	3	Marketing	1	406	3	Male	96	3	3	Sales Executive	2	Married	8020	5100	0	Y	No	15	3	3	80	2	12	3	2	11	9	6	9
+36	No	Travel_Frequently	566	Research & Development	18	4	Life Sciences	1	407	3	Male	81	4	1	Laboratory Technician	4	Married	3688	7122	4	Y	No	18	3	4	80	2	4	2	3	1	0	0	0
+51	No	Travel_Rarely	1302	Research & Development	2	3	Medical	1	408	4	Male	84	1	2	Manufacturing Director	2	Divorced	5482	16321	5	Y	No	18	3	4	80	1	13	3	3	4	1	1	2
+41	No	Travel_Rarely	334	Sales	2	4	Life Sciences	1	410	4	Male	88	3	4	Manager	2	Single	16015	15896	1	Y	No	19	3	2	80	0	22	2	3	22	10	0	4
+18	No	Travel_Rarely	812	Sales	10	3	Medical	1	411	4	Female	69	2	1	Sales Representative	3	Single	1200	9724	1	Y	No	12	3	1	80	0	0	2	3	0	0	0	0
+28	No	Travel_Rarely	1476	Research & Development	16	2	Medical	1	412	2	Male	68	4	2	Healthcare Representative	1	Single	5661	4824	0	Y	No	19	3	3	80	0	9	2	3	8	3	0	7
+31	No	Travel_Rarely	218	Sales	7	3	Technical Degree	1	416	2	Male	100	4	2	Sales Executive	4	Married	6929	12241	4	Y	No	11	3	2	80	1	10	3	2	8	7	7	7
+39	No	Travel_Rarely	1132	Research & Development	1	3	Medical	1	417	3	Male	48	4	3	Healthcare Representative	4	Divorced	9613	10942	0	Y	No	17	3	1	80	3	19	5	2	18	10	3	7
+36	No	Non-Travel	1105	Research & Development	24	4	Life Sciences	1	419	2	Female	47	3	2	Laboratory Technician	2	Married	5674	6927	7	Y	No	15	3	3	80	1	11	3	3	9	8	0	8
+32	No	Travel_Rarely	906	Sales	7	3	Life Sciences	1	420	4	Male	91	2	2	Sales Executive	3	Married	5484	16985	1	Y	No	14	3	3	80	1	13	3	2	13	8	4	8
+38	No	Travel_Rarely	849	Research & Development	25	2	Life Sciences	1	421	1	Female	81	2	3	Research Director	2	Married	12061	26707	3	Y	No	17	3	3	80	1	19	2	3	10	8	0	1
+58	No	Non-Travel	390	Research & Development	1	4	Life Sciences	1	422	4	Male	32	1	2	Healthcare Representative	3	Divorced	5660	17056	2	Y	Yes	13	3	4	80	1	12	2	3	5	3	1	2
+31	No	Travel_Rarely	691	Research & Development	5	4	Technical Degree	1	423	3	Male	86	3	1	Research Scientist	4	Married	4821	10077	0	Y	Yes	12	3	3	80	1	6	4	3	5	2	0	3
+31	No	Travel_Rarely	106	Human Resources	2	3	Human Resources	1	424	1	Male	62	2	2	Human Resources	1	Married	6410	17822	3	Y	No	12	3	4	80	0	9	1	3	2	2	1	0
+45	No	Travel_Frequently	1249	Research & Development	7	3	Life Sciences	1	425	1	Male	97	3	3	Laboratory Technician	1	Divorced	5210	20308	1	Y	No	18	3	1	80	1	24	2	3	24	9	9	11
+31	No	Travel_Rarely	192	Research & Development	2	4	Life Sciences	1	426	3	Male	32	3	1	Research Scientist	4	Divorced	2695	7747	0	Y	Yes	18	3	2	80	1	3	2	1	2	2	2	2
+33	No	Travel_Frequently	553	Research & Development	5	4	Life Sciences	1	428	4	Female	74	3	3	Manager	2	Married	11878	23364	6	Y	No	11	3	2	80	2	12	2	3	10	6	8	8
+39	No	Travel_Rarely	117	Research & Development	10	1	Medical	1	429	3	Male	99	3	4	Manager	1	Married	17068	5355	1	Y	Yes	14	3	4	80	0	21	3	3	21	9	11	10
+43	No	Travel_Frequently	185	Research & Development	10	4	Life Sciences	1	430	3	Female	33	3	1	Laboratory Technician	4	Single	2455	10675	0	Y	No	19	3	1	80	0	9	5	3	8	7	1	7
+49	No	Travel_Rarely	1091	Research & Development	1	2	Technical Degree	1	431	3	Female	90	2	4	Healthcare Representative	3	Single	13964	17810	7	Y	Yes	12	3	4	80	0	25	2	3	7	1	0	7
+52	Yes	Travel_Rarely	723	Research & Development	8	4	Medical	1	433	3	Male	85	2	2	Research Scientist	2	Married	4941	17747	2	Y	No	15	3	1	80	0	11	3	2	8	2	7	7
+27	No	Travel_Rarely	1220	Research & Development	5	3	Life Sciences	1	434	3	Female	85	3	1	Research Scientist	2	Single	2478	20938	1	Y	Yes	12	3	2	80	0	4	2	2	4	3	1	2
+32	No	Travel_Rarely	588	Sales	8	2	Technical Degree	1	436	3	Female	65	2	2	Sales Executive	2	Married	5228	24624	1	Y	Yes	11	3	4	80	0	13	2	3	13	12	11	9
+27	No	Travel_Rarely	1377	Sales	2	3	Life Sciences	1	437	4	Male	74	3	2	Sales Executive	3	Single	4478	5242	1	Y	Yes	11	3	1	80	0	5	3	3	5	4	0	4
+31	No	Travel_Rarely	691	Sales	7	3	Marketing	1	438	4	Male	73	3	2	Sales Executive	4	Divorced	7547	7143	4	Y	No	12	3	4	80	3	13	3	3	7	7	1	7
+32	No	Travel_Rarely	1018	Research & Development	2	4	Medical	1	439	1	Female	74	4	2	Research Scientist	4	Single	5055	10557	7	Y	No	16	3	3	80	0	10	0	2	7	7	0	7
+28	Yes	Travel_Rarely	1157	Research & Development	2	4	Medical	1	440	1	Male	84	1	1	Research Scientist	4	Married	3464	24737	5	Y	Yes	13	3	4	80	0	5	4	2	3	2	2	2
+30	No	Travel_Rarely	1275	Research & Development	28	2	Medical	1	441	4	Female	64	3	2	Research Scientist	4	Married	5775	11934	1	Y	No	13	3	4	80	2	11	2	3	10	8	1	9
+31	No	Travel_Frequently	798	Research & Development	7	2	Life Sciences	1	442	3	Female	48	2	3	Manufacturing Director	3	Married	8943	14034	1	Y	No	24	4	1	80	1	10	2	3	10	9	8	9
+39	No	Travel_Frequently	672	Research & Development	7	2	Medical	1	444	3	Male	54	2	5	Manager	4	Married	19272	21141	1	Y	No	15	3	1	80	1	21	2	3	21	9	13	3
+39	Yes	Travel_Rarely	1162	Sales	3	2	Medical	1	445	4	Female	41	3	2	Sales Executive	3	Married	5238	17778	4	Y	Yes	18	3	1	80	0	12	3	2	1	0	0	0
+33	No	Travel_Frequently	508	Sales	10	3	Marketing	1	446	2	Male	46	2	2	Sales Executive	4	Single	4682	4317	3	Y	No	14	3	3	80	0	9	6	2	7	7	0	1
+47	No	Travel_Rarely	1482	Research & Development	5	5	Life Sciences	1	447	4	Male	42	3	5	Research Director	3	Married	18300	16375	4	Y	No	11	3	2	80	1	21	2	3	3	2	1	1
+43	No	Travel_Frequently	559	Research & Development	10	4	Life Sciences	1	448	3	Female	82	2	2	Laboratory Technician	3	Divorced	5257	6227	1	Y	No	11	3	2	80	1	9	3	4	9	7	0	0
+27	No	Non-Travel	210	Sales	1	1	Marketing	1	449	3	Male	73	3	2	Sales Executive	2	Married	6349	22107	0	Y	Yes	13	3	4	80	1	6	0	3	5	4	1	4
+54	No	Travel_Frequently	928	Research & Development	20	4	Life Sciences	1	450	4	Female	31	3	2	Research Scientist	3	Single	4869	16885	3	Y	No	12	3	4	80	0	20	4	2	4	3	0	3
+43	No	Travel_Rarely	1001	Research & Development	7	3	Life Sciences	1	451	3	Female	43	3	3	Healthcare Representative	1	Married	9985	9262	8	Y	No	16	3	1	80	1	10	1	2	1	0	0	0
+45	No	Travel_Rarely	549	Research & Development	8	4	Other	1	452	4	Male	75	3	2	Research Scientist	4	Married	3697	9278	9	Y	No	14	3	1	80	2	12	3	3	10	9	9	8
+40	No	Travel_Rarely	1124	Sales	1	2	Medical	1	453	2	Male	57	1	2	Sales Executive	4	Married	7457	13273	2	Y	Yes	22	4	3	80	3	6	2	2	4	3	0	2
+29	Yes	Travel_Rarely	318	Research & Development	8	4	Other	1	454	2	Male	77	1	1	Laboratory Technician	1	Married	2119	4759	1	Y	Yes	11	3	4	80	0	7	4	2	7	7	0	7
+29	No	Travel_Rarely	738	Research & Development	9	5	Other	1	455	2	Male	30	2	1	Laboratory Technician	4	Single	3983	7621	0	Y	No	17	3	3	80	0	4	2	3	3	2	2	2
+30	No	Travel_Rarely	570	Sales	5	3	Marketing	1	456	4	Female	30	2	2	Sales Executive	3	Divorced	6118	5431	1	Y	No	13	3	3	80	3	10	2	3	10	9	1	2
+27	No	Travel_Rarely	1130	Sales	8	4	Marketing	1	458	2	Female	56	3	2	Sales Executive	2	Married	6214	3415	1	Y	No	18	3	1	80	1	8	3	3	8	7	0	7
+37	No	Travel_Rarely	1192	Research & Development	5	2	Medical	1	460	4	Male	61	3	2	Manufacturing Director	4	Divorced	6347	23177	7	Y	No	16	3	3	80	2	8	2	2	6	2	0	4
+38	No	Travel_Rarely	343	Research & Development	15	2	Life Sciences	1	461	3	Male	92	2	3	Research Director	4	Divorced	11510	15682	0	Y	Yes	14	3	2	80	1	12	3	3	11	10	2	9
+31	No	Travel_Rarely	1232	Research & Development	7	4	Medical	1	462	3	Female	39	3	3	Manufacturing Director	4	Single	7143	25713	1	Y	Yes	14	3	3	80	0	11	2	2	11	9	4	10
+29	No	Travel_Rarely	144	Sales	10	1	Marketing	1	463	4	Female	39	2	2	Sales Executive	2	Divorced	8268	11866	1	Y	Yes	14	3	1	80	2	7	2	3	7	7	1	7
+35	No	Travel_Rarely	1296	Research & Development	5	4	Technical Degree	1	464	3	Male	62	3	3	Manufacturing Director	2	Single	8095	18264	0	Y	No	13	3	4	80	0	17	5	3	16	6	0	13
+23	No	Travel_Rarely	1309	Research & Development	26	1	Life Sciences	1	465	3	Male	83	3	1	Research Scientist	4	Divorced	2904	16092	1	Y	No	12	3	3	80	2	4	2	2	4	2	0	2
+41	No	Travel_Rarely	483	Research & Development	6	3	Medical	1	466	4	Male	95	2	2	Manufacturing Director	2	Single	6032	10110	6	Y	Yes	15	3	4	80	0	8	3	3	5	4	1	2
+47	No	Travel_Frequently	1309	Sales	4	1	Medical	1	467	2	Male	99	3	2	Sales Representative	3	Single	2976	25751	3	Y	No	19	3	1	80	0	5	3	3	0	0	0	0
+42	No	Travel_Rarely	810	Research & Development	23	5	Life Sciences	1	468	1	Female	44	3	4	Research Director	4	Single	15992	15901	2	Y	No	14	3	2	80	0	16	2	3	1	0	0	0
+29	No	Non-Travel	746	Sales	2	3	Life Sciences	1	469	4	Male	61	3	2	Sales Executive	3	Married	4649	16928	1	Y	No	14	3	1	80	1	4	3	2	4	3	0	2
+42	No	Travel_Rarely	544	Human Resources	2	1	Technical Degree	1	470	3	Male	52	3	1	Human Resources	3	Divorced	2696	24017	0	Y	Yes	11	3	3	80	1	4	5	3	3	2	1	0
+32	No	Travel_Rarely	1062	Research & Development	2	3	Medical	1	471	3	Female	75	3	1	Laboratory Technician	2	Married	2370	3956	1	Y	No	13	3	3	80	1	8	4	3	8	0	0	7
+48	No	Travel_Rarely	530	Sales	29	1	Medical	1	473	1	Female	91	3	3	Manager	3	Married	12504	23978	3	Y	No	21	4	2	80	1	15	3	1	0	0	0	0
+37	No	Travel_Rarely	1319	Research & Development	6	3	Medical	1	474	3	Male	51	4	2	Research Scientist	1	Divorced	5974	17001	4	Y	Yes	13	3	1	80	2	13	2	3	7	7	6	7
+30	No	Non-Travel	641	Sales	25	2	Technical Degree	1	475	4	Female	85	3	2	Sales Executive	3	Married	4736	6069	7	Y	Yes	12	3	2	80	1	4	2	4	2	2	2	2
+26	No	Travel_Rarely	933	Sales	1	3	Life Sciences	1	476	3	Male	57	3	2	Sales Executive	3	Married	5296	20156	1	Y	No	17	3	2	80	1	8	3	3	8	7	7	7
+42	No	Travel_Rarely	1332	Research & Development	2	4	Other	1	477	1	Male	98	2	2	Healthcare Representative	4	Single	6781	17078	3	Y	No	23	4	2	80	0	14	6	3	1	0	0	0
+21	Yes	Travel_Frequently	756	Sales	1	1	Technical Degree	1	478	1	Female	99	2	1	Sales Representative	2	Single	2174	9150	1	Y	Yes	11	3	3	80	0	3	3	3	3	2	1	2
+36	No	Non-Travel	845	Sales	1	5	Medical	1	479	4	Female	45	3	2	Sales Executive	4	Single	6653	15276	4	Y	No	15	3	2	80	0	7	6	3	1	0	0	0
+36	No	Travel_Frequently	541	Sales	3	4	Medical	1	481	1	Male	48	2	3	Sales Executive	4	Married	9699	7246	4	Y	No	11	3	1	80	1	16	2	3	13	9	1	12
+57	No	Travel_Rarely	593	Research & Development	1	4	Medical	1	482	4	Male	88	3	2	Healthcare Representative	3	Married	6755	2967	2	Y	No	11	3	3	80	0	15	2	3	3	2	1	2
+40	No	Travel_Rarely	1171	Research & Development	10	4	Life Sciences	1	483	4	Female	46	4	1	Laboratory Technician	3	Married	2213	22495	3	Y	Yes	13	3	3	80	1	10	3	3	7	7	1	7
+21	No	Non-Travel	895	Sales	9	2	Medical	1	484	1	Male	39	3	1	Sales Representative	4	Single	2610	2851	1	Y	No	24	4	3	80	0	3	3	2	3	2	2	2
+33	Yes	Travel_Rarely	350	Sales	5	3	Marketing	1	485	4	Female	34	3	1	Sales Representative	3	Single	2851	9150	1	Y	Yes	13	3	2	80	0	1	2	3	1	0	0	0
+37	No	Travel_Rarely	921	Research & Development	10	3	Medical	1	486	3	Female	98	3	1	Laboratory Technician	1	Married	3452	17663	6	Y	No	20	4	2	80	1	17	3	3	5	4	0	3
+46	No	Non-Travel	1144	Research & Development	7	4	Medical	1	487	3	Female	30	3	2	Manufacturing Director	3	Married	5258	16044	2	Y	No	14	3	3	80	0	7	2	4	1	0	0	0
+41	Yes	Travel_Frequently	143	Sales	4	3	Marketing	1	488	1	Male	56	3	2	Sales Executive	2	Single	9355	9558	1	Y	No	18	3	3	80	0	8	5	3	8	7	7	7
+50	No	Travel_Rarely	1046	Research & Development	10	3	Technical Degree	1	491	4	Male	100	2	3	Healthcare Representative	4	Single	10496	2755	6	Y	No	15	3	4	80	0	20	2	3	4	3	1	3
+40	Yes	Travel_Rarely	575	Sales	22	2	Marketing	1	492	3	Male	68	2	2	Sales Executive	3	Married	6380	6110	2	Y	Yes	12	3	1	80	2	8	6	3	6	4	1	0
+31	No	Travel_Rarely	408	Research & Development	9	4	Life Sciences	1	493	3	Male	42	2	1	Research Scientist	2	Single	2657	7551	0	Y	Yes	16	3	4	80	0	3	5	3	2	2	2	2
+21	Yes	Travel_Rarely	156	Sales	12	3	Life Sciences	1	494	3	Female	90	4	1	Sales Representative	2	Single	2716	25422	1	Y	No	15	3	4	80	0	1	0	3	1	0	0	0
+29	No	Travel_Rarely	1283	Research & Development	23	3	Life Sciences	1	495	4	Male	54	3	1	Research Scientist	4	Single	2201	18168	9	Y	No	16	3	4	80	0	6	4	3	3	2	1	2
+35	No	Travel_Rarely	755	Research & Development	9	4	Life Sciences	1	496	3	Male	97	2	2	Healthcare Representative	2	Single	6540	19394	9	Y	No	19	3	3	80	0	10	5	3	1	1	0	0
+27	No	Travel_Rarely	1469	Research & Development	1	2	Medical	1	497	4	Male	82	3	1	Laboratory Technician	2	Divorced	3816	17881	1	Y	No	11	3	2	80	1	5	2	3	5	2	0	4
+28	No	Travel_Rarely	304	Sales	9	4	Life Sciences	1	498	2	Male	92	3	2	Sales Executive	4	Single	5253	20750	1	Y	No	16	3	4	80	0	7	1	3	7	5	0	7
+49	No	Travel_Rarely	1261	Research & Development	7	3	Other	1	499	2	Male	31	2	3	Healthcare Representative	3	Single	10965	12066	8	Y	No	24	4	3	80	0	26	2	3	5	2	0	0
+51	No	Travel_Rarely	1178	Sales	14	2	Life Sciences	1	500	3	Female	87	3	2	Sales Executive	4	Married	4936	14862	4	Y	No	11	3	3	80	1	18	2	2	7	7	0	7
+36	No	Travel_Rarely	329	Research & Development	2	3	Life Sciences	1	501	4	Female	96	3	1	Research Scientist	3	Married	2543	11868	4	Y	No	13	3	2	80	1	6	3	3	2	2	2	2
+34	Yes	Non-Travel	1362	Sales	19	3	Marketing	1	502	1	Male	67	4	2	Sales Executive	4	Single	5304	4652	8	Y	Yes	13	3	2	80	0	9	3	2	5	2	0	4
+55	No	Travel_Rarely	1311	Research & Development	2	3	Life Sciences	1	505	3	Female	97	3	4	Manager	4	Single	16659	23258	2	Y	Yes	13	3	3	80	0	30	2	3	5	4	1	2
+24	No	Travel_Rarely	1371	Sales	10	4	Marketing	1	507	4	Female	77	3	2	Sales Executive	3	Divorced	4260	5915	1	Y	Yes	12	3	4	80	1	5	2	4	5	2	0	3
+30	No	Travel_Rarely	202	Sales	2	1	Technical Degree	1	508	3	Male	72	3	1	Sales Representative	2	Married	2476	17434	1	Y	No	18	3	1	80	1	1	3	3	1	0	0	0
+26	Yes	Travel_Frequently	575	Research & Development	3	1	Technical Degree	1	510	3	Male	73	3	1	Research Scientist	1	Single	3102	6582	0	Y	No	22	4	3	80	0	7	2	3	6	4	0	4
+22	No	Travel_Rarely	253	Research & Development	11	3	Medical	1	511	1	Female	43	3	1	Research Scientist	2	Married	2244	24440	1	Y	No	13	3	4	80	1	2	1	3	2	1	1	2
+36	No	Travel_Rarely	164	Sales	2	2	Medical	1	513	2	Male	61	2	3	Sales Executive	3	Married	7596	3809	1	Y	No	13	3	2	80	2	10	2	3	10	9	9	0
+30	Yes	Travel_Frequently	464	Research & Development	4	3	Technical Degree	1	514	3	Male	40	3	1	Research Scientist	4	Single	2285	3427	9	Y	Yes	23	4	3	80	0	3	4	3	1	0	0	0
+37	No	Travel_Rarely	1107	Research & Development	14	3	Life Sciences	1	515	4	Female	95	3	1	Laboratory Technician	1	Divorced	3034	26914	1	Y	No	12	3	3	80	1	18	2	2	18	7	12	17
+40	No	Travel_Rarely	759	Sales	2	2	Marketing	1	516	4	Female	46	3	2	Sales Executive	2	Divorced	5715	22553	7	Y	No	12	3	3	80	2	8	5	3	5	4	1	3
+42	No	Travel_Rarely	201	Research & Development	1	4	Life Sciences	1	517	2	Female	95	3	1	Laboratory Technician	1	Divorced	2576	20490	3	Y	No	16	3	2	80	1	8	5	3	5	2	1	2
+37	No	Travel_Rarely	1305	Research & Development	10	4	Life Sciences	1	518	3	Male	49	3	2	Manufacturing Director	2	Single	4197	21123	2	Y	Yes	12	3	4	80	0	18	2	2	1	0	0	1
+43	No	Travel_Rarely	982	Research & Development	12	3	Life Sciences	1	520	1	Male	59	2	4	Research Director	2	Divorced	14336	4345	1	Y	No	11	3	3	80	1	25	3	3	25	10	3	9
+40	No	Travel_Rarely	555	Research & Development	2	3	Medical	1	521	2	Female	78	2	2	Laboratory Technician	3	Married	3448	13436	6	Y	No	22	4	2	80	1	20	3	3	1	0	0	0
+54	No	Travel_Rarely	821	Research & Development	5	2	Medical	1	522	1	Male	86	3	5	Research Director	1	Married	19406	8509	4	Y	No	11	3	3	80	1	24	4	2	4	2	1	2
+34	No	Non-Travel	1381	Sales	4	4	Marketing	1	523	3	Female	72	3	2	Sales Executive	3	Married	6538	12740	9	Y	No	15	3	1	80	1	6	3	3	3	2	1	2
+31	No	Travel_Rarely	480	Research & Development	7	2	Medical	1	524	2	Female	31	3	2	Manufacturing Director	1	Married	4306	4156	1	Y	No	12	3	2	80	1	13	5	1	13	10	3	12
+43	No	Travel_Frequently	313	Research & Development	21	3	Medical	1	525	4	Male	61	3	1	Laboratory Technician	4	Married	2258	15238	7	Y	No	20	4	1	80	1	8	1	3	3	2	1	2
+43	No	Travel_Rarely	1473	Research & Development	8	4	Other	1	526	3	Female	74	3	2	Healthcare Representative	3	Divorced	4522	2227	4	Y	Yes	14	3	4	80	0	8	3	3	5	2	0	2
+25	No	Travel_Rarely	891	Sales	4	2	Life Sciences	1	527	2	Female	99	2	2	Sales Executive	4	Single	4487	12090	1	Y	Yes	11	3	2	80	0	5	3	3	5	4	1	3
+37	No	Non-Travel	1063	Research & Development	25	5	Medical	1	529	2	Female	72	3	2	Research Scientist	3	Married	4449	23866	3	Y	Yes	15	3	1	80	2	15	2	3	13	11	10	7
+31	No	Travel_Rarely	329	Research & Development	1	2	Life Sciences	1	530	4	Male	98	2	1	Laboratory Technician	1	Married	2218	16193	1	Y	No	12	3	3	80	1	4	3	3	4	2	3	2
+39	No	Travel_Frequently	1218	Research & Development	1	1	Life Sciences	1	531	2	Male	52	3	5	Manager	3	Divorced	19197	8213	1	Y	Yes	14	3	3	80	1	21	3	3	21	8	1	6
+56	No	Travel_Frequently	906	Sales	6	3	Life Sciences	1	532	3	Female	86	4	4	Sales Executive	1	Married	13212	18256	9	Y	No	11	3	4	80	3	36	0	2	7	7	7	7
+30	No	Travel_Rarely	1082	Sales	12	3	Technical Degree	1	533	2	Female	83	3	2	Sales Executive	3	Single	6577	19558	0	Y	No	11	3	2	80	0	6	6	3	5	4	4	4
+41	No	Travel_Rarely	645	Sales	1	3	Marketing	1	534	2	Male	49	4	3	Sales Executive	1	Married	8392	19566	1	Y	No	16	3	3	80	1	10	2	3	10	7	0	7
+28	No	Travel_Rarely	1300	Research & Development	17	2	Medical	1	536	3	Male	79	3	2	Laboratory Technician	1	Divorced	4558	13535	1	Y	No	12	3	4	80	1	10	2	3	10	0	1	8
+25	Yes	Travel_Rarely	688	Research & Development	3	3	Medical	1	538	1	Male	91	3	1	Laboratory Technician	1	Married	4031	9396	5	Y	No	13	3	3	80	1	6	5	3	2	2	0	2
+52	No	Travel_Rarely	319	Research & Development	3	3	Medical	1	543	4	Male	39	2	3	Manufacturing Director	3	Married	7969	19609	2	Y	Yes	14	3	3	80	0	28	4	3	5	4	0	4
+45	No	Travel_Rarely	192	Research & Development	10	2	Life Sciences	1	544	1	Male	69	3	1	Research Scientist	4	Married	2654	9655	3	Y	No	21	4	4	80	2	8	3	2	2	2	0	2
+52	No	Travel_Rarely	1490	Research & Development	4	2	Life Sciences	1	546	4	Female	30	3	4	Manager	4	Married	16555	10310	2	Y	No	13	3	4	80	0	31	2	1	5	2	1	4
+42	No	Travel_Frequently	532	Research & Development	29	2	Life Sciences	1	547	1	Female	92	3	2	Research Scientist	3	Divorced	4556	12932	2	Y	No	11	3	2	80	1	19	3	3	5	4	0	2
+30	No	Travel_Rarely	317	Research & Development	2	3	Life Sciences	1	548	3	Female	43	1	2	Manufacturing Director	4	Single	6091	24793	2	Y	No	20	4	3	80	0	11	2	3	5	4	0	2
+60	No	Travel_Rarely	422	Research & Development	7	3	Life Sciences	1	549	1	Female	41	3	5	Manager	1	Married	19566	3854	5	Y	No	11	3	4	80	0	33	5	1	29	8	11	10
+46	No	Travel_Rarely	1485	Research & Development	18	3	Medical	1	550	3	Female	87	3	2	Manufacturing Director	3	Divorced	4810	26314	2	Y	No	14	3	3	80	1	19	5	2	10	7	0	8
+42	No	Travel_Frequently	1368	Research & Development	28	4	Technical Degree	1	551	4	Female	88	2	2	Healthcare Representative	4	Married	4523	4386	0	Y	No	11	3	4	80	3	7	4	4	6	5	0	4
+24	Yes	Travel_Rarely	1448	Sales	1	1	Technical Degree	1	554	1	Female	62	3	1	Sales Representative	2	Single	3202	21972	1	Y	Yes	16	3	2	80	0	6	4	3	5	3	1	4
+34	Yes	Travel_Frequently	296	Sales	6	2	Marketing	1	555	4	Female	33	1	1	Sales Representative	3	Divorced	2351	12253	0	Y	No	16	3	4	80	1	3	3	2	2	2	1	0
+38	No	Travel_Frequently	1490	Research & Development	2	2	Life Sciences	1	556	4	Male	42	3	1	Laboratory Technician	4	Married	1702	12106	1	Y	Yes	23	4	3	80	1	1	3	3	1	0	0	0
+40	No	Travel_Rarely	1398	Sales	2	4	Life Sciences	1	558	3	Female	79	3	5	Manager	3	Married	18041	13022	0	Y	No	14	3	4	80	0	21	2	3	20	15	1	12
+26	No	Travel_Rarely	1349	Research & Development	23	3	Life Sciences	1	560	1	Female	90	3	1	Research Scientist	4	Divorced	2886	3032	1	Y	No	22	4	2	80	2	3	3	1	3	2	0	2
+30	No	Non-Travel	1400	Research & Development	3	3	Life Sciences	1	562	3	Male	53	3	1	Laboratory Technician	4	Married	2097	16734	4	Y	No	15	3	3	80	1	9	3	1	5	3	1	4
+29	No	Travel_Rarely	986	Research & Development	3	4	Medical	1	564	2	Male	93	2	3	Research Director	3	Married	11935	21526	1	Y	No	18	3	3	80	0	10	2	3	10	2	0	7
+29	Yes	Travel_Rarely	408	Research & Development	25	5	Technical Degree	1	565	3	Female	71	2	1	Research Scientist	2	Married	2546	18300	5	Y	No	16	3	2	80	0	6	2	4	2	2	1	1
+19	Yes	Travel_Rarely	489	Human Resources	2	2	Technical Degree	1	566	1	Male	52	2	1	Human Resources	4	Single	2564	18437	1	Y	No	12	3	3	80	0	1	3	4	1	0	0	0
+30	No	Non-Travel	1398	Sales	22	4	Other	1	567	3	Female	69	3	3	Sales Executive	1	Married	8412	2890	0	Y	No	11	3	3	80	0	10	3	3	9	8	7	8
+57	No	Travel_Rarely	210	Sales	29	3	Marketing	1	568	1	Male	56	2	4	Manager	4	Divorced	14118	22102	3	Y	No	12	3	3	80	1	32	3	2	1	0	0	0
+50	No	Travel_Rarely	1099	Research & Development	29	4	Life Sciences	1	569	2	Male	88	2	4	Manager	3	Married	17046	9314	0	Y	No	15	3	2	80	1	28	2	3	27	10	15	7
+30	No	Non-Travel	1116	Research & Development	2	3	Medical	1	571	3	Female	49	3	1	Laboratory Technician	4	Single	2564	7181	0	Y	No	14	3	3	80	0	12	2	2	11	7	6	7
+60	No	Travel_Frequently	1499	Sales	28	3	Marketing	1	573	3	Female	80	2	3	Sales Executive	1	Married	10266	2845	4	Y	No	19	3	4	80	0	22	5	4	18	13	13	11
+47	No	Travel_Rarely	983	Research & Development	2	2	Medical	1	574	1	Female	65	3	2	Manufacturing Director	4	Divorced	5070	7389	5	Y	No	13	3	3	80	3	20	2	3	5	0	0	4
+46	No	Travel_Rarely	1009	Research & Development	2	3	Life Sciences	1	575	1	Male	51	3	4	Research Director	3	Married	17861	2288	6	Y	No	13	3	3	80	0	26	2	1	3	2	0	1
+35	No	Travel_Rarely	144	Research & Development	22	3	Life Sciences	1	577	4	Male	46	1	1	Laboratory Technician	3	Single	4230	19225	0	Y	No	15	3	3	80	0	6	2	3	5	4	4	3
+54	No	Travel_Rarely	548	Research & Development	8	4	Life Sciences	1	578	3	Female	42	3	2	Laboratory Technician	3	Single	3780	23428	7	Y	No	11	3	3	80	0	19	3	3	1	0	0	0
+34	No	Travel_Rarely	1303	Research & Development	2	4	Life Sciences	1	579	4	Male	62	2	1	Research Scientist	3	Divorced	2768	8416	3	Y	No	12	3	3	80	1	14	3	3	7	3	5	7
+46	No	Travel_Rarely	1125	Sales	10	3	Marketing	1	580	3	Female	94	2	3	Sales Executive	4	Married	9071	11563	2	Y	Yes	19	3	3	80	1	15	3	3	3	2	1	2
+31	No	Travel_Rarely	1274	Research & Development	9	1	Life Sciences	1	581	3	Male	33	3	3	Manufacturing Director	2	Divorced	10648	14394	1	Y	No	25	4	4	80	1	13	6	4	13	8	0	8
+33	Yes	Travel_Rarely	1277	Research & Development	15	1	Medical	1	582	2	Male	56	3	3	Manager	3	Married	13610	24619	7	Y	Yes	12	3	4	80	0	15	2	4	7	6	7	7
+33	Yes	Travel_Rarely	587	Research & Development	10	1	Medical	1	584	1	Male	38	1	1	Laboratory Technician	4	Divorced	3408	6705	7	Y	No	13	3	1	80	3	8	2	3	4	3	1	3
+30	No	Travel_Rarely	413	Sales	7	1	Marketing	1	585	4	Male	57	3	1	Sales Representative	2	Single	2983	18398	0	Y	No	14	3	1	80	0	4	3	3	3	2	1	2
+35	No	Travel_Rarely	1276	Research & Development	16	3	Life Sciences	1	586	4	Male	72	3	3	Healthcare Representative	3	Married	7632	14295	4	Y	Yes	12	3	3	80	0	10	2	3	8	7	0	0
+31	Yes	Travel_Frequently	534	Research & Development	20	3	Life Sciences	1	587	1	Male	66	3	3	Healthcare Representative	3	Married	9824	22908	3	Y	No	12	3	1	80	0	12	2	3	1	0	0	0
+34	Yes	Travel_Frequently	988	Human Resources	23	3	Human Resources	1	590	2	Female	43	3	3	Human Resources	1	Divorced	9950	11533	9	Y	Yes	15	3	3	80	3	11	2	3	3	2	0	2
+42	No	Travel_Frequently	1474	Research & Development	5	2	Other	1	591	2	Male	97	3	1	Laboratory Technician	3	Married	2093	9260	4	Y	No	17	3	4	80	1	8	4	3	2	2	2	0
+36	No	Non-Travel	635	Sales	10	4	Medical	1	592	2	Male	32	3	3	Sales Executive	4	Single	9980	15318	1	Y	No	14	3	4	80	0	10	3	2	10	3	9	7
+22	Yes	Travel_Frequently	1368	Research & Development	4	1	Technical Degree	1	593	3	Male	99	2	1	Laboratory Technician	3	Single	3894	9129	5	Y	No	16	3	3	80	0	4	3	3	2	2	1	2
+48	No	Travel_Rarely	163	Sales	2	5	Marketing	1	595	2	Female	37	3	2	Sales Executive	4	Married	4051	19658	2	Y	No	14	3	1	80	1	14	2	3	9	7	6	7
+55	No	Travel_Rarely	1117	Sales	18	5	Life Sciences	1	597	1	Female	83	3	4	Manager	2	Single	16835	9873	3	Y	No	23	4	4	80	0	37	2	3	10	9	7	7
+41	No	Non-Travel	267	Sales	10	2	Life Sciences	1	599	4	Male	56	3	2	Sales Executive	4	Single	6230	13430	7	Y	No	14	3	4	80	0	16	3	3	14	3	1	10
+35	No	Travel_Rarely	619	Sales	1	3	Marketing	1	600	2	Male	85	3	2	Sales Executive	3	Married	4717	18659	9	Y	No	11	3	3	80	0	15	2	3	11	9	6	9
+40	No	Travel_Rarely	302	Research & Development	6	3	Life Sciences	1	601	2	Female	75	3	4	Manufacturing Director	3	Single	13237	20364	7	Y	No	15	3	3	80	0	22	3	3	20	6	5	13
+39	No	Travel_Frequently	443	Research & Development	8	1	Life Sciences	1	602	3	Female	48	3	1	Laboratory Technician	3	Married	3755	17872	1	Y	No	11	3	1	80	1	8	3	3	8	3	0	7
+31	No	Travel_Rarely	828	Sales	2	1	Life Sciences	1	604	2	Male	77	3	2	Sales Executive	4	Single	6582	8346	4	Y	Yes	13	3	3	80	0	10	2	4	6	5	0	5
+42	No	Travel_Rarely	319	Research & Development	24	3	Medical	1	605	4	Male	56	3	3	Manufacturing Director	1	Married	7406	6950	1	Y	Yes	21	4	4	80	1	10	5	2	10	9	5	8
+45	No	Travel_Rarely	561	Sales	2	3	Other	1	606	4	Male	61	3	2	Sales Executive	2	Married	4805	16177	0	Y	No	19	3	2	80	1	9	3	4	8	7	3	7
+26	Yes	Travel_Frequently	426	Human Resources	17	4	Life Sciences	1	608	2	Female	58	3	1	Human Resources	3	Divorced	2741	22808	0	Y	Yes	11	3	2	80	1	8	2	2	7	7	1	0
+29	No	Travel_Rarely	232	Research & Development	19	3	Technical Degree	1	611	4	Male	34	3	2	Manufacturing Director	4	Divorced	4262	22645	4	Y	No	12	3	2	80	2	8	2	4	3	2	1	2
+33	No	Travel_Rarely	922	Research & Development	1	5	Medical	1	612	1	Female	95	4	4	Research Director	3	Divorced	16184	22578	4	Y	No	19	3	3	80	1	10	2	3	6	1	0	5
+31	No	Travel_Rarely	688	Sales	7	3	Life Sciences	1	613	3	Male	44	2	3	Manager	4	Divorced	11557	25291	9	Y	No	21	4	3	80	1	10	3	2	5	4	0	1
+18	Yes	Travel_Frequently	1306	Sales	5	3	Marketing	1	614	2	Male	69	3	1	Sales Representative	2	Single	1878	8059	1	Y	Yes	14	3	4	80	0	0	3	3	0	0	0	0
+40	No	Non-Travel	1094	Sales	28	3	Other	1	615	3	Male	58	1	3	Sales Executive	1	Divorced	10932	11373	3	Y	No	15	3	3	80	1	20	2	3	1	0	0	1
+41	No	Non-Travel	509	Research & Development	2	4	Other	1	616	1	Female	62	2	2	Healthcare Representative	3	Single	6811	2112	2	Y	Yes	17	3	1	80	0	10	3	3	8	7	0	7
+26	No	Travel_Rarely	775	Sales	29	2	Medical	1	618	1	Male	45	3	2	Sales Executive	3	Divorced	4306	4267	5	Y	No	12	3	1	80	2	8	5	3	0	0	0	0
+35	No	Travel_Rarely	195	Sales	1	3	Medical	1	620	1	Female	80	3	2	Sales Executive	3	Single	4859	6698	1	Y	No	16	3	4	80	0	5	3	3	5	4	0	3
+34	No	Travel_Rarely	258	Sales	21	4	Life Sciences	1	621	4	Male	74	4	2	Sales Executive	4	Single	5337	19921	1	Y	No	12	3	4	80	0	10	3	3	10	7	5	7
+26	Yes	Travel_Rarely	471	Research & Development	24	3	Technical Degree	1	622	3	Male	66	1	1	Laboratory Technician	4	Single	2340	23213	1	Y	Yes	18	3	2	80	0	1	3	1	1	0	0	0
+37	No	Travel_Rarely	799	Research & Development	1	3	Technical Degree	1	623	2	Female	59	3	3	Manufacturing Director	4	Single	7491	23848	4	Y	No	17	3	4	80	0	12	3	4	6	5	1	2
+46	No	Travel_Frequently	1034	Research & Development	18	1	Medical	1	624	1	Female	86	3	3	Healthcare Representative	3	Married	10527	8984	5	Y	No	11	3	4	80	0	28	3	2	2	2	1	2
+41	No	Travel_Rarely	1276	Sales	2	5	Life Sciences	1	625	2	Female	91	3	4	Manager	1	Married	16595	5626	7	Y	No	16	3	2	80	1	22	2	3	18	16	11	8
+37	No	Non-Travel	142	Sales	9	4	Medical	1	626	1	Male	69	3	3	Sales Executive	2	Divorced	8834	24666	1	Y	No	13	3	4	80	1	9	6	3	9	5	7	7
+52	No	Travel_Rarely	956	Research & Development	6	2	Technical Degree	1	630	4	Male	78	3	2	Research Scientist	1	Divorced	5577	22087	3	Y	Yes	12	3	2	80	2	18	3	3	10	9	6	9
+32	Yes	Non-Travel	1474	Sales	11	4	Other	1	631	4	Male	60	4	2	Sales Executive	3	Married	4707	23914	8	Y	No	12	3	4	80	0	6	2	3	4	2	1	2
+24	No	Travel_Frequently	535	Sales	24	3	Medical	1	632	4	Male	38	3	1	Sales Representative	4	Married	2400	5530	0	Y	No	13	3	3	80	2	3	3	3	2	2	2	1
+38	No	Travel_Rarely	1495	Research & Development	10	3	Medical	1	634	3	Female	76	3	2	Healthcare Representative	3	Married	9824	22174	3	Y	No	19	3	3	80	1	18	4	3	1	0	0	0
+37	No	Travel_Rarely	446	Research & Development	1	4	Life Sciences	1	635	2	Female	65	3	2	Manufacturing Director	2	Married	6447	15701	6	Y	No	12	3	2	80	1	8	2	2	6	5	4	3
+49	No	Travel_Rarely	1245	Research & Development	18	4	Life Sciences	1	638	4	Male	58	2	5	Research Director	3	Divorced	19502	2125	1	Y	Yes	17	3	3	80	1	31	5	3	31	9	0	9
+24	No	Travel_Rarely	691	Research & Development	23	3	Medical	1	639	2	Male	89	4	1	Research Scientist	4	Married	2725	21630	1	Y	Yes	11	3	2	80	2	6	3	3	6	5	1	4
+26	No	Travel_Rarely	703	Sales	28	2	Marketing	1	641	1	Male	66	3	2	Sales Executive	2	Married	6272	7428	1	Y	No	20	4	4	80	2	6	5	4	5	3	1	4
+24	No	Travel_Rarely	823	Research & Development	17	2	Other	1	643	4	Male	94	2	1	Laboratory Technician	2	Married	2127	9100	1	Y	No	21	4	4	80	1	1	2	3	1	0	0	0
+50	No	Travel_Frequently	1246	Human Resources	3	3	Medical	1	644	1	Male	99	3	5	Manager	2	Married	18200	7999	1	Y	No	11	3	3	80	1	32	2	3	32	5	10	7
+25	No	Travel_Rarely	622	Sales	13	1	Medical	1	645	2	Male	40	3	1	Sales Representative	3	Married	2096	26376	1	Y	No	11	3	3	80	0	7	1	3	7	4	0	6
+24	Yes	Travel_Frequently	1287	Research & Development	7	3	Life Sciences	1	647	1	Female	55	3	1	Laboratory Technician	3	Married	2886	14168	1	Y	Yes	16	3	4	80	1	6	4	3	6	3	1	2
+30	Yes	Travel_Frequently	448	Sales	12	4	Life Sciences	1	648	2	Male	74	2	1	Sales Representative	1	Married	2033	14470	1	Y	No	18	3	3	80	1	1	2	4	1	0	0	0
+34	No	Travel_Rarely	254	Research & Development	1	2	Life Sciences	1	649	2	Male	83	2	1	Research Scientist	4	Married	3622	22794	1	Y	Yes	13	3	4	80	1	6	3	3	6	5	1	3
+31	Yes	Travel_Rarely	1365	Sales	13	4	Medical	1	650	2	Male	46	3	2	Sales Executive	1	Divorced	4233	11512	2	Y	No	17	3	3	80	0	9	2	1	3	1	1	2
+35	No	Travel_Rarely	538	Research & Development	25	2	Other	1	652	1	Male	54	2	2	Laboratory Technician	4	Single	3681	14004	4	Y	No	14	3	4	80	0	9	3	3	3	2	0	2
+31	No	Travel_Rarely	525	Sales	6	4	Medical	1	653	1	Male	66	4	2	Sales Executive	4	Divorced	5460	6219	4	Y	No	22	4	4	80	2	13	4	4	7	7	5	7
+27	No	Travel_Rarely	798	Research & Development	6	4	Medical	1	655	1	Female	66	2	1	Research Scientist	3	Divorced	2187	5013	0	Y	No	12	3	3	80	2	6	5	2	5	3	0	3
+37	No	Travel_Rarely	558	Sales	2	3	Marketing	1	656	4	Male	75	3	2	Sales Executive	3	Married	9602	3010	4	Y	Yes	11	3	3	80	1	17	3	2	3	0	1	0
+20	No	Travel_Rarely	959	Research & Development	1	3	Life Sciences	1	657	4	Female	83	2	1	Research Scientist	2	Single	2836	11757	1	Y	No	13	3	4	80	0	1	0	4	1	0	0	0
+42	No	Travel_Rarely	622	Research & Development	2	4	Life Sciences	1	659	3	Female	81	3	2	Healthcare Representative	4	Married	4089	5718	1	Y	No	13	3	2	80	2	10	4	3	10	2	2	2
+43	No	Travel_Rarely	782	Research & Development	6	4	Other	1	661	2	Male	50	2	4	Research Director	4	Divorced	16627	2671	4	Y	Yes	14	3	3	80	1	21	3	2	1	0	0	0
+38	No	Travel_Rarely	362	Research & Development	1	1	Life Sciences	1	662	3	Female	43	3	1	Research Scientist	1	Single	2619	14561	3	Y	No	17	3	4	80	0	8	3	2	0	0	0	0
+43	No	Travel_Frequently	1001	Research & Development	9	5	Medical	1	663	4	Male	72	3	2	Laboratory Technician	3	Divorced	5679	19627	3	Y	Yes	13	3	2	80	1	10	3	3	8	7	4	7
+48	No	Travel_Rarely	1236	Research & Development	1	4	Life Sciences	1	664	4	Female	40	2	4	Manager	1	Married	15402	17997	7	Y	No	11	3	1	80	1	21	3	1	3	2	0	2
+44	No	Travel_Rarely	1112	Human Resources	1	4	Life Sciences	1	665	1	Female	50	2	2	Human Resources	3	Single	5985	26894	4	Y	No	11	3	2	80	0	10	1	4	2	2	0	2
+34	No	Travel_Rarely	204	Sales	14	3	Technical Degree	1	666	3	Female	31	3	1	Sales Representative	3	Divorced	2579	2912	1	Y	Yes	18	3	4	80	2	8	3	3	8	2	0	6
+27	Yes	Travel_Rarely	1420	Sales	2	1	Marketing	1	667	3	Male	85	3	1	Sales Representative	1	Divorced	3041	16346	0	Y	No	11	3	2	80	1	5	3	3	4	3	0	2
+21	No	Travel_Rarely	1343	Sales	22	1	Technical Degree	1	669	3	Male	49	3	1	Sales Representative	3	Single	3447	24444	1	Y	No	11	3	3	80	0	3	2	3	3	2	1	2
+44	No	Travel_Rarely	1315	Research & Development	3	4	Other	1	671	4	Male	35	3	5	Manager	4	Married	19513	9358	4	Y	Yes	12	3	1	80	1	26	2	4	2	2	0	1
+22	No	Travel_Rarely	604	Research & Development	6	1	Medical	1	675	1	Male	69	3	1	Research Scientist	3	Married	2773	12145	0	Y	No	20	4	4	80	0	3	3	3	2	2	2	2
+33	No	Travel_Rarely	1216	Sales	8	4	Marketing	1	677	3	Male	39	3	2	Sales Executive	3	Divorced	7104	20431	0	Y	No	12	3	4	80	0	6	3	3	5	0	1	2
+32	No	Travel_Rarely	646	Research & Development	9	4	Life Sciences	1	679	1	Female	92	3	2	Research Scientist	4	Married	6322	18089	1	Y	Yes	12	3	4	80	1	6	2	2	6	4	0	5
+30	No	Travel_Frequently	160	Research & Development	3	3	Medical	1	680	3	Female	71	3	1	Research Scientist	3	Divorced	2083	22653	1	Y	No	20	4	3	80	1	1	2	3	1	0	0	0
+53	No	Travel_Rarely	238	Sales	1	1	Medical	1	682	4	Female	34	3	2	Sales Executive	1	Single	8381	7507	7	Y	No	20	4	4	80	0	18	2	4	14	7	8	10
+34	No	Travel_Rarely	1397	Research & Development	1	5	Life Sciences	1	683	2	Male	42	3	1	Research Scientist	4	Married	2691	7660	1	Y	No	12	3	4	80	1	10	4	2	10	9	8	8
+45	Yes	Travel_Frequently	306	Sales	26	4	Life Sciences	1	684	1	Female	100	3	2	Sales Executive	1	Married	4286	5630	2	Y	No	14	3	4	80	2	5	4	3	1	1	0	0
+26	No	Travel_Rarely	991	Research & Development	6	3	Life Sciences	1	686	3	Female	71	3	1	Laboratory Technician	4	Married	2659	17759	1	Y	Yes	13	3	3	80	1	3	2	3	3	2	0	2
+37	No	Travel_Rarely	482	Research & Development	3	3	Other	1	689	3	Male	36	3	3	Manufacturing Director	3	Married	9434	9606	1	Y	No	15	3	3	80	1	10	2	3	10	7	7	8
+29	No	Travel_Rarely	1176	Sales	3	2	Medical	1	690	2	Female	62	3	2	Sales Executive	3	Married	5561	3487	1	Y	No	14	3	1	80	1	6	5	2	6	0	1	2
+35	No	Travel_Rarely	1017	Research & Development	6	4	Life Sciences	1	691	2	Male	82	1	2	Research Scientist	4	Single	6646	19368	1	Y	No	13	3	2	80	0	17	3	3	17	11	11	8
+33	No	Travel_Frequently	1296	Research & Development	6	3	Life Sciences	1	692	3	Male	30	3	2	Healthcare Representative	4	Divorced	7725	5335	3	Y	No	23	4	3	80	1	15	2	1	13	11	4	7
+54	No	Travel_Rarely	397	Human Resources	19	4	Medical	1	698	3	Male	88	3	3	Human Resources	2	Married	10725	6729	2	Y	No	15	3	3	80	1	16	1	4	9	7	7	1
+36	No	Travel_Rarely	913	Research & Development	9	2	Medical	1	699	2	Male	48	2	2	Manufacturing Director	2	Divorced	8847	13934	2	Y	Yes	11	3	3	80	1	13	2	3	3	2	0	2
+27	No	Travel_Rarely	1115	Research & Development	3	4	Medical	1	700	1	Male	54	2	1	Research Scientist	4	Single	2045	15174	0	Y	No	13	3	4	80	0	5	0	3	4	2	1	1
+20	Yes	Travel_Rarely	1362	Research & Development	10	1	Medical	1	701	4	Male	32	3	1	Research Scientist	3	Single	1009	26999	1	Y	Yes	11	3	4	80	0	1	5	3	1	0	1	1
+33	Yes	Travel_Frequently	1076	Research & Development	3	3	Life Sciences	1	702	1	Male	70	3	1	Research Scientist	1	Single	3348	3164	1	Y	Yes	11	3	1	80	0	10	3	3	10	8	9	7
+35	No	Non-Travel	727	Research & Development	3	3	Life Sciences	1	704	3	Male	41	2	1	Laboratory Technician	3	Married	1281	16900	1	Y	No	18	3	3	80	2	1	3	3	1	0	0	0
+23	No	Travel_Rarely	885	Research & Development	4	3	Medical	1	705	1	Male	58	4	1	Research Scientist	1	Married	2819	8544	2	Y	No	16	3	1	80	1	5	3	4	3	2	0	2
+25	No	Travel_Rarely	810	Sales	8	3	Life Sciences	1	707	4	Male	57	4	2	Sales Executive	2	Married	4851	15678	0	Y	No	22	4	3	80	1	4	4	3	3	2	1	2
+38	No	Travel_Rarely	243	Sales	7	4	Marketing	1	709	4	Female	46	2	2	Sales Executive	4	Single	4028	7791	0	Y	No	20	4	1	80	0	8	2	3	7	7	0	5
+29	No	Travel_Frequently	806	Research & Development	1	4	Life Sciences	1	710	2	Male	76	1	1	Research Scientist	4	Divorced	2720	18959	1	Y	No	18	3	4	80	1	10	5	3	10	7	2	8
+48	No	Travel_Rarely	817	Sales	2	1	Marketing	1	712	2	Male	56	4	2	Sales Executive	2	Married	8120	18597	3	Y	No	12	3	4	80	0	12	3	3	2	2	2	2
+27	No	Travel_Frequently	1410	Sales	3	1	Medical	1	714	4	Female	71	4	2	Sales Executive	4	Divorced	4647	16673	1	Y	Yes	20	4	2	80	2	6	3	3	6	5	0	4
+37	No	Travel_Rarely	1225	Research & Development	10	2	Life Sciences	1	715	4	Male	80	4	1	Research Scientist	4	Single	4680	15232	3	Y	No	17	3	1	80	0	4	2	3	1	0	0	0
+50	No	Travel_Rarely	1207	Research & Development	28	1	Medical	1	716	4	Male	74	4	1	Laboratory Technician	3	Married	3221	3297	1	Y	Yes	11	3	3	80	3	20	3	3	20	8	3	8
+34	No	Travel_Rarely	1442	Research & Development	9	3	Medical	1	717	4	Female	46	2	3	Healthcare Representative	2	Single	8621	17654	1	Y	No	14	3	2	80	0	9	3	4	8	7	7	7
+24	Yes	Travel_Rarely	693	Sales	3	2	Life Sciences	1	720	1	Female	65	3	2	Sales Executive	3	Single	4577	24785	9	Y	No	14	3	1	80	0	4	3	3	2	2	2	0
+39	No	Travel_Rarely	408	Research & Development	2	4	Technical Degree	1	721	4	Female	80	2	2	Healthcare Representative	3	Single	4553	20978	1	Y	No	11	3	1	80	0	20	4	3	20	7	11	10
+32	No	Travel_Rarely	929	Sales	10	3	Marketing	1	722	4	Male	55	3	2	Sales Executive	4	Single	5396	21703	1	Y	No	12	3	4	80	0	10	2	2	10	7	0	8
+50	Yes	Travel_Frequently	562	Sales	8	2	Technical Degree	1	723	2	Male	50	3	2	Sales Executive	3	Married	6796	23452	3	Y	Yes	14	3	1	80	1	18	4	3	4	3	1	3
+38	No	Travel_Rarely	827	Research & Development	1	4	Life Sciences	1	724	2	Female	33	4	2	Healthcare Representative	4	Single	7625	19383	0	Y	No	13	3	3	80	0	10	4	2	9	7	1	8
+27	No	Travel_Rarely	608	Research & Development	1	2	Life Sciences	1	725	3	Female	68	3	3	Manufacturing Director	1	Married	7412	6009	1	Y	No	11	3	4	80	0	9	3	3	9	7	0	7
+32	No	Travel_Rarely	1018	Research & Development	3	2	Life Sciences	1	727	3	Female	39	3	3	Research Director	4	Single	11159	19373	3	Y	No	15	3	4	80	0	10	6	3	7	7	7	7
+47	No	Travel_Rarely	703	Sales	14	4	Marketing	1	728	4	Male	42	3	2	Sales Executive	1	Single	4960	11825	2	Y	No	12	3	4	80	0	20	2	3	7	7	1	7
+40	No	Travel_Frequently	580	Sales	5	4	Life Sciences	1	729	4	Male	48	2	3	Sales Executive	1	Married	10475	23772	5	Y	Yes	21	4	3	80	1	20	2	3	18	13	1	12
+53	No	Travel_Rarely	970	Research & Development	7	3	Life Sciences	1	730	3	Male	59	4	4	Research Director	3	Married	14814	13514	3	Y	No	19	3	3	80	0	32	3	3	5	1	1	3
+41	No	Travel_Rarely	427	Human Resources	10	4	Human Resources	1	731	2	Male	73	2	5	Manager	4	Divorced	19141	8861	3	Y	No	15	3	2	80	3	23	2	2	21	6	12	6
+60	No	Travel_Rarely	1179	Sales	16	4	Marketing	1	732	1	Male	84	3	2	Sales Executive	1	Single	5405	11924	8	Y	No	14	3	4	80	0	10	1	3	2	2	2	2
+27	No	Travel_Frequently	294	Research & Development	10	2	Life Sciences	1	733	4	Male	32	3	3	Manufacturing Director	1	Divorced	8793	4809	1	Y	No	21	4	3	80	2	9	4	2	9	7	1	7
+41	No	Travel_Rarely	314	Human Resources	1	3	Human Resources	1	734	4	Male	59	2	5	Manager	3	Married	19189	19562	1	Y	No	12	3	2	80	1	22	3	3	22	7	2	10
+50	No	Travel_Rarely	316	Sales	8	4	Marketing	1	738	4	Male	54	3	1	Sales Representative	2	Married	3875	9983	7	Y	No	15	3	4	80	1	4	2	3	2	2	2	2
+28	Yes	Travel_Rarely	654	Research & Development	1	2	Life Sciences	1	741	1	Female	67	1	1	Research Scientist	2	Single	2216	3872	7	Y	Yes	13	3	4	80	0	10	4	3	7	7	3	7
+36	No	Non-Travel	427	Research & Development	8	3	Life Sciences	1	742	1	Female	63	4	3	Research Director	1	Married	11713	20335	9	Y	No	14	3	1	80	1	10	2	3	8	7	0	5
+38	No	Travel_Rarely	168	Research & Development	1	3	Life Sciences	1	743	3	Female	81	3	3	Manufacturing Director	3	Single	7861	15397	4	Y	Yes	14	3	4	80	0	10	4	4	1	0	0	0
+44	No	Non-Travel	381	Research & Development	24	3	Medical	1	744	1	Male	49	1	1	Laboratory Technician	3	Single	3708	2104	2	Y	No	14	3	3	80	0	9	5	3	5	2	1	4
+47	No	Travel_Frequently	217	Sales	3	3	Medical	1	746	4	Female	49	3	4	Sales Executive	3	Divorced	13770	10225	9	Y	Yes	12	3	4	80	2	28	2	2	22	2	11	13
+30	No	Travel_Rarely	501	Sales	27	5	Marketing	1	747	3	Male	99	3	2	Sales Executive	4	Divorced	5304	25275	7	Y	No	23	4	4	80	1	10	2	2	8	7	7	7
+29	No	Travel_Rarely	1396	Sales	10	3	Life Sciences	1	749	3	Male	99	3	1	Sales Representative	3	Single	2642	2755	1	Y	No	11	3	3	80	0	1	6	3	1	0	0	0
+42	Yes	Travel_Frequently	933	Research & Development	19	3	Medical	1	752	3	Male	57	4	1	Research Scientist	3	Divorced	2759	20366	6	Y	Yes	12	3	4	80	0	7	2	3	2	2	2	2
+43	No	Travel_Frequently	775	Sales	15	3	Life Sciences	1	754	4	Male	47	2	2	Sales Executive	4	Married	6804	23683	3	Y	No	18	3	3	80	1	7	5	3	2	2	2	2
+34	No	Travel_Rarely	970	Research & Development	8	2	Medical	1	757	2	Female	96	3	2	Healthcare Representative	3	Single	6142	7360	3	Y	No	11	3	4	80	0	10	2	3	5	1	4	3
+23	No	Travel_Rarely	650	Research & Development	9	1	Medical	1	758	2	Male	37	3	1	Laboratory Technician	1	Married	2500	4344	1	Y	No	14	3	4	80	1	5	2	4	4	3	0	2
+39	No	Travel_Rarely	141	Human Resources	3	3	Human Resources	1	760	3	Female	44	4	2	Human Resources	2	Married	6389	18767	9	Y	No	15	3	3	80	1	12	3	1	8	3	3	6
+56	No	Travel_Rarely	832	Research & Development	9	3	Medical	1	762	3	Male	81	3	4	Healthcare Representative	4	Married	11103	20420	7	Y	No	11	3	3	80	0	30	1	2	10	7	1	1
+40	No	Travel_Rarely	804	Research & Development	2	1	Medical	1	763	4	Female	86	2	1	Research Scientist	4	Single	2342	22929	0	Y	Yes	20	4	4	80	0	5	2	2	4	2	2	3
+27	No	Travel_Rarely	975	Research & Development	7	3	Medical	1	764	4	Female	55	2	2	Healthcare Representative	1	Single	6811	23398	8	Y	No	19	3	1	80	0	9	2	1	7	6	0	7
+29	No	Travel_Rarely	1090	Sales	10	3	Marketing	1	766	4	Male	83	3	1	Sales Representative	2	Divorced	2297	17967	1	Y	No	14	3	4	80	2	2	2	3	2	2	2	2
+53	No	Travel_Rarely	346	Research & Development	6	3	Life Sciences	1	769	4	Male	86	3	2	Laboratory Technician	4	Single	2450	10919	2	Y	No	17	3	4	80	0	19	4	3	2	2	2	2
+35	No	Non-Travel	1225	Research & Development	2	4	Life Sciences	1	771	4	Female	61	3	2	Healthcare Representative	1	Divorced	5093	4761	2	Y	No	11	3	1	80	1	16	2	4	1	0	0	0
+32	No	Travel_Frequently	430	Research & Development	24	4	Life Sciences	1	772	1	Male	80	3	2	Laboratory Technician	4	Married	5309	21146	1	Y	No	15	3	4	80	2	10	2	3	10	8	4	7
+38	No	Travel_Rarely	268	Research & Development	2	5	Medical	1	773	4	Male	92	3	1	Research Scientist	3	Married	3057	20471	6	Y	Yes	13	3	2	80	1	6	0	1	1	0	0	1
+34	No	Travel_Rarely	167	Research & Development	8	5	Life Sciences	1	775	2	Female	32	3	2	Manufacturing Director	1	Divorced	5121	4187	3	Y	No	14	3	3	80	1	7	3	3	0	0	0	0
+52	No	Travel_Rarely	621	Sales	3	4	Marketing	1	776	3	Male	31	2	4	Manager	1	Married	16856	10084	1	Y	No	11	3	1	80	0	34	3	4	34	6	1	16
+33	Yes	Travel_Rarely	527	Research & Development	1	4	Other	1	780	4	Male	63	3	1	Research Scientist	4	Single	2686	5207	1	Y	Yes	13	3	3	80	0	10	2	2	10	9	7	8
+25	No	Travel_Rarely	883	Sales	26	1	Medical	1	781	3	Female	32	3	2	Sales Executive	4	Single	6180	22807	1	Y	No	23	4	2	80	0	6	5	2	6	5	1	4
+45	No	Travel_Rarely	954	Sales	2	2	Technical Degree	1	783	2	Male	46	1	2	Sales Representative	3	Single	6632	12388	0	Y	No	13	3	1	80	0	9	3	3	8	7	3	1
+23	No	Travel_Rarely	310	Research & Development	10	1	Medical	1	784	1	Male	79	4	1	Research Scientist	3	Single	3505	19630	1	Y	No	18	3	4	80	0	2	3	3	2	2	0	2
+47	Yes	Travel_Frequently	719	Sales	27	2	Life Sciences	1	785	2	Female	77	4	2	Sales Executive	3	Single	6397	10339	4	Y	Yes	12	3	4	80	0	8	2	3	5	4	1	3
+34	No	Travel_Rarely	304	Sales	2	3	Other	1	786	4	Male	60	3	2	Sales Executive	4	Single	6274	18686	1	Y	No	22	4	3	80	0	6	5	3	6	5	1	4
+55	Yes	Travel_Rarely	725	Research & Development	2	3	Medical	1	787	4	Male	78	3	5	Manager	1	Married	19859	21199	5	Y	Yes	13	3	4	80	1	24	2	3	5	2	1	4
+36	No	Non-Travel	1434	Sales	8	4	Life Sciences	1	789	1	Male	76	2	3	Sales Executive	1	Single	7587	14229	1	Y	No	15	3	2	80	0	10	1	3	10	7	0	9
+52	No	Non-Travel	715	Research & Development	19	4	Medical	1	791	4	Male	41	3	1	Research Scientist	4	Married	4258	26589	0	Y	No	18	3	1	80	1	5	3	3	4	3	1	2
+26	No	Travel_Frequently	575	Research & Development	1	2	Life Sciences	1	792	1	Female	71	1	1	Laboratory Technician	4	Divorced	4364	5288	3	Y	No	14	3	1	80	1	5	2	3	2	2	2	0
+29	No	Travel_Rarely	657	Research & Development	27	3	Medical	1	793	2	Female	66	3	2	Healthcare Representative	3	Married	4335	25549	4	Y	No	12	3	1	80	1	11	3	2	8	7	1	1
+26	Yes	Travel_Rarely	1146	Sales	8	3	Technical Degree	1	796	4	Male	38	2	2	Sales Executive	1	Single	5326	3064	6	Y	No	17	3	3	80	0	6	2	2	4	3	1	2
+34	No	Travel_Rarely	182	Research & Development	1	4	Life Sciences	1	797	2	Female	72	4	1	Research Scientist	4	Single	3280	13551	2	Y	No	16	3	3	80	0	10	2	3	4	2	1	3
+54	No	Travel_Rarely	376	Research & Development	19	4	Medical	1	799	4	Female	95	3	2	Manufacturing Director	1	Divorced	5485	22670	9	Y	Yes	11	3	2	80	2	9	4	3	5	3	1	4
+27	No	Travel_Frequently	829	Sales	8	1	Marketing	1	800	3	Male	84	3	2	Sales Executive	4	Married	4342	24008	0	Y	No	19	3	2	80	1	5	3	3	4	2	1	1
+37	No	Travel_Rarely	571	Research & Development	10	1	Life Sciences	1	802	4	Female	82	3	1	Research Scientist	1	Divorced	2782	19905	0	Y	Yes	13	3	2	80	2	6	3	2	5	3	4	3
+38	No	Travel_Frequently	240	Research & Development	2	4	Life Sciences	1	803	1	Female	75	4	2	Manufacturing Director	1	Single	5980	26085	6	Y	Yes	12	3	4	80	0	17	2	3	15	7	4	12
+34	No	Travel_Rarely	121	Research & Development	2	4	Medical	1	804	3	Female	86	2	1	Research Scientist	1	Single	4381	7530	1	Y	No	11	3	3	80	0	6	3	3	6	5	1	3
+35	No	Travel_Rarely	384	Sales	8	4	Life Sciences	1	805	1	Female	72	3	1	Sales Representative	4	Married	2572	20317	1	Y	No	16	3	2	80	1	3	1	2	3	2	0	2
+30	No	Travel_Rarely	921	Research & Development	1	3	Life Sciences	1	806	4	Male	38	1	1	Laboratory Technician	3	Married	3833	24375	3	Y	No	21	4	3	80	2	7	2	3	2	2	0	2
+40	No	Travel_Frequently	791	Research & Development	2	2	Medical	1	807	3	Female	38	4	2	Healthcare Representative	2	Married	4244	9931	1	Y	No	24	4	4	80	1	8	2	3	8	7	3	7
+34	No	Travel_Rarely	1111	Sales	8	2	Life Sciences	1	808	3	Female	93	3	2	Sales Executive	1	Married	6500	13305	5	Y	No	17	3	2	80	1	6	1	3	3	2	1	2
+42	No	Travel_Frequently	570	Research & Development	8	3	Life Sciences	1	809	2	Male	66	3	5	Manager	4	Divorced	18430	16225	1	Y	No	13	3	2	80	1	24	4	2	24	7	14	9
+23	Yes	Travel_Rarely	1243	Research & Development	6	3	Life Sciences	1	811	3	Male	63	4	1	Laboratory Technician	1	Married	1601	3445	1	Y	Yes	21	4	3	80	2	1	2	3	0	0	0	0
+24	No	Non-Travel	1092	Research & Development	9	3	Life Sciences	1	812	3	Male	60	2	1	Laboratory Technician	2	Divorced	2694	26551	1	Y	No	11	3	3	80	3	1	4	3	1	0	0	0
+52	No	Travel_Rarely	1325	Research & Development	11	4	Life Sciences	1	813	4	Female	82	3	2	Laboratory Technician	3	Married	3149	21821	8	Y	No	20	4	2	80	1	9	3	3	5	2	1	4
+50	No	Travel_Rarely	691	Research & Development	2	3	Medical	1	815	3	Male	64	3	4	Research Director	3	Married	17639	6881	5	Y	No	16	3	4	80	0	30	3	3	4	3	0	3
+29	Yes	Travel_Rarely	805	Research & Development	1	2	Life Sciences	1	816	2	Female	36	2	1	Laboratory Technician	1	Married	2319	6689	1	Y	Yes	11	3	4	80	1	1	1	3	1	0	0	0
+33	No	Travel_Rarely	213	Research & Development	7	3	Medical	1	817	3	Male	49	3	3	Research Director	3	Married	11691	25995	0	Y	No	11	3	4	80	0	14	3	4	13	9	3	7
+33	Yes	Travel_Rarely	118	Sales	16	3	Marketing	1	819	1	Female	69	3	2	Sales Executive	1	Single	5324	26507	5	Y	No	15	3	3	80	0	6	3	3	3	2	0	2
+47	No	Travel_Rarely	202	Research & Development	2	2	Other	1	820	3	Female	33	3	4	Manager	4	Married	16752	12982	1	Y	Yes	11	3	3	80	1	26	3	2	26	14	3	0
+36	No	Travel_Rarely	676	Research & Development	1	3	Other	1	823	3	Female	35	3	2	Manufacturing Director	2	Married	5228	23361	0	Y	No	15	3	1	80	1	10	2	3	9	7	0	5
+29	No	Travel_Rarely	1252	Research & Development	23	2	Life Sciences	1	824	3	Male	81	4	1	Research Scientist	3	Married	2700	23779	1	Y	No	24	4	3	80	1	10	3	3	10	7	0	7
+58	Yes	Travel_Rarely	286	Research & Development	2	4	Life Sciences	1	825	4	Male	31	3	5	Research Director	2	Single	19246	25761	7	Y	Yes	12	3	4	80	0	40	2	3	31	15	13	8
+35	No	Travel_Rarely	1258	Research & Development	1	4	Life Sciences	1	826	4	Female	40	4	1	Research Scientist	3	Single	2506	13301	3	Y	No	13	3	3	80	0	7	0	3	2	2	2	2
+42	No	Travel_Rarely	932	Research & Development	1	2	Life Sciences	1	827	4	Female	43	2	2	Manufacturing Director	4	Married	6062	4051	9	Y	Yes	13	3	4	80	1	8	4	3	4	3	0	2
+28	Yes	Travel_Rarely	890	Research & Development	2	4	Medical	1	828	3	Male	46	3	1	Research Scientist	3	Single	4382	16374	6	Y	No	17	3	4	80	0	5	3	2	2	2	2	1
+36	No	Travel_Rarely	1041	Human Resources	13	3	Human Resources	1	829	3	Male	36	3	1	Human Resources	2	Married	2143	25527	4	Y	No	13	3	2	80	1	8	2	3	5	2	0	4
+32	No	Travel_Rarely	859	Research & Development	4	3	Life Sciences	1	830	3	Female	98	2	2	Manufacturing Director	3	Married	6162	19124	1	Y	No	12	3	3	80	1	14	3	3	14	13	6	8
+40	No	Travel_Frequently	720	Research & Development	16	4	Medical	1	832	1	Male	51	2	2	Laboratory Technician	3	Single	5094	11983	6	Y	No	14	3	4	80	0	10	6	3	1	0	0	0
+30	No	Travel_Rarely	946	Research & Development	2	3	Medical	1	833	3	Female	52	2	2	Manufacturing Director	4	Single	6877	20234	5	Y	Yes	24	4	2	80	0	12	4	2	0	0	0	0
+45	No	Travel_Rarely	252	Research & Development	2	3	Life Sciences	1	834	2	Female	95	2	1	Research Scientist	3	Single	2274	6153	1	Y	No	14	3	4	80	0	1	3	3	1	0	0	0
+42	No	Travel_Rarely	933	Research & Development	29	3	Life Sciences	1	836	2	Male	98	3	2	Manufacturing Director	2	Married	4434	11806	1	Y	No	13	3	4	80	1	10	3	2	9	8	7	8
+38	No	Travel_Frequently	471	Research & Development	12	3	Life Sciences	1	837	1	Male	45	2	2	Healthcare Representative	1	Divorced	6288	4284	2	Y	No	15	3	3	80	1	13	3	2	4	3	1	2
+34	No	Travel_Frequently	702	Research & Development	16	4	Life Sciences	1	838	3	Female	100	2	1	Research Scientist	4	Single	2553	8306	1	Y	No	16	3	3	80	0	6	3	3	5	2	1	3
+49	Yes	Travel_Rarely	1184	Sales	11	3	Marketing	1	840	3	Female	43	3	3	Sales Executive	4	Married	7654	5860	1	Y	No	18	3	1	80	2	9	3	4	9	8	7	7
+55	Yes	Travel_Rarely	436	Sales	2	1	Medical	1	842	3	Male	37	3	2	Sales Executive	4	Single	5160	21519	4	Y	No	16	3	3	80	0	12	3	2	9	7	7	3
+43	No	Travel_Rarely	589	Research & Development	14	2	Life Sciences	1	843	2	Male	94	3	4	Research Director	1	Married	17159	5200	6	Y	No	24	4	3	80	1	22	3	3	4	1	1	0
+27	No	Travel_Rarely	269	Research & Development	5	1	Technical Degree	1	844	3	Male	42	2	3	Research Director	4	Divorced	12808	8842	1	Y	Yes	16	3	2	80	1	9	3	3	9	8	0	8
+35	No	Travel_Rarely	950	Research & Development	7	3	Other	1	845	3	Male	59	3	3	Manufacturing Director	3	Single	10221	18869	3	Y	No	21	4	2	80	0	17	3	4	8	5	1	6
+28	No	Travel_Rarely	760	Sales	2	4	Marketing	1	846	2	Female	81	3	2	Sales Executive	2	Married	4779	3698	1	Y	Yes	20	4	1	80	0	8	2	3	8	7	7	5
+34	No	Travel_Rarely	829	Human Resources	3	2	Human Resources	1	847	3	Male	88	3	1	Human Resources	4	Married	3737	2243	0	Y	No	19	3	3	80	1	4	1	1	3	2	0	2
+26	Yes	Travel_Frequently	887	Research & Development	5	2	Medical	1	848	3	Female	88	2	1	Research Scientist	3	Married	2366	20898	1	Y	Yes	14	3	1	80	1	8	2	3	8	7	1	7
+27	No	Non-Travel	443	Research & Development	3	3	Medical	1	850	4	Male	50	3	1	Research Scientist	4	Married	1706	16571	1	Y	No	11	3	3	80	3	0	6	2	0	0	0	0
+51	No	Travel_Rarely	1318	Sales	26	4	Marketing	1	851	1	Female	66	3	4	Manager	3	Married	16307	5594	2	Y	No	14	3	3	80	1	29	2	2	20	6	4	17
+44	No	Travel_Rarely	625	Research & Development	4	3	Medical	1	852	4	Male	50	3	2	Healthcare Representative	2	Single	5933	5197	9	Y	No	12	3	4	80	0	10	2	2	5	2	2	3
+25	No	Travel_Rarely	180	Research & Development	2	1	Medical	1	854	1	Male	65	4	1	Research Scientist	1	Single	3424	21632	7	Y	No	13	3	3	80	0	6	3	2	4	3	0	1
+33	No	Travel_Rarely	586	Sales	1	3	Medical	1	855	1	Male	48	4	2	Sales Executive	1	Divorced	4037	21816	1	Y	No	22	4	1	80	1	9	5	3	9	8	0	8
+35	No	Travel_Rarely	1343	Research & Development	27	1	Medical	1	856	3	Female	53	2	1	Research Scientist	1	Single	2559	17852	1	Y	No	11	3	4	80	0	6	3	2	6	5	1	1
+36	No	Travel_Rarely	928	Sales	1	2	Life Sciences	1	857	2	Male	56	3	2	Sales Executive	4	Married	6201	2823	1	Y	Yes	14	3	4	80	1	18	1	2	18	14	4	11
+32	No	Travel_Rarely	117	Sales	13	4	Life Sciences	1	859	2	Male	73	3	2	Sales Executive	4	Divorced	4403	9250	2	Y	No	11	3	3	80	1	8	3	2	5	2	0	3
+30	No	Travel_Frequently	1012	Research & Development	5	4	Life Sciences	1	861	2	Male	75	2	1	Research Scientist	4	Divorced	3761	2373	9	Y	No	12	3	2	80	1	10	3	2	5	4	0	3
+53	No	Travel_Rarely	661	Sales	7	2	Marketing	1	862	1	Female	78	2	3	Sales Executive	4	Married	10934	20715	7	Y	Yes	18	3	4	80	1	35	3	3	5	2	0	4
+45	No	Travel_Rarely	930	Sales	9	3	Marketing	1	864	4	Male	74	3	3	Sales Executive	1	Divorced	10761	19239	4	Y	Yes	12	3	3	80	1	18	2	3	5	4	0	2
+32	No	Travel_Rarely	638	Research & Development	8	2	Medical	1	865	3	Female	91	4	2	Research Scientist	3	Married	5175	22162	5	Y	No	12	3	3	80	1	9	3	2	5	3	1	3
+52	No	Travel_Frequently	890	Research & Development	25	4	Medical	1	867	3	Female	81	2	4	Manufacturing Director	4	Married	13826	19028	3	Y	No	22	4	3	80	0	31	3	3	9	8	0	0
+37	No	Travel_Rarely	342	Sales	16	4	Marketing	1	868	4	Male	66	2	2	Sales Executive	3	Divorced	6334	24558	4	Y	No	19	3	4	80	2	9	2	3	1	0	0	0
+28	No	Travel_Rarely	1169	Human Resources	8	2	Medical	1	869	2	Male	63	2	1	Human Resources	4	Divorced	4936	23965	1	Y	No	13	3	4	80	1	6	6	3	5	1	0	4
+22	No	Travel_Rarely	1230	Research & Development	1	2	Life Sciences	1	872	4	Male	33	2	2	Manufacturing Director	4	Married	4775	19146	6	Y	No	22	4	1	80	2	4	2	1	2	2	2	2
+44	No	Travel_Rarely	986	Research & Development	8	4	Life Sciences	1	874	1	Male	62	4	1	Laboratory Technician	4	Married	2818	5044	2	Y	Yes	24	4	3	80	1	10	2	2	3	2	0	2
+42	No	Travel_Frequently	1271	Research & Development	2	1	Medical	1	875	2	Male	35	3	1	Research Scientist	4	Single	2515	9068	5	Y	Yes	14	3	4	80	0	8	2	3	2	1	2	2
+36	No	Travel_Rarely	1278	Human Resources	8	3	Life Sciences	1	878	1	Male	77	2	1	Human Resources	1	Married	2342	8635	0	Y	No	21	4	3	80	0	6	3	3	5	4	0	3
+25	No	Travel_Rarely	141	Sales	3	1	Other	1	879	3	Male	98	3	2	Sales Executive	1	Married	4194	14363	1	Y	Yes	18	3	4	80	0	5	3	3	5	3	0	3
+35	No	Travel_Rarely	607	Research & Development	9	3	Life Sciences	1	880	4	Female	66	2	3	Manufacturing Director	3	Married	10685	23457	1	Y	Yes	20	4	2	80	1	17	2	3	17	14	5	15
+35	Yes	Travel_Frequently	130	Research & Development	25	4	Life Sciences	1	881	4	Female	96	3	1	Research Scientist	2	Divorced	2022	16612	1	Y	Yes	19	3	1	80	1	10	3	2	10	2	7	8
+32	No	Non-Travel	300	Research & Development	1	3	Life Sciences	1	882	4	Male	61	3	1	Laboratory Technician	4	Divorced	2314	9148	0	Y	No	12	3	2	80	1	4	2	3	3	0	0	2
+25	No	Travel_Rarely	583	Sales	4	1	Marketing	1	885	3	Male	87	2	2	Sales Executive	1	Married	4256	18154	1	Y	No	12	3	1	80	0	5	1	4	5	2	0	3
+49	No	Travel_Rarely	1418	Research & Development	1	3	Technical Degree	1	887	3	Female	36	3	1	Research Scientist	1	Married	3580	10554	2	Y	No	16	3	2	80	1	7	2	3	4	2	0	2
+24	No	Non-Travel	1269	Research & Development	4	1	Life Sciences	1	888	1	Male	46	2	1	Laboratory Technician	4	Married	3162	10778	0	Y	No	17	3	4	80	0	6	2	2	5	2	3	4
+32	No	Travel_Frequently	379	Sales	5	2	Life Sciences	1	889	2	Male	48	3	2	Sales Executive	2	Married	6524	8891	1	Y	No	14	3	4	80	1	10	3	3	10	8	5	3
+38	No	Travel_Rarely	395	Sales	9	3	Marketing	1	893	2	Male	98	2	1	Sales Representative	2	Married	2899	12102	0	Y	No	19	3	4	80	1	3	3	3	2	2	1	2
+42	No	Travel_Rarely	1265	Research & Development	3	3	Life Sciences	1	894	3	Female	95	4	2	Laboratory Technician	4	Married	5231	23726	2	Y	Yes	13	3	2	80	1	17	1	2	5	3	1	3
+31	No	Travel_Rarely	1222	Research & Development	11	4	Life Sciences	1	895	4	Male	48	3	1	Research Scientist	4	Married	2356	14871	3	Y	Yes	19	3	2	80	1	8	2	3	6	4	0	2
+29	Yes	Travel_Rarely	341	Sales	1	3	Medical	1	896	2	Female	48	2	1	Sales Representative	3	Divorced	2800	23522	6	Y	Yes	19	3	3	80	3	5	3	3	3	2	0	2
+53	No	Travel_Rarely	868	Sales	8	3	Marketing	1	897	1	Male	73	3	4	Sales Executive	4	Married	11836	22789	5	Y	No	14	3	3	80	1	28	3	3	2	0	2	2
+35	No	Travel_Rarely	672	Research & Development	25	3	Technical Degree	1	899	4	Male	78	2	3	Manufacturing Director	2	Married	10903	9129	3	Y	No	16	3	1	80	0	16	2	3	13	10	4	8
+37	No	Travel_Frequently	1231	Sales	21	2	Medical	1	900	3	Female	54	3	1	Sales Representative	4	Married	2973	21222	5	Y	No	15	3	2	80	1	10	3	3	5	4	0	0
+53	No	Travel_Rarely	102	Research & Development	23	4	Life Sciences	1	901	4	Female	72	3	4	Research Director	4	Single	14275	20206	6	Y	No	18	3	3	80	0	33	0	3	12	9	3	8
+43	No	Travel_Frequently	422	Research & Development	1	3	Life Sciences	1	902	4	Female	33	3	2	Healthcare Representative	4	Married	5562	21782	4	Y	No	13	3	2	80	1	12	2	2	5	2	2	2
+47	No	Travel_Rarely	249	Sales	2	2	Marketing	1	903	3	Female	35	3	2	Sales Executive	4	Married	4537	17783	0	Y	Yes	22	4	1	80	1	8	2	3	7	6	7	7
+37	No	Non-Travel	1252	Sales	19	2	Medical	1	904	1	Male	32	3	3	Sales Executive	2	Single	7642	4814	1	Y	Yes	13	3	4	80	0	10	2	3	10	0	0	9
+50	No	Non-Travel	881	Research & Development	2	4	Life Sciences	1	905	1	Male	98	3	4	Manager	1	Divorced	17924	4544	1	Y	No	11	3	4	80	1	31	3	3	31	6	14	7
+39	No	Travel_Rarely	1383	Human Resources	2	3	Life Sciences	1	909	4	Female	42	2	2	Human Resources	4	Married	5204	7790	8	Y	No	11	3	3	80	2	13	2	3	5	4	0	4
+33	No	Travel_Rarely	1075	Human Resources	3	2	Human Resources	1	910	4	Male	57	3	1	Human Resources	2	Divorced	2277	22650	3	Y	Yes	11	3	3	80	1	7	4	4	4	3	0	3
+32	Yes	Travel_Rarely	374	Research & Development	25	4	Life Sciences	1	911	1	Male	87	3	1	Laboratory Technician	4	Single	2795	18016	1	Y	Yes	24	4	3	80	0	1	2	1	1	0	0	1
+29	No	Travel_Rarely	1086	Research & Development	7	1	Medical	1	912	1	Female	62	2	1	Laboratory Technician	4	Divorced	2532	6054	6	Y	No	14	3	3	80	3	8	5	3	4	3	0	3
+44	No	Travel_Rarely	661	Research & Development	9	2	Life Sciences	1	913	2	Male	61	3	1	Research Scientist	1	Married	2559	7508	1	Y	Yes	13	3	4	80	0	8	0	3	8	7	7	1
+28	No	Travel_Rarely	821	Sales	5	4	Medical	1	916	1	Male	98	3	2	Sales Executive	4	Single	4908	24252	1	Y	No	14	3	2	80	0	4	3	3	4	2	0	2
+58	Yes	Travel_Frequently	781	Research & Development	2	1	Life Sciences	1	918	4	Male	57	2	1	Laboratory Technician	4	Divorced	2380	13384	9	Y	Yes	14	3	4	80	1	3	3	2	1	0	0	0
+43	No	Travel_Rarely	177	Research & Development	8	3	Life Sciences	1	920	1	Female	55	3	2	Manufacturing Director	2	Divorced	4765	23814	4	Y	No	21	4	3	80	1	4	2	4	1	0	0	0
+20	Yes	Travel_Rarely	500	Sales	2	3	Medical	1	922	3	Female	49	2	1	Sales Representative	3	Single	2044	22052	1	Y	No	13	3	4	80	0	2	3	2	2	2	0	2
+21	Yes	Travel_Rarely	1427	Research & Development	18	1	Other	1	923	4	Female	65	3	1	Research Scientist	4	Single	2693	8870	1	Y	No	19	3	1	80	0	1	3	2	1	0	0	0
+36	No	Travel_Rarely	1425	Research & Development	14	1	Life Sciences	1	924	3	Male	68	3	2	Healthcare Representative	4	Married	6586	4821	0	Y	Yes	17	3	1	80	1	17	2	2	16	8	4	11
+47	No	Travel_Rarely	1454	Sales	2	4	Life Sciences	1	925	4	Female	65	2	1	Sales Representative	4	Single	3294	13137	1	Y	Yes	18	3	1	80	0	3	3	2	3	2	1	2
+22	Yes	Travel_Rarely	617	Research & Development	3	1	Life Sciences	1	926	2	Female	34	3	2	Manufacturing Director	3	Married	4171	10022	0	Y	Yes	19	3	1	80	1	4	3	4	3	2	0	2
+41	Yes	Travel_Rarely	1085	Research & Development	2	4	Life Sciences	1	927	2	Female	57	1	1	Laboratory Technician	4	Divorced	2778	17725	4	Y	Yes	13	3	3	80	1	10	1	2	7	7	1	0
+28	No	Travel_Rarely	995	Research & Development	9	3	Medical	1	930	3	Female	77	3	1	Research Scientist	3	Divorced	2377	9834	5	Y	No	18	3	2	80	1	6	2	3	2	2	2	2
+39	Yes	Travel_Rarely	1122	Research & Development	6	3	Medical	1	932	4	Male	70	3	1	Laboratory Technician	1	Married	2404	4303	7	Y	Yes	21	4	4	80	0	8	2	1	2	2	2	2
+27	No	Travel_Rarely	618	Research & Development	4	3	Life Sciences	1	933	2	Female	76	3	1	Research Scientist	3	Single	2318	17808	1	Y	No	19	3	3	80	0	1	2	3	1	1	0	0
+34	No	Travel_Rarely	546	Research & Development	10	3	Life Sciences	1	934	2	Male	83	3	1	Laboratory Technician	2	Divorced	2008	6896	1	Y	No	14	3	2	80	2	1	3	3	1	0	1	0
+42	No	Travel_Rarely	462	Sales	14	2	Medical	1	936	3	Female	68	2	2	Sales Executive	3	Single	6244	7824	7	Y	No	17	3	1	80	0	10	6	3	5	4	0	3
+33	No	Travel_Rarely	1198	Research & Development	1	4	Other	1	939	3	Male	100	2	1	Research Scientist	1	Single	2799	3339	3	Y	Yes	11	3	2	80	0	6	1	3	3	2	0	2
+58	No	Travel_Rarely	1272	Research & Development	5	3	Technical Degree	1	940	3	Female	37	2	3	Healthcare Representative	2	Divorced	10552	9255	2	Y	Yes	13	3	4	80	1	24	3	3	6	0	0	4
+31	No	Travel_Rarely	154	Sales	7	4	Life Sciences	1	941	2	Male	41	2	1	Sales Representative	3	Married	2329	11737	3	Y	No	15	3	2	80	0	13	2	4	7	7	5	2
+35	No	Travel_Rarely	1137	Research & Development	21	1	Life Sciences	1	942	4	Female	51	3	2	Healthcare Representative	4	Married	4014	19170	1	Y	Yes	25	4	4	80	1	10	2	1	10	6	0	7
+49	No	Travel_Rarely	527	Research & Development	8	2	Other	1	944	1	Female	51	3	3	Laboratory Technician	2	Married	7403	22477	4	Y	No	11	3	3	80	1	29	3	2	26	9	1	7
+48	No	Travel_Rarely	1469	Research & Development	20	4	Medical	1	945	4	Male	51	3	1	Research Scientist	3	Married	2259	5543	4	Y	No	17	3	1	80	2	13	2	2	0	0	0	0
+31	No	Non-Travel	1188	Sales	20	2	Marketing	1	947	4	Female	45	3	2	Sales Executive	3	Married	6932	24406	1	Y	No	13	3	4	80	1	9	2	2	9	8	0	0
+36	No	Travel_Rarely	188	Research & Development	7	4	Other	1	949	2	Male	65	3	1	Research Scientist	4	Single	4678	23293	2	Y	No	18	3	3	80	0	8	6	3	6	2	0	1
+38	No	Travel_Rarely	1333	Research & Development	1	3	Technical Degree	1	950	4	Female	80	3	3	Research Director	1	Married	13582	16292	1	Y	No	13	3	2	80	1	15	3	3	15	12	5	11
+32	No	Non-Travel	1184	Research & Development	1	3	Life Sciences	1	951	3	Female	70	2	1	Laboratory Technician	2	Married	2332	3974	6	Y	No	20	4	3	80	0	5	3	3	3	0	0	2
+25	Yes	Travel_Rarely	867	Sales	19	2	Marketing	1	952	3	Male	36	2	1	Sales Representative	2	Married	2413	18798	1	Y	Yes	18	3	3	80	3	1	2	3	1	0	0	0
+40	No	Travel_Rarely	658	Sales	10	4	Marketing	1	954	1	Male	67	2	3	Sales Executive	2	Divorced	9705	20652	2	Y	No	12	3	2	80	1	11	2	2	1	0	0	0
+26	No	Travel_Frequently	1283	Sales	1	3	Medical	1	956	3	Male	52	2	2	Sales Executive	1	Single	4294	11148	1	Y	No	12	3	2	80	0	7	2	3	7	7	0	7
+41	No	Travel_Rarely	263	Research & Development	6	3	Medical	1	957	4	Male	59	3	1	Laboratory Technician	1	Single	4721	3119	2	Y	Yes	13	3	3	80	0	20	3	3	18	13	2	17
+36	No	Travel_Rarely	938	Research & Development	2	4	Medical	1	958	3	Male	79	3	1	Laboratory Technician	3	Single	2519	12287	4	Y	No	21	4	3	80	0	16	6	3	11	8	3	9
+19	Yes	Travel_Rarely	419	Sales	21	3	Other	1	959	4	Male	37	2	1	Sales Representative	2	Single	2121	9947	1	Y	Yes	13	3	2	80	0	1	3	4	1	0	0	0
+20	Yes	Travel_Rarely	129	Research & Development	4	3	Technical Degree	1	960	1	Male	84	3	1	Laboratory Technician	1	Single	2973	13008	1	Y	No	19	3	2	80	0	1	2	3	1	0	0	0
+31	No	Travel_Rarely	616	Research & Development	12	3	Medical	1	961	4	Female	41	3	2	Healthcare Representative	4	Married	5855	17369	0	Y	Yes	11	3	3	80	2	10	2	1	9	7	8	5
+40	No	Travel_Frequently	1469	Research & Development	9	4	Medical	1	964	4	Male	35	3	1	Research Scientist	2	Divorced	3617	25063	8	Y	Yes	14	3	4	80	1	3	2	3	1	1	0	0
+32	No	Travel_Rarely	498	Research & Development	3	4	Medical	1	966	3	Female	93	3	2	Manufacturing Director	1	Married	6725	13554	1	Y	No	12	3	3	80	1	8	2	4	8	7	6	3
+36	Yes	Travel_Rarely	530	Sales	3	1	Life Sciences	1	967	3	Male	51	2	3	Sales Executive	4	Married	10325	5518	1	Y	Yes	11	3	1	80	1	16	6	3	16	7	3	7
+33	No	Travel_Rarely	1069	Research & Development	1	3	Life Sciences	1	969	2	Female	42	2	2	Healthcare Representative	4	Single	6949	12291	0	Y	No	14	3	1	80	0	6	3	3	5	0	1	4
+37	Yes	Travel_Rarely	625	Sales	1	4	Life Sciences	1	970	1	Male	46	2	3	Sales Executive	3	Married	10609	14922	5	Y	No	11	3	3	80	0	17	2	1	14	1	11	7
+45	No	Non-Travel	805	Research & Development	4	2	Life Sciences	1	972	3	Male	57	3	2	Laboratory Technician	2	Married	4447	23163	1	Y	No	12	3	2	80	0	9	5	2	9	7	0	8
+29	No	Travel_Frequently	1404	Sales	20	3	Technical Degree	1	974	3	Female	84	3	1	Sales Representative	4	Married	2157	18203	1	Y	No	15	3	2	80	1	3	5	3	3	1	0	2
+35	No	Travel_Rarely	1219	Sales	18	3	Medical	1	975	3	Female	86	3	2	Sales Executive	3	Married	4601	6179	1	Y	No	16	3	2	80	0	5	3	3	5	2	1	0
+52	No	Travel_Rarely	1053	Research & Development	1	2	Life Sciences	1	976	4	Male	70	3	4	Manager	4	Married	17099	13829	2	Y	No	15	3	2	80	1	26	2	2	9	8	7	8
+58	Yes	Travel_Rarely	289	Research & Development	2	3	Technical Degree	1	977	4	Male	51	3	1	Research Scientist	3	Single	2479	26227	4	Y	No	24	4	1	80	0	7	4	3	1	0	0	0
+53	No	Travel_Rarely	1376	Sales	2	2	Medical	1	981	3	Male	45	3	4	Manager	3	Divorced	14852	13938	6	Y	No	13	3	3	80	1	22	3	4	17	13	15	2
+30	No	Travel_Rarely	231	Sales	8	2	Other	1	982	3	Male	62	3	3	Sales Executive	3	Divorced	7264	9977	5	Y	No	11	3	1	80	1	10	2	4	8	4	7	7
+38	No	Non-Travel	152	Sales	10	3	Technical Degree	1	983	3	Female	85	3	2	Sales Executive	4	Single	5666	19899	1	Y	Yes	13	3	2	80	0	6	1	3	5	3	1	3
+35	No	Travel_Rarely	882	Sales	3	4	Life Sciences	1	984	4	Male	92	3	3	Sales Executive	4	Divorced	7823	6812	6	Y	No	13	3	2	80	1	12	2	3	10	9	0	8
+39	No	Travel_Rarely	903	Sales	2	5	Life Sciences	1	985	1	Male	41	4	3	Sales Executive	3	Single	7880	2560	0	Y	No	18	3	4	80	0	9	3	3	8	7	0	7
+40	Yes	Non-Travel	1479	Sales	24	3	Life Sciences	1	986	2	Female	100	4	4	Sales Executive	2	Single	13194	17071	4	Y	Yes	16	3	4	80	0	22	2	2	1	0	0	0
+47	No	Travel_Frequently	1379	Research & Development	16	4	Medical	1	987	3	Male	64	4	2	Manufacturing Director	3	Divorced	5067	6759	1	Y	Yes	19	3	3	80	0	20	3	4	19	10	2	7
+36	No	Non-Travel	1229	Sales	8	4	Technical Degree	1	990	1	Male	84	3	2	Sales Executive	4	Divorced	5079	25952	4	Y	No	13	3	4	80	2	12	3	3	7	7	0	7
+31	Yes	Non-Travel	335	Research & Development	9	2	Medical	1	991	3	Male	46	2	1	Research Scientist	1	Single	2321	10322	0	Y	Yes	22	4	1	80	0	4	0	3	3	2	1	2
+33	No	Non-Travel	722	Sales	17	3	Life Sciences	1	992	4	Male	38	3	4	Manager	3	Single	17444	20489	1	Y	No	11	3	4	80	0	10	2	3	10	8	6	0
+29	Yes	Travel_Rarely	906	Research & Development	10	3	Life Sciences	1	994	4	Female	92	2	1	Research Scientist	1	Single	2404	11479	6	Y	Yes	20	4	3	80	0	3	5	3	0	0	0	0
+33	No	Travel_Rarely	461	Research & Development	13	1	Life Sciences	1	995	2	Female	53	3	1	Research Scientist	4	Single	3452	17241	3	Y	No	18	3	1	80	0	5	4	3	3	2	0	2
+45	No	Travel_Rarely	974	Research & Development	1	4	Medical	1	996	4	Female	91	3	1	Laboratory Technician	4	Divorced	2270	11005	3	Y	No	14	3	4	80	2	8	2	3	5	3	0	2
+50	No	Travel_Rarely	1126	Research & Development	1	2	Medical	1	997	4	Male	66	3	4	Research Director	4	Divorced	17399	6615	9	Y	No	22	4	3	80	1	32	1	2	5	4	1	3
+33	No	Travel_Frequently	827	Research & Development	1	4	Other	1	998	3	Female	84	4	2	Healthcare Representative	2	Married	5488	20161	1	Y	Yes	13	3	1	80	1	6	2	3	6	5	1	2
+41	No	Travel_Frequently	840	Research & Development	9	3	Medical	1	999	1	Male	64	3	5	Research Director	3	Divorced	19419	3735	2	Y	No	17	3	2	80	1	21	2	4	18	16	0	11
+27	No	Travel_Rarely	1134	Research & Development	16	4	Technical Degree	1	1001	3	Female	37	3	1	Laboratory Technician	2	Married	2811	12086	9	Y	No	14	3	2	80	1	4	2	3	2	2	2	2
+45	No	Non-Travel	248	Research & Development	23	2	Life Sciences	1	1002	4	Male	42	3	2	Laboratory Technician	1	Married	3633	14039	1	Y	Yes	15	3	3	80	1	9	2	3	9	8	0	8
+47	No	Travel_Rarely	955	Sales	4	2	Life Sciences	1	1003	4	Female	83	3	2	Sales Executive	4	Single	4163	8571	1	Y	Yes	17	3	3	80	0	9	0	3	9	0	0	7
+30	Yes	Travel_Rarely	138	Research & Development	22	3	Life Sciences	1	1004	1	Female	48	3	1	Research Scientist	3	Married	2132	11539	4	Y	Yes	11	3	2	80	0	7	2	3	5	2	0	1
+50	No	Travel_Rarely	939	Research & Development	24	3	Life Sciences	1	1005	4	Male	95	3	4	Manufacturing Director	3	Married	13973	4161	3	Y	Yes	18	3	4	80	1	22	2	3	12	11	1	5
+38	No	Travel_Frequently	1391	Research & Development	10	1	Medical	1	1006	3	Male	66	3	1	Research Scientist	3	Married	2684	12127	0	Y	No	17	3	2	80	1	3	0	2	2	1	0	2
+46	No	Travel_Rarely	566	Research & Development	7	2	Medical	1	1007	4	Male	75	3	3	Manufacturing Director	3	Divorced	10845	24208	6	Y	No	13	3	2	80	1	13	3	3	8	7	0	7
+24	No	Travel_Rarely	1206	Research & Development	17	1	Medical	1	1009	4	Female	41	2	2	Manufacturing Director	3	Divorced	4377	24117	1	Y	No	15	3	2	80	2	5	6	3	4	2	3	2
+35	Yes	Travel_Rarely	622	Research & Development	14	4	Other	1	1010	3	Male	39	2	1	Laboratory Technician	2	Divorced	3743	10074	1	Y	Yes	24	4	4	80	1	5	2	1	4	2	0	2
+31	No	Travel_Frequently	853	Research & Development	1	1	Life Sciences	1	1011	3	Female	96	3	2	Manufacturing Director	1	Married	4148	11275	1	Y	No	12	3	3	80	1	4	1	3	4	3	0	3
+18	No	Non-Travel	287	Research & Development	5	2	Life Sciences	1	1012	2	Male	73	3	1	Research Scientist	4	Single	1051	13493	1	Y	No	15	3	4	80	0	0	2	3	0	0	0	0
+54	No	Travel_Rarely	1441	Research & Development	17	3	Technical Degree	1	1013	3	Female	56	3	3	Manufacturing Director	3	Married	10739	13943	8	Y	No	11	3	3	80	1	22	2	3	10	7	0	8
+35	No	Travel_Rarely	583	Research & Development	25	4	Medical	1	1014	3	Female	57	3	3	Healthcare Representative	3	Divorced	10388	6975	1	Y	Yes	11	3	3	80	1	16	3	2	16	10	10	1
+30	No	Travel_Rarely	153	Research & Development	8	2	Life Sciences	1	1015	2	Female	73	4	3	Research Director	1	Married	11416	17802	0	Y	Yes	12	3	3	80	3	9	4	2	8	7	1	7
+20	Yes	Travel_Rarely	1097	Research & Development	11	3	Medical	1	1016	4	Female	98	2	1	Research Scientist	1	Single	2600	18275	1	Y	Yes	15	3	1	80	0	1	2	3	1	0	0	0
+30	Yes	Travel_Frequently	109	Research & Development	5	3	Medical	1	1017	2	Female	60	3	1	Laboratory Technician	2	Single	2422	25725	0	Y	No	17	3	1	80	0	4	3	3	3	2	1	2
+26	No	Travel_Rarely	1066	Research & Development	2	2	Medical	1	1018	4	Male	32	4	2	Manufacturing Director	4	Married	5472	3334	1	Y	No	12	3	2	80	0	8	2	3	8	7	1	3
+22	No	Travel_Rarely	217	Research & Development	8	1	Life Sciences	1	1019	2	Male	94	1	1	Laboratory Technician	1	Married	2451	6881	1	Y	No	15	3	1	80	1	4	3	2	4	3	1	1
+48	No	Travel_Rarely	277	Research & Development	6	3	Life Sciences	1	1022	1	Male	97	2	2	Healthcare Representative	3	Single	4240	13119	2	Y	No	13	3	4	80	0	19	0	3	2	2	2	2
+48	No	Travel_Rarely	1355	Research & Development	4	4	Life Sciences	1	1024	3	Male	78	2	3	Healthcare Representative	3	Single	10999	22245	7	Y	No	14	3	2	80	0	27	3	3	15	11	4	8
+41	No	Travel_Rarely	549	Research & Development	7	2	Medical	1	1025	4	Female	42	3	2	Manufacturing Director	3	Single	5003	23371	6	Y	No	14	3	2	80	0	8	6	3	2	2	2	1
+39	No	Travel_Rarely	466	Research & Development	1	1	Life Sciences	1	1026	4	Female	65	2	4	Manufacturing Director	4	Married	12742	7060	1	Y	No	16	3	3	80	1	21	3	3	21	6	11	8
+27	No	Travel_Rarely	1055	Research & Development	2	4	Life Sciences	1	1027	1	Female	47	3	2	Manufacturing Director	4	Married	4227	4658	0	Y	No	18	3	2	80	1	4	2	3	3	2	2	2
+35	No	Travel_Rarely	802	Research & Development	10	3	Other	1	1028	2	Male	45	3	1	Laboratory Technician	4	Divorced	3917	9541	1	Y	No	20	4	1	80	1	3	4	2	3	2	1	2
+42	No	Travel_Rarely	265	Sales	5	2	Marketing	1	1029	4	Male	90	3	5	Manager	3	Married	18303	7770	6	Y	No	13	3	2	80	0	21	3	4	1	0	0	0
+50	No	Travel_Rarely	804	Research & Development	9	3	Life Sciences	1	1030	1	Male	64	3	1	Laboratory Technician	4	Married	2380	20165	4	Y	No	18	3	2	80	0	8	5	3	1	0	0	0
+59	No	Travel_Rarely	715	Research & Development	2	3	Life Sciences	1	1032	3	Female	69	2	4	Manufacturing Director	4	Single	13726	21829	3	Y	Yes	13	3	1	80	0	30	4	3	5	3	4	3
+37	Yes	Travel_Rarely	1141	Research & Development	11	2	Medical	1	1033	1	Female	61	1	2	Healthcare Representative	2	Married	4777	14382	5	Y	No	15	3	1	80	0	15	2	1	1	0	0	0
+55	No	Travel_Frequently	135	Research & Development	18	4	Medical	1	1034	3	Male	62	3	2	Healthcare Representative	2	Married	6385	12992	3	Y	Yes	14	3	4	80	2	17	3	3	8	7	6	7
+41	No	Non-Travel	247	Research & Development	7	1	Life Sciences	1	1035	2	Female	55	1	5	Research Director	3	Divorced	19973	20284	1	Y	No	22	4	2	80	2	21	3	3	21	16	5	10
+38	No	Travel_Rarely	1035	Sales	3	4	Life Sciences	1	1036	2	Male	42	3	2	Sales Executive	4	Single	6861	4981	8	Y	Yes	12	3	3	80	0	19	1	3	1	0	0	0
+26	Yes	Non-Travel	265	Sales	29	2	Medical	1	1037	2	Male	79	1	2	Sales Executive	1	Single	4969	21813	8	Y	No	18	3	4	80	0	7	6	3	2	2	2	2
+52	Yes	Travel_Rarely	266	Sales	2	1	Marketing	1	1038	1	Female	57	1	5	Manager	4	Married	19845	25846	1	Y	No	15	3	4	80	1	33	3	3	32	14	6	9
+44	No	Travel_Rarely	1448	Sales	28	3	Medical	1	1039	4	Female	53	4	4	Sales Executive	4	Married	13320	11737	3	Y	Yes	18	3	3	80	1	23	2	3	12	11	11	11
+50	No	Non-Travel	145	Sales	1	3	Life Sciences	1	1040	4	Female	95	3	2	Sales Executive	3	Married	6347	24920	0	Y	No	12	3	1	80	1	19	3	3	18	7	0	13
+36	Yes	Travel_Rarely	885	Research & Development	16	4	Life Sciences	1	1042	3	Female	43	4	1	Laboratory Technician	1	Single	2743	8269	1	Y	No	16	3	3	80	0	18	1	3	17	13	15	14
+39	No	Travel_Frequently	945	Research & Development	22	3	Medical	1	1043	4	Female	82	3	3	Manufacturing Director	1	Single	10880	5083	1	Y	Yes	13	3	3	80	0	21	2	3	21	6	2	8
+33	No	Non-Travel	1038	Sales	8	1	Life Sciences	1	1044	2	Female	88	2	1	Sales Representative	4	Single	2342	21437	0	Y	No	19	3	4	80	0	3	2	2	2	2	2	2
+45	No	Travel_Rarely	1234	Sales	11	2	Life Sciences	1	1045	4	Female	90	3	4	Manager	4	Married	17650	5404	3	Y	No	13	3	2	80	1	26	4	4	9	3	1	1
+32	No	Non-Travel	1109	Research & Development	29	4	Medical	1	1046	4	Female	69	3	1	Laboratory Technician	3	Single	4025	11135	9	Y	No	12	3	2	80	0	10	2	3	8	7	7	7
+34	No	Travel_Rarely	216	Sales	1	4	Marketing	1	1047	2	Male	75	4	2	Sales Executive	4	Divorced	9725	12278	0	Y	No	11	3	4	80	1	16	2	2	15	1	0	9
+59	No	Travel_Rarely	1089	Sales	1	2	Technical Degree	1	1048	2	Male	66	3	3	Manager	4	Married	11904	11038	3	Y	Yes	14	3	3	80	1	14	1	1	6	4	0	4
+45	No	Travel_Rarely	788	Human Resources	24	4	Medical	1	1049	2	Male	36	3	1	Human Resources	2	Single	2177	8318	1	Y	No	16	3	1	80	0	6	3	3	6	3	0	4
+53	No	Travel_Frequently	124	Sales	2	3	Marketing	1	1050	3	Female	38	2	3	Sales Executive	2	Married	7525	23537	2	Y	No	12	3	1	80	1	30	2	3	15	7	6	12
+36	Yes	Travel_Rarely	660	Research & Development	15	3	Other	1	1052	1	Male	81	3	2	Laboratory Technician	3	Divorced	4834	7858	7	Y	No	14	3	2	80	1	9	3	2	1	0	0	0
+26	Yes	Travel_Frequently	342	Research & Development	2	3	Life Sciences	1	1053	1	Male	57	3	1	Research Scientist	1	Married	2042	15346	6	Y	Yes	14	3	2	80	1	6	2	3	3	2	1	2
+34	No	Travel_Rarely	1333	Sales	10	4	Life Sciences	1	1055	3	Female	87	3	1	Sales Representative	3	Married	2220	18410	1	Y	Yes	19	3	4	80	1	1	2	3	1	1	0	0
+28	No	Travel_Rarely	1144	Sales	10	1	Medical	1	1056	4	Male	74	3	1	Sales Representative	2	Married	1052	23384	1	Y	No	22	4	2	80	0	1	5	3	1	0	0	0
+38	No	Travel_Frequently	1186	Research & Development	3	4	Other	1	1060	3	Male	44	3	1	Research Scientist	3	Married	2821	2997	3	Y	No	16	3	1	80	1	8	2	3	2	2	2	2
+50	No	Travel_Rarely	1464	Research & Development	2	4	Medical	1	1061	2	Male	62	3	5	Research Director	3	Married	19237	12853	2	Y	Yes	11	3	4	80	1	29	2	2	8	1	7	7
+37	No	Travel_Rarely	124	Research & Development	3	3	Other	1	1062	4	Female	35	3	2	Healthcare Representative	2	Single	4107	13848	3	Y	No	15	3	1	80	0	8	3	2	4	3	0	1
+40	No	Travel_Rarely	300	Sales	26	3	Marketing	1	1066	3	Male	74	3	2	Sales Executive	1	Married	8396	22217	1	Y	No	14	3	2	80	1	8	3	2	7	7	7	5
+26	No	Travel_Frequently	921	Research & Development	1	1	Medical	1	1068	1	Female	66	2	1	Research Scientist	3	Divorced	2007	25265	1	Y	No	13	3	3	80	2	5	5	3	5	3	1	3
+46	No	Travel_Rarely	430	Research & Development	1	4	Medical	1	1069	4	Male	40	3	5	Research Director	4	Divorced	19627	21445	9	Y	No	17	3	4	80	2	23	0	3	2	2	2	2
+54	No	Travel_Rarely	1082	Sales	2	4	Life Sciences	1	1070	3	Female	41	2	3	Sales Executive	3	Married	10686	8392	6	Y	No	11	3	2	80	1	13	4	3	9	4	7	0
+56	No	Travel_Frequently	1240	Research & Development	9	3	Medical	1	1071	1	Female	63	3	1	Research Scientist	3	Married	2942	12154	2	Y	No	19	3	2	80	1	18	4	3	5	4	0	3
+36	No	Travel_Rarely	796	Research & Development	12	5	Medical	1	1073	4	Female	51	2	3	Manufacturing Director	4	Single	8858	15669	0	Y	No	11	3	2	80	0	15	2	2	14	8	7	8
+55	No	Non-Travel	444	Research & Development	2	1	Medical	1	1074	3	Male	40	2	4	Manager	1	Single	16756	17323	7	Y	No	15	3	2	80	0	31	3	4	9	7	6	2
+43	No	Travel_Rarely	415	Sales	25	3	Medical	1	1076	3	Male	79	2	3	Sales Executive	4	Divorced	10798	5268	5	Y	No	13	3	3	80	1	18	5	3	1	0	0	0
+20	Yes	Travel_Frequently	769	Sales	9	3	Marketing	1	1077	4	Female	54	3	1	Sales Representative	4	Single	2323	17205	1	Y	Yes	14	3	2	80	0	2	3	3	2	2	0	2
+21	Yes	Travel_Rarely	1334	Research & Development	10	3	Life Sciences	1	1079	3	Female	36	2	1	Laboratory Technician	1	Single	1416	17258	1	Y	No	13	3	1	80	0	1	6	2	1	0	1	0
+46	No	Travel_Rarely	1003	Research & Development	8	4	Life Sciences	1	1080	4	Female	74	2	2	Research Scientist	1	Divorced	4615	21029	8	Y	Yes	23	4	1	80	3	19	2	3	16	13	1	7
+51	Yes	Travel_Rarely	1323	Research & Development	4	4	Life Sciences	1	1081	1	Male	34	3	1	Research Scientist	3	Married	2461	10332	9	Y	Yes	12	3	3	80	3	18	2	4	10	0	2	7
+28	Yes	Non-Travel	1366	Research & Development	24	2	Technical Degree	1	1082	2	Male	72	2	3	Healthcare Representative	1	Single	8722	12355	1	Y	No	12	3	1	80	0	10	2	2	10	7	1	9
+26	No	Travel_Rarely	192	Research & Development	1	2	Medical	1	1083	1	Male	59	2	1	Laboratory Technician	1	Married	3955	11141	1	Y	No	16	3	1	80	2	6	2	3	5	3	1	3
+30	No	Travel_Rarely	1176	Research & Development	20	3	Other	1	1084	3	Male	85	3	2	Manufacturing Director	1	Married	9957	9096	0	Y	No	15	3	3	80	1	7	1	2	6	2	0	2
+41	No	Travel_Rarely	509	Research & Development	7	2	Technical Degree	1	1085	2	Female	43	4	1	Research Scientist	3	Married	3376	18863	1	Y	No	13	3	3	80	0	10	3	3	10	6	0	8
+38	No	Travel_Rarely	330	Research & Development	17	1	Life Sciences	1	1088	3	Female	65	2	3	Healthcare Representative	3	Married	8823	24608	0	Y	No	18	3	1	80	1	20	4	2	19	9	1	9
+40	No	Travel_Rarely	1492	Research & Development	20	4	Technical Degree	1	1092	1	Male	61	3	3	Healthcare Representative	4	Married	10322	26542	4	Y	No	20	4	4	80	1	14	6	3	11	10	11	1
+27	No	Non-Travel	1277	Research & Development	8	5	Life Sciences	1	1094	1	Male	87	1	1	Laboratory Technician	3	Married	4621	5869	1	Y	No	19	3	4	80	3	3	4	3	3	2	1	2
+55	No	Travel_Frequently	1091	Research & Development	2	1	Life Sciences	1	1096	4	Male	65	3	3	Manufacturing Director	2	Married	10976	15813	3	Y	No	18	3	2	80	1	23	4	3	3	2	1	2
+28	No	Travel_Rarely	857	Research & Development	10	3	Other	1	1097	3	Female	59	3	2	Research Scientist	3	Single	3660	7909	3	Y	No	13	3	4	80	0	10	4	4	8	7	1	7
+44	Yes	Travel_Rarely	1376	Human Resources	1	2	Medical	1	1098	2	Male	91	2	3	Human Resources	1	Married	10482	2326	9	Y	No	14	3	4	80	1	24	1	3	20	6	3	6
+33	No	Travel_Rarely	654	Research & Development	5	3	Life Sciences	1	1099	4	Male	34	2	3	Healthcare Representative	4	Divorced	7119	21214	4	Y	No	15	3	3	80	1	9	2	3	3	2	1	2
+35	Yes	Travel_Rarely	1204	Sales	4	3	Technical Degree	1	1100	4	Male	86	3	3	Sales Executive	1	Single	9582	10333	0	Y	Yes	22	4	1	80	0	9	2	3	8	7	4	7
+33	Yes	Travel_Frequently	827	Research & Development	29	4	Medical	1	1101	1	Female	54	2	2	Research Scientist	3	Single	4508	3129	1	Y	No	22	4	2	80	0	14	4	3	13	7	3	8
+28	No	Travel_Rarely	895	Research & Development	15	2	Life Sciences	1	1102	1	Male	50	3	1	Laboratory Technician	3	Divorced	2207	22482	1	Y	No	16	3	4	80	1	4	5	2	4	2	2	2
+34	No	Travel_Frequently	618	Research & Development	3	1	Life Sciences	1	1103	1	Male	45	3	2	Healthcare Representative	4	Single	7756	22266	0	Y	No	17	3	3	80	0	7	1	2	6	2	0	4
+37	No	Travel_Rarely	309	Sales	10	4	Life Sciences	1	1105	4	Female	88	2	2	Sales Executive	4	Divorced	6694	24223	2	Y	Yes	14	3	3	80	3	8	5	3	1	0	0	0
+25	Yes	Travel_Rarely	1219	Research & Development	4	1	Technical Degree	1	1106	4	Male	32	3	1	Laboratory Technician	4	Married	3691	4605	1	Y	Yes	15	3	2	80	1	7	3	4	7	7	5	6
+26	Yes	Travel_Rarely	1330	Research & Development	21	3	Medical	1	1107	1	Male	37	3	1	Laboratory Technician	3	Divorced	2377	19373	1	Y	No	20	4	3	80	1	1	0	2	1	1	0	0
+33	Yes	Travel_Rarely	1017	Research & Development	25	3	Medical	1	1108	1	Male	55	2	1	Research Scientist	2	Single	2313	2993	4	Y	Yes	20	4	2	80	0	5	0	3	2	2	2	2
+42	No	Travel_Rarely	469	Research & Development	2	2	Medical	1	1109	4	Male	35	3	4	Manager	1	Married	17665	14399	0	Y	No	17	3	4	80	1	23	3	3	22	6	13	7
+28	Yes	Travel_Frequently	1009	Research & Development	1	3	Medical	1	1111	1	Male	45	2	1	Laboratory Technician	2	Divorced	2596	7160	1	Y	No	15	3	1	80	2	1	2	3	1	0	0	0
+50	Yes	Travel_Frequently	959	Sales	1	4	Other	1	1113	4	Male	81	3	2	Sales Executive	3	Single	4728	17251	3	Y	Yes	14	3	4	80	0	5	4	3	0	0	0	0
+33	No	Travel_Frequently	970	Sales	7	3	Life Sciences	1	1114	4	Female	30	3	2	Sales Executive	2	Married	4302	13401	0	Y	No	17	3	3	80	1	4	3	3	3	2	0	2
+34	No	Non-Travel	697	Research & Development	3	4	Life Sciences	1	1115	3	Male	40	2	1	Research Scientist	4	Married	2979	22478	3	Y	No	17	3	4	80	3	6	2	3	0	0	0	0
+48	No	Non-Travel	1262	Research & Development	1	4	Medical	1	1116	1	Male	35	4	4	Manager	4	Single	16885	16154	2	Y	No	22	4	3	80	0	27	3	2	5	4	2	1
+45	No	Non-Travel	1050	Sales	9	4	Life Sciences	1	1117	2	Female	65	2	2	Sales Executive	3	Married	5593	17970	1	Y	No	13	3	4	80	1	15	2	3	15	10	4	12
+52	No	Travel_Rarely	994	Research & Development	7	4	Life Sciences	1	1118	2	Male	87	3	3	Healthcare Representative	2	Single	10445	15322	7	Y	No	19	3	4	80	0	18	4	3	8	6	4	0
+38	No	Travel_Rarely	770	Sales	10	4	Marketing	1	1119	3	Male	73	2	3	Sales Executive	3	Divorced	8740	5569	0	Y	Yes	14	3	2	80	2	9	2	3	8	7	2	7
+29	No	Travel_Rarely	1107	Research & Development	28	4	Life Sciences	1	1120	3	Female	93	3	1	Research Scientist	4	Divorced	2514	26968	4	Y	No	22	4	1	80	1	11	1	3	7	5	1	7
+28	No	Travel_Rarely	950	Research & Development	3	3	Medical	1	1121	4	Female	93	3	3	Manufacturing Director	2	Divorced	7655	8039	0	Y	No	17	3	2	80	3	10	3	2	9	7	1	7
+46	No	Travel_Rarely	406	Sales	3	1	Marketing	1	1124	1	Male	52	3	4	Manager	3	Married	17465	15596	3	Y	No	12	3	4	80	1	23	3	3	12	9	4	9
+38	No	Travel_Rarely	130	Sales	2	2	Marketing	1	1125	4	Male	32	3	3	Sales Executive	2	Single	7351	20619	7	Y	No	16	3	3	80	0	10	2	3	1	0	0	0
+43	No	Travel_Frequently	1082	Research & Development	27	3	Life Sciences	1	1126	3	Female	83	3	3	Manufacturing Director	1	Married	10820	11535	8	Y	No	11	3	3	80	1	18	1	3	8	7	0	1
+39	Yes	Travel_Frequently	203	Research & Development	2	3	Life Sciences	1	1127	1	Male	84	3	4	Healthcare Representative	4	Divorced	12169	13547	7	Y	No	11	3	4	80	3	21	4	3	18	7	11	5
+40	No	Travel_Rarely	1308	Research & Development	14	3	Medical	1	1128	3	Male	44	2	5	Research Director	3	Single	19626	17544	1	Y	No	14	3	1	80	0	21	2	4	20	7	4	9
+21	No	Travel_Rarely	984	Research & Development	1	1	Technical Degree	1	1131	4	Female	70	2	1	Research Scientist	2	Single	2070	25326	1	Y	Yes	11	3	3	80	0	2	6	4	2	2	2	2
+39	No	Non-Travel	439	Research & Development	9	3	Life Sciences	1	1132	3	Male	70	3	2	Laboratory Technician	2	Single	6782	8770	9	Y	No	15	3	3	80	0	9	2	2	5	4	0	3
+36	No	Non-Travel	217	Research & Development	18	4	Life Sciences	1	1133	1	Male	78	3	2	Manufacturing Director	4	Single	7779	23238	2	Y	No	20	4	1	80	0	18	0	3	11	9	0	9
+31	No	Travel_Frequently	793	Sales	20	3	Life Sciences	1	1135	3	Male	67	4	1	Sales Representative	4	Married	2791	21981	0	Y	No	12	3	1	80	1	3	4	3	2	2	2	2
+28	No	Travel_Rarely	1451	Research & Development	2	1	Life Sciences	1	1136	1	Male	67	2	1	Research Scientist	2	Married	3201	19911	0	Y	No	17	3	1	80	0	6	2	1	5	3	0	4
+35	No	Travel_Frequently	1182	Sales	11	2	Marketing	1	1137	4	Male	54	3	2	Sales Executive	4	Divorced	4968	18500	1	Y	No	11	3	4	80	1	5	3	3	5	2	0	2
+49	No	Travel_Rarely	174	Sales	8	4	Technical Degree	1	1138	4	Male	56	2	4	Sales Executive	2	Married	13120	11879	6	Y	No	17	3	2	80	1	22	3	3	9	8	2	3
+34	No	Travel_Frequently	1003	Research & Development	2	2	Life Sciences	1	1140	4	Male	95	3	2	Manufacturing Director	3	Single	4033	15834	2	Y	No	11	3	4	80	0	5	3	2	3	2	0	2
+29	No	Travel_Frequently	490	Research & Development	10	3	Life Sciences	1	1143	4	Female	61	3	1	Research Scientist	2	Divorced	3291	17940	0	Y	No	14	3	4	80	2	8	2	2	7	5	1	1
+42	No	Travel_Rarely	188	Research & Development	29	3	Medical	1	1148	2	Male	56	1	2	Laboratory Technician	4	Single	4272	9558	4	Y	No	19	3	1	80	0	16	3	3	1	0	0	0
+29	No	Travel_Rarely	718	Research & Development	8	1	Medical	1	1150	2	Male	79	2	2	Manufacturing Director	4	Married	5056	17689	1	Y	Yes	15	3	3	80	1	10	2	2	10	7	1	2
+38	No	Travel_Rarely	433	Human Resources	1	3	Human Resources	1	1152	3	Male	37	4	1	Human Resources	3	Married	2844	6004	1	Y	No	13	3	4	80	1	7	2	4	7	6	5	0
+28	No	Travel_Frequently	773	Research & Development	6	3	Life Sciences	1	1154	3	Male	39	2	1	Research Scientist	3	Divorced	2703	22088	1	Y	Yes	14	3	4	80	1	3	2	3	3	1	0	2
+18	Yes	Non-Travel	247	Research & Development	8	1	Medical	1	1156	3	Male	80	3	1	Laboratory Technician	3	Single	1904	13556	1	Y	No	12	3	4	80	0	0	0	3	0	0	0	0
+33	Yes	Travel_Rarely	603	Sales	9	4	Marketing	1	1157	1	Female	77	3	2	Sales Executive	1	Single	8224	18385	0	Y	Yes	17	3	1	80	0	6	3	3	5	2	0	3
+41	No	Travel_Rarely	167	Research & Development	12	4	Life Sciences	1	1158	2	Male	46	3	1	Laboratory Technician	4	Married	4766	9051	3	Y	Yes	11	3	1	80	1	6	4	3	1	0	0	0
+31	Yes	Travel_Frequently	874	Research & Development	15	3	Medical	1	1160	3	Male	72	3	1	Laboratory Technician	3	Married	2610	6233	1	Y	No	12	3	3	80	1	2	5	2	2	2	2	2
+37	No	Travel_Rarely	367	Research & Development	25	2	Medical	1	1161	3	Female	52	2	2	Healthcare Representative	4	Divorced	5731	17171	7	Y	No	13	3	3	80	2	9	2	3	6	2	1	3
+27	No	Travel_Rarely	199	Research & Development	6	3	Life Sciences	1	1162	4	Male	55	2	1	Research Scientist	3	Married	2539	7950	1	Y	No	13	3	3	80	1	4	0	3	4	2	2	2
+34	No	Travel_Rarely	1400	Sales	9	1	Life Sciences	1	1163	2	Female	70	3	2	Sales Executive	3	Married	5714	5829	1	Y	No	20	4	1	80	0	6	3	2	6	5	1	3
+35	No	Travel_Rarely	528	Human Resources	8	4	Technical Degree	1	1164	3	Male	100	3	1	Human Resources	3	Single	4323	7108	1	Y	No	17	3	2	80	0	6	2	1	5	4	1	4
+29	Yes	Travel_Rarely	408	Sales	23	1	Life Sciences	1	1165	4	Female	45	2	3	Sales Executive	1	Married	7336	11162	1	Y	No	13	3	1	80	1	11	3	1	11	8	3	10
+40	No	Travel_Frequently	593	Research & Development	9	4	Medical	1	1166	2	Female	88	3	3	Research Director	3	Single	13499	13782	9	Y	No	17	3	3	80	0	20	3	2	18	7	2	13
+42	Yes	Travel_Frequently	481	Sales	12	3	Life Sciences	1	1167	3	Male	44	3	4	Sales Executive	1	Single	13758	2447	0	Y	Yes	12	3	2	80	0	22	2	2	21	9	13	14
+42	No	Travel_Rarely	647	Sales	4	4	Marketing	1	1171	2	Male	45	3	2	Sales Executive	1	Single	5155	2253	7	Y	No	13	3	4	80	0	9	3	4	6	4	1	5
+35	No	Travel_Rarely	982	Research & Development	1	4	Medical	1	1172	4	Male	58	2	1	Laboratory Technician	3	Married	2258	16340	6	Y	No	12	3	2	80	1	10	2	3	8	0	1	7
+24	No	Travel_Rarely	477	Research & Development	24	3	Medical	1	1173	4	Male	49	3	1	Laboratory Technician	2	Single	3597	6409	8	Y	No	22	4	4	80	0	6	2	3	4	3	1	2
+28	Yes	Travel_Rarely	1485	Research & Development	12	1	Life Sciences	1	1175	3	Female	79	3	1	Laboratory Technician	4	Married	2515	22955	1	Y	Yes	11	3	4	80	0	1	4	2	1	1	0	0
+26	No	Travel_Rarely	1384	Research & Development	3	4	Medical	1	1177	1	Male	82	4	1	Laboratory Technician	4	Married	4420	13421	1	Y	No	22	4	2	80	1	8	2	3	8	7	0	7
+30	No	Travel_Rarely	852	Sales	10	3	Marketing	1	1179	3	Male	72	2	2	Sales Executive	3	Married	6578	2706	1	Y	No	18	3	1	80	1	10	3	3	10	3	1	4
+40	No	Travel_Frequently	902	Research & Development	26	2	Medical	1	1180	3	Female	92	2	2	Research Scientist	4	Married	4422	21203	3	Y	Yes	13	3	4	80	1	16	3	1	1	1	0	0
+35	No	Travel_Rarely	819	Research & Development	2	3	Life Sciences	1	1182	3	Male	44	2	3	Manufacturing Director	2	Divorced	10274	19588	2	Y	No	18	3	2	80	1	15	2	4	7	7	6	4
+34	No	Travel_Frequently	669	Research & Development	1	3	Medical	1	1184	4	Male	97	2	2	Healthcare Representative	1	Single	5343	25755	0	Y	No	20	4	3	80	0	14	3	3	13	9	4	9
+35	No	Travel_Frequently	636	Research & Development	4	4	Other	1	1185	4	Male	47	2	1	Laboratory Technician	4	Married	2376	26537	1	Y	No	13	3	2	80	1	2	2	4	2	2	2	2
+43	Yes	Travel_Rarely	1372	Sales	9	3	Marketing	1	1188	1	Female	85	1	2	Sales Executive	3	Single	5346	9489	8	Y	No	13	3	2	80	0	7	2	2	4	3	1	3
+32	No	Non-Travel	862	Sales	2	1	Life Sciences	1	1190	3	Female	76	3	1	Sales Representative	1	Divorced	2827	14947	1	Y	No	12	3	3	80	3	1	3	3	1	0	0	0
+56	No	Travel_Rarely	718	Research & Development	4	4	Technical Degree	1	1191	4	Female	92	3	5	Manager	1	Divorced	19943	18575	4	Y	No	13	3	4	80	1	28	2	3	5	2	4	2
+29	No	Travel_Rarely	1401	Research & Development	6	1	Medical	1	1192	2	Female	54	3	1	Laboratory Technician	4	Married	3131	26342	1	Y	No	13	3	1	80	1	10	5	3	10	8	0	8
+19	No	Travel_Rarely	645	Research & Development	9	2	Life Sciences	1	1193	3	Male	54	3	1	Research Scientist	1	Single	2552	7172	1	Y	No	25	4	3	80	0	1	4	3	1	1	0	0
+45	No	Travel_Rarely	1457	Research & Development	7	3	Medical	1	1195	1	Female	83	3	1	Research Scientist	3	Married	4477	20100	4	Y	Yes	19	3	3	80	1	7	2	2	3	2	0	2
+37	No	Travel_Rarely	977	Research & Development	1	3	Life Sciences	1	1196	4	Female	56	2	2	Manufacturing Director	4	Married	6474	9961	1	Y	No	13	3	2	80	1	14	2	2	14	8	3	11
+20	No	Travel_Rarely	805	Research & Development	3	3	Life Sciences	1	1198	1	Male	87	2	1	Laboratory Technician	3	Single	3033	12828	1	Y	No	12	3	1	80	0	2	2	2	2	2	1	2
+44	Yes	Travel_Rarely	1097	Research & Development	10	4	Life Sciences	1	1200	3	Male	96	3	1	Research Scientist	3	Single	2936	10826	1	Y	Yes	11	3	3	80	0	6	4	3	6	4	0	2
+53	No	Travel_Rarely	1223	Research & Development	7	2	Medical	1	1201	4	Female	50	3	5	Manager	3	Divorced	18606	18640	3	Y	No	18	3	2	80	1	26	6	3	7	7	4	7
+29	No	Travel_Rarely	942	Research & Development	15	1	Life Sciences	1	1202	2	Female	69	1	1	Research Scientist	4	Married	2168	26933	0	Y	Yes	18	3	1	80	1	6	2	2	5	4	1	3
+22	Yes	Travel_Frequently	1256	Research & Development	3	4	Life Sciences	1	1203	3	Male	48	2	1	Research Scientist	4	Married	2853	4223	0	Y	Yes	11	3	2	80	1	1	5	3	0	0	0	0
+46	No	Travel_Rarely	1402	Sales	2	3	Marketing	1	1204	3	Female	69	3	4	Manager	1	Married	17048	24097	8	Y	No	23	4	1	80	0	28	2	3	26	15	15	9
+44	No	Non-Travel	111	Research & Development	17	3	Life Sciences	1	1206	4	Male	74	1	1	Research Scientist	3	Single	2290	4279	2	Y	No	13	3	4	80	0	6	3	3	0	0	0	0
+33	No	Travel_Rarely	147	Human Resources	2	3	Human Resources	1	1207	2	Male	99	3	1	Human Resources	3	Married	3600	8429	1	Y	No	13	3	4	80	1	5	2	3	5	4	1	4
+41	Yes	Non-Travel	906	Research & Development	5	2	Life Sciences	1	1210	1	Male	95	2	1	Research Scientist	1	Divorced	2107	20293	6	Y	No	17	3	1	80	1	5	2	1	1	0	0	0
+30	No	Travel_Rarely	1329	Sales	29	4	Life Sciences	1	1211	3	Male	61	3	2	Sales Executive	1	Divorced	4115	13192	8	Y	No	19	3	3	80	3	8	3	3	4	3	0	3
+40	No	Travel_Frequently	1184	Sales	2	4	Medical	1	1212	2	Male	62	3	2	Sales Executive	2	Married	4327	25440	5	Y	No	12	3	4	80	3	5	2	3	0	0	0	0
+50	No	Travel_Frequently	1421	Research & Development	2	3	Medical	1	1215	4	Female	30	3	4	Manager	1	Married	17856	9490	2	Y	No	22	4	3	80	1	32	3	3	2	2	2	2
+28	No	Travel_Rarely	1179	Research & Development	19	4	Medical	1	1216	4	Male	78	2	1	Laboratory Technician	1	Married	3196	12449	1	Y	No	12	3	3	80	3	6	2	3	6	5	3	3
+46	No	Travel_Rarely	1450	Research & Development	15	2	Life Sciences	1	1217	4	Male	52	3	5	Research Director	2	Married	19081	10849	5	Y	No	11	3	1	80	1	25	2	3	4	2	0	3
+35	No	Travel_Rarely	1361	Sales	17	4	Life Sciences	1	1218	3	Male	94	3	2	Sales Executive	1	Married	8966	21026	3	Y	Yes	15	3	4	80	3	15	2	3	7	7	1	7
+24	Yes	Travel_Rarely	984	Research & Development	17	2	Life Sciences	1	1219	4	Female	97	3	1	Laboratory Technician	2	Married	2210	3372	1	Y	No	13	3	1	80	1	1	3	1	1	0	0	0
+33	No	Travel_Frequently	1146	Sales	25	3	Medical	1	1220	2	Female	82	3	2	Sales Executive	3	Married	4539	4905	1	Y	No	12	3	1	80	1	10	3	2	10	7	0	1
+36	No	Travel_Rarely	917	Research & Development	6	4	Life Sciences	1	1221	3	Male	60	1	1	Laboratory Technician	3	Divorced	2741	6865	1	Y	No	14	3	3	80	1	7	4	3	7	7	1	7
+30	No	Travel_Rarely	853	Research & Development	7	4	Life Sciences	1	1224	3	Male	49	3	2	Laboratory Technician	3	Divorced	3491	11309	1	Y	No	13	3	1	80	3	10	4	2	10	7	8	9
+44	No	Travel_Rarely	200	Research & Development	29	4	Other	1	1225	4	Male	32	3	2	Research Scientist	4	Single	4541	7744	1	Y	No	25	4	2	80	0	20	3	3	20	11	13	17
+20	No	Travel_Rarely	654	Sales	21	3	Marketing	1	1226	3	Male	43	4	1	Sales Representative	4	Single	2678	5050	1	Y	No	17	3	4	80	0	2	2	3	2	1	2	2
+46	No	Travel_Rarely	150	Research & Development	2	4	Technical Degree	1	1228	4	Male	60	3	2	Manufacturing Director	4	Divorced	7379	17433	2	Y	No	11	3	3	80	1	12	3	2	6	3	1	4
+42	No	Non-Travel	179	Human Resources	2	5	Medical	1	1231	4	Male	79	4	2	Human Resources	1	Married	6272	12858	7	Y	No	16	3	1	80	1	10	3	4	4	3	0	3
+60	No	Travel_Rarely	696	Sales	7	4	Marketing	1	1233	2	Male	52	4	2	Sales Executive	4	Divorced	5220	10893	0	Y	Yes	18	3	2	80	1	12	3	3	11	7	1	9
+32	No	Travel_Frequently	116	Research & Development	13	3	Other	1	1234	3	Female	77	2	1	Laboratory Technician	2	Married	2743	7331	1	Y	No	20	4	3	80	1	2	2	3	2	2	2	2
+32	No	Travel_Frequently	1316	Research & Development	2	2	Life Sciences	1	1235	4	Female	38	3	2	Research Scientist	3	Single	4998	2338	4	Y	Yes	14	3	4	80	0	10	2	3	8	7	0	7
+36	No	Travel_Rarely	363	Research & Development	1	3	Technical Degree	1	1237	3	Female	77	1	3	Manufacturing Director	1	Divorced	10252	4235	2	Y	Yes	21	4	3	80	1	17	2	3	7	7	7	7
+33	No	Travel_Rarely	117	Research & Development	9	3	Medical	1	1238	1	Male	60	3	1	Research Scientist	4	Married	2781	6311	0	Y	No	13	3	2	80	1	15	5	3	14	10	4	10
+40	No	Travel_Rarely	107	Sales	10	3	Technical Degree	1	1239	2	Female	84	2	2	Sales Executive	2	Divorced	6852	11591	7	Y	No	12	3	2	80	1	7	2	4	5	1	1	3
+25	No	Travel_Rarely	1356	Sales	10	4	Life Sciences	1	1240	3	Male	57	3	2	Sales Executive	4	Single	4950	20623	0	Y	No	14	3	2	80	0	5	4	3	4	3	1	1
+30	No	Travel_Rarely	1465	Research & Development	1	3	Medical	1	1241	4	Male	63	3	1	Research Scientist	2	Married	3579	9369	0	Y	Yes	21	4	1	80	1	12	2	3	11	9	5	7
+42	No	Travel_Frequently	458	Research & Development	26	5	Medical	1	1242	1	Female	60	3	3	Research Director	1	Married	13191	23281	3	Y	Yes	17	3	3	80	0	20	6	3	1	0	0	0
+35	No	Non-Travel	1212	Sales	8	2	Marketing	1	1243	3	Female	78	2	3	Sales Executive	4	Married	10377	13755	4	Y	Yes	11	3	2	80	1	16	6	2	13	2	4	12
+27	No	Travel_Rarely	1103	Research & Development	14	3	Life Sciences	1	1244	1	Male	42	3	1	Research Scientist	1	Married	2235	14377	1	Y	Yes	14	3	4	80	2	9	3	2	9	7	6	8
+54	No	Travel_Frequently	966	Research & Development	1	4	Life Sciences	1	1245	4	Female	53	3	3	Manufacturing Director	3	Divorced	10502	9659	7	Y	No	17	3	1	80	1	33	2	1	5	4	1	4
+44	No	Travel_Rarely	1117	Research & Development	2	1	Life Sciences	1	1246	1	Female	72	4	1	Research Scientist	4	Married	2011	19982	1	Y	No	13	3	4	80	1	10	5	3	10	5	7	7
+19	Yes	Non-Travel	504	Research & Development	10	3	Medical	1	1248	1	Female	96	2	1	Research Scientist	2	Single	1859	6148	1	Y	Yes	25	4	2	80	0	1	2	4	1	1	0	0
+29	No	Travel_Rarely	1010	Research & Development	1	3	Life Sciences	1	1249	1	Female	97	3	1	Research Scientist	4	Divorced	3760	5598	1	Y	No	15	3	1	80	3	3	5	3	3	2	1	2
+54	No	Travel_Rarely	685	Research & Development	3	3	Life Sciences	1	1250	4	Male	85	3	4	Research Director	4	Married	17779	23474	3	Y	No	14	3	1	80	0	36	2	3	10	9	0	9
+31	No	Travel_Rarely	1332	Research & Development	11	2	Medical	1	1251	3	Male	80	3	2	Healthcare Representative	1	Married	6833	17089	1	Y	Yes	12	3	4	80	0	6	2	2	6	5	0	1
+31	No	Travel_Rarely	1062	Research & Development	24	3	Medical	1	1252	3	Female	96	2	2	Healthcare Representative	1	Single	6812	17198	1	Y	No	19	3	2	80	0	10	2	3	10	9	1	8
+59	No	Travel_Rarely	326	Sales	3	3	Life Sciences	1	1254	3	Female	48	2	2	Sales Executive	4	Single	5171	16490	5	Y	No	17	3	4	80	0	13	2	3	6	1	0	5
+43	No	Travel_Rarely	920	Research & Development	3	3	Life Sciences	1	1255	3	Male	96	1	5	Research Director	4	Married	19740	18625	3	Y	No	14	3	2	80	1	25	2	3	8	7	0	7
+49	No	Travel_Rarely	1098	Research & Development	4	2	Medical	1	1256	1	Male	85	2	5	Manager	3	Married	18711	12124	2	Y	No	13	3	3	80	1	23	2	4	1	0	0	0
+36	No	Travel_Frequently	469	Research & Development	3	3	Technical Degree	1	1257	3	Male	46	3	1	Research Scientist	2	Married	3692	9256	1	Y	No	12	3	3	80	0	12	2	2	11	10	0	7
+48	No	Travel_Rarely	969	Research & Development	2	2	Technical Degree	1	1258	4	Male	76	4	1	Laboratory Technician	2	Single	2559	16620	5	Y	No	11	3	3	80	0	7	4	2	1	0	0	0
+27	No	Travel_Rarely	1167	Research & Development	4	2	Life Sciences	1	1259	1	Male	76	3	1	Research Scientist	3	Divorced	2517	3208	1	Y	No	11	3	2	80	3	5	2	3	5	3	0	3
+29	No	Travel_Rarely	1329	Research & Development	7	3	Life Sciences	1	1260	3	Male	82	3	2	Healthcare Representative	4	Divorced	6623	4204	1	Y	Yes	11	3	2	80	2	6	2	3	6	0	1	0
+48	No	Travel_Rarely	715	Research & Development	1	3	Life Sciences	1	1263	4	Male	76	2	5	Research Director	4	Single	18265	8733	6	Y	No	12	3	3	80	0	25	3	4	1	0	0	0
+29	No	Travel_Rarely	694	Research & Development	1	3	Life Sciences	1	1264	4	Female	87	2	4	Research Director	4	Divorced	16124	3423	3	Y	No	14	3	2	80	2	9	2	2	7	7	1	7
+34	No	Travel_Rarely	1320	Research & Development	20	3	Technical Degree	1	1265	3	Female	89	4	1	Research Scientist	3	Married	2585	21643	0	Y	No	17	3	4	80	0	2	5	2	1	0	0	0
+44	No	Travel_Rarely	1099	Sales	5	3	Marketing	1	1267	2	Male	88	3	5	Manager	2	Married	18213	8751	7	Y	No	11	3	3	80	1	26	5	3	22	9	3	10
+33	No	Travel_Rarely	536	Sales	10	5	Marketing	1	1268	4	Male	82	4	3	Sales Executive	3	Divorced	8380	21708	0	Y	Yes	14	3	4	80	2	10	3	3	9	8	0	8
+19	No	Travel_Rarely	265	Research & Development	25	3	Life Sciences	1	1269	2	Female	57	4	1	Research Scientist	4	Single	2994	21221	1	Y	Yes	12	3	4	80	0	1	2	3	1	0	0	1
+23	No	Travel_Rarely	373	Research & Development	1	2	Life Sciences	1	1270	4	Male	47	3	1	Research Scientist	3	Married	1223	16901	1	Y	No	22	4	4	80	1	1	2	3	1	0	0	1
+25	Yes	Travel_Frequently	599	Sales	24	1	Life Sciences	1	1273	3	Male	73	1	1	Sales Representative	4	Single	1118	8040	1	Y	Yes	14	3	4	80	0	1	4	3	1	0	1	0
+26	No	Travel_Rarely	583	Research & Development	4	2	Life Sciences	1	1275	3	Male	53	3	1	Research Scientist	4	Single	2875	9973	1	Y	Yes	20	4	2	80	0	8	2	2	8	5	2	2
+45	Yes	Travel_Rarely	1449	Sales	2	3	Marketing	1	1277	1	Female	94	1	5	Manager	2	Single	18824	2493	2	Y	Yes	16	3	1	80	0	26	2	3	24	10	1	11
+55	No	Non-Travel	177	Research & Development	8	1	Medical	1	1278	4	Male	37	2	4	Healthcare Representative	2	Divorced	13577	25592	1	Y	Yes	15	3	4	80	1	34	3	3	33	9	15	0
+21	Yes	Travel_Frequently	251	Research & Development	10	2	Life Sciences	1	1279	1	Female	45	2	1	Laboratory Technician	3	Single	2625	25308	1	Y	No	20	4	3	80	0	2	2	1	2	2	2	2
+46	No	Travel_Rarely	168	Sales	4	2	Marketing	1	1280	4	Female	33	2	5	Manager	2	Married	18789	9946	2	Y	No	14	3	3	80	1	26	2	3	11	4	0	8
+34	No	Travel_Rarely	131	Sales	2	3	Marketing	1	1281	3	Female	86	3	2	Sales Executive	1	Single	4538	6039	0	Y	Yes	12	3	4	80	0	4	3	3	3	2	0	2
+51	No	Travel_Frequently	237	Sales	9	3	Life Sciences	1	1282	4	Male	83	3	5	Manager	2	Divorced	19847	19196	4	Y	Yes	24	4	1	80	1	31	5	2	29	10	11	10
+59	No	Travel_Rarely	1429	Research & Development	18	4	Medical	1	1283	4	Male	67	3	3	Manufacturing Director	4	Single	10512	20002	6	Y	No	12	3	4	80	0	25	6	2	9	7	5	4
+34	No	Travel_Frequently	135	Research & Development	19	3	Medical	1	1285	3	Female	46	3	2	Laboratory Technician	2	Divorced	4444	22534	4	Y	No	13	3	3	80	2	15	2	4	11	8	5	10
+28	No	Travel_Frequently	791	Research & Development	1	4	Medical	1	1286	4	Male	44	3	1	Laboratory Technician	3	Single	2154	6842	0	Y	Yes	11	3	3	80	0	5	2	2	4	2	0	2
+44	No	Travel_Rarely	1199	Research & Development	4	2	Life Sciences	1	1288	3	Male	92	4	5	Manager	1	Divorced	19190	17477	1	Y	No	14	3	4	80	2	26	4	2	25	9	14	13
+34	No	Travel_Frequently	648	Human Resources	11	3	Life Sciences	1	1289	3	Male	56	2	2	Human Resources	2	Married	4490	21833	4	Y	No	11	3	4	80	2	14	5	4	10	9	1	8
+35	No	Travel_Rarely	735	Research & Development	6	1	Life Sciences	1	1291	3	Male	66	3	1	Research Scientist	3	Married	3506	6020	0	Y	Yes	14	3	4	80	0	4	3	3	3	2	2	2
+42	No	Travel_Rarely	603	Research & Development	7	4	Medical	1	1292	2	Female	78	4	2	Research Scientist	2	Married	2372	5628	6	Y	Yes	16	3	4	80	0	18	2	3	1	0	0	0
+43	No	Travel_Rarely	531	Sales	4	4	Marketing	1	1293	4	Female	56	2	3	Sales Executive	4	Single	10231	20364	3	Y	No	14	3	4	80	0	23	3	4	21	7	15	17
+36	No	Travel_Rarely	429	Research & Development	2	4	Life Sciences	1	1294	3	Female	53	3	2	Manufacturing Director	2	Single	5410	2323	9	Y	Yes	11	3	4	80	0	18	2	3	16	14	5	12
+44	Yes	Travel_Rarely	621	Research & Development	15	3	Medical	1	1295	1	Female	73	3	3	Healthcare Representative	4	Married	7978	14075	1	Y	No	11	3	4	80	1	10	2	3	10	7	0	5
+28	No	Travel_Frequently	193	Research & Development	2	3	Life Sciences	1	1296	4	Male	52	2	1	Laboratory Technician	4	Married	3867	14222	1	Y	Yes	12	3	2	80	1	2	2	3	2	2	2	2
+51	No	Travel_Frequently	968	Research & Development	6	2	Medical	1	1297	2	Female	40	2	1	Laboratory Technician	3	Single	2838	4257	0	Y	No	14	3	2	80	0	8	6	2	7	0	7	7
+30	No	Non-Travel	879	Research & Development	9	2	Medical	1	1298	3	Female	72	3	2	Manufacturing Director	3	Single	4695	12858	7	Y	Yes	18	3	3	80	0	10	3	3	8	4	1	7
+29	Yes	Travel_Rarely	806	Research & Development	7	3	Technical Degree	1	1299	2	Female	39	3	1	Laboratory Technician	3	Divorced	3339	17285	3	Y	Yes	13	3	1	80	2	10	2	3	7	7	7	7
+28	No	Travel_Rarely	640	Research & Development	1	3	Technical Degree	1	1301	4	Male	84	3	1	Research Scientist	1	Single	2080	4732	2	Y	No	11	3	2	80	0	5	2	2	3	2	1	2
+25	No	Travel_Rarely	266	Research & Development	1	3	Medical	1	1303	4	Female	40	3	1	Research Scientist	2	Single	2096	18830	1	Y	No	18	3	4	80	0	2	3	2	2	2	2	1
+32	No	Travel_Rarely	604	Sales	8	3	Medical	1	1304	3	Male	56	4	2	Sales Executive	4	Married	6209	11693	1	Y	No	15	3	3	80	2	10	4	4	10	7	0	8
+45	No	Travel_Frequently	364	Research & Development	25	3	Medical	1	1306	2	Female	83	3	5	Manager	2	Single	18061	13035	3	Y	No	22	4	3	80	0	22	4	3	0	0	0	0
+39	No	Travel_Rarely	412	Research & Development	13	4	Medical	1	1307	3	Female	94	2	4	Manager	2	Divorced	17123	17334	6	Y	Yes	13	3	4	80	2	21	4	3	19	9	15	2
+58	No	Travel_Rarely	848	Research & Development	23	4	Life Sciences	1	1308	1	Male	88	3	1	Research Scientist	3	Divorced	2372	26076	1	Y	No	12	3	4	80	2	2	3	3	2	2	2	2
+32	Yes	Travel_Rarely	1089	Research & Development	7	2	Life Sciences	1	1309	4	Male	79	3	2	Laboratory Technician	3	Married	4883	22845	1	Y	No	18	3	1	80	1	10	3	3	10	4	1	1
+39	Yes	Travel_Rarely	360	Research & Development	23	3	Medical	1	1310	3	Male	93	3	1	Research Scientist	1	Single	3904	22154	0	Y	No	13	3	1	80	0	6	2	3	5	2	0	3
+30	No	Travel_Rarely	1138	Research & Development	6	3	Technical Degree	1	1311	1	Female	48	2	2	Laboratory Technician	4	Married	4627	23631	0	Y	No	12	3	1	80	1	10	6	3	9	2	6	7
+36	No	Travel_Rarely	325	Research & Development	10	4	Technical Degree	1	1312	4	Female	63	3	3	Healthcare Representative	3	Married	7094	5747	3	Y	No	12	3	1	80	0	10	0	3	7	7	1	7
+46	No	Travel_Rarely	991	Human Resources	1	2	Life Sciences	1	1314	4	Female	44	3	1	Human Resources	1	Single	3423	22957	6	Y	No	12	3	3	80	0	10	3	4	7	6	5	7
+28	No	Non-Travel	1476	Research & Development	1	3	Life Sciences	1	1315	3	Female	55	1	2	Laboratory Technician	4	Married	6674	16392	0	Y	No	11	3	1	80	3	10	6	3	9	8	7	5
+50	No	Travel_Rarely	1322	Research & Development	28	3	Life Sciences	1	1317	4	Female	43	3	4	Research Director	1	Married	16880	22422	4	Y	Yes	11	3	2	80	0	25	2	3	3	2	1	2
+40	Yes	Travel_Rarely	299	Sales	25	4	Marketing	1	1318	4	Male	57	2	3	Sales Executive	2	Single	9094	17235	2	Y	Yes	12	3	3	80	0	9	2	3	5	4	1	0
+52	Yes	Travel_Rarely	1030	Sales	5	3	Life Sciences	1	1319	2	Male	64	3	3	Sales Executive	2	Single	8446	21534	9	Y	Yes	19	3	3	80	0	10	2	2	8	7	7	7
+30	No	Travel_Rarely	634	Research & Development	17	4	Medical	1	1321	2	Female	95	3	3	Manager	1	Married	11916	25927	1	Y	Yes	23	4	4	80	2	9	2	3	9	1	0	8
+39	No	Travel_Rarely	524	Research & Development	18	2	Life Sciences	1	1322	1	Male	32	3	2	Manufacturing Director	3	Single	4534	13352	0	Y	No	11	3	1	80	0	9	6	3	8	7	1	7
+31	No	Non-Travel	587	Sales	2	4	Life Sciences	1	1324	4	Female	57	3	3	Sales Executive	3	Divorced	9852	8935	1	Y	Yes	19	3	1	80	1	10	5	2	10	8	9	6
+41	No	Non-Travel	256	Sales	10	2	Medical	1	1329	3	Male	40	1	2	Sales Executive	2	Single	6151	22074	1	Y	No	13	3	1	80	0	19	4	3	19	2	11	9
+31	Yes	Travel_Frequently	1060	Sales	1	3	Life Sciences	1	1331	4	Female	54	3	1	Sales Representative	2	Single	2302	8319	1	Y	Yes	11	3	1	80	0	3	2	4	3	2	2	2
+44	Yes	Travel_Rarely	935	Research & Development	3	3	Life Sciences	1	1333	1	Male	89	3	1	Laboratory Technician	1	Married	2362	14669	4	Y	No	12	3	3	80	0	10	4	4	3	2	1	2
+42	No	Non-Travel	495	Research & Development	2	1	Life Sciences	1	1334	3	Male	37	3	4	Manager	3	Married	17861	26582	0	Y	Yes	13	3	4	80	0	21	3	2	20	8	2	10
+55	No	Travel_Rarely	282	Research & Development	2	2	Medical	1	1336	4	Female	58	1	5	Manager	3	Married	19187	6992	4	Y	No	14	3	4	80	1	23	5	3	19	9	9	11
+56	No	Travel_Rarely	206	Human Resources	8	4	Life Sciences	1	1338	4	Male	99	3	5	Manager	2	Single	19717	4022	6	Y	No	14	3	1	80	0	36	4	3	7	3	7	7
+40	No	Non-Travel	458	Research & Development	16	2	Life Sciences	1	1340	3	Male	74	3	1	Research Scientist	3	Divorced	3544	8532	9	Y	No	16	3	2	80	1	6	0	3	4	2	0	0
+34	No	Travel_Rarely	943	Research & Development	9	3	Life Sciences	1	1344	4	Male	86	3	3	Healthcare Representative	4	Divorced	8500	5494	0	Y	No	11	3	4	80	1	10	0	2	9	7	1	6
+40	No	Travel_Rarely	523	Research & Development	2	3	Life Sciences	1	1346	3	Male	98	3	2	Research Scientist	4	Single	4661	22455	1	Y	No	13	3	3	80	0	9	4	3	9	8	8	8
+41	No	Travel_Frequently	1018	Sales	1	3	Marketing	1	1349	3	Female	66	3	2	Sales Executive	1	Divorced	4103	4297	0	Y	No	17	3	4	80	1	10	2	3	9	3	1	7
+35	No	Travel_Frequently	482	Research & Development	4	4	Life Sciences	1	1350	3	Male	87	3	2	Research Scientist	3	Single	4249	2690	1	Y	Yes	11	3	2	80	0	9	3	3	9	6	1	1
+51	No	Travel_Rarely	770	Human Resources	5	3	Life Sciences	1	1352	3	Male	84	3	4	Manager	2	Divorced	14026	17588	1	Y	Yes	11	3	2	80	1	33	2	3	33	9	0	10
+38	No	Travel_Rarely	1009	Sales	2	2	Life Sciences	1	1355	2	Female	31	3	2	Sales Executive	1	Divorced	6893	19461	3	Y	No	15	3	4	80	1	11	3	3	7	7	1	7
+34	No	Travel_Rarely	507	Sales	15	2	Medical	1	1356	3	Female	66	3	2	Sales Executive	1	Single	6125	23553	1	Y	No	12	3	4	80	0	10	6	4	10	8	9	6
+25	No	Travel_Rarely	882	Research & Development	19	1	Medical	1	1358	4	Male	67	3	1	Laboratory Technician	4	Married	3669	9075	3	Y	No	11	3	3	80	3	7	6	2	3	2	1	2
+58	Yes	Travel_Rarely	601	Research & Development	7	4	Medical	1	1360	3	Female	53	2	3	Manufacturing Director	1	Married	10008	12023	7	Y	Yes	14	3	4	80	0	31	0	2	10	9	5	9
+40	No	Travel_Rarely	329	Research & Development	1	4	Life Sciences	1	1361	2	Male	88	3	1	Laboratory Technician	2	Married	2387	6762	3	Y	No	22	4	3	80	1	7	3	3	4	2	0	3
+36	No	Travel_Frequently	607	Sales	7	3	Marketing	1	1362	1	Female	83	4	2	Sales Executive	1	Married	4639	2261	2	Y	No	16	3	4	80	1	17	2	2	15	7	6	13
+48	No	Travel_Rarely	855	Research & Development	4	3	Life Sciences	1	1363	4	Male	54	3	3	Manufacturing Director	4	Single	7898	18706	1	Y	No	11	3	3	80	0	11	2	3	10	9	0	8
+27	No	Travel_Rarely	1291	Sales	11	3	Medical	1	1364	3	Female	98	4	1	Sales Representative	4	Married	2534	6527	8	Y	No	14	3	2	80	1	5	4	3	1	0	0	0
+51	No	Travel_Rarely	1405	Research & Development	11	2	Technical Degree	1	1367	4	Female	82	2	4	Manufacturing Director	2	Single	13142	24439	3	Y	No	16	3	2	80	0	29	1	2	5	2	0	3
+18	No	Non-Travel	1124	Research & Development	1	3	Life Sciences	1	1368	4	Female	97	3	1	Laboratory Technician	4	Single	1611	19305	1	Y	No	15	3	3	80	0	0	5	4	0	0	0	0
+35	No	Travel_Rarely	817	Research & Development	1	3	Medical	1	1369	4	Female	60	2	2	Laboratory Technician	4	Married	5363	10846	0	Y	No	12	3	2	80	1	10	0	3	9	7	0	0
+27	No	Travel_Frequently	793	Sales	2	1	Life Sciences	1	1371	4	Male	43	1	2	Sales Executive	4	Single	5071	20392	3	Y	No	20	4	2	80	0	8	3	3	6	2	0	0
+55	Yes	Travel_Rarely	267	Sales	13	4	Marketing	1	1372	1	Male	85	4	4	Sales Executive	3	Single	13695	9277	6	Y	Yes	17	3	3	80	0	24	2	2	19	7	3	8
+56	No	Travel_Rarely	1369	Research & Development	23	3	Life Sciences	1	1373	4	Male	68	3	4	Manufacturing Director	2	Married	13402	18235	4	Y	Yes	12	3	1	80	1	33	0	3	19	16	15	9
+34	No	Non-Travel	999	Research & Development	26	1	Technical Degree	1	1374	1	Female	92	2	1	Research Scientist	3	Divorced	2029	15891	1	Y	No	20	4	3	80	3	5	2	3	5	4	0	0
+40	No	Travel_Rarely	1202	Research & Development	2	1	Medical	1	1375	2	Female	89	4	2	Healthcare Representative	3	Divorced	6377	13888	5	Y	No	20	4	2	80	3	15	0	3	12	11	11	8
+34	No	Travel_Rarely	285	Research & Development	29	3	Medical	1	1377	2	Male	86	3	2	Laboratory Technician	3	Married	5429	17491	4	Y	No	13	3	1	80	2	10	1	3	8	7	7	7
+31	Yes	Travel_Frequently	703	Sales	2	3	Life Sciences	1	1379	3	Female	90	2	1	Sales Representative	4	Single	2785	11882	7	Y	No	14	3	3	80	0	3	3	4	1	0	0	0
+35	Yes	Travel_Frequently	662	Sales	18	4	Marketing	1	1380	4	Female	67	3	2	Sales Executive	3	Married	4614	23288	0	Y	Yes	18	3	3	80	1	5	0	2	4	2	3	2
+38	No	Travel_Frequently	693	Research & Development	7	3	Life Sciences	1	1382	4	Male	57	4	1	Research Scientist	3	Divorced	2610	15748	1	Y	No	11	3	4	80	3	4	2	3	4	2	0	3
+34	No	Travel_Rarely	404	Research & Development	2	4	Technical Degree	1	1383	3	Female	98	3	2	Healthcare Representative	4	Single	6687	6163	1	Y	No	11	3	4	80	0	14	2	4	14	11	4	11
+28	No	Travel_Rarely	736	Sales	26	3	Life Sciences	1	1387	3	Male	48	2	2	Sales Executive	1	Married	4724	24232	1	Y	No	11	3	3	80	1	5	0	3	5	3	0	4
+31	Yes	Travel_Rarely	330	Research & Development	22	4	Medical	1	1389	4	Male	98	3	2	Manufacturing Director	3	Married	6179	21057	1	Y	Yes	15	3	4	80	2	10	3	2	10	2	6	7
+39	No	Travel_Rarely	1498	Sales	21	4	Life Sciences	1	1390	1	Male	44	2	2	Sales Executive	4	Married	6120	3567	3	Y	Yes	12	3	4	80	2	8	2	4	5	4	1	4
+51	No	Travel_Frequently	541	Sales	2	3	Marketing	1	1391	2	Male	52	3	3	Sales Executive	2	Married	10596	15395	2	Y	No	11	3	2	80	0	14	5	3	4	2	3	2
+41	No	Travel_Frequently	1200	Research & Development	22	3	Life Sciences	1	1392	4	Female	75	3	2	Research Scientist	4	Divorced	5467	13953	3	Y	Yes	14	3	1	80	2	12	4	2	6	2	3	3
+37	No	Travel_Rarely	1439	Research & Development	4	1	Life Sciences	1	1394	3	Male	54	3	1	Research Scientist	3	Married	2996	5182	7	Y	Yes	15	3	4	80	0	8	2	3	6	4	1	3
+33	No	Travel_Frequently	1111	Sales	5	1	Life Sciences	1	1395	2	Male	61	3	2	Sales Executive	4	Married	9998	19293	6	Y	No	13	3	1	80	0	8	2	4	5	4	1	2
+32	No	Travel_Rarely	499	Sales	2	1	Marketing	1	1396	3	Male	36	3	2	Sales Executive	2	Married	4078	20497	0	Y	Yes	13	3	1	80	3	4	3	2	3	2	1	2
+39	No	Non-Travel	1485	Research & Development	25	2	Life Sciences	1	1397	3	Male	71	3	3	Healthcare Representative	3	Married	10920	3449	3	Y	No	21	4	2	80	1	13	2	3	6	4	0	5
+25	No	Travel_Rarely	1372	Sales	18	1	Life Sciences	1	1399	1	Male	93	4	2	Sales Executive	3	Married	6232	12477	2	Y	No	11	3	2	80	0	6	3	2	3	2	1	2
+52	No	Travel_Frequently	322	Research & Development	28	2	Medical	1	1401	4	Female	59	4	4	Manufacturing Director	3	Married	13247	9731	2	Y	Yes	11	3	2	80	1	24	3	2	5	3	0	2
+43	No	Travel_Rarely	930	Research & Development	6	3	Medical	1	1402	1	Female	73	2	2	Research Scientist	3	Single	4081	20003	1	Y	Yes	14	3	1	80	0	20	3	1	20	7	1	8
+27	No	Travel_Rarely	205	Sales	10	3	Marketing	1	1403	4	Female	98	2	2	Sales Executive	4	Married	5769	7100	1	Y	Yes	11	3	4	80	0	6	3	3	6	2	4	4
+27	Yes	Travel_Rarely	135	Research & Development	17	4	Life Sciences	1	1405	4	Female	51	3	1	Research Scientist	3	Single	2394	25681	1	Y	Yes	13	3	4	80	0	8	2	3	8	2	7	7
+26	No	Travel_Rarely	683	Research & Development	2	1	Medical	1	1407	1	Male	36	2	1	Research Scientist	4	Single	3904	4050	0	Y	No	12	3	4	80	0	5	2	3	4	3	1	1
+42	No	Travel_Rarely	1147	Human Resources	10	3	Human Resources	1	1408	3	Female	31	3	4	Manager	1	Married	16799	16616	0	Y	No	14	3	3	80	1	21	5	3	20	7	0	9
+52	No	Travel_Rarely	258	Research & Development	8	4	Other	1	1409	3	Female	54	3	1	Laboratory Technician	1	Married	2950	17363	9	Y	No	13	3	3	80	0	12	2	1	5	4	0	4
+37	No	Travel_Rarely	1462	Research & Development	11	3	Medical	1	1411	1	Female	94	3	1	Laboratory Technician	3	Single	3629	19106	4	Y	No	18	3	1	80	0	8	6	3	3	2	0	2
+35	No	Travel_Frequently	200	Research & Development	18	2	Life Sciences	1	1412	3	Male	60	3	3	Manufacturing Director	4	Single	9362	19944	2	Y	No	11	3	3	80	0	10	2	3	2	2	2	2
+25	No	Travel_Rarely	949	Research & Development	1	3	Technical Degree	1	1415	1	Male	81	3	1	Laboratory Technician	4	Married	3229	4910	4	Y	No	11	3	2	80	1	7	2	2	3	2	0	2
+26	No	Travel_Rarely	652	Research & Development	7	3	Other	1	1417	3	Male	100	4	1	Laboratory Technician	1	Single	3578	23577	0	Y	No	12	3	4	80	0	8	2	3	7	7	0	7
+29	No	Travel_Rarely	332	Human Resources	17	3	Other	1	1419	2	Male	51	2	3	Human Resources	1	Single	7988	9769	1	Y	No	13	3	1	80	0	10	3	2	10	9	0	9
+49	Yes	Travel_Frequently	1475	Research & Development	28	2	Life Sciences	1	1420	1	Male	97	2	2	Laboratory Technician	1	Single	4284	22710	3	Y	No	20	4	1	80	0	20	2	3	4	3	1	3
+29	Yes	Travel_Frequently	337	Research & Development	14	1	Other	1	1421	3	Female	84	3	3	Healthcare Representative	4	Single	7553	22930	0	Y	Yes	12	3	1	80	0	9	1	3	8	7	7	7
+54	No	Travel_Rarely	971	Research & Development	1	3	Medical	1	1422	4	Female	54	3	4	Research Director	4	Single	17328	5652	6	Y	No	19	3	4	80	0	29	3	2	20	7	12	7
+58	No	Travel_Rarely	1055	Research & Development	1	3	Medical	1	1423	4	Female	76	3	5	Research Director	1	Married	19701	22456	3	Y	Yes	21	4	3	80	1	32	3	3	9	8	1	5
+55	No	Travel_Rarely	1136	Research & Development	1	4	Medical	1	1424	2	Male	81	4	4	Research Director	4	Divorced	14732	12414	2	Y	No	13	3	4	80	2	31	4	4	7	7	0	0
+36	No	Travel_Rarely	1174	Sales	3	4	Marketing	1	1425	1	Female	99	3	2	Sales Executive	2	Single	9278	20763	3	Y	Yes	16	3	4	80	0	15	3	3	5	4	0	1
+31	Yes	Travel_Frequently	667	Sales	1	4	Life Sciences	1	1427	2	Female	50	1	1	Sales Representative	3	Single	1359	16154	1	Y	No	12	3	2	80	0	1	3	3	1	0	0	0
+30	No	Travel_Rarely	855	Sales	7	4	Marketing	1	1428	4	Female	73	3	2	Sales Executive	1	Divorced	4779	12761	7	Y	No	14	3	2	80	2	8	3	3	3	2	0	2
+31	No	Travel_Rarely	182	Research & Development	8	5	Life Sciences	1	1430	1	Female	93	3	4	Research Director	2	Single	16422	8847	3	Y	No	11	3	3	80	0	9	3	4	3	2	1	0
+34	No	Travel_Frequently	560	Research & Development	1	4	Other	1	1431	4	Male	91	3	1	Research Scientist	1	Divorced	2996	20284	5	Y	No	14	3	3	80	2	10	2	3	4	3	1	3
+31	Yes	Travel_Rarely	202	Research & Development	8	3	Life Sciences	1	1433	1	Female	34	2	1	Research Scientist	2	Single	1261	22262	1	Y	No	12	3	3	80	0	1	3	4	1	0	0	0
+27	No	Travel_Rarely	1377	Research & Development	11	1	Life Sciences	1	1434	2	Male	91	3	1	Laboratory Technician	1	Married	2099	7679	0	Y	No	14	3	2	80	0	6	3	4	5	0	1	4
+36	No	Travel_Rarely	172	Research & Development	4	4	Life Sciences	1	1435	1	Male	37	2	2	Laboratory Technician	4	Single	5810	22604	1	Y	No	16	3	3	80	0	10	2	2	10	4	1	8
+36	No	Travel_Rarely	329	Sales	16	4	Marketing	1	1436	3	Female	98	2	2	Sales Executive	1	Married	5647	13494	4	Y	No	13	3	1	80	2	11	3	2	3	2	0	2
+47	No	Travel_Rarely	465	Research & Development	1	3	Technical Degree	1	1438	1	Male	74	3	1	Research Scientist	4	Married	3420	10205	7	Y	No	12	3	3	80	1	17	2	2	6	5	1	2
+25	Yes	Travel_Rarely	383	Sales	9	2	Life Sciences	1	1439	1	Male	68	2	1	Sales Representative	1	Married	4400	15182	3	Y	No	12	3	1	80	0	6	2	3	3	2	2	2
+37	No	Non-Travel	1413	Research & Development	5	2	Technical Degree	1	1440	3	Male	84	4	1	Laboratory Technician	3	Single	3500	25470	0	Y	No	14	3	1	80	0	7	2	1	6	5	1	3
+56	No	Travel_Rarely	1255	Research & Development	1	2	Life Sciences	1	1441	1	Female	90	3	1	Research Scientist	1	Married	2066	10494	2	Y	No	22	4	4	80	1	5	3	4	3	2	1	0
+47	No	Travel_Rarely	359	Research & Development	2	4	Medical	1	1443	1	Female	82	3	4	Research Director	3	Married	17169	26703	3	Y	No	19	3	2	80	2	26	2	4	20	17	5	6
+24	No	Travel_Rarely	1476	Sales	4	1	Medical	1	1445	4	Female	42	3	2	Sales Executive	3	Married	4162	15211	1	Y	Yes	12	3	3	80	2	5	3	3	5	4	0	3
+32	No	Travel_Rarely	601	Sales	7	5	Marketing	1	1446	4	Male	97	3	2	Sales Executive	4	Married	9204	23343	4	Y	No	12	3	3	80	1	7	3	2	4	3	0	3
+34	No	Travel_Rarely	401	Research & Development	1	3	Life Sciences	1	1447	4	Female	86	2	1	Laboratory Technician	2	Married	3294	3708	5	Y	No	17	3	1	80	1	7	2	2	5	4	0	2
+41	No	Travel_Rarely	1283	Research & Development	5	5	Medical	1	1448	2	Male	90	4	1	Research Scientist	3	Married	2127	5561	2	Y	Yes	12	3	1	80	0	7	5	2	4	2	0	3
+40	No	Non-Travel	663	Research & Development	9	4	Other	1	1449	3	Male	81	3	2	Laboratory Technician	3	Divorced	3975	23099	3	Y	No	11	3	3	80	2	11	2	4	8	7	0	7
+31	No	Travel_Rarely	326	Sales	8	2	Life Sciences	1	1453	1	Male	31	3	3	Sales Executive	4	Divorced	10793	8386	1	Y	No	18	3	1	80	1	13	5	3	13	7	9	9
+46	Yes	Travel_Rarely	377	Sales	9	3	Marketing	1	1457	1	Male	52	3	3	Sales Executive	4	Divorced	10096	15986	4	Y	No	11	3	1	80	1	28	1	4	7	7	4	3
+39	Yes	Non-Travel	592	Research & Development	2	3	Life Sciences	1	1458	1	Female	54	2	1	Laboratory Technician	1	Single	3646	17181	2	Y	Yes	23	4	2	80	0	11	2	4	1	0	0	0
+31	Yes	Travel_Frequently	1445	Research & Development	1	5	Life Sciences	1	1459	3	Female	100	4	3	Manufacturing Director	2	Single	7446	8931	1	Y	No	11	3	1	80	0	10	2	3	10	8	4	7
+45	No	Travel_Rarely	1038	Research & Development	20	3	Medical	1	1460	2	Male	95	1	3	Healthcare Representative	1	Divorced	10851	19863	2	Y	Yes	18	3	2	80	1	24	2	3	7	7	0	7
+31	No	Travel_Rarely	1398	Human Resources	8	2	Medical	1	1461	4	Female	96	4	1	Human Resources	2	Single	2109	24609	9	Y	No	18	3	4	80	0	8	3	3	3	2	0	2
+31	Yes	Travel_Frequently	523	Research & Development	2	3	Life Sciences	1	1464	2	Male	94	3	1	Laboratory Technician	4	Married	3722	21081	6	Y	Yes	13	3	3	80	1	7	2	1	2	2	2	2
+45	No	Travel_Rarely	1448	Research & Development	29	3	Technical Degree	1	1465	2	Male	55	3	3	Manufacturing Director	4	Married	9380	14720	4	Y	Yes	18	3	4	80	2	10	4	4	3	1	1	2
+48	No	Travel_Rarely	1221	Sales	7	3	Marketing	1	1466	3	Male	96	3	2	Sales Executive	1	Divorced	5486	24795	4	Y	No	11	3	1	80	3	15	3	3	2	2	2	2
+34	Yes	Travel_Rarely	1107	Human Resources	9	4	Technical Degree	1	1467	1	Female	52	3	1	Human Resources	3	Married	2742	3072	1	Y	No	15	3	4	80	0	2	0	3	2	2	2	2
+40	No	Non-Travel	218	Research & Development	8	1	Medical	1	1468	4	Male	55	2	3	Research Director	2	Divorced	13757	25178	2	Y	No	11	3	3	80	1	16	5	3	9	8	4	8
+28	No	Travel_Rarely	866	Sales	5	3	Medical	1	1469	4	Male	84	3	2	Sales Executive	1	Single	8463	23490	0	Y	No	18	3	4	80	0	6	4	3	5	4	1	3
+44	No	Non-Travel	981	Research & Development	5	3	Life Sciences	1	1471	3	Male	90	2	1	Laboratory Technician	3	Single	3162	7973	3	Y	No	14	3	4	80	0	7	5	3	5	2	0	3
+53	No	Travel_Rarely	447	Research & Development	2	3	Medical	1	1472	4	Male	39	4	4	Research Director	2	Single	16598	19764	4	Y	No	12	3	2	80	0	35	2	2	9	8	8	8
+49	No	Travel_Rarely	1495	Research & Development	5	4	Technical Degree	1	1473	1	Male	96	3	2	Healthcare Representative	3	Married	6651	21534	2	Y	No	14	3	2	80	1	20	0	2	3	2	1	2
+40	No	Travel_Rarely	896	Research & Development	2	3	Medical	1	1474	3	Male	68	3	1	Research Scientist	3	Divorced	2345	8045	2	Y	No	14	3	3	80	1	8	3	4	3	1	1	2
+44	No	Travel_Rarely	1467	Research & Development	20	3	Life Sciences	1	1475	4	Male	49	3	1	Research Scientist	2	Single	3420	21158	1	Y	No	13	3	3	80	0	6	3	2	5	2	1	3
+33	No	Travel_Frequently	430	Sales	7	3	Medical	1	1477	4	Male	54	3	2	Sales Executive	1	Married	4373	17456	0	Y	No	14	3	1	80	2	5	2	3	4	3	0	3
+34	No	Travel_Rarely	1326	Sales	3	3	Other	1	1478	4	Male	81	1	2	Sales Executive	1	Single	4759	15891	3	Y	No	18	3	4	80	0	15	2	3	13	9	3	12
+30	No	Travel_Rarely	1358	Sales	16	1	Life Sciences	1	1479	4	Male	96	3	2	Sales Executive	3	Married	5301	2939	8	Y	No	15	3	3	80	2	4	2	2	2	1	2	2
+42	No	Travel_Frequently	748	Research & Development	9	2	Medical	1	1480	1	Female	74	3	1	Laboratory Technician	4	Single	3673	16458	1	Y	No	13	3	3	80	0	12	3	3	12	9	5	8
+44	No	Travel_Frequently	383	Sales	1	5	Marketing	1	1481	1	Female	79	3	2	Sales Executive	3	Married	4768	9282	7	Y	No	12	3	3	80	1	11	4	2	1	0	0	0
+30	No	Non-Travel	990	Research & Development	7	3	Technical Degree	1	1482	3	Male	64	3	1	Research Scientist	3	Divorced	1274	7152	1	Y	No	13	3	2	80	2	1	2	2	1	0	0	0
+57	No	Travel_Rarely	405	Research & Development	1	2	Life Sciences	1	1483	2	Male	93	4	2	Research Scientist	3	Married	4900	2721	0	Y	No	24	4	1	80	1	13	2	2	12	9	2	8
+49	No	Travel_Rarely	1490	Research & Development	7	4	Life Sciences	1	1484	3	Male	35	3	3	Healthcare Representative	2	Divorced	10466	20948	3	Y	No	14	3	2	80	2	29	3	3	8	7	0	7
+34	No	Travel_Frequently	829	Research & Development	15	3	Medical	1	1485	2	Male	71	3	4	Research Director	1	Divorced	17007	11929	7	Y	No	14	3	4	80	2	16	3	2	14	8	6	9
+28	Yes	Travel_Frequently	1496	Sales	1	3	Technical Degree	1	1486	1	Male	92	3	1	Sales Representative	3	Married	2909	15747	3	Y	No	15	3	4	80	1	5	3	4	3	2	1	2
+29	Yes	Travel_Frequently	115	Sales	13	3	Technical Degree	1	1487	1	Female	51	3	2	Sales Executive	2	Single	5765	17485	5	Y	No	11	3	1	80	0	7	4	1	5	3	0	0
+34	Yes	Travel_Rarely	790	Sales	24	4	Medical	1	1489	1	Female	40	2	2	Sales Executive	2	Single	4599	7815	0	Y	Yes	23	4	3	80	0	16	2	4	15	9	10	10
+35	No	Travel_Rarely	660	Sales	7	1	Life Sciences	1	1492	4	Male	76	3	1	Sales Representative	3	Married	2404	16192	1	Y	No	13	3	1	80	1	1	3	3	1	0	0	0
+24	Yes	Travel_Frequently	381	Research & Development	9	3	Medical	1	1494	2	Male	89	3	1	Laboratory Technician	1	Single	3172	16998	2	Y	Yes	11	3	3	80	0	4	2	2	0	0	0	0
+24	No	Non-Travel	830	Sales	13	2	Life Sciences	1	1495	4	Female	78	3	1	Sales Representative	2	Married	2033	7103	1	Y	No	13	3	3	80	1	1	2	3	1	0	0	0
+44	No	Travel_Frequently	1193	Research & Development	2	1	Medical	1	1496	2	Male	86	3	3	Manufacturing Director	3	Single	10209	19719	5	Y	Yes	18	3	2	80	0	16	2	2	2	2	2	2
+29	No	Travel_Rarely	1246	Sales	19	3	Life Sciences	1	1497	3	Male	77	2	2	Sales Executive	3	Divorced	8620	23757	1	Y	No	14	3	3	80	2	10	3	3	10	7	0	4
+30	No	Travel_Rarely	330	Human Resources	1	3	Life Sciences	1	1499	3	Male	46	3	1	Human Resources	3	Divorced	2064	15428	0	Y	No	21	4	1	80	1	6	3	4	5	3	1	3
+55	No	Travel_Rarely	1229	Research & Development	4	4	Life Sciences	1	1501	4	Male	30	3	2	Healthcare Representative	3	Married	4035	16143	0	Y	Yes	16	3	2	80	0	4	2	3	3	2	1	2
+33	No	Travel_Rarely	1099	Research & Development	4	4	Medical	1	1502	1	Female	82	2	1	Laboratory Technician	2	Married	3838	8192	8	Y	No	11	3	4	80	0	8	5	3	5	4	0	2
+47	No	Travel_Rarely	571	Sales	14	3	Medical	1	1503	3	Female	78	3	2	Sales Executive	3	Married	4591	24200	3	Y	Yes	17	3	3	80	1	11	4	2	5	4	1	2
+28	Yes	Travel_Frequently	289	Research & Development	2	2	Medical	1	1504	3	Male	38	2	1	Laboratory Technician	1	Single	2561	5355	7	Y	No	11	3	3	80	0	8	2	2	0	0	0	0
+28	No	Travel_Rarely	1423	Research & Development	1	3	Life Sciences	1	1506	1	Male	72	2	1	Research Scientist	3	Divorced	1563	12530	1	Y	No	14	3	4	80	1	1	2	1	1	0	0	0
+28	No	Travel_Frequently	467	Sales	7	3	Life Sciences	1	1507	3	Male	55	3	2	Sales Executive	1	Single	4898	11827	0	Y	No	14	3	4	80	0	5	5	3	4	2	1	3
+49	No	Travel_Rarely	271	Research & Development	3	2	Medical	1	1509	3	Female	43	2	2	Laboratory Technician	1	Married	4789	23070	4	Y	No	25	4	1	80	1	10	3	3	3	2	1	2
+29	No	Travel_Frequently	410	Research & Development	2	1	Life Sciences	1	1513	4	Female	97	3	1	Laboratory Technician	2	Married	3180	4668	0	Y	No	13	3	3	80	3	4	3	3	3	2	0	2
+28	No	Travel_Rarely	1083	Research & Development	29	1	Life Sciences	1	1514	3	Male	96	1	2	Manufacturing Director	2	Married	6549	3173	1	Y	No	14	3	2	80	2	8	2	2	8	6	1	7
+33	No	Travel_Rarely	516	Research & Development	8	5	Life Sciences	1	1515	4	Male	69	3	2	Healthcare Representative	3	Single	6388	22049	2	Y	Yes	17	3	1	80	0	14	6	3	0	0	0	0
+32	No	Travel_Rarely	495	Research & Development	10	3	Medical	1	1516	3	Male	64	3	3	Manager	4	Single	11244	21072	2	Y	No	25	4	2	80	0	10	5	4	5	2	0	0
+54	No	Travel_Frequently	1050	Research & Development	11	4	Medical	1	1520	2	Female	87	3	4	Manager	4	Divorced	16032	24456	3	Y	No	20	4	1	80	1	26	2	3	14	9	1	12
+29	Yes	Travel_Rarely	224	Research & Development	1	4	Technical Degree	1	1522	1	Male	100	2	1	Research Scientist	1	Single	2362	7568	6	Y	No	13	3	3	80	0	11	2	1	9	7	0	7
+44	No	Travel_Rarely	136	Research & Development	28	3	Life Sciences	1	1523	4	Male	32	3	4	Research Director	1	Married	16328	22074	3	Y	No	13	3	3	80	1	24	1	4	20	6	14	17
+39	No	Travel_Rarely	1089	Research & Development	6	3	Life Sciences	1	1525	2	Female	32	3	3	Manufacturing Director	2	Single	8376	9150	4	Y	No	18	3	4	80	0	9	3	3	2	0	2	2
+46	No	Travel_Rarely	228	Sales	3	3	Life Sciences	1	1527	3	Female	51	3	4	Manager	2	Married	16606	11380	8	Y	No	12	3	4	80	1	23	2	4	13	12	5	1
+35	No	Travel_Rarely	1029	Research & Development	16	3	Life Sciences	1	1529	4	Female	91	2	3	Healthcare Representative	2	Single	8606	21195	1	Y	No	19	3	4	80	0	11	3	1	11	8	3	3
+23	No	Travel_Rarely	507	Research & Development	20	1	Life Sciences	1	1533	1	Male	97	3	2	Laboratory Technician	3	Single	2272	24812	0	Y	No	14	3	2	80	0	5	2	3	4	3	1	2
+40	Yes	Travel_Rarely	676	Research & Development	9	4	Life Sciences	1	1534	4	Male	86	3	1	Laboratory Technician	1	Single	2018	21831	3	Y	No	14	3	2	80	0	15	3	1	5	4	1	0
+34	No	Travel_Rarely	971	Sales	1	3	Technical Degree	1	1535	4	Male	64	2	3	Sales Executive	3	Married	7083	12288	1	Y	Yes	14	3	4	80	0	10	3	3	10	9	8	6
+31	Yes	Travel_Frequently	561	Research & Development	3	3	Life Sciences	1	1537	4	Female	33	3	1	Research Scientist	3	Single	4084	4156	1	Y	No	12	3	1	80	0	7	2	1	7	2	7	7
+50	No	Travel_Frequently	333	Research & Development	22	5	Medical	1	1539	3	Male	88	1	4	Research Director	4	Single	14411	24450	1	Y	Yes	13	3	4	80	0	32	2	3	32	6	13	9
+34	No	Travel_Rarely	1440	Sales	7	2	Technical Degree	1	1541	2	Male	55	3	1	Sales Representative	3	Married	2308	4944	0	Y	Yes	25	4	2	80	1	12	4	3	11	10	5	7
+42	No	Travel_Rarely	1210	Research & Development	2	3	Medical	1	1542	3	Male	68	2	1	Laboratory Technician	2	Married	4841	24052	4	Y	No	14	3	2	80	1	4	3	3	1	0	0	0
+37	No	Travel_Rarely	674	Research & Development	13	3	Medical	1	1543	1	Male	47	3	2	Research Scientist	4	Married	4285	3031	1	Y	No	17	3	1	80	0	10	2	3	10	8	3	7
+29	No	Travel_Rarely	441	Research & Development	8	1	Other	1	1544	3	Female	39	1	2	Healthcare Representative	1	Married	9715	7288	3	Y	No	13	3	3	80	1	9	3	3	7	7	0	7
+33	No	Travel_Rarely	575	Research & Development	25	3	Life Sciences	1	1545	4	Male	44	2	2	Manufacturing Director	2	Single	4320	24152	1	Y	No	13	3	4	80	0	5	2	3	5	3	0	2
+45	No	Travel_Rarely	950	Research & Development	28	3	Technical Degree	1	1546	4	Male	97	3	1	Research Scientist	4	Married	2132	4585	4	Y	No	20	4	4	80	1	8	3	3	5	4	0	3
+42	No	Travel_Frequently	288	Research & Development	2	3	Life Sciences	1	1547	4	Male	40	3	3	Healthcare Representative	4	Married	10124	18611	2	Y	Yes	14	3	3	80	1	24	3	1	20	8	13	9
+40	No	Travel_Rarely	1342	Sales	9	2	Medical	1	1548	1	Male	47	3	2	Sales Executive	1	Married	5473	19345	0	Y	No	12	3	4	80	0	9	5	4	8	4	7	1
+33	No	Travel_Rarely	589	Research & Development	28	4	Life Sciences	1	1549	2	Male	79	3	2	Laboratory Technician	3	Married	5207	22949	1	Y	Yes	12	3	2	80	1	15	3	3	15	14	5	7
+40	No	Travel_Rarely	898	Human Resources	6	2	Medical	1	1550	3	Male	38	3	4	Manager	4	Single	16437	17381	1	Y	Yes	21	4	4	80	0	21	2	3	21	7	7	7
+24	No	Travel_Rarely	350	Research & Development	21	2	Technical Degree	1	1551	3	Male	57	2	1	Laboratory Technician	1	Divorced	2296	10036	0	Y	No	14	3	2	80	3	2	3	3	1	1	0	0
+40	No	Non-Travel	1142	Research & Development	8	2	Life Sciences	1	1552	4	Male	72	3	2	Healthcare Representative	4	Divorced	4069	8841	3	Y	Yes	18	3	3	80	0	8	2	3	2	2	2	2
+45	No	Travel_Rarely	538	Research & Development	1	4	Technical Degree	1	1553	1	Male	66	3	3	Healthcare Representative	2	Divorced	7441	20933	1	Y	No	12	3	1	80	3	10	4	3	10	8	7	7
+35	No	Travel_Rarely	1402	Sales	28	4	Life Sciences	1	1554	2	Female	98	2	1	Sales Representative	3	Married	2430	26204	0	Y	No	23	4	1	80	2	6	5	3	5	3	4	2
+32	No	Travel_Rarely	824	Research & Development	5	2	Life Sciences	1	1555	4	Female	67	2	2	Research Scientist	2	Married	5878	15624	3	Y	No	12	3	1	80	1	12	2	3	7	1	2	5
+36	No	Travel_Rarely	1157	Sales	2	4	Life Sciences	1	1556	3	Male	70	3	1	Sales Representative	4	Single	2644	17001	3	Y	Yes	21	4	4	80	0	7	3	2	3	2	1	2
+48	No	Travel_Rarely	492	Sales	16	4	Life Sciences	1	1557	3	Female	96	3	2	Sales Executive	3	Divorced	6439	13693	8	Y	No	14	3	3	80	1	18	2	3	8	7	7	7
+29	No	Travel_Rarely	598	Research & Development	9	3	Life Sciences	1	1558	3	Male	91	4	1	Research Scientist	3	Married	2451	22376	6	Y	No	18	3	1	80	2	5	2	2	1	0	0	0
+33	No	Travel_Rarely	1242	Sales	8	4	Life Sciences	1	1560	1	Male	46	3	2	Sales Executive	1	Married	6392	10589	2	Y	No	13	3	4	80	1	8	6	1	2	2	2	2
+30	Yes	Travel_Rarely	740	Sales	1	3	Life Sciences	1	1562	2	Male	64	2	2	Sales Executive	1	Married	9714	5323	1	Y	No	11	3	4	80	1	10	4	3	10	8	6	7
+38	No	Travel_Frequently	888	Human Resources	10	4	Human Resources	1	1563	3	Male	71	3	2	Human Resources	3	Married	6077	14814	3	Y	No	11	3	3	80	0	10	2	3	6	3	1	2
+35	No	Travel_Rarely	992	Research & Development	1	3	Medical	1	1564	4	Male	68	2	1	Laboratory Technician	1	Single	2450	21731	1	Y	No	19	3	2	80	0	3	3	3	3	0	1	2
+30	No	Travel_Rarely	1288	Sales	29	4	Technical Degree	1	1568	3	Male	33	3	3	Sales Executive	2	Married	9250	17799	3	Y	No	12	3	2	80	1	9	3	3	4	2	1	3
+35	Yes	Travel_Rarely	104	Research & Development	2	3	Life Sciences	1	1569	1	Female	69	3	1	Laboratory Technician	1	Divorced	2074	26619	1	Y	Yes	12	3	4	80	1	1	2	3	1	0	0	0
+53	Yes	Travel_Rarely	607	Research & Development	2	5	Technical Degree	1	1572	3	Female	78	2	3	Manufacturing Director	4	Married	10169	14618	0	Y	No	16	3	2	80	1	34	4	3	33	7	1	9
+38	Yes	Travel_Rarely	903	Research & Development	2	3	Medical	1	1573	3	Male	81	3	2	Manufacturing Director	2	Married	4855	7653	4	Y	No	11	3	1	80	2	7	2	3	5	2	1	4
+32	No	Non-Travel	1200	Research & Development	1	4	Technical Degree	1	1574	4	Male	62	3	2	Research Scientist	1	Married	4087	25174	4	Y	No	14	3	2	80	1	9	3	2	6	5	1	2
+48	No	Travel_Rarely	1108	Research & Development	15	4	Other	1	1576	3	Female	65	3	1	Research Scientist	1	Married	2367	16530	8	Y	No	12	3	4	80	1	10	3	2	8	2	7	6
+34	No	Travel_Rarely	479	Research & Development	7	4	Medical	1	1577	1	Male	35	3	1	Research Scientist	4	Single	2972	22061	1	Y	No	13	3	3	80	0	1	4	1	1	0	0	0
+55	No	Travel_Rarely	685	Sales	26	5	Marketing	1	1578	3	Male	60	2	5	Manager	4	Married	19586	23037	1	Y	No	21	4	3	80	1	36	3	3	36	6	2	13
+34	No	Travel_Rarely	1351	Research & Development	1	4	Life Sciences	1	1580	2	Male	45	3	2	Research Scientist	4	Married	5484	13008	9	Y	No	17	3	2	80	1	9	3	2	2	2	2	1
+26	No	Travel_Rarely	474	Research & Development	3	3	Life Sciences	1	1581	1	Female	89	3	1	Research Scientist	4	Married	2061	11133	1	Y	No	21	4	1	80	0	1	5	3	1	0	0	0
+38	No	Travel_Rarely	1245	Sales	14	3	Life Sciences	1	1582	3	Male	80	3	2	Sales Executive	2	Married	9924	12355	0	Y	No	11	3	4	80	1	10	3	3	9	8	7	7
+38	No	Travel_Rarely	437	Sales	16	3	Life Sciences	1	1583	2	Female	90	3	2	Sales Executive	2	Single	4198	16379	2	Y	No	12	3	2	80	0	8	5	4	3	2	1	2
+36	No	Travel_Rarely	884	Sales	1	4	Life Sciences	1	1585	2	Female	73	3	2	Sales Executive	3	Single	6815	21447	6	Y	No	13	3	1	80	0	15	5	3	1	0	0	0
+29	No	Travel_Rarely	1370	Research & Development	3	1	Medical	1	1586	2	Male	87	3	1	Laboratory Technician	1	Single	4723	16213	1	Y	Yes	18	3	4	80	0	10	3	3	10	9	1	5
+35	No	Travel_Rarely	670	Research & Development	10	4	Medical	1	1587	1	Female	51	3	2	Healthcare Representative	3	Single	6142	4223	3	Y	Yes	16	3	3	80	0	10	4	3	5	2	0	4
+39	No	Travel_Rarely	1462	Sales	6	3	Medical	1	1588	4	Male	38	4	3	Sales Executive	3	Married	8237	4658	2	Y	No	11	3	1	80	1	11	3	3	7	6	7	6
+29	No	Travel_Frequently	995	Research & Development	2	1	Life Sciences	1	1590	1	Male	87	3	2	Healthcare Representative	4	Divorced	8853	24483	1	Y	No	19	3	4	80	1	6	0	4	6	4	1	3
+50	No	Travel_Rarely	264	Sales	9	3	Marketing	1	1591	3	Male	59	3	5	Manager	3	Married	19331	19519	4	Y	Yes	16	3	3	80	1	27	2	3	1	0	0	0
+23	No	Travel_Rarely	977	Research & Development	10	3	Technical Degree	1	1592	4	Male	45	4	1	Research Scientist	3	Married	2073	12826	2	Y	No	16	3	4	80	1	4	2	3	2	2	2	2
+36	No	Travel_Frequently	1302	Research & Development	6	4	Life Sciences	1	1594	1	Male	80	4	2	Laboratory Technician	1	Married	5562	19711	3	Y	Yes	13	3	4	80	1	9	3	3	3	2	0	2
+42	No	Travel_Rarely	1059	Research & Development	9	2	Other	1	1595	4	Male	93	2	5	Manager	4	Single	19613	26362	8	Y	No	22	4	4	80	0	24	2	3	1	0	0	1
+35	No	Travel_Rarely	750	Research & Development	28	3	Life Sciences	1	1596	2	Male	46	4	2	Laboratory Technician	3	Married	3407	25348	1	Y	No	17	3	4	80	2	10	3	2	10	9	6	8
+34	No	Travel_Frequently	653	Research & Development	10	4	Technical Degree	1	1597	4	Male	92	2	2	Healthcare Representative	3	Married	5063	15332	1	Y	No	14	3	2	80	1	8	3	2	8	2	7	7
+40	No	Travel_Rarely	118	Sales	14	2	Life Sciences	1	1598	4	Female	84	3	2	Sales Executive	1	Married	4639	11262	1	Y	No	15	3	3	80	1	5	2	3	5	4	1	2
+43	No	Travel_Rarely	990	Research & Development	27	3	Technical Degree	1	1599	4	Male	87	4	1	Laboratory Technician	2	Divorced	4876	5855	5	Y	No	12	3	3	80	1	8	0	3	6	4	0	2
+35	No	Travel_Rarely	1349	Research & Development	7	2	Life Sciences	1	1601	3	Male	63	2	1	Laboratory Technician	4	Married	2690	7713	1	Y	No	18	3	4	80	1	1	5	2	1	0	0	1
+46	No	Travel_Rarely	563	Sales	1	4	Life Sciences	1	1602	4	Male	56	4	4	Manager	1	Single	17567	3156	1	Y	No	15	3	2	80	0	27	5	1	26	0	0	12
+28	Yes	Travel_Rarely	329	Research & Development	24	3	Medical	1	1604	3	Male	51	3	1	Laboratory Technician	2	Married	2408	7324	1	Y	Yes	17	3	3	80	3	1	3	3	1	1	0	0
+22	No	Non-Travel	457	Research & Development	26	2	Other	1	1605	2	Female	85	2	1	Research Scientist	3	Married	2814	10293	1	Y	Yes	14	3	2	80	0	4	2	2	4	2	1	3
+50	No	Travel_Frequently	1234	Research & Development	20	5	Medical	1	1606	2	Male	41	3	4	Healthcare Representative	3	Married	11245	20689	2	Y	Yes	15	3	3	80	1	32	3	3	30	8	12	13
+32	No	Travel_Rarely	634	Research & Development	5	4	Other	1	1607	2	Female	35	4	1	Research Scientist	4	Married	3312	18783	3	Y	No	17	3	4	80	2	6	3	3	3	2	0	2
+44	No	Travel_Rarely	1313	Research & Development	7	3	Medical	1	1608	2	Female	31	3	5	Research Director	4	Divorced	19049	3549	0	Y	Yes	14	3	4	80	1	23	4	2	22	7	1	10
+30	No	Travel_Rarely	241	Research & Development	7	3	Medical	1	1609	2	Male	48	2	1	Research Scientist	2	Married	2141	5348	1	Y	No	12	3	2	80	1	6	3	2	6	4	1	1
+45	No	Travel_Rarely	1015	Research & Development	5	5	Medical	1	1611	3	Female	50	1	2	Laboratory Technician	1	Single	5769	23447	1	Y	Yes	14	3	1	80	0	10	3	3	10	7	1	4
+45	No	Non-Travel	336	Sales	26	3	Marketing	1	1612	1	Male	52	2	2	Sales Executive	1	Married	4385	24162	1	Y	No	15	3	1	80	1	10	2	3	10	7	4	5
+31	No	Travel_Frequently	715	Sales	2	4	Other	1	1613	4	Male	54	3	2	Sales Executive	1	Single	5332	21602	7	Y	No	13	3	4	80	0	10	3	3	5	2	0	3
+36	No	Travel_Rarely	559	Research & Development	12	4	Life Sciences	1	1614	3	Female	76	3	2	Manufacturing Director	3	Married	4663	12421	9	Y	Yes	12	3	2	80	2	7	2	3	3	2	1	1
+34	No	Travel_Frequently	426	Research & Development	10	4	Life Sciences	1	1615	3	Male	42	4	2	Manufacturing Director	4	Divorced	4724	17000	1	Y	No	13	3	1	80	1	9	3	3	9	7	7	2
+49	No	Travel_Rarely	722	Research & Development	25	4	Life Sciences	1	1617	3	Female	84	3	1	Laboratory Technician	1	Married	3211	22102	1	Y	No	14	3	4	80	1	10	3	2	9	6	1	4
+39	No	Travel_Rarely	1387	Research & Development	10	5	Medical	1	1618	2	Male	76	3	2	Manufacturing Director	1	Married	5377	3835	2	Y	No	13	3	4	80	3	10	3	3	7	7	7	7
+27	No	Travel_Rarely	1302	Research & Development	19	3	Other	1	1619	4	Male	67	2	1	Laboratory Technician	1	Divorced	4066	16290	1	Y	No	11	3	1	80	2	7	3	3	7	7	0	7
+35	No	Travel_Rarely	819	Research & Development	18	5	Life Sciences	1	1621	2	Male	48	4	2	Research Scientist	1	Married	5208	26312	1	Y	No	11	3	4	80	0	16	2	3	16	15	1	10
+28	No	Travel_Rarely	580	Research & Development	27	3	Medical	1	1622	2	Female	39	1	2	Manufacturing Director	1	Divorced	4877	20460	0	Y	No	21	4	2	80	1	6	5	2	5	3	0	0
+21	No	Travel_Rarely	546	Research & Development	5	1	Medical	1	1623	3	Male	97	3	1	Research Scientist	4	Single	3117	26009	1	Y	No	18	3	3	80	0	3	2	3	2	2	2	2
+18	Yes	Travel_Frequently	544	Sales	3	2	Medical	1	1624	2	Female	70	3	1	Sales Representative	4	Single	1569	18420	1	Y	Yes	12	3	3	80	0	0	2	4	0	0	0	0
+47	No	Travel_Rarely	1176	Human Resources	26	4	Life Sciences	1	1625	4	Female	98	3	5	Manager	3	Married	19658	5220	3	Y	No	11	3	3	80	1	27	2	3	5	2	1	0
+39	No	Travel_Rarely	170	Research & Development	3	2	Medical	1	1627	3	Male	76	2	2	Laboratory Technician	3	Divorced	3069	10302	0	Y	No	15	3	4	80	1	11	3	3	10	8	0	7
+40	No	Travel_Rarely	884	Research & Development	15	3	Life Sciences	1	1628	1	Female	80	2	3	Manufacturing Director	3	Married	10435	25800	1	Y	No	13	3	4	80	2	18	2	3	18	15	14	12
+35	No	Non-Travel	208	Research & Development	8	4	Life Sciences	1	1630	3	Female	52	3	2	Healthcare Representative	3	Married	4148	12250	1	Y	No	12	3	4	80	1	15	5	3	14	11	2	9
+37	No	Travel_Rarely	671	Research & Development	19	3	Life Sciences	1	1631	3	Male	85	3	2	Manufacturing Director	3	Married	5768	26493	3	Y	No	17	3	1	80	3	9	2	2	4	3	0	2
+39	No	Travel_Frequently	711	Research & Development	4	3	Medical	1	1633	1	Female	81	3	2	Manufacturing Director	3	Single	5042	3140	0	Y	No	13	3	4	80	0	10	2	1	9	2	3	8
+45	No	Travel_Rarely	1329	Research & Development	2	2	Other	1	1635	4	Female	59	2	2	Manufacturing Director	4	Divorced	5770	5388	1	Y	No	19	3	1	80	2	10	3	3	10	7	3	9
+38	No	Travel_Rarely	397	Research & Development	2	2	Medical	1	1638	4	Female	54	2	3	Manufacturing Director	3	Married	7756	14199	3	Y	Yes	19	3	4	80	1	10	6	4	5	4	0	2
+35	Yes	Travel_Rarely	737	Sales	10	3	Medical	1	1639	4	Male	55	2	3	Sales Executive	1	Married	10306	21530	9	Y	No	17	3	3	80	0	15	3	3	13	12	6	0
+37	No	Travel_Rarely	1470	Research & Development	10	3	Medical	1	1640	2	Female	71	3	1	Research Scientist	2	Married	3936	9953	1	Y	No	11	3	1	80	1	8	2	1	8	4	7	7
+40	No	Travel_Rarely	448	Research & Development	16	3	Life Sciences	1	1641	3	Female	84	3	3	Manufacturing Director	4	Single	7945	19948	6	Y	Yes	15	3	4	80	0	18	2	2	4	2	3	3
+44	No	Travel_Frequently	602	Human Resources	1	5	Human Resources	1	1642	1	Male	37	3	2	Human Resources	4	Married	5743	10503	4	Y	Yes	11	3	3	80	0	14	3	3	10	7	0	2
+48	No	Travel_Frequently	365	Research & Development	4	5	Medical	1	1644	3	Male	89	2	4	Manager	4	Married	15202	5602	2	Y	No	25	4	2	80	1	23	3	3	2	2	2	2
+35	Yes	Travel_Rarely	763	Sales	15	2	Medical	1	1645	1	Male	59	1	2	Sales Executive	4	Divorced	5440	22098	6	Y	Yes	14	3	4	80	2	7	2	2	2	2	2	2
+24	No	Travel_Frequently	567	Research & Development	2	1	Technical Degree	1	1646	1	Female	32	3	1	Research Scientist	4	Single	3760	17218	1	Y	Yes	13	3	3	80	0	6	2	3	6	3	1	3
+27	No	Travel_Rarely	486	Research & Development	8	3	Medical	1	1647	2	Female	86	4	1	Research Scientist	3	Married	3517	22490	7	Y	No	17	3	1	80	0	5	0	3	3	2	0	2
+27	No	Travel_Frequently	591	Research & Development	2	3	Medical	1	1648	4	Male	87	3	1	Research Scientist	4	Single	2580	6297	2	Y	No	13	3	3	80	0	6	0	2	4	2	1	2
+40	Yes	Travel_Rarely	1329	Research & Development	7	3	Life Sciences	1	1649	1	Male	73	3	1	Laboratory Technician	1	Single	2166	3339	3	Y	Yes	14	3	2	80	0	10	3	1	4	2	0	3
+29	No	Travel_Rarely	469	Sales	10	3	Medical	1	1650	3	Male	42	2	2	Sales Executive	3	Single	5869	23413	9	Y	No	11	3	3	80	0	8	2	3	5	2	1	4
+36	No	Travel_Rarely	711	Research & Development	5	4	Life Sciences	1	1651	2	Female	42	3	3	Healthcare Representative	1	Married	8008	22792	4	Y	No	12	3	3	80	2	9	6	3	3	2	0	2
+25	No	Travel_Frequently	772	Research & Development	2	1	Life Sciences	1	1653	4	Male	77	4	2	Manufacturing Director	3	Divorced	5206	4973	1	Y	No	17	3	3	80	2	7	6	3	7	7	0	7
+39	No	Travel_Rarely	492	Research & Development	12	3	Medical	1	1654	4	Male	66	3	2	Manufacturing Director	2	Married	5295	7693	4	Y	No	21	4	3	80	0	7	3	3	5	4	1	0
+49	No	Travel_Rarely	301	Research & Development	22	4	Other	1	1655	1	Female	72	3	4	Research Director	2	Married	16413	3498	3	Y	No	16	3	2	80	2	27	2	3	4	2	1	2
+50	No	Travel_Rarely	813	Research & Development	17	5	Life Sciences	1	1656	4	Female	50	2	3	Research Director	1	Divorced	13269	21981	5	Y	No	15	3	3	80	3	19	3	3	14	11	1	11
+20	No	Travel_Rarely	1141	Sales	2	3	Medical	1	1657	3	Female	31	3	1	Sales Representative	3	Single	2783	13251	1	Y	No	19	3	1	80	0	2	3	3	2	2	2	2
+34	No	Travel_Rarely	1130	Research & Development	3	3	Life Sciences	1	1658	4	Female	66	3	2	Research Scientist	2	Divorced	5433	19332	1	Y	No	12	3	3	80	1	11	2	3	11	8	7	9
+36	No	Travel_Rarely	311	Research & Development	7	3	Life Sciences	1	1659	1	Male	77	3	1	Laboratory Technician	2	Single	2013	10950	2	Y	No	11	3	3	80	0	15	4	3	4	3	1	3
+49	No	Travel_Rarely	465	Research & Development	6	1	Life Sciences	1	1661	3	Female	41	2	4	Healthcare Representative	3	Married	13966	11652	2	Y	Yes	19	3	2	80	1	30	3	3	15	11	2	12
+36	No	Non-Travel	894	Research & Development	1	4	Medical	1	1662	4	Female	33	2	2	Manufacturing Director	3	Married	4374	15411	0	Y	No	15	3	3	80	0	4	6	3	3	2	1	2
+36	No	Travel_Rarely	1040	Research & Development	3	2	Life Sciences	1	1664	4	Male	79	4	2	Healthcare Representative	1	Divorced	6842	26308	6	Y	No	20	4	1	80	1	13	3	3	5	4	0	4
+54	No	Travel_Rarely	584	Research & Development	22	5	Medical	1	1665	2	Female	91	3	4	Manager	3	Married	17426	18685	3	Y	No	25	4	3	80	1	36	6	3	10	8	4	7
+43	No	Travel_Rarely	1291	Research & Development	15	2	Life Sciences	1	1666	3	Male	65	2	4	Research Director	3	Married	17603	3525	1	Y	No	24	4	1	80	1	14	3	3	14	10	6	11
+35	Yes	Travel_Frequently	880	Sales	12	4	Other	1	1667	4	Male	36	3	2	Sales Executive	4	Single	4581	10414	3	Y	Yes	24	4	1	80	0	13	2	4	11	9	6	7
+38	No	Travel_Frequently	1189	Research & Development	1	3	Life Sciences	1	1668	4	Male	90	3	2	Research Scientist	4	Married	4735	9867	7	Y	No	15	3	4	80	2	19	4	4	13	11	2	9
+29	No	Travel_Rarely	991	Sales	5	3	Medical	1	1669	1	Male	43	2	2	Sales Executive	2	Divorced	4187	3356	1	Y	Yes	13	3	2	80	1	10	3	2	10	0	0	9
+33	No	Travel_Rarely	392	Sales	2	4	Medical	1	1670	4	Male	93	3	2	Sales Executive	4	Divorced	5505	3921	1	Y	No	14	3	3	80	2	6	5	3	6	2	0	4
+32	No	Travel_Rarely	977	Research & Development	2	3	Medical	1	1671	4	Male	45	3	2	Research Scientist	2	Divorced	5470	25518	0	Y	No	13	3	3	80	2	10	4	2	9	5	1	6
+31	No	Travel_Rarely	1112	Sales	5	4	Life Sciences	1	1673	1	Female	67	3	2	Sales Executive	4	Married	5476	22589	1	Y	No	11	3	1	80	2	10	2	3	10	0	0	2
+49	No	Travel_Rarely	464	Research & Development	16	3	Medical	1	1674	4	Female	74	3	1	Laboratory Technician	1	Divorced	2587	24941	4	Y	Yes	16	3	2	80	1	17	2	2	2	2	2	2
+38	No	Travel_Frequently	148	Research & Development	2	3	Medical	1	1675	4	Female	42	2	1	Laboratory Technician	2	Single	2440	23826	1	Y	No	22	4	2	80	0	4	3	3	4	3	3	3
+47	No	Travel_Rarely	1225	Sales	2	4	Life Sciences	1	1676	2	Female	47	4	4	Manager	2	Divorced	15972	21086	6	Y	No	14	3	3	80	3	29	2	3	3	2	1	2
+49	No	Travel_Rarely	809	Research & Development	1	3	Life Sciences	1	1677	3	Male	36	3	4	Manager	3	Single	15379	22384	4	Y	No	14	3	1	80	0	23	2	3	8	7	0	0
+41	No	Travel_Rarely	1206	Sales	23	2	Life Sciences	1	1678	4	Male	80	3	3	Sales Executive	3	Single	7082	11591	3	Y	Yes	16	3	4	80	0	21	2	3	2	0	0	2
+20	No	Travel_Rarely	727	Sales	9	1	Life Sciences	1	1680	4	Male	54	3	1	Sales Representative	1	Single	2728	21082	1	Y	No	11	3	1	80	0	2	3	3	2	2	0	2
+33	No	Non-Travel	530	Sales	16	3	Life Sciences	1	1681	3	Female	36	3	2	Sales Executive	4	Divorced	5368	16130	1	Y	Yes	25	4	3	80	1	7	2	3	6	5	1	2
+36	No	Travel_Rarely	1351	Research & Development	26	4	Life Sciences	1	1682	1	Male	80	3	2	Healthcare Representative	3	Married	5347	7419	6	Y	No	14	3	2	80	2	10	2	2	3	2	0	2
+44	No	Travel_Rarely	528	Human Resources	1	3	Life Sciences	1	1683	3	Female	44	3	1	Human Resources	4	Divorced	3195	4167	4	Y	Yes	18	3	1	80	3	8	2	3	2	2	2	2
+23	Yes	Travel_Rarely	1320	Research & Development	8	1	Medical	1	1684	4	Male	93	2	1	Laboratory Technician	3	Single	3989	20586	1	Y	Yes	11	3	1	80	0	5	2	3	5	4	1	2
+38	No	Travel_Rarely	1495	Research & Development	4	2	Medical	1	1687	4	Female	87	3	1	Laboratory Technician	3	Married	3306	26176	7	Y	No	19	3	4	80	1	7	5	2	0	0	0	0
+53	No	Travel_Rarely	1395	Research & Development	24	4	Medical	1	1689	2	Male	48	4	3	Healthcare Representative	4	Married	7005	3458	3	Y	No	15	3	3	80	0	11	2	3	4	3	1	2
+48	Yes	Travel_Frequently	708	Sales	7	2	Medical	1	1691	4	Female	95	3	1	Sales Representative	3	Married	2655	11740	2	Y	Yes	11	3	3	80	2	19	3	3	9	7	7	7
+32	Yes	Travel_Rarely	1259	Research & Development	2	4	Life Sciences	1	1692	4	Male	95	3	1	Laboratory Technician	2	Single	1393	24852	1	Y	No	12	3	1	80	0	1	2	3	1	0	0	0
+26	No	Non-Travel	786	Research & Development	7	3	Medical	1	1693	4	Male	76	3	1	Laboratory Technician	4	Single	2570	11925	1	Y	No	20	4	3	80	0	7	5	3	7	7	5	7
+55	No	Travel_Rarely	1441	Research & Development	22	3	Technical Degree	1	1694	1	Male	94	2	1	Research Scientist	2	Divorced	3537	23737	5	Y	No	12	3	4	80	1	8	1	3	4	2	1	2
+34	No	Travel_Rarely	1157	Research & Development	5	2	Medical	1	1696	2	Male	57	2	2	Laboratory Technician	4	Married	3986	11912	1	Y	No	14	3	3	80	1	15	3	4	15	10	4	13
+60	No	Travel_Rarely	370	Research & Development	1	4	Medical	1	1697	3	Male	92	1	3	Healthcare Representative	4	Divorced	10883	20467	3	Y	No	20	4	3	80	1	19	2	4	1	0	0	0
+33	No	Travel_Rarely	267	Research & Development	21	3	Medical	1	1698	2	Male	79	4	1	Laboratory Technician	2	Married	2028	13637	1	Y	No	18	3	4	80	3	14	6	3	14	11	2	13
+37	No	Travel_Frequently	1278	Sales	1	4	Medical	1	1700	3	Male	31	1	2	Sales Executive	4	Divorced	9525	7677	1	Y	No	14	3	3	80	2	6	2	2	6	3	1	3
+34	No	Travel_Rarely	678	Research & Development	19	3	Life Sciences	1	1701	2	Female	35	2	1	Research Scientist	4	Married	2929	20338	1	Y	No	12	3	2	80	0	10	3	3	10	9	8	7
+23	Yes	Travel_Rarely	427	Sales	7	3	Life Sciences	1	1702	3	Male	99	3	1	Sales Representative	4	Divorced	2275	25103	1	Y	Yes	21	4	2	80	1	3	2	3	3	2	0	2
+44	No	Travel_Rarely	921	Research & Development	2	3	Life Sciences	1	1703	3	Female	96	4	3	Healthcare Representative	4	Married	7879	14810	1	Y	Yes	19	3	2	80	1	9	2	3	8	7	6	7
+35	No	Travel_Frequently	146	Research & Development	2	4	Medical	1	1704	1	Male	79	2	1	Research Scientist	4	Single	4930	13970	0	Y	Yes	14	3	3	80	0	6	2	4	5	4	1	4
+43	No	Travel_Rarely	1179	Sales	2	3	Medical	1	1706	4	Male	73	3	2	Sales Executive	4	Married	7847	6069	1	Y	Yes	17	3	1	80	1	10	3	3	10	9	8	8
+24	No	Travel_Rarely	581	Research & Development	9	3	Medical	1	1707	3	Male	62	4	1	Research Scientist	3	Married	4401	17616	1	Y	No	16	3	4	80	1	5	1	3	5	3	0	4
+41	No	Travel_Rarely	918	Sales	6	3	Marketing	1	1708	4	Male	35	3	3	Sales Executive	3	Single	9241	15869	1	Y	No	12	3	2	80	0	10	3	3	10	8	8	7
+29	No	Travel_Rarely	1082	Research & Development	9	4	Medical	1	1709	4	Female	43	3	1	Laboratory Technician	3	Married	2974	25412	9	Y	No	17	3	3	80	1	9	2	3	5	3	1	2
+36	No	Travel_Rarely	530	Sales	2	4	Life Sciences	1	1710	3	Female	51	3	2	Sales Representative	4	Single	4502	7439	3	Y	No	15	3	3	80	0	17	2	2	13	7	6	7
+45	No	Non-Travel	1238	Research & Development	1	1	Life Sciences	1	1712	3	Male	74	2	3	Healthcare Representative	3	Married	10748	3395	3	Y	No	23	4	4	80	1	25	3	2	23	15	14	4
+24	Yes	Travel_Rarely	240	Human Resources	22	1	Human Resources	1	1714	4	Male	58	1	1	Human Resources	3	Married	1555	11585	1	Y	No	11	3	3	80	1	1	2	3	1	0	0	0
+47	Yes	Travel_Frequently	1093	Sales	9	3	Life Sciences	1	1716	3	Male	82	1	4	Sales Executive	3	Married	12936	24164	7	Y	No	11	3	3	80	0	25	3	1	23	5	14	10
+26	No	Travel_Rarely	390	Research & Development	17	4	Medical	1	1718	4	Male	62	1	1	Laboratory Technician	3	Married	2305	6217	1	Y	No	15	3	3	80	3	3	3	4	3	2	0	2
+45	No	Travel_Rarely	1005	Research & Development	28	2	Technical Degree	1	1719	4	Female	48	2	4	Research Director	2	Single	16704	17119	1	Y	No	11	3	3	80	0	21	2	3	21	6	8	6
+32	No	Travel_Frequently	585	Research & Development	10	3	Life Sciences	1	1720	1	Male	56	3	1	Research Scientist	3	Married	3433	17360	6	Y	No	13	3	1	80	1	10	3	2	5	2	1	3
+31	No	Travel_Rarely	741	Research & Development	2	4	Life Sciences	1	1721	2	Male	69	3	1	Laboratory Technician	3	Married	3477	18103	1	Y	No	14	3	4	80	1	6	2	4	5	2	0	3
+41	No	Non-Travel	552	Human Resources	4	3	Human Resources	1	1722	3	Male	60	1	2	Human Resources	2	Married	6430	20794	6	Y	No	19	3	2	80	1	10	4	3	3	2	1	2
+40	No	Travel_Rarely	369	Research & Development	8	2	Life Sciences	1	1724	2	Female	92	3	2	Manufacturing Director	1	Married	6516	5041	2	Y	Yes	16	3	2	80	1	18	3	3	1	0	0	0
+24	No	Travel_Rarely	506	Research & Development	29	1	Medical	1	1725	2	Male	91	3	1	Laboratory Technician	1	Divorced	3907	3622	1	Y	No	13	3	2	80	3	6	2	4	6	2	1	2
+46	No	Travel_Rarely	717	Research & Development	13	4	Life Sciences	1	1727	3	Male	34	3	2	Healthcare Representative	2	Single	5562	9697	6	Y	No	14	3	4	80	0	19	3	3	10	7	0	9
+35	No	Travel_Rarely	1370	Research & Development	27	4	Life Sciences	1	1728	4	Male	49	3	2	Manufacturing Director	3	Married	6883	5151	2	Y	No	16	3	2	80	1	17	3	3	7	7	0	7
+30	No	Travel_Rarely	793	Research & Development	16	1	Life Sciences	1	1729	2	Male	33	3	1	Research Scientist	4	Married	2862	3811	1	Y	No	12	3	2	80	1	10	2	2	10	0	0	8
+47	No	Non-Travel	543	Sales	2	4	Marketing	1	1731	3	Male	87	3	2	Sales Executive	2	Married	4978	3536	7	Y	No	11	3	4	80	1	4	3	1	1	0	0	0
+46	No	Travel_Rarely	1277	Sales	2	3	Life Sciences	1	1732	3	Male	74	3	3	Sales Executive	4	Divorced	10368	5596	4	Y	Yes	12	3	2	80	1	13	5	2	10	6	0	3
+36	Yes	Travel_Rarely	1456	Sales	13	5	Marketing	1	1733	2	Male	96	2	2	Sales Executive	1	Divorced	6134	8658	5	Y	Yes	13	3	2	80	3	16	3	3	2	2	2	2
+32	Yes	Travel_Rarely	964	Sales	1	2	Life Sciences	1	1734	1	Male	34	1	2	Sales Executive	2	Single	6735	12147	6	Y	No	15	3	2	80	0	10	2	3	0	0	0	0
+23	No	Travel_Rarely	160	Research & Development	4	1	Medical	1	1735	3	Female	51	3	1	Laboratory Technician	2	Single	3295	12862	1	Y	No	13	3	3	80	0	3	3	1	3	2	1	2
+31	No	Travel_Frequently	163	Research & Development	24	1	Technical Degree	1	1736	4	Female	30	3	2	Manufacturing Director	4	Single	5238	6670	2	Y	No	20	4	4	80	0	9	3	2	5	4	1	4
+39	No	Non-Travel	792	Research & Development	1	3	Life Sciences	1	1737	4	Male	77	3	2	Laboratory Technician	4	Married	6472	8989	1	Y	Yes	15	3	4	80	1	9	2	3	9	8	5	8
+32	No	Travel_Rarely	371	Sales	19	3	Life Sciences	1	1739	4	Male	80	1	3	Sales Executive	3	Married	9610	3840	3	Y	No	13	3	3	80	1	10	2	1	4	3	0	2
+40	No	Travel_Rarely	611	Sales	7	4	Medical	1	1740	2	Male	88	3	5	Manager	2	Single	19833	4349	1	Y	No	14	3	2	80	0	21	3	2	21	8	12	8
+45	No	Travel_Rarely	176	Human Resources	4	3	Life Sciences	1	1744	3	Female	56	1	3	Human Resources	3	Married	9756	6595	4	Y	No	21	4	3	80	2	9	2	4	5	0	0	3
+30	No	Travel_Frequently	1312	Research & Development	2	4	Technical Degree	1	1745	4	Female	78	2	1	Research Scientist	1	Single	4968	26427	0	Y	No	16	3	4	80	0	10	2	3	9	7	0	7
+24	No	Travel_Frequently	897	Human Resources	10	3	Medical	1	1746	1	Male	59	3	1	Human Resources	4	Married	2145	2097	0	Y	No	14	3	4	80	1	3	2	3	2	2	2	1
+30	Yes	Travel_Frequently	600	Human Resources	8	3	Human Resources	1	1747	3	Female	66	2	1	Human Resources	4	Divorced	2180	9732	6	Y	No	11	3	3	80	1	6	0	2	4	2	1	2
+31	No	Travel_Rarely	1003	Sales	5	3	Technical Degree	1	1749	1	Male	51	3	2	Sales Executive	3	Married	8346	20943	1	Y	No	19	3	3	80	1	6	3	3	5	2	0	2
+27	No	Travel_Rarely	1054	Research & Development	8	3	Medical	1	1751	3	Female	67	3	1	Research Scientist	4	Single	3445	6152	1	Y	No	11	3	3	80	0	6	5	2	6	2	1	4
+29	Yes	Travel_Rarely	428	Sales	9	3	Marketing	1	1752	2	Female	52	1	1	Sales Representative	2	Single	2760	14630	1	Y	No	13	3	3	80	0	2	3	3	2	2	2	2
+29	No	Travel_Frequently	461	Research & Development	1	3	Life Sciences	1	1753	4	Male	70	4	2	Healthcare Representative	3	Single	6294	23060	8	Y	Yes	12	3	4	80	0	10	5	4	3	2	0	2
+30	No	Travel_Rarely	979	Sales	15	2	Marketing	1	1754	3	Male	94	2	3	Sales Executive	1	Divorced	7140	3088	2	Y	No	11	3	1	80	1	12	2	3	7	7	1	7
+34	No	Travel_Rarely	181	Research & Development	2	4	Medical	1	1755	4	Male	97	4	1	Research Scientist	4	Married	2932	5586	0	Y	Yes	14	3	1	80	3	6	3	3	5	0	1	2
+33	No	Non-Travel	1283	Sales	2	3	Marketing	1	1756	4	Female	62	3	2	Sales Executive	2	Single	5147	10697	8	Y	No	15	3	4	80	0	13	2	2	11	7	1	7
+49	No	Travel_Rarely	1313	Sales	11	4	Marketing	1	1757	4	Female	80	3	2	Sales Executive	4	Single	4507	8191	3	Y	No	12	3	3	80	0	8	1	4	5	1	0	4
+33	Yes	Travel_Rarely	211	Sales	16	3	Life Sciences	1	1758	1	Female	74	3	3	Sales Executive	1	Single	8564	10092	2	Y	Yes	20	4	3	80	0	11	2	2	0	0	0	0
+38	No	Travel_Frequently	594	Research & Development	2	2	Medical	1	1760	3	Female	75	2	1	Laboratory Technician	2	Married	2468	15963	4	Y	No	14	3	2	80	1	9	4	2	6	1	0	5
+31	Yes	Travel_Rarely	1079	Sales	16	4	Marketing	1	1761	1	Male	70	3	3	Sales Executive	3	Married	8161	19002	2	Y	No	13	3	1	80	3	10	2	3	1	0	0	0
+29	No	Travel_Rarely	590	Research & Development	4	3	Technical Degree	1	1762	4	Female	91	2	1	Research Scientist	1	Divorced	2109	10007	1	Y	No	13	3	3	80	1	1	2	3	1	0	0	0
+30	No	Travel_Rarely	305	Research & Development	16	3	Life Sciences	1	1763	3	Male	58	4	2	Healthcare Representative	3	Married	5294	9128	3	Y	No	16	3	3	80	1	10	3	3	7	0	1	7
+32	No	Non-Travel	953	Research & Development	5	4	Technical Degree	1	1764	2	Male	65	3	1	Research Scientist	2	Single	2718	17674	2	Y	No	14	3	2	80	0	12	3	3	7	7	0	7
+38	No	Travel_Rarely	833	Research & Development	18	3	Medical	1	1766	2	Male	60	1	2	Healthcare Representative	4	Married	5811	24539	3	Y	Yes	16	3	3	80	1	15	2	3	1	0	1	0
+43	Yes	Travel_Frequently	807	Research & Development	17	3	Technical Degree	1	1767	3	Male	38	2	1	Research Scientist	3	Married	2437	15587	9	Y	Yes	16	3	4	80	1	6	4	3	1	0	0	0
+42	No	Travel_Rarely	855	Research & Development	12	3	Medical	1	1768	2	Male	57	3	1	Laboratory Technician	2	Divorced	2766	8952	8	Y	No	22	4	2	80	3	7	6	2	5	3	0	4
+55	No	Travel_Rarely	478	Research & Development	2	3	Medical	1	1770	3	Male	60	2	5	Research Director	1	Married	19038	19805	8	Y	No	12	3	2	80	3	34	2	3	1	0	0	0
+33	No	Non-Travel	775	Research & Development	4	3	Technical Degree	1	1771	4	Male	90	3	2	Research Scientist	2	Divorced	3055	6194	5	Y	No	15	3	4	80	2	11	2	2	9	8	1	7
+41	No	Travel_Rarely	548	Research & Development	9	4	Life Sciences	1	1772	3	Male	94	3	1	Laboratory Technician	1	Divorced	2289	20520	1	Y	No	20	4	2	80	2	5	2	3	5	3	0	4
+34	No	Non-Travel	1375	Sales	10	3	Life Sciences	1	1774	4	Male	87	3	2	Sales Executive	3	Divorced	4001	12313	1	Y	Yes	14	3	3	80	1	15	3	3	15	14	0	7
+53	No	Non-Travel	661	Research & Development	1	4	Medical	1	1775	1	Female	60	2	4	Manufacturing Director	3	Married	12965	22308	4	Y	Yes	20	4	4	80	3	27	2	2	3	2	0	2
+43	No	Travel_Rarely	244	Human Resources	2	3	Life Sciences	1	1778	2	Male	97	3	1	Human Resources	4	Single	3539	5033	0	Y	No	13	3	2	80	0	10	5	3	9	7	1	8
+34	No	Travel_Rarely	511	Sales	3	2	Life Sciences	1	1779	4	Female	32	1	2	Sales Executive	4	Single	6029	25353	5	Y	No	12	3	1	80	0	6	3	3	2	2	2	2
+21	Yes	Travel_Rarely	337	Sales	7	1	Marketing	1	1780	2	Male	31	3	1	Sales Representative	2	Single	2679	4567	1	Y	No	13	3	2	80	0	1	3	3	1	0	1	0
+38	No	Travel_Rarely	1153	Research & Development	6	2	Other	1	1782	4	Female	40	2	1	Laboratory Technician	3	Married	3702	16376	1	Y	No	11	3	2	80	1	5	3	3	5	4	0	4
+22	Yes	Travel_Rarely	1294	Research & Development	8	1	Medical	1	1783	3	Female	79	3	1	Laboratory Technician	1	Married	2398	15999	1	Y	Yes	17	3	3	80	0	1	6	3	1	0	0	0
+31	No	Travel_Rarely	196	Sales	29	4	Marketing	1	1784	1	Female	91	2	2	Sales Executive	4	Married	5468	13402	1	Y	No	14	3	1	80	2	13	3	3	12	7	5	7
+51	No	Travel_Rarely	942	Research & Development	3	3	Technical Degree	1	1786	1	Female	53	3	3	Manager	3	Married	13116	22984	2	Y	No	11	3	4	80	0	15	2	3	2	2	2	2
+37	No	Travel_Rarely	589	Sales	9	2	Marketing	1	1787	2	Male	46	2	2	Sales Executive	2	Married	4189	8800	1	Y	No	14	3	1	80	2	5	2	3	5	2	0	3
+46	No	Travel_Rarely	734	Research & Development	2	4	Medical	1	1789	3	Male	46	3	5	Research Director	4	Divorced	19328	14218	7	Y	Yes	17	3	3	80	1	24	3	3	2	1	2	2
+36	No	Travel_Rarely	1383	Research & Development	10	3	Life Sciences	1	1790	4	Male	90	3	3	Healthcare Representative	1	Married	8321	25949	7	Y	Yes	13	3	4	80	1	15	1	3	12	8	5	7
+44	Yes	Travel_Frequently	429	Research & Development	1	2	Medical	1	1792	3	Male	99	3	1	Research Scientist	2	Divorced	2342	11092	1	Y	Yes	12	3	3	80	3	6	2	2	5	3	2	3
+37	No	Travel_Rarely	1239	Human Resources	8	2	Other	1	1794	3	Male	89	3	2	Human Resources	2	Divorced	4071	12832	2	Y	No	13	3	3	80	0	19	4	2	10	0	4	7
+35	Yes	Travel_Rarely	303	Sales	27	3	Life Sciences	1	1797	3	Male	84	3	2	Sales Executive	4	Single	5813	13492	1	Y	Yes	18	3	4	80	0	10	2	3	10	7	7	7
+33	No	Travel_Rarely	867	Research & Development	8	4	Life Sciences	1	1798	4	Male	90	4	1	Research Scientist	1	Married	3143	6076	6	Y	No	19	3	2	80	1	14	1	3	10	8	7	6
+28	No	Travel_Rarely	1181	Research & Development	1	3	Life Sciences	1	1799	3	Male	82	3	1	Research Scientist	4	Married	2044	5531	1	Y	No	11	3	3	80	1	5	6	4	5	3	0	3
+39	No	Travel_Rarely	1253	Research & Development	10	1	Medical	1	1800	3	Male	65	3	3	Research Director	3	Single	13464	7914	7	Y	No	21	4	3	80	0	9	3	3	4	3	2	2
+46	No	Non-Travel	849	Sales	26	2	Life Sciences	1	1801	2	Male	98	2	2	Sales Executive	2	Single	7991	25166	8	Y	No	15	3	3	80	0	6	3	3	2	2	2	2
+40	No	Travel_Rarely	616	Research & Development	2	2	Life Sciences	1	1802	3	Female	99	3	1	Laboratory Technician	1	Married	3377	25605	4	Y	No	17	3	4	80	1	7	5	2	4	3	0	2
+42	No	Travel_Rarely	1128	Research & Development	13	3	Medical	1	1803	2	Male	95	4	2	Healthcare Representative	1	Married	5538	5696	5	Y	No	18	3	3	80	2	10	2	2	0	0	0	0
+35	No	Non-Travel	1180	Research & Development	2	2	Medical	1	1804	2	Male	90	3	2	Manufacturing Director	4	Divorced	5762	24442	2	Y	No	14	3	3	80	1	15	6	3	7	7	1	7
+38	No	Non-Travel	1336	Human Resources	2	3	Human Resources	1	1805	1	Male	100	3	1	Human Resources	2	Divorced	2592	7129	5	Y	No	13	3	4	80	3	13	3	3	11	10	3	8
+34	Yes	Travel_Frequently	234	Research & Development	9	4	Life Sciences	1	1807	4	Male	93	3	2	Laboratory Technician	1	Married	5346	6208	4	Y	No	17	3	3	80	1	11	3	2	7	1	0	7
+37	Yes	Travel_Rarely	370	Research & Development	10	4	Medical	1	1809	4	Male	58	3	2	Manufacturing Director	1	Single	4213	4992	1	Y	No	15	3	2	80	0	10	4	1	10	3	0	8
+39	No	Travel_Frequently	766	Sales	20	3	Life Sciences	1	1812	3	Male	83	3	2	Sales Executive	4	Divorced	4127	19188	2	Y	No	18	3	4	80	1	7	6	3	2	1	2	2
+43	No	Non-Travel	343	Research & Development	9	3	Life Sciences	1	1813	1	Male	52	3	1	Research Scientist	3	Single	2438	24978	4	Y	No	13	3	3	80	0	7	2	2	3	2	1	2
+41	No	Travel_Rarely	447	Research & Development	5	3	Life Sciences	1	1814	2	Male	85	4	2	Healthcare Representative	2	Single	6870	15530	3	Y	No	12	3	1	80	0	11	3	1	3	2	1	2
+41	No	Travel_Rarely	796	Sales	4	1	Marketing	1	1815	3	Female	81	3	3	Sales Executive	3	Divorced	10447	26458	0	Y	Yes	13	3	4	80	1	23	3	4	22	14	13	5
+30	No	Travel_Rarely	1092	Research & Development	10	3	Medical	1	1816	1	Female	64	3	3	Manufacturing Director	3	Single	9667	2739	9	Y	No	14	3	2	80	0	9	3	3	7	7	0	2
+26	Yes	Travel_Rarely	920	Human Resources	20	2	Medical	1	1818	4	Female	69	3	1	Human Resources	2	Married	2148	6889	0	Y	Yes	11	3	3	80	0	6	3	3	5	1	1	4
+46	Yes	Travel_Rarely	261	Research & Development	21	2	Medical	1	1821	4	Female	66	3	2	Healthcare Representative	2	Married	8926	10842	4	Y	No	22	4	4	80	1	13	2	4	9	7	3	7
+40	No	Travel_Rarely	1194	Research & Development	1	3	Life Sciences	1	1822	3	Female	52	3	2	Healthcare Representative	4	Divorced	6513	9060	4	Y	No	17	3	4	80	1	12	3	3	5	3	0	3
+34	No	Travel_Rarely	810	Sales	8	2	Technical Degree	1	1823	2	Male	92	4	2	Sales Executive	3	Married	6799	22128	1	Y	No	21	4	3	80	2	10	5	3	10	8	4	8
+58	No	Non-Travel	350	Sales	2	3	Medical	1	1824	2	Male	52	3	4	Manager	2	Divorced	16291	22577	4	Y	No	22	4	4	80	1	37	0	2	16	9	14	14
+35	No	Travel_Rarely	185	Research & Development	23	4	Medical	1	1826	2	Male	91	1	1	Laboratory Technician	3	Married	2705	9696	0	Y	No	16	3	2	80	1	6	2	4	5	4	0	3
+47	No	Travel_Rarely	1001	Research & Development	4	3	Life Sciences	1	1827	3	Female	92	2	3	Manufacturing Director	2	Divorced	10333	19271	8	Y	Yes	12	3	3	80	1	28	4	3	22	11	14	10
+40	No	Travel_Rarely	750	Research & Development	12	3	Life Sciences	1	1829	2	Female	47	3	2	Healthcare Representative	1	Divorced	4448	10748	2	Y	No	12	3	2	80	1	15	3	3	7	4	7	7
+54	No	Travel_Rarely	431	Research & Development	7	4	Medical	1	1830	4	Female	68	3	2	Research Scientist	4	Married	6854	15696	4	Y	No	15	3	2	80	1	14	2	2	7	1	1	7
+31	No	Travel_Frequently	1125	Sales	7	4	Marketing	1	1833	1	Female	68	3	3	Sales Executive	1	Married	9637	8277	2	Y	No	14	3	4	80	2	9	3	3	3	2	2	2
+28	No	Travel_Rarely	1217	Research & Development	1	3	Medical	1	1834	3	Female	67	3	1	Research Scientist	1	Married	3591	12719	1	Y	No	25	4	3	80	1	3	3	3	3	2	1	2
+38	No	Travel_Rarely	723	Sales	2	4	Marketing	1	1835	2	Female	77	1	2	Sales Representative	4	Married	5405	4244	2	Y	Yes	20	4	1	80	2	20	4	2	4	2	0	3
+26	No	Travel_Rarely	572	Sales	10	3	Medical	1	1836	3	Male	46	3	2	Sales Executive	4	Single	4684	9125	1	Y	No	13	3	1	80	0	5	4	3	5	3	1	2
+58	No	Travel_Frequently	1216	Research & Development	15	4	Life Sciences	1	1837	1	Male	87	3	4	Research Director	3	Married	15787	21624	2	Y	Yes	14	3	2	80	0	23	3	3	2	2	2	2
+18	No	Non-Travel	1431	Research & Development	14	3	Medical	1	1839	2	Female	33	3	1	Research Scientist	3	Single	1514	8018	1	Y	No	16	3	3	80	0	0	4	1	0	0	0	0
+31	Yes	Travel_Rarely	359	Human Resources	18	5	Human Resources	1	1842	4	Male	89	4	1	Human Resources	1	Married	2956	21495	0	Y	No	17	3	3	80	0	2	4	3	1	0	0	0
+29	Yes	Travel_Rarely	350	Human Resources	13	3	Human Resources	1	1844	1	Male	56	2	1	Human Resources	1	Divorced	2335	3157	4	Y	Yes	15	3	4	80	3	4	3	3	2	2	2	0
+45	No	Non-Travel	589	Sales	2	4	Life Sciences	1	1845	3	Female	67	3	2	Sales Executive	3	Married	5154	19665	4	Y	No	22	4	2	80	2	10	3	4	8	7	5	7
+36	No	Travel_Rarely	430	Research & Development	2	4	Other	1	1847	4	Female	73	3	2	Research Scientist	2	Married	6962	19573	4	Y	Yes	22	4	4	80	1	15	2	3	1	0	0	0
+43	No	Travel_Frequently	1422	Sales	2	4	Life Sciences	1	1849	1	Male	92	3	2	Sales Executive	4	Married	5675	19246	1	Y	No	20	4	3	80	1	7	5	3	7	7	7	7
+27	No	Travel_Frequently	1297	Research & Development	5	2	Life Sciences	1	1850	4	Female	53	3	1	Laboratory Technician	4	Single	2379	19826	0	Y	Yes	14	3	3	80	0	6	3	2	5	4	0	2
+29	No	Travel_Frequently	574	Research & Development	20	1	Medical	1	1852	4	Male	40	3	1	Laboratory Technician	4	Married	3812	7003	1	Y	No	13	3	2	80	0	11	3	4	11	8	3	10
+32	No	Travel_Frequently	1318	Sales	10	4	Marketing	1	1853	4	Male	79	3	2	Sales Executive	4	Single	4648	26075	8	Y	No	13	3	3	80	0	4	2	4	0	0	0	0
+42	No	Non-Travel	355	Research & Development	10	4	Technical Degree	1	1854	3	Male	38	3	1	Research Scientist	3	Married	2936	6161	3	Y	No	22	4	2	80	2	10	1	2	6	3	3	3
+47	No	Travel_Rarely	207	Research & Development	9	4	Life Sciences	1	1856	2	Female	64	3	1	Laboratory Technician	3	Single	2105	5411	4	Y	No	12	3	3	80	0	7	2	3	2	2	2	0
+46	No	Travel_Rarely	706	Research & Development	2	2	Life Sciences	1	1857	4	Male	82	3	3	Manufacturing Director	4	Divorced	8578	19989	3	Y	No	14	3	3	80	1	12	4	2	9	8	4	7
+28	No	Non-Travel	280	Human Resources	1	2	Life Sciences	1	1858	3	Male	43	3	1	Human Resources	4	Divorced	2706	10494	1	Y	No	15	3	2	80	1	3	2	3	3	2	2	2
+29	No	Travel_Rarely	726	Research & Development	29	1	Life Sciences	1	1859	4	Male	93	1	2	Healthcare Representative	3	Divorced	6384	21143	8	Y	No	17	3	4	80	2	11	3	3	7	0	1	6
+42	No	Travel_Rarely	1142	Research & Development	8	3	Life Sciences	1	1860	4	Male	81	3	1	Laboratory Technician	3	Single	3968	13624	4	Y	No	13	3	4	80	0	8	3	3	0	0	0	0
+32	Yes	Travel_Rarely	414	Sales	2	4	Marketing	1	1862	3	Male	82	2	2	Sales Executive	2	Single	9907	26186	7	Y	Yes	12	3	3	80	0	7	3	2	2	2	2	2
+46	No	Travel_Rarely	1319	Sales	3	3	Technical Degree	1	1863	1	Female	45	4	4	Sales Executive	1	Divorced	13225	7739	2	Y	No	12	3	4	80	1	25	5	3	19	17	2	8
+27	No	Travel_Rarely	728	Sales	23	1	Medical	1	1864	2	Female	36	2	2	Sales Representative	3	Married	3540	7018	1	Y	No	21	4	4	80	1	9	5	3	9	8	5	8
+29	No	Travel_Rarely	352	Human Resources	6	1	Medical	1	1865	4	Male	87	2	1	Human Resources	2	Married	2804	15434	1	Y	No	11	3	4	80	0	1	3	3	1	0	0	0
+43	No	Travel_Rarely	823	Research & Development	6	3	Medical	1	1866	1	Female	81	2	5	Manager	3	Married	19392	22539	7	Y	No	13	3	4	80	0	21	2	3	16	12	6	14
+48	No	Travel_Rarely	1224	Research & Development	10	3	Life Sciences	1	1867	4	Male	91	2	5	Research Director	2	Married	19665	13583	4	Y	No	12	3	4	80	0	29	3	3	22	10	12	9
+29	Yes	Travel_Frequently	459	Research & Development	24	2	Life Sciences	1	1868	4	Male	73	2	1	Research Scientist	4	Single	2439	14753	1	Y	Yes	24	4	2	80	0	1	3	2	1	0	1	0
+46	Yes	Travel_Rarely	1254	Sales	10	3	Life Sciences	1	1869	3	Female	64	3	3	Sales Executive	2	Married	7314	14011	5	Y	No	21	4	3	80	3	14	2	3	8	7	0	7
+27	No	Travel_Frequently	1131	Research & Development	15	3	Life Sciences	1	1870	4	Female	77	2	1	Research Scientist	1	Married	4774	23844	0	Y	No	19	3	4	80	1	8	2	2	7	6	7	3
+39	No	Travel_Rarely	835	Research & Development	19	4	Other	1	1871	4	Male	41	3	2	Research Scientist	4	Divorced	3902	5141	8	Y	No	14	3	2	80	3	7	2	3	2	2	2	2
+55	No	Travel_Rarely	836	Research & Development	2	4	Technical Degree	1	1873	2	Male	98	2	1	Research Scientist	4	Married	2662	7975	8	Y	No	20	4	2	80	1	19	2	4	5	2	0	4
+28	No	Travel_Rarely	1172	Sales	3	3	Medical	1	1875	2	Female	78	3	1	Sales Representative	2	Married	2856	3692	1	Y	No	19	3	4	80	1	1	3	3	1	0	0	0
+30	Yes	Travel_Rarely	945	Sales	9	3	Medical	1	1876	2	Male	89	3	1	Sales Representative	4	Single	1081	16019	1	Y	No	13	3	3	80	0	1	3	2	1	0	0	0
+22	Yes	Travel_Rarely	391	Research & Development	7	1	Life Sciences	1	1878	4	Male	75	3	1	Research Scientist	2	Single	2472	26092	1	Y	Yes	23	4	1	80	0	1	2	3	1	0	0	0
+36	No	Travel_Rarely	1266	Sales	10	4	Technical Degree	1	1880	2	Female	63	2	2	Sales Executive	3	Married	5673	6060	1	Y	Yes	13	3	1	80	1	10	4	3	10	9	1	7
+31	No	Travel_Rarely	311	Research & Development	20	3	Life Sciences	1	1881	2	Male	89	3	2	Laboratory Technician	3	Divorced	4197	18624	1	Y	No	11	3	1	80	1	10	2	3	10	8	0	2
+34	No	Travel_Rarely	1480	Sales	4	3	Life Sciences	1	1882	3	Male	64	3	3	Sales Executive	4	Married	9713	24444	2	Y	Yes	13	3	4	80	3	9	3	3	5	3	1	0
+29	No	Travel_Rarely	592	Research & Development	7	3	Life Sciences	1	1883	4	Male	59	3	1	Laboratory Technician	1	Single	2062	19384	3	Y	No	14	3	2	80	0	11	2	3	3	2	1	2
+37	No	Travel_Rarely	783	Research & Development	7	4	Medical	1	1885	4	Male	78	3	2	Research Scientist	1	Married	4284	13588	5	Y	Yes	22	4	3	80	1	16	2	3	5	3	0	4
+35	No	Travel_Rarely	219	Research & Development	16	2	Other	1	1886	4	Female	44	2	2	Manufacturing Director	2	Married	4788	25388	0	Y	Yes	11	3	4	80	0	4	2	3	3	2	0	2
+45	No	Travel_Rarely	556	Research & Development	25	2	Life Sciences	1	1888	2	Female	93	2	2	Manufacturing Director	4	Married	5906	23888	0	Y	No	13	3	4	80	2	10	2	2	9	8	3	8
+36	No	Travel_Frequently	1213	Human Resources	2	1	Human Resources	1	1890	2	Male	94	2	2	Human Resources	4	Single	3886	4223	1	Y	No	21	4	4	80	0	10	2	2	10	1	0	8
+40	No	Travel_Rarely	1137	Research & Development	1	4	Life Sciences	1	1892	1	Male	98	3	4	Manager	1	Divorced	16823	18991	2	Y	No	11	3	1	80	1	22	3	3	19	7	11	16
+26	No	Travel_Rarely	482	Research & Development	1	2	Life Sciences	1	1893	2	Female	90	2	1	Research Scientist	3	Married	2933	14908	1	Y	Yes	13	3	3	80	1	1	3	2	1	0	1	0
+27	No	Travel_Rarely	511	Sales	2	2	Medical	1	1898	1	Female	89	4	2	Sales Executive	3	Single	6500	26997	0	Y	No	14	3	2	80	0	9	5	2	8	7	0	7
+48	No	Travel_Frequently	117	Research & Development	22	3	Medical	1	1900	4	Female	58	3	4	Manager	4	Divorced	17174	2437	3	Y	No	11	3	2	80	1	24	3	3	22	17	4	7
+44	No	Travel_Rarely	170	Research & Development	1	4	Life Sciences	1	1903	2	Male	78	4	2	Healthcare Representative	1	Married	5033	9364	2	Y	No	15	3	4	80	1	10	5	3	2	0	2	2
+34	Yes	Non-Travel	967	Research & Development	16	4	Technical Degree	1	1905	4	Male	85	1	1	Research Scientist	1	Married	2307	14460	1	Y	Yes	23	4	2	80	1	5	2	3	5	2	3	0
+56	Yes	Travel_Rarely	1162	Research & Development	24	2	Life Sciences	1	1907	1	Male	97	3	1	Laboratory Technician	4	Single	2587	10261	1	Y	No	16	3	4	80	0	5	3	3	4	2	1	0
+36	No	Travel_Rarely	335	Sales	17	2	Marketing	1	1908	3	Male	33	2	2	Sales Executive	2	Married	5507	16822	2	Y	No	16	3	3	80	2	12	1	1	4	2	1	3
+41	No	Travel_Rarely	337	Sales	8	3	Marketing	1	1909	3	Female	54	3	2	Sales Executive	2	Married	4393	26841	5	Y	No	21	4	3	80	1	14	3	3	5	4	1	4
+42	No	Travel_Rarely	1396	Research & Development	6	3	Medical	1	1911	3	Male	83	3	3	Research Director	1	Married	13348	14842	9	Y	No	13	3	2	80	1	18	3	4	13	7	5	7
+31	No	Travel_Rarely	1079	Sales	10	2	Medical	1	1912	3	Female	86	3	2	Sales Executive	4	Divorced	6583	20115	2	Y	Yes	11	3	4	80	1	8	2	3	5	2	1	4
+34	No	Travel_Rarely	735	Sales	3	1	Medical	1	1915	4	Female	75	2	2	Sales Executive	4	Married	8103	16495	3	Y	Yes	12	3	3	80	0	9	3	2	4	2	0	1
+31	No	Travel_Rarely	471	Research & Development	4	3	Medical	1	1916	1	Female	62	4	1	Laboratory Technician	3	Divorced	3978	16031	8	Y	No	12	3	2	80	1	4	0	2	2	2	2	2
+26	No	Travel_Frequently	1096	Research & Development	6	3	Other	1	1918	3	Male	61	4	1	Laboratory Technician	4	Married	2544	7102	0	Y	No	18	3	1	80	1	8	3	3	7	7	7	7
+45	No	Travel_Frequently	1297	Research & Development	1	4	Medical	1	1922	2	Male	44	3	2	Healthcare Representative	3	Single	5399	14511	4	Y	No	12	3	3	80	0	12	3	3	4	2	0	3
+33	No	Travel_Rarely	217	Sales	10	4	Marketing	1	1924	2	Male	43	3	2	Sales Executive	3	Single	5487	10410	1	Y	No	14	3	2	80	0	10	2	2	10	4	0	9
+28	No	Travel_Frequently	783	Sales	1	2	Life Sciences	1	1927	3	Male	42	2	2	Sales Executive	4	Married	6834	19255	1	Y	Yes	12	3	3	80	1	7	2	3	7	7	0	7
+29	Yes	Travel_Frequently	746	Sales	24	3	Technical Degree	1	1928	3	Male	45	4	1	Sales Representative	1	Single	1091	10642	1	Y	No	17	3	4	80	0	1	3	3	1	0	0	0
+39	No	Non-Travel	1251	Sales	21	4	Life Sciences	1	1929	1	Female	32	1	2	Sales Executive	3	Married	5736	3987	6	Y	No	19	3	3	80	1	10	1	3	3	2	1	2
+27	No	Travel_Rarely	1354	Research & Development	2	4	Technical Degree	1	1931	2	Male	41	3	1	Research Scientist	2	Married	2226	6073	1	Y	No	11	3	3	80	1	6	3	2	5	3	1	2
+34	No	Travel_Frequently	735	Research & Development	22	4	Other	1	1932	3	Male	86	2	2	Research Scientist	4	Married	5747	26496	1	Y	Yes	15	3	2	80	0	16	3	3	15	10	6	11
+28	Yes	Travel_Rarely	1475	Sales	13	2	Marketing	1	1933	4	Female	84	3	2	Sales Executive	3	Single	9854	23352	3	Y	Yes	11	3	4	80	0	6	0	3	2	0	2	2
+47	No	Non-Travel	1169	Research & Development	14	4	Technical Degree	1	1934	3	Male	64	3	2	Research Scientist	2	Married	5467	2125	8	Y	No	18	3	3	80	1	16	4	4	8	7	1	7
+56	No	Travel_Rarely	1443	Sales	11	5	Marketing	1	1935	4	Female	89	2	2	Sales Executive	1	Married	5380	20328	4	Y	No	16	3	3	80	1	6	3	3	0	0	0	0
+39	No	Travel_Rarely	867	Research & Development	9	2	Medical	1	1936	1	Male	87	3	2	Manufacturing Director	1	Married	5151	12315	1	Y	No	25	4	4	80	1	10	3	3	10	0	7	9
+38	No	Travel_Frequently	1394	Research & Development	8	3	Medical	1	1937	4	Female	58	2	2	Research Scientist	2	Divorced	2133	18115	1	Y	Yes	16	3	3	80	1	20	3	3	20	11	0	7
+58	No	Travel_Rarely	605	Sales	21	3	Life Sciences	1	1938	4	Female	72	3	4	Manager	4	Married	17875	11761	4	Y	Yes	13	3	3	80	1	29	2	2	1	0	0	0
+32	Yes	Travel_Frequently	238	Research & Development	5	2	Life Sciences	1	1939	1	Female	47	4	1	Research Scientist	3	Single	2432	15318	3	Y	Yes	14	3	1	80	0	8	2	3	4	1	0	3
+38	No	Travel_Rarely	1206	Research & Development	9	2	Life Sciences	1	1940	2	Male	71	3	1	Research Scientist	4	Divorced	4771	14293	2	Y	No	19	3	4	80	2	10	0	4	5	2	0	3
+49	No	Travel_Frequently	1064	Research & Development	2	1	Life Sciences	1	1941	2	Male	42	3	5	Research Director	4	Married	19161	13738	3	Y	No	15	3	4	80	0	28	3	3	5	4	4	3
+42	No	Travel_Rarely	419	Sales	12	4	Marketing	1	1943	2	Male	77	3	2	Sales Executive	4	Divorced	5087	2900	3	Y	Yes	12	3	3	80	2	14	4	3	0	0	0	0
+27	Yes	Travel_Frequently	1337	Human Resources	22	3	Human Resources	1	1944	1	Female	58	2	1	Human Resources	2	Married	2863	19555	1	Y	No	12	3	1	80	0	1	2	3	1	0	0	0
+35	No	Travel_Rarely	682	Sales	18	4	Medical	1	1945	2	Male	71	3	2	Sales Executive	1	Married	5561	15975	0	Y	No	16	3	4	80	1	6	2	1	5	3	0	4
+28	No	Non-Travel	1103	Research & Development	16	3	Medical	1	1947	3	Male	49	3	1	Research Scientist	3	Single	2144	2122	1	Y	No	14	3	3	80	0	5	3	2	5	3	1	4
+31	No	Non-Travel	976	Research & Development	3	2	Medical	1	1948	3	Male	48	3	1	Research Scientist	1	Divorced	3065	3995	1	Y	Yes	13	3	4	80	1	4	3	4	4	2	2	3
+36	No	Non-Travel	1351	Research & Development	9	4	Life Sciences	1	1949	1	Male	66	4	1	Laboratory Technician	2	Married	2810	9238	1	Y	No	22	4	2	80	0	5	3	3	5	4	0	2
+34	No	Travel_Rarely	937	Sales	1	3	Marketing	1	1950	1	Male	32	3	3	Sales Executive	4	Single	9888	6770	1	Y	No	21	4	1	80	0	14	3	2	14	8	2	1
+34	No	Travel_Rarely	1239	Sales	13	4	Medical	1	1951	4	Male	39	3	3	Sales Executive	3	Divorced	8628	22914	1	Y	No	18	3	3	80	1	9	2	2	8	7	1	1
+26	No	Travel_Rarely	157	Research & Development	1	3	Medical	1	1952	3	Male	95	3	1	Laboratory Technician	1	Single	2867	20006	0	Y	No	13	3	4	80	0	8	6	2	7	7	7	6
+29	No	Travel_Rarely	136	Research & Development	1	3	Life Sciences	1	1954	1	Male	89	3	2	Healthcare Representative	1	Married	5373	6225	0	Y	No	12	3	1	80	1	6	5	2	5	3	0	2
+32	No	Non-Travel	1146	Research & Development	15	4	Medical	1	1955	3	Female	34	3	2	Healthcare Representative	4	Divorced	6667	16542	5	Y	No	18	3	2	80	1	9	6	3	5	1	1	2
+31	No	Travel_Frequently	1125	Research & Development	1	3	Life Sciences	1	1956	4	Male	48	1	2	Research Scientist	1	Married	5003	5771	1	Y	No	21	4	2	80	0	10	6	3	10	8	8	7
+28	Yes	Travel_Rarely	1404	Research & Development	17	3	Technical Degree	1	1960	3	Male	32	2	1	Laboratory Technician	4	Divorced	2367	18779	5	Y	No	12	3	1	80	1	6	2	2	4	1	0	3
+38	No	Travel_Rarely	1404	Sales	1	3	Life Sciences	1	1961	1	Male	59	2	1	Sales Representative	1	Single	2858	11473	4	Y	No	14	3	1	80	0	20	3	2	1	0	0	0
+35	No	Travel_Rarely	1224	Sales	7	4	Life Sciences	1	1962	3	Female	55	3	2	Sales Executive	4	Married	5204	13586	1	Y	Yes	11	3	4	80	0	10	2	3	10	8	0	9
+27	No	Travel_Rarely	954	Sales	9	3	Marketing	1	1965	4	Male	44	3	2	Sales Executive	4	Single	4105	5099	1	Y	No	14	3	1	80	0	7	5	3	7	7	0	7
+32	No	Travel_Rarely	1373	Research & Development	5	4	Life Sciences	1	1966	4	Male	56	2	2	Manufacturing Director	4	Single	9679	10138	8	Y	No	24	4	2	80	0	8	1	3	1	0	0	0
+31	Yes	Travel_Frequently	754	Sales	26	4	Marketing	1	1967	1	Male	63	3	2	Sales Executive	4	Married	5617	21075	1	Y	Yes	11	3	3	80	0	10	4	3	10	7	0	8
+53	Yes	Travel_Rarely	1168	Sales	24	4	Life Sciences	1	1968	1	Male	66	3	3	Sales Executive	1	Single	10448	5843	6	Y	Yes	13	3	2	80	0	15	2	2	2	2	2	2
+54	No	Travel_Rarely	155	Research & Development	9	2	Life Sciences	1	1969	1	Female	67	3	2	Research Scientist	3	Married	2897	22474	3	Y	No	11	3	3	80	2	9	6	2	4	3	2	3
+33	No	Travel_Frequently	1303	Research & Development	7	2	Life Sciences	1	1970	4	Male	36	3	2	Healthcare Representative	3	Divorced	5968	18079	1	Y	No	20	4	3	80	3	9	2	3	9	7	2	8
+43	No	Travel_Rarely	574	Research & Development	11	3	Life Sciences	1	1971	1	Male	30	3	3	Healthcare Representative	3	Married	7510	16873	1	Y	No	17	3	2	80	1	10	1	3	10	9	0	9
+38	No	Travel_Frequently	1444	Human Resources	1	4	Other	1	1972	4	Male	88	3	1	Human Resources	2	Married	2991	5224	0	Y	Yes	11	3	2	80	1	7	2	3	6	2	1	2
+55	No	Travel_Rarely	189	Human Resources	26	4	Human Resources	1	1973	3	Male	71	4	5	Manager	2	Married	19636	25811	4	Y	Yes	18	3	1	80	1	35	0	3	10	9	1	4
+31	No	Travel_Rarely	1276	Research & Development	2	1	Medical	1	1974	4	Female	59	1	1	Laboratory Technician	4	Divorced	1129	17536	1	Y	Yes	11	3	3	80	3	1	4	3	1	0	0	0
+39	No	Travel_Rarely	119	Sales	15	4	Marketing	1	1975	2	Male	77	3	4	Sales Executive	1	Single	13341	25098	0	Y	No	12	3	1	80	0	21	3	3	20	8	11	10
+42	No	Non-Travel	335	Research & Development	23	2	Life Sciences	1	1976	4	Male	37	2	2	Research Scientist	3	Single	4332	14811	1	Y	No	12	3	4	80	0	20	2	3	20	9	3	7
+31	No	Non-Travel	697	Research & Development	10	3	Medical	1	1979	3	Female	40	3	3	Research Director	3	Married	11031	26862	4	Y	No	20	4	3	80	1	13	2	4	11	7	4	8
+54	No	Travel_Rarely	157	Research & Development	10	3	Medical	1	1980	3	Female	77	3	2	Manufacturing Director	1	Single	4440	25198	6	Y	Yes	19	3	4	80	0	9	3	3	5	2	1	4
+24	No	Travel_Rarely	771	Research & Development	1	2	Life Sciences	1	1981	2	Male	45	2	2	Healthcare Representative	3	Single	4617	14120	1	Y	No	12	3	2	80	0	4	2	2	4	3	1	2
+23	No	Travel_Rarely	571	Research & Development	12	2	Other	1	1982	4	Male	78	3	1	Laboratory Technician	4	Single	2647	13672	1	Y	No	13	3	3	80	0	5	6	4	5	2	1	4
+40	No	Travel_Frequently	692	Research & Development	11	3	Technical Degree	1	1985	4	Female	73	3	2	Laboratory Technician	3	Married	6323	26849	1	Y	No	11	3	1	80	1	10	2	4	10	9	9	4
+40	No	Travel_Rarely	444	Sales	2	2	Marketing	1	1986	2	Female	92	3	2	Sales Executive	2	Married	5677	4258	3	Y	No	14	3	3	80	1	15	4	3	11	8	5	10
+25	No	Travel_Rarely	309	Human Resources	2	3	Human Resources	1	1987	3	Female	82	3	1	Human Resources	2	Married	2187	19655	4	Y	No	14	3	3	80	0	6	3	3	2	0	1	2
+30	No	Travel_Rarely	911	Research & Development	1	2	Medical	1	1989	4	Male	76	3	1	Laboratory Technician	2	Married	3748	4077	1	Y	No	13	3	3	80	0	12	6	2	12	8	1	7
+25	No	Travel_Rarely	977	Research & Development	2	1	Other	1	1992	4	Male	57	3	1	Laboratory Technician	3	Divorced	3977	7298	6	Y	Yes	19	3	3	80	1	7	2	2	2	2	0	2
+47	No	Travel_Rarely	1180	Research & Development	25	3	Medical	1	1993	1	Male	84	3	3	Healthcare Representative	3	Single	8633	13084	2	Y	No	23	4	2	80	0	25	3	3	17	14	12	11
+33	No	Non-Travel	1313	Research & Development	1	2	Medical	1	1994	2	Male	59	2	1	Laboratory Technician	3	Divorced	2008	20439	1	Y	No	12	3	3	80	3	1	2	2	1	1	0	0
+38	No	Travel_Rarely	1321	Sales	1	4	Life Sciences	1	1995	4	Male	86	3	2	Sales Executive	2	Married	4440	7636	0	Y	No	15	3	1	80	2	16	3	3	15	13	5	8
+31	No	Travel_Rarely	1154	Sales	2	2	Life Sciences	1	1996	1	Male	54	3	1	Sales Representative	3	Married	3067	6393	0	Y	No	19	3	3	80	1	3	1	3	2	2	1	2
+38	No	Travel_Frequently	508	Research & Development	6	4	Life Sciences	1	1997	1	Male	72	2	2	Manufacturing Director	3	Married	5321	14284	2	Y	No	11	3	4	80	1	10	1	3	8	3	7	7
+42	No	Travel_Rarely	557	Research & Development	18	4	Life Sciences	1	1998	4	Male	35	3	2	Research Scientist	1	Divorced	5410	11189	6	Y	Yes	17	3	3	80	1	9	3	2	4	3	1	2
+41	No	Travel_Rarely	642	Research & Development	1	3	Life Sciences	1	1999	4	Male	76	3	1	Research Scientist	4	Married	2782	21412	3	Y	No	22	4	1	80	1	12	3	3	5	3	1	0
+47	No	Non-Travel	1162	Research & Development	1	1	Medical	1	2000	3	Female	98	3	3	Research Director	2	Married	11957	17231	0	Y	No	18	3	1	80	2	14	3	1	13	8	5	12
+35	No	Travel_Rarely	1490	Research & Development	11	4	Medical	1	2003	4	Male	43	3	1	Laboratory Technician	3	Married	2660	20232	7	Y	Yes	11	3	3	80	1	5	3	3	2	2	2	2
+22	No	Travel_Rarely	581	Research & Development	1	2	Life Sciences	1	2007	4	Male	63	3	1	Research Scientist	3	Single	3375	17624	0	Y	No	12	3	4	80	0	4	2	4	3	2	1	2
+35	No	Travel_Rarely	1395	Research & Development	9	4	Medical	1	2008	2	Male	48	3	2	Research Scientist	3	Single	5098	18698	1	Y	No	19	3	2	80	0	10	5	3	10	7	0	8
+33	No	Travel_Rarely	501	Research & Development	15	2	Medical	1	2009	2	Female	95	3	2	Healthcare Representative	4	Married	4878	21653	0	Y	Yes	13	3	1	80	1	10	6	3	9	7	8	1
+32	No	Travel_Rarely	267	Research & Development	29	4	Life Sciences	1	2010	3	Female	49	2	1	Laboratory Technician	2	Single	2837	15919	1	Y	No	13	3	3	80	0	6	3	3	6	2	4	1
+40	No	Travel_Rarely	543	Research & Development	1	4	Life Sciences	1	2012	1	Male	83	3	1	Laboratory Technician	4	Married	2406	4060	8	Y	No	19	3	3	80	2	8	3	2	1	0	0	0
+32	No	Travel_Rarely	234	Sales	1	4	Medical	1	2013	2	Male	68	2	1	Sales Representative	2	Married	2269	18024	0	Y	No	14	3	2	80	1	3	2	3	2	2	2	2
+39	No	Travel_Rarely	116	Research & Development	24	1	Life Sciences	1	2014	1	Male	52	3	2	Research Scientist	4	Single	4108	5340	7	Y	No	13	3	1	80	0	18	2	3	7	7	1	7
+38	No	Travel_Rarely	201	Research & Development	10	3	Medical	1	2015	2	Female	99	1	3	Research Director	3	Married	13206	3376	3	Y	No	12	3	1	80	1	20	3	3	18	16	1	11
+32	No	Travel_Rarely	801	Sales	1	4	Marketing	1	2016	3	Female	48	3	3	Sales Executive	4	Married	10422	24032	1	Y	No	19	3	3	80	2	14	3	3	14	10	5	7
+37	No	Travel_Rarely	161	Research & Development	10	3	Life Sciences	1	2017	3	Female	42	4	3	Research Director	4	Married	13744	15471	1	Y	Yes	25	4	1	80	1	16	2	3	16	11	6	8
+25	No	Travel_Rarely	1382	Sales	8	2	Other	1	2018	1	Female	85	3	2	Sales Executive	3	Divorced	4907	13684	0	Y	Yes	22	4	2	80	1	6	3	2	5	3	0	4
+52	No	Non-Travel	585	Sales	29	4	Life Sciences	1	2019	1	Male	40	3	1	Sales Representative	4	Divorced	3482	19788	2	Y	No	15	3	2	80	2	16	3	2	9	8	0	0
+44	No	Travel_Rarely	1037	Research & Development	1	3	Medical	1	2020	2	Male	42	3	1	Research Scientist	4	Single	2436	13422	6	Y	Yes	12	3	3	80	0	6	2	3	4	3	1	2
+21	No	Travel_Rarely	501	Sales	5	1	Medical	1	2021	3	Male	58	3	1	Sales Representative	1	Single	2380	25479	1	Y	Yes	11	3	4	80	0	2	6	3	2	2	1	2
+39	No	Non-Travel	105	Research & Development	9	3	Life Sciences	1	2022	4	Male	87	3	5	Manager	4	Single	19431	15302	2	Y	No	13	3	3	80	0	21	3	2	6	0	1	3
+23	Yes	Travel_Frequently	638	Sales	9	3	Marketing	1	2023	4	Male	33	3	1	Sales Representative	1	Married	1790	26956	1	Y	No	19	3	1	80	1	1	3	2	1	0	1	0
+36	No	Travel_Rarely	557	Sales	3	3	Medical	1	2024	1	Female	94	2	3	Sales Executive	4	Married	7644	12695	0	Y	No	19	3	3	80	2	10	2	3	9	7	3	4
+36	No	Travel_Frequently	688	Research & Development	4	2	Life Sciences	1	2025	4	Female	97	3	2	Manufacturing Director	2	Divorced	5131	9192	7	Y	No	13	3	2	80	3	18	3	3	4	2	0	2
+56	No	Non-Travel	667	Research & Development	1	4	Life Sciences	1	2026	3	Male	57	3	2	Healthcare Representative	3	Divorced	6306	26236	1	Y	No	21	4	1	80	1	13	2	2	13	12	1	9
+29	Yes	Travel_Rarely	1092	Research & Development	1	4	Medical	1	2027	1	Male	36	3	1	Research Scientist	4	Married	4787	26124	9	Y	Yes	14	3	2	80	3	4	3	4	2	2	2	2
+42	No	Travel_Rarely	300	Research & Development	2	3	Life Sciences	1	2031	1	Male	56	3	5	Manager	3	Married	18880	17312	5	Y	No	11	3	1	80	0	24	2	2	22	6	4	14
+56	Yes	Travel_Rarely	310	Research & Development	7	2	Technical Degree	1	2032	4	Male	72	3	1	Laboratory Technician	3	Married	2339	3666	8	Y	No	11	3	4	80	1	14	4	1	10	9	9	8
+41	No	Travel_Rarely	582	Research & Development	28	4	Life Sciences	1	2034	1	Female	60	2	4	Manufacturing Director	2	Married	13570	5640	0	Y	No	23	4	3	80	1	21	3	3	20	7	0	10
+34	No	Travel_Rarely	704	Sales	28	3	Marketing	1	2035	4	Female	95	2	2	Sales Executive	3	Married	6712	8978	1	Y	No	21	4	4	80	2	8	2	3	8	7	1	7
+36	No	Non-Travel	301	Sales	15	4	Marketing	1	2036	4	Male	88	1	2	Sales Executive	4	Divorced	5406	10436	1	Y	No	24	4	1	80	1	15	4	2	15	12	11	11
+41	No	Travel_Rarely	930	Sales	3	3	Life Sciences	1	2037	3	Male	57	2	2	Sales Executive	2	Divorced	8938	12227	2	Y	No	11	3	3	80	1	14	5	3	5	4	0	4
+32	No	Travel_Rarely	529	Research & Development	2	3	Technical Degree	1	2038	4	Male	78	3	1	Research Scientist	1	Single	2439	11288	1	Y	No	14	3	4	80	0	4	4	3	4	2	1	2
+35	No	Travel_Rarely	1146	Human Resources	26	4	Life Sciences	1	2040	3	Female	31	3	3	Human Resources	4	Single	8837	16642	1	Y	Yes	16	3	3	80	0	9	2	3	9	0	1	7
+38	No	Travel_Rarely	345	Sales	10	2	Life Sciences	1	2041	1	Female	100	3	2	Sales Executive	4	Married	5343	5982	1	Y	No	11	3	3	80	1	10	1	3	10	7	1	9
+50	Yes	Travel_Frequently	878	Sales	1	4	Life Sciences	1	2044	2	Male	94	3	2	Sales Executive	3	Divorced	6728	14255	7	Y	No	12	3	4	80	2	12	3	3	6	3	0	1
+36	No	Travel_Rarely	1120	Sales	11	4	Marketing	1	2045	2	Female	100	2	2	Sales Executive	4	Married	6652	14369	4	Y	No	13	3	1	80	1	8	2	2	6	3	0	0
+45	No	Travel_Rarely	374	Sales	20	3	Life Sciences	1	2046	4	Female	50	3	2	Sales Executive	3	Single	4850	23333	8	Y	No	15	3	3	80	0	8	3	3	5	3	0	1
+40	No	Travel_Rarely	1322	Research & Development	2	4	Life Sciences	1	2048	3	Male	52	2	1	Research Scientist	3	Single	2809	2725	2	Y	No	14	3	4	80	0	8	2	3	2	2	2	2
+35	No	Travel_Frequently	1199	Research & Development	18	4	Life Sciences	1	2049	3	Male	80	3	2	Healthcare Representative	3	Married	5689	24594	1	Y	Yes	14	3	4	80	2	10	2	4	10	2	0	2
+40	No	Travel_Rarely	1194	Research & Development	2	4	Medical	1	2051	3	Female	98	3	1	Research Scientist	3	Married	2001	12549	2	Y	No	14	3	2	80	3	20	2	3	5	3	0	2
+35	No	Travel_Rarely	287	Research & Development	1	4	Life Sciences	1	2052	3	Female	62	1	1	Research Scientist	4	Married	2977	8952	1	Y	No	12	3	4	80	1	4	5	3	4	3	1	1
+29	No	Travel_Rarely	1378	Research & Development	13	2	Other	1	2053	4	Male	46	2	2	Laboratory Technician	2	Married	4025	23679	4	Y	Yes	13	3	1	80	1	10	2	3	4	3	0	3
+29	No	Travel_Rarely	468	Research & Development	28	4	Medical	1	2054	4	Female	73	2	1	Research Scientist	1	Single	3785	8489	1	Y	No	14	3	2	80	0	5	3	1	5	4	0	4
+50	Yes	Travel_Rarely	410	Sales	28	3	Marketing	1	2055	4	Male	39	2	3	Sales Executive	1	Divorced	10854	16586	4	Y	Yes	13	3	2	80	1	20	3	3	3	2	2	0
+39	No	Travel_Rarely	722	Sales	24	1	Marketing	1	2056	2	Female	60	2	4	Sales Executive	4	Married	12031	8828	0	Y	No	11	3	1	80	1	21	2	2	20	9	9	6
+31	No	Non-Travel	325	Research & Development	5	3	Medical	1	2057	2	Male	74	3	2	Manufacturing Director	1	Single	9936	3787	0	Y	No	19	3	2	80	0	10	2	3	9	4	1	7
+26	No	Travel_Rarely	1167	Sales	5	3	Other	1	2060	4	Female	30	2	1	Sales Representative	3	Single	2966	21378	0	Y	No	18	3	4	80	0	5	2	3	4	2	0	0
+36	No	Travel_Frequently	884	Research & Development	23	2	Medical	1	2061	3	Male	41	4	2	Laboratory Technician	4	Married	2571	12290	4	Y	No	17	3	3	80	1	17	3	3	5	2	0	3
+39	No	Travel_Rarely	613	Research & Development	6	1	Medical	1	2062	4	Male	42	2	3	Healthcare Representative	1	Married	9991	21457	4	Y	No	15	3	1	80	1	9	5	3	7	7	1	7
+27	No	Travel_Rarely	155	Research & Development	4	3	Life Sciences	1	2064	2	Male	87	4	2	Manufacturing Director	2	Married	6142	5174	1	Y	Yes	20	4	2	80	1	6	0	3	6	2	0	3
+49	No	Travel_Frequently	1023	Sales	2	3	Medical	1	2065	4	Male	63	2	2	Sales Executive	2	Married	5390	13243	2	Y	No	14	3	4	80	0	17	3	2	9	6	0	8
+34	No	Travel_Rarely	628	Research & Development	8	3	Medical	1	2068	2	Male	82	4	2	Laboratory Technician	3	Married	4404	10228	2	Y	No	12	3	1	80	0	6	3	4	4	3	1	2
\ No newline at end of file
diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index cb1cfed0e..c68d25655 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -2,16 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING (pytensor.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`\n",
-      "WARNING (pytensor.configdefaults): g++ not detected!  PyTensor will be unable to compile C-implementations and will default to Python. Performance may be severely degraded. To remove this warning, set PyTensor flags cxx to an empty string.\n",
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
      ]
     }
    ],
@@ -78,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +101,7 @@
        "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,14 +122,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\stechsga\\AppData\\Local\\Temp\\ipykernel_9160\\1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_32825/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
       "  logit_func = lambda x: np.log(x / (1 - x))\n"
      ]
     },
@@ -141,7 +140,7 @@
        "        1.76938091,         nan])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,17 +155,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\stechsga\\Miniconda3\\envs\\bambi\\Lib\\site-packages\\bambi\\formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
+      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
       "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
-      "c:\\Users\\stechsga\\Miniconda3\\envs\\bambi\\Lib\\site-packages\\formulae\\terms\\variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
@@ -206,7 +203,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 19:14&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -221,7 +218,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1174 seconds.\n"
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
      ]
     }
    ],
@@ -232,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -270,74 +279,74 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>response_threshold[0]</th>\n",
-       "      <td>-1.923</td>\n",
+       "      <td>-1.922</td>\n",
        "      <td>0.030</td>\n",
        "      <td>-1.979</td>\n",
-       "      <td>-1.868</td>\n",
+       "      <td>-1.865</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4152.0</td>\n",
-       "      <td>3065.0</td>\n",
+       "      <td>4234.0</td>\n",
+       "      <td>2969.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[1]</th>\n",
-       "      <td>-1.270</td>\n",
+       "      <td>-1.269</td>\n",
        "      <td>0.024</td>\n",
-       "      <td>-1.315</td>\n",
-       "      <td>-1.223</td>\n",
+       "      <td>-1.316</td>\n",
+       "      <td>-1.225</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5031.0</td>\n",
-       "      <td>3389.0</td>\n",
+       "      <td>5204.0</td>\n",
+       "      <td>3592.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[2]</th>\n",
        "      <td>-0.719</td>\n",
-       "      <td>0.021</td>\n",
-       "      <td>-0.758</td>\n",
+       "      <td>0.022</td>\n",
+       "      <td>-0.757</td>\n",
        "      <td>-0.678</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5272.0</td>\n",
-       "      <td>3462.0</td>\n",
+       "      <td>5581.0</td>\n",
+       "      <td>3758.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[3]</th>\n",
-       "      <td>0.248</td>\n",
+       "      <td>0.249</td>\n",
        "      <td>0.020</td>\n",
-       "      <td>0.208</td>\n",
-       "      <td>0.285</td>\n",
+       "      <td>0.212</td>\n",
+       "      <td>0.287</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4636.0</td>\n",
-       "      <td>3102.0</td>\n",
+       "      <td>5138.0</td>\n",
+       "      <td>3361.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[4]</th>\n",
-       "      <td>0.892</td>\n",
+       "      <td>0.893</td>\n",
        "      <td>0.022</td>\n",
        "      <td>0.852</td>\n",
        "      <td>0.935</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4861.0</td>\n",
-       "      <td>3639.0</td>\n",
+       "      <td>5173.0</td>\n",
+       "      <td>3303.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[5]</th>\n",
-       "      <td>1.774</td>\n",
+       "      <td>1.775</td>\n",
        "      <td>0.029</td>\n",
-       "      <td>1.721</td>\n",
-       "      <td>1.829</td>\n",
+       "      <td>1.717</td>\n",
+       "      <td>1.825</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5602.0</td>\n",
-       "      <td>3655.0</td>\n",
+       "      <td>5318.0</td>\n",
+       "      <td>3331.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -345,24 +354,24 @@
        "</div>"
       ],
       "text/plain": [
-       "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "response_threshold[0] -1.923  0.030  -1.979   -1.868        0.0      0.0   \n",
-       "response_threshold[1] -1.270  0.024  -1.315   -1.223        0.0      0.0   \n",
-       "response_threshold[2] -0.719  0.021  -0.758   -0.678        0.0      0.0   \n",
-       "response_threshold[3]  0.248  0.020   0.208    0.285        0.0      0.0   \n",
-       "response_threshold[4]  0.892  0.022   0.852    0.935        0.0      0.0   \n",
-       "response_threshold[5]  1.774  0.029   1.721    1.829        0.0      0.0   \n",
+       "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
+       "response_threshold[0] -1.922  0.030  -1.979   -1.865        0.0      0.0  \\\n",
+       "response_threshold[1] -1.269  0.024  -1.316   -1.225        0.0      0.0   \n",
+       "response_threshold[2] -0.719  0.022  -0.757   -0.678        0.0      0.0   \n",
+       "response_threshold[3]  0.249  0.020   0.212    0.287        0.0      0.0   \n",
+       "response_threshold[4]  0.893  0.022   0.852    0.935        0.0      0.0   \n",
+       "response_threshold[5]  1.775  0.029   1.717    1.825        0.0      0.0   \n",
        "\n",
        "                       ess_bulk  ess_tail  r_hat  \n",
-       "response_threshold[0]    4152.0    3065.0    1.0  \n",
-       "response_threshold[1]    5031.0    3389.0    1.0  \n",
-       "response_threshold[2]    5272.0    3462.0    1.0  \n",
-       "response_threshold[3]    4636.0    3102.0    1.0  \n",
-       "response_threshold[4]    4861.0    3639.0    1.0  \n",
-       "response_threshold[5]    5602.0    3655.0    1.0  "
+       "response_threshold[0]    4234.0    2969.0    1.0  \n",
+       "response_threshold[1]    5204.0    3592.0    1.0  \n",
+       "response_threshold[2]    5581.0    3758.0    1.0  \n",
+       "response_threshold[3]    5138.0    3361.0    1.0  \n",
+       "response_threshold[4]    5173.0    3303.0    1.0  \n",
+       "response_threshold[5]    5318.0    3331.0    1.0  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -382,19 +391,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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0Bz9QO+jub0cWlYjEVWl5JX/O38yM+YVsKD5AVqd23HXJEG4ek03vru3jHZ5IzGJJcmeHv++vM+bAJU0ejYjE1fvb9/HovAKee7uIgxXVjM7O4Bc3ns3kkb1p10ZTktL6xNJP7qR7yJnZZII2PanAQ+7+QD37fAz4LkHiXOruN5/s94nIiauucf6+Olhua+66XbRtk8JHzurLJ8fncFb/jHiHJ3JKYlnxJBO4D7iAIBG9Cdzv7ruO87lUghY9E4HNwCIzm+Xuq+rsMwS4F5jg7nvMrOdJn4mInJA9Byp4atEmHptfSFFJGX27tmfqZUO58bwBZHZqF+/wRJpELNOVTwFvANeG2x8nuD/3oeN8bgywtra5qpk9BVwNrKqzz+3Ar919D4C774g9dBE5GSuK9vLo3AJmLd3Coaoaxp2WyX9eeQYfOqMXbep0ABBJBLEkuT7u/v062z8wsxti+Fw/YFOd7c0c22j1dAAze4tgSvO77v7y0QcyszuAOwCys7Nj+GoRqauiqoaXV27j0bkFLC7cQ3paKted259PjMtlaO/O8Q5PJDKxJLk5ZnYj8HS4fR0wuwm/fwhwMdAfeMPMznT3kro7ufuDwIMQtNppou8WSXg7Sst5IuwAsHPfIXIyO/DtD5/B9XkD6JqeFu/wRCIXS5K7HfgyUNsdPBU4YGafI+gr11AjqCJgQJ3t/uFYXZuBBWFHgw1m9h5B0lsUW/gicjR35+2Ne3h0biEvLt9KVY1z8dAefHJcLhed3oMUdQCQJBJLdeXJzmUsAoaY2UCC5HYjcHTl5EzgJuCPZpZFMH25/iS/TySplVdWM2tpsNzWiqJSOrdrwyfG5XLruBwGZnWMd3gicRHZ8uDuXmVmdxJMbaYCD7v7SjO7H8h391nhe5PMbBXBos9Tj1e1KZLsZi4pYtrsNWwpKaNvRjqfuSCXHfsq+NOijew5WMmQnp34wZSRXDO6Hx3VAUCSnLm3rltceXl5np+fH+8wROJi5pIi7n1uOWWVxzYCuWxELz45Ppdxp6kDgCQfM1vs7nlHj+uveSKtyLTZa+pNcL26tON3tx7z37dI0ovpoRgzu8DMPhW+7hHeZxORZuTuFJWU1fvejlI1BhGpz3GTnJndB3yDYGUSgDTgsSiDEpEj7dhXzh0zFjf4ft+M9GaMRqT1iOVK7hrgKsIOBO6+BdDToyLNwN2ZtXQLk37+Bq+/t5OrR/UlPe3I/2zT01KZetnQBo4gktxiuSdX4e5uZg5gZqpFFmkGu/Yf4tszV/DSim2MGpDBz64fxeCenY6prpx62VCmjO4X73BFWqRYktzTZvY7IMPMbgc+Dfw+2rBEkttLy7fy7Zkr2FdexTcmD+P2Dww8vK7klNH9lNREYhTLw+A/NbOJQCkwFPiOu78SeWQiSWjPgQq+M2slLyzdwpn9uvKzj43i9F66OyBysmJptfNV4E9KbCLRmrNyG998fgV7yyr42sTT+fzFg0hTVwCRUxLLdGVngkWadxO02HnG3bdHG5ZI8th7sJLvvrCS55cUMbxPF6Z/egzD+za0JKyInIhYpiu/B3zPzM4CbgBeN7PN7n68fnIichyvvrude55dzu4DFXzp0iF84YODadtGV28iTeVEVjzZAWwDdgHq4C1yCvaWVfKDv67imcWbGda7Mw/fdh4j+3WNd1giCSeWe3L/D/gY0AN4Brjd3Vc1/ikRacjr7+3knmeXsWPfIe784GDuunQw7dqkxjsskYQUy5XcAODL7v5OxLGIJLR95ZX814ureXLhJgb37MRzt5zLqAEZ8Q5LJKE1mOTMrIu7lwLTwu3udd93990RxyaSMN5aW8zX/7yMrXvL+NxFp/GVD51O+zRdvYlErbEruSeAK4HFgAN1e3c4cFqEcYkkhAOHqvjRS6t5bP5GTsvqyDOfH8+5Od3iHZZI0mgwybn7leFvdRwQOQnz1u3i688uZfOeMj57wUDuvmyort5EmlkshSf/cPdLjzcmIoGDFVX85OU1PDK3gNzMDjz9uXGcl9v9+B8UkSbX2D259kAHIMvMuvHv6cougBbOE6nHooLdTH1mKQW7DnLb+Fy+PnkoHdqqN7FIvDT2X9/ngC8DfQnuy9UmuVLgf6MNS6R1Ka+s5qez1/CHtzbQv1s6T95+PuMGZcY7LJGk19g9uV8AvzCzu9z9V80Yk0ir8vbGPdz99FLWFx/g1vNzuOfyYXRsp6s3kZYglmW9fmVmI4HhQPs649OjDEykpSuvrObnf3+P37+xnj5d03n8s2OZMDgr3mGJSB2xFJ7cB1xMkOReBC4H3gSU5CRpLd1UwteeWcraHfu5aUw237xiGJ3bp8U7LBE5SixzKtcBo4Al7v4pM+sFPBZtWCIt06Gqan75j/f57evr6dGpHY9+egwXnd4j3mGJSANiSXJl7l5jZlVm1oVgoeYBEccl0uKsKNrL3c8s5d1t+7j+3P58+8rhdE3X1ZtISxZLkss3swzg9wRVlvuBeVEGJdKSVFTV8OvX1vLr19bSvWNbHr4tj0uG9Yp3WCISg1gKT/5f+PK3ZvYy0MXdl8VycDObDPwCSAUecvcHGtjvWuDPwHnunh9T5CLNYNWWUu5+Zimrtpby0dH9uO8jI+jaQVdvIq1FYw+Dn9PYe+7+dmMHNrNU4NfARGAzsMjMZh3dpsfMOgNfAhacSOAiUaqsruG3/1zHL199n67paTx467lMGtE73mGJyAlq7EruZ42858Alxzn2GGCtu68HMLOngKuBo3vRfR/4MTD1OMcTaRbvbd/H155eyvKivXxkVF++d9UIundsG++wROQkNPYw+AdP8dj9gE11tjcDY+vuEF4tDnD3v5lZg0nOzO4A7gDIzs4+xbBE6ldVXcPv/7WBn7/yHp3at+H/Pn4OV5zZJ95hicgpiOU5uU/UN36qD4ObWQrw38Btx9vX3R8EHgTIy8vzU/lekfqs3bGfu59ZyjubSrh8ZG++P2UkWZ3axTssETlFsVRXnlfndXvgUuBtjv8weBFHPmrQPxyr1RkYCfzTzAB6A7PM7CoVn0hzqa5xHn5zA9PmrKFD21R+ddNorjyrD+G/kyLSysVSXXlX3e3wcYKnYjj2ImCImQ0kSG43AjfXOe5e4PAaSGb2T+BuJThpLhuKD3D3M0tZXLiHicN78cNrRtKzc/vjf1BEWo2TWUX2AHDcRqruXmVmdwKzCR4heNjdV5rZ/UC+u886ie8WOWU1Nc4jcwv4yex3aZuaws9vGMWUs/vp6k0kAcVyT+4FgmpKgBSCNSyfjuXg7v4iwXqXdce+08C+F8dyTJFTUbjrAFP/vIyFG3ZzybCe/OijZ9Kri67eRBJVLFdyP63zugoodPfNEcUjEomaGuexBYX86MV3aZNiTLvuLK47t7+u3kQSXCz35F4HCNetbBO+7u7uuyOOTaRJbNp9kG88u4y563Zx4ek9+PG1Z9Kna3q8wxKRZhDLdOUdwP1AOVBD0CHcgdOiDU3k1Lg7Ty7cxA//tgoz44GPnskN5w3Q1ZtIEollunIqMNLdi6MORuRkzVxSxLTZa9hSUkbfjHQ++4GBvPruDv71fjETBmfy42vPon+3DvEOU0SaWSxJbh1wMOpARE7WzCVF3PvccsoqqwEoKinjey+sIi3V+MGUkXx8bLau3kSSVCxJ7l5grpktAA7VDrr7FyOLSuQETJu95nCCq6t7x7bccn5OHCISkZYiliT3O+BVYDnBPTmRFqG6xllcuIeikrJ6399ReqjecRFJHrEkuTR3/2rkkYjEoKyimjfXFjNn5Tb+8e4Odh+oaHDfvhmqoBRJdrEkuZfCCssXOHK6Uo8QSLPYfaCCf6zeziurtvPG+zspr6yhc/s2XDqsJxOH92ZfeSXfe2HVEVOW6WmpTL1saByjFpGWIJYkd1P4+946Y3qEQCK1cddB5qzaxpxV28kv2E2NQ5+u7bkhbwCTRvRmzMDupKWmHN6/fVrqEdWVUy8bypTR/eJ4BiLSEsTyMPhx16kUOVXuzoqiUuas2sYrq7bz7rZ9AAzr3Zk7PziYSSN6M6JvlwarJKeM7qekJiLHiFs/OZGKqhoWbNjFK6uCqcite8tJMTgvtzvf/vAZTBrem+xMPdsmIicvyn5yIsfYV17J6+/tZM7K7by2Zgf7yqton5bChUN68LVJQ7lkWE+6d2wb7zBFJEFE2U9OBIDtpeWHr9bmrdtFRXUN3Tu25fKRvZk4vDcXDM4ivW1qvMMUkQQUWT85SV7uzrqd+5m9Mkhs72wqASAnswOfHJ/DpBG9OSe7G6kpWoVERKIVaT85SR7VNc6SjXt4ZdV25qzazobiAwCM6t+VuyedzqQRvRnSs5OW1xKRZqV+cnLSyiureWttMXNWbucf726neH8FaanG+adl8ukLBjLxjF707qqGpCISPw0mOTMbDPSq7SdXZ3yCmbVz93WRRyctzp4DFbz67g5eWbWd19/bSVllNZ3ateHioT2YNKI3Fw/tQZf2afEOU0QEaPxK7n848gHwWqXhex+JIB5pgTbtPhhOQ25jUcEeqmucXl3ace25/Zg4vDfnn9addm1UOCIiLU9jSa6Xuy8/etDdl5tZbnQhSby5Oyu3lB6+v7Z6aykAp/fqxOcvOo1Jw3tzZr+upKhwRERauMaSXEYj72nl2wRTWV3Dwg27D5f6F5WUYQZ5Od341hVnMHF4L3KzOsY7TBGRE9JYkss3s9vd/fd1B83ss8DiaMOS5rD/UBVvvLeTOSu38eq7Oygtr6JdmxQ+MCSLL106hEvO6ElWp3bxDlNE5KQ1luS+DDxvZh/n30ktD2gLXBNxXNIEZi4pOmbR4vGDM/nH6h3MWbmNt9btoqKqhowOaUwc3puJw3tx4elZdGh7Mo9Pioi0PObuje9g9kFgZLi50t1fjTyqRuTl5Xl+fn48Q2gVZi4p4t7nlh/RfsYMav9x9++WzqThvZk0ohd5Od1oU2dFfxGR1sbMFrt73tHjsSzr9RrwWiRRSST2HKjg/hdWHpHgIEhwndu34enPjWNY7856MFtEEl6k81JmNhn4BZAKPOTuDxz1/leBzxI8ZL4T+LS7F0YZUyIqq6hmYcFu5q4t5q11xazcUkpDF+j7y6s4o0+X5g1QRCROIktyZpYK/BqYCGwGFpnZLHdfVWe3JUCeux80s/8AfgLcEFVMiaKquoalm/cyd20xb64tZsnGEiqqa0hLNUZnd+PLl57OjPkFFO+vOOazfTNUGCsiySPKK7kxwFp3Xw9gZk8BVwOHk1w4FVprPnBLhPG0Wu7O+zv289baYt5aW8yC9bvZd6gKgOF9unDbhFzGD8pkzMDuh4tGcjI7HHNPLj0tlamXDY3LOYiIxEOUSa4fsKnO9mZgbCP7fwZ4qb43zOwO4A6A7OzspoqvRdtSUnY4qb21bhc79x0CILt7B64c1ZcJgzMZd1ommQ2U+Nd2yT66ulLds0UkmbSIWnEzu4Xg8YSL6nvf3R8EHoSgurIZQ2s2JQcrmL9+F2+uLWbu2l2sD1fxz+zYlvGDs5gwKJMJg7MY0D32TtlTRvdTUhORpBZlkisCBtTZ7h+OHcHMPgR8C7jI3Q9FGE+LUl5ZzaKC3by1dhdz1xWzvGgv7tChbSpjB3bn5rHZTBicxdBenbV8lojISYoyyS0ChpjZQILkdiNwc90dzGw08DtgsrvviDCWuKuqrmF50V7mrtvFm+8Xs3jjHiqqamiTYozOzuCLlwzhgiFZjOqfQds2emZNRKQpRJbk3L3KzO4EZhM8QvCwu680s/uBfHefBUwDOgHPhM9sbXT3q6KKqTnVdsd+a20wBTl//S72lQfFIsN6d+YT5+cwYXAWYwZ2p2O7FjFrLCKScCL9v6u7vwi8eNTYd+q8/lCU39/ctu0tr1MsUsz20mD2tX+3dD58Zh/GD85i/KBMrQcpItJMdAlxCvaWVTJ//a7DiW3dzqBYpFuHtLBYJIsLBmeRnRl7sYiIiDQdJbkTUF5ZzeLCPYfL+pdvLqHGg+fPxgzszo3nZTN+cCZn9O6iYhERkRZASa4R1TXOiqK9vLUuuFLLL9jDoaoaUlOMswdkcOclQ5gwKJPR2d1ULCIi0gIlXZKrr/1M7bNk7s764gOHl8uat24XpWGxyNBenfn42BwmDA5WFuncPi2epyEiIjFIqiR3dPuZopIy7nl2GYsKd1Fe4cxdV8zWveUA9MtIZ/LI3kwYnMW4QZn07Nw+nqGLiMhJSKokN232mmPaz5RX1fD4/E1kdEhj/KBMvhAWi+RkdlArGhGRVi6pktyWkrJ6xw14+9sTVSwiIpJgkqpaoqE2M30z0pXgREQSUFIluamXDSU9LfWIMbWfERFJXEk1Xan2MyIiySWpkhyo/YyISDJJqulKERFJLkpyIiKSsJTkREQkYZm7xzuGE2JmO4HCJjhUFlDcBMdpDXSuiSmZzhWS63x1ricux917HD3Y6pJcUzGzfHfPi3cczUHnmpiS6Vwhuc5X59p0NF0pIiIJS0lOREQSVjInuQfjHUAz0rkmpmQ6V0iu89W5NpGkvScnIiKJL5mv5EREJMElXZIzs4fNbIeZrYh3LFEzswFm9pqZrTKzlWb2pXjHFBUza29mC81saXiu34t3TFEzs1QzW2Jmf413LFEyswIzW25m75hZfrzjiZKZZZjZn83sXTNbbWbj4h1TVMxsaPjPtPan1My+3OTfk2zTlWZ2IbAfmO7uI+MdT5TMrA/Qx93fNrPOwGJgiruvinNoTc6CDrcd3X2/maUBbwJfcvf5cQ4tMmb2VSAP6OLuV8Y7nqiYWQGQ5+4J/9yYmT0K/MvdHzKztkAHdy+Jc1iRM7NUoAgY6+5N8Rz0YUl3JefubwC74x1Hc3D3re7+dvh6H7AaSMjVqT2wP9xMC38S9m9wZtYf+DDwULxjkaZhZl2BC4E/ALh7RTIkuNClwLqmTnCQhEkuWZlZLjAaWBDnUCITTt+9A+wAXnH3hD1X4H+ArwM1cY6jOTgwx8wWm9kd8Q4mQgOBncAfw2noh8ysY7yDaiY3Ak9GcWAluSRgZp2AZ4Evu3tpvOOJirtXu/vZQH9gjJkl5HS0mV0J7HD3xfGOpZlc4O7nAJcDXwhvOSSiNsA5wG/cfTRwALgnviFFL5yWvQp4JorjK8kluPD+1LPA4+7+XLzjaQ7hFM9rwOQ4hxKVCcBV4b2qp4BLzOyx+IYUHXcvCn/vAJ4HxsQ3oshsBjbXmYH4M0HSS3SXA2+7+/YoDq4kl8DCYow/AKvd/b/jHU+UzKyHmWWEr9OBicC7cQ0qIu5+r7v3d/dcgmmeV939ljiHFQkz6xgWTRFO3U0CErIy2t23AZvMbGg4dCmQcEVi9biJiKYqIQk7g5vZk8DFQJaZbQbuc/c/xDeqyEwAbgWWh/eqAL7p7i/GL6TI9AEeDau0UoCn3T2hS+uTRC/g+eDva7QBnnD3l+MbUqTuAh4Pp/DWA5+KczyRCv/iMhH4XGTfkWyPEIiISPLQdKWIiCQsJTkREUlYSnIiIpKwlORERCRhKcmJiEjCUpKTpGdm1eEq6CvM7IXa5+0SnZlNMbPh8Y5DJEpKciJQ5u5nh10pdgNfiHdAzWQKEGmSM7OkexZXWhYlOZEjzSPs1GBmg8zs5XBh4H+Z2bBw/Prwqm+pmb0Rjt1mZn8xs3+a2ftmdl/tAc3sq+H+K2r7ZZlZbtgv7Pdh/7s54UotmNkXwx6Ay8zsqXCsY9gLcWG4eO/V9QVvZt8Ie68tNbMHwrHbzWxROPasmXUws/EE6wVOC69iBzVyvoPMbH543B+Y2f5w3MxsWnhey83shnD84vDzs4BVZnZ/3T5hZvZDS+DehtLCuLt+9JPUP8D+8HcqwSKxk8PtfwBDwtdjCZbPAlgO9AtfZ4S/bwO2AplAOsHSU3nAueH+HYFOwEqCbhC5QBVwdvj5p4FbwtdbgHZHHf+/6ryfAbxH0D+v7nlcDswl6EEG0D38nVlnnx8Ad4WvHwGuq/NeQ+f7V+Cm8PXn6/x5XQu8Ev659QI2Eqw8czHB4sIDw/1yCdYmhOAv1uvqxqQf/UT5o6kEEUgPlz3rR9Bz75Wwc8N44JlwSSmAduHvt4BHzOxpoO6i16+4+y4AM3sOuICgTczz7n6gzvgHgFnABnd/J/zsYoJkALCMYGmnmcDMcGwSwaLMd4fb7YHsMN5aHwL+6O4HAdy9tm/iSDP7AUFy7ATMPvoP4DjnO45gahPgCeCn4esLgCfdvRrYbmavA+cBpcBCd98QxlFgZrvMbDRBMlxS++ckEjUlOZHwnpyZdSBIAF8guMop8aB1zxHc/fNmNpagaeliMzu39q2jdz3O9x6q87qa4AqQ8LgXAh8BvmVmZwIGXOvua2I+q397hKAj/FIzu43gSutoKTRwvifpwFHbDxFc7fYGHm6i7xA5Lt2TEwmFV0BfBL4GHAQ2mNn1cPj+06jw9SB3X+Du3yFocjkgPMREM+se3lubQnDF9y9gSngfrCNwTThWLzNLAQa4+2vAN4Cu/Pvq6y4LL7PCq6KjvQJ8KkzWmFn3cLwzsNWCtksfr7P/vvA9POgzWO/5AvMJpiYh6HpQ61/ADRY0q+1BkJgXNnBqzxO0PjqPeq4kRaKiJCdSh7svIZguvIkgIXzGzJYS3EurLfaYFhZarCC4B7Y0HF9I0LtvGfCsu+e7+9sEV1ILCbqyPxR+R0NSgcfMbDmwBPilB/3xvg+kAcvMbGW4fXTsLxNMg+aH06+1U5v/GX73WxzZfugpYGpYyDKokfP9MvBVM1sGDAb2huPPh+e6FHgV+LoH7WKO4e4VBD3+ng6nN0WahboQiDSBcBowz93vjHcsTS28MixzdzezGwmKUOqt7mzkGCnA28D17v5+FHGK1Ef35ETkeM4F/jecKi0BPn0iH7bggfO/EhTgKMFJs9KVnIiIJCzdkxMRkYSlJCciIglLSU5ERBKWkpyIiCQsJTkREUlYSnIiIpKw/j9ocJ2btES7WQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
-       "<Figure size 504x216 with 1 Axes>"
+       "<Figure size 700x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -411,19 +418,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 504x216 with 1 Axes>"
+       "<Figure size 700x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -466,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -529,8 +534,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
       "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 79 seconds.\n"
@@ -555,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -593,62 +596,62 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>action[1]</th>\n",
-       "      <td>-0.466</td>\n",
-       "      <td>0.055</td>\n",
-       "      <td>-0.567</td>\n",
-       "      <td>-0.361</td>\n",
+       "      <td>-0.464</td>\n",
+       "      <td>0.057</td>\n",
+       "      <td>-0.574</td>\n",
+       "      <td>-0.357</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2387.0</td>\n",
-       "      <td>2733.0</td>\n",
+       "      <td>2537.0</td>\n",
+       "      <td>2856.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>intention[1]</th>\n",
-       "      <td>-0.276</td>\n",
-       "      <td>0.058</td>\n",
-       "      <td>-0.392</td>\n",
-       "      <td>-0.174</td>\n",
+       "      <td>-0.274</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>-0.386</td>\n",
+       "      <td>-0.169</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2376.0</td>\n",
-       "      <td>2645.0</td>\n",
+       "      <td>2705.0</td>\n",
+       "      <td>2756.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact[1]</th>\n",
-       "      <td>-0.325</td>\n",
-       "      <td>0.071</td>\n",
-       "      <td>-0.459</td>\n",
-       "      <td>-0.195</td>\n",
+       "      <td>-0.324</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>-0.456</td>\n",
+       "      <td>-0.190</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2709.0</td>\n",
-       "      <td>2889.0</td>\n",
+       "      <td>2918.0</td>\n",
+       "      <td>2525.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>action:intention[1, 1]</th>\n",
-       "      <td>-0.455</td>\n",
-       "      <td>0.080</td>\n",
-       "      <td>-0.600</td>\n",
-       "      <td>-0.299</td>\n",
+       "      <td>-0.458</td>\n",
+       "      <td>0.084</td>\n",
+       "      <td>-0.611</td>\n",
+       "      <td>-0.296</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2483.0</td>\n",
-       "      <td>2882.0</td>\n",
+       "      <td>2895.0</td>\n",
+       "      <td>2917.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact:intention[1, 1]</th>\n",
-       "      <td>-1.284</td>\n",
-       "      <td>0.102</td>\n",
-       "      <td>-1.461</td>\n",
-       "      <td>-1.078</td>\n",
+       "      <td>-1.286</td>\n",
+       "      <td>0.099</td>\n",
+       "      <td>-1.473</td>\n",
+       "      <td>-1.104</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2759.0</td>\n",
-       "      <td>2926.0</td>\n",
+       "      <td>3275.0</td>\n",
+       "      <td>3357.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -657,21 +660,21 @@
       ],
       "text/plain": [
        "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "action[1]               -0.466  0.055  -0.567   -0.361      0.001    0.001  \\\n",
-       "intention[1]            -0.276  0.058  -0.392   -0.174      0.001    0.001   \n",
-       "contact[1]              -0.325  0.071  -0.459   -0.195      0.001    0.001   \n",
-       "action:intention[1, 1]  -0.455  0.080  -0.600   -0.299      0.002    0.001   \n",
-       "contact:intention[1, 1] -1.284  0.102  -1.461   -1.078      0.002    0.001   \n",
+       "action[1]               -0.464  0.057  -0.574   -0.357      0.001    0.001  \\\n",
+       "intention[1]            -0.274  0.059  -0.386   -0.169      0.001    0.001   \n",
+       "contact[1]              -0.324  0.072  -0.456   -0.190      0.001    0.001   \n",
+       "action:intention[1, 1]  -0.458  0.084  -0.611   -0.296      0.002    0.001   \n",
+       "contact:intention[1, 1] -1.286  0.099  -1.473   -1.104      0.002    0.001   \n",
        "\n",
        "                         ess_bulk  ess_tail  r_hat  \n",
-       "action[1]                  2387.0    2733.0    1.0  \n",
-       "intention[1]               2376.0    2645.0    1.0  \n",
-       "contact[1]                 2709.0    2889.0    1.0  \n",
-       "action:intention[1, 1]     2483.0    2882.0    1.0  \n",
-       "contact:intention[1, 1]    2759.0    2926.0    1.0  "
+       "action[1]                  2537.0    2856.0    1.0  \n",
+       "intention[1]               2705.0    2756.0    1.0  \n",
+       "contact[1]                 2918.0    2525.0    1.0  \n",
+       "action:intention[1, 1]     2895.0    2917.0    1.0  \n",
+       "contact:intention[1, 1]    3275.0    3357.0    1.0  "
       ]
      },
-     "execution_count": 54,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -693,12 +696,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -727,12 +730,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -763,12 +766,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -848,6 +851,893 @@
     "Thus, the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$ rather than continuing past it."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Human resources attrition dataset\n",
+    "\n",
+    "To illustrate an sequential model with a stopping ratio link function, we will use data from the IBM human resources employee attrition and performance [dataset](https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset). The original dataset contains 1470 rows and 35 columns. However, our goal is to model the total working years of employees using age as a predictor. This data lends itself to a sequential model as the response, total working years, is a sequential process. In order to have 10 years of working experience, it is necessarily true that the employee had 9 years of working experience. Additionally, age is choosen as a predictor as it is positively correlated with total working years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "attrition = pd.read_csv(\"data/hr_employee_attrition.tsv.txt\", sep=\"\\t\")\n",
+    "attrition = attrition[attrition[\"Attrition\"] == \"No\"]\n",
+    "attrition[\"YearsAtCompany\"] = pd.Categorical(attrition[\"YearsAtCompany\"], ordered=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(attrition.TotalWorkingYears, attrition.YearsAtCompany, alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>YearsAtCompany</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>TotalWorkingYears</th>\n",
+       "      <th>YearsInCurrentRole</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "      <td>41</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>49</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>37</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8</td>\n",
+       "      <td>33</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>27</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1465</th>\n",
+       "      <td>5</td>\n",
+       "      <td>36</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1466</th>\n",
+       "      <td>7</td>\n",
+       "      <td>39</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1467</th>\n",
+       "      <td>6</td>\n",
+       "      <td>27</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1468</th>\n",
+       "      <td>9</td>\n",
+       "      <td>49</td>\n",
+       "      <td>17</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1469</th>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1470 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      YearsAtCompany  Age TotalWorkingYears  YearsInCurrentRole\n",
+       "0                  6   41                 8                   4\n",
+       "1                 10   49                10                   7\n",
+       "2                  0   37                 7                   0\n",
+       "3                  8   33                 8                   7\n",
+       "4                  2   27                 6                   2\n",
+       "...              ...  ...               ...                 ...\n",
+       "1465               5   36                17                   2\n",
+       "1466               7   39                 9                   7\n",
+       "1467               6   27                 6                   2\n",
+       "1468               9   49                17                   6\n",
+       "1469               4   34                 6                   3\n",
+       "\n",
+       "[1470 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "attrition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
+      "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [YearsAtCompany_threshold, TotalWorkingYears]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 03:57&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 238 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "sequence_model = bmb.Model(\"YearsAtCompany ~ TotalWorkingYears\", data=attrition, family=\"sratio\")\n",
+    "sequence_idata = sequence_model.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[0]</th>\n",
+       "      <td>-2.086</td>\n",
+       "      <td>0.304</td>\n",
+       "      <td>-2.669</td>\n",
+       "      <td>-1.541</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>4099.0</td>\n",
+       "      <td>2853.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[1]</th>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.149</td>\n",
+       "      <td>-0.254</td>\n",
+       "      <td>0.304</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1677.0</td>\n",
+       "      <td>2547.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[2]</th>\n",
+       "      <td>-0.903</td>\n",
+       "      <td>0.201</td>\n",
+       "      <td>-1.275</td>\n",
+       "      <td>-0.523</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>2099.0</td>\n",
+       "      <td>2739.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[3]</th>\n",
+       "      <td>-0.529</td>\n",
+       "      <td>0.187</td>\n",
+       "      <td>-0.894</td>\n",
+       "      <td>-0.194</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1870.0</td>\n",
+       "      <td>1959.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[4]</th>\n",
+       "      <td>-0.038</td>\n",
+       "      <td>0.160</td>\n",
+       "      <td>-0.340</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1735.0</td>\n",
+       "      <td>2493.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[5]</th>\n",
+       "      <td>0.419</td>\n",
+       "      <td>0.151</td>\n",
+       "      <td>0.131</td>\n",
+       "      <td>0.699</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1455.0</td>\n",
+       "      <td>2298.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[6]</th>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.142</td>\n",
+       "      <td>0.671</td>\n",
+       "      <td>1.205</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1250.0</td>\n",
+       "      <td>2195.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[7]</th>\n",
+       "      <td>0.663</td>\n",
+       "      <td>0.159</td>\n",
+       "      <td>0.363</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1386.0</td>\n",
+       "      <td>2334.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[8]</th>\n",
+       "      <td>1.062</td>\n",
+       "      <td>0.151</td>\n",
+       "      <td>0.764</td>\n",
+       "      <td>1.336</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1259.0</td>\n",
+       "      <td>2106.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[9]</th>\n",
+       "      <td>1.156</td>\n",
+       "      <td>0.157</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>1.451</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1314.0</td>\n",
+       "      <td>2440.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[10]</th>\n",
+       "      <td>2.325</td>\n",
+       "      <td>0.143</td>\n",
+       "      <td>2.063</td>\n",
+       "      <td>2.591</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1109.0</td>\n",
+       "      <td>2126.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[11]</th>\n",
+       "      <td>0.730</td>\n",
+       "      <td>0.207</td>\n",
+       "      <td>0.351</td>\n",
+       "      <td>1.127</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1984.0</td>\n",
+       "      <td>2533.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[12]</th>\n",
+       "      <td>1.184</td>\n",
+       "      <td>0.194</td>\n",
+       "      <td>0.817</td>\n",
+       "      <td>1.544</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1658.0</td>\n",
+       "      <td>2354.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[13]</th>\n",
+       "      <td>1.011</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>0.637</td>\n",
+       "      <td>1.436</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>2068.0</td>\n",
+       "      <td>2421.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[14]</th>\n",
+       "      <td>0.974</td>\n",
+       "      <td>0.229</td>\n",
+       "      <td>0.519</td>\n",
+       "      <td>1.377</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2081.0</td>\n",
+       "      <td>2651.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[15]</th>\n",
+       "      <td>1.385</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>0.988</td>\n",
+       "      <td>1.790</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1712.0</td>\n",
+       "      <td>2312.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[16]</th>\n",
+       "      <td>1.486</td>\n",
+       "      <td>0.215</td>\n",
+       "      <td>1.076</td>\n",
+       "      <td>1.883</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1738.0</td>\n",
+       "      <td>2604.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[17]</th>\n",
+       "      <td>1.538</td>\n",
+       "      <td>0.223</td>\n",
+       "      <td>1.151</td>\n",
+       "      <td>1.981</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1886.0</td>\n",
+       "      <td>2311.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[18]</th>\n",
+       "      <td>1.504</td>\n",
+       "      <td>0.246</td>\n",
+       "      <td>1.015</td>\n",
+       "      <td>1.952</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1739.0</td>\n",
+       "      <td>2268.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[19]</th>\n",
+       "      <td>1.417</td>\n",
+       "      <td>0.263</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>1.878</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2220.0</td>\n",
+       "      <td>2866.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[20]</th>\n",
+       "      <td>1.897</td>\n",
+       "      <td>0.237</td>\n",
+       "      <td>1.447</td>\n",
+       "      <td>2.332</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1874.0</td>\n",
+       "      <td>2381.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[21]</th>\n",
+       "      <td>2.260</td>\n",
+       "      <td>0.243</td>\n",
+       "      <td>1.817</td>\n",
+       "      <td>2.724</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1926.0</td>\n",
+       "      <td>2200.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[22]</th>\n",
+       "      <td>1.978</td>\n",
+       "      <td>0.279</td>\n",
+       "      <td>1.470</td>\n",
+       "      <td>2.484</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2066.0</td>\n",
+       "      <td>2272.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[23]</th>\n",
+       "      <td>2.245</td>\n",
+       "      <td>0.269</td>\n",
+       "      <td>1.729</td>\n",
+       "      <td>2.746</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2183.0</td>\n",
+       "      <td>2773.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[24]</th>\n",
+       "      <td>2.223</td>\n",
+       "      <td>0.295</td>\n",
+       "      <td>1.703</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2419.0</td>\n",
+       "      <td>3004.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[25]</th>\n",
+       "      <td>2.121</td>\n",
+       "      <td>0.327</td>\n",
+       "      <td>1.509</td>\n",
+       "      <td>2.723</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>2661.0</td>\n",
+       "      <td>2898.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[26]</th>\n",
+       "      <td>2.327</td>\n",
+       "      <td>0.325</td>\n",
+       "      <td>1.740</td>\n",
+       "      <td>2.950</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>2351.0</td>\n",
+       "      <td>2678.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[27]</th>\n",
+       "      <td>1.736</td>\n",
+       "      <td>0.416</td>\n",
+       "      <td>0.953</td>\n",
+       "      <td>2.484</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>2749.0</td>\n",
+       "      <td>2672.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[28]</th>\n",
+       "      <td>2.694</td>\n",
+       "      <td>0.332</td>\n",
+       "      <td>2.036</td>\n",
+       "      <td>3.300</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>1912.0</td>\n",
+       "      <td>2288.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[29]</th>\n",
+       "      <td>2.537</td>\n",
+       "      <td>0.383</td>\n",
+       "      <td>1.833</td>\n",
+       "      <td>3.248</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3087.0</td>\n",
+       "      <td>2747.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[30]</th>\n",
+       "      <td>2.341</td>\n",
+       "      <td>0.431</td>\n",
+       "      <td>1.521</td>\n",
+       "      <td>3.129</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3526.0</td>\n",
+       "      <td>2538.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[31]</th>\n",
+       "      <td>2.804</td>\n",
+       "      <td>0.396</td>\n",
+       "      <td>2.076</td>\n",
+       "      <td>3.573</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3354.0</td>\n",
+       "      <td>2859.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[32]</th>\n",
+       "      <td>3.073</td>\n",
+       "      <td>0.408</td>\n",
+       "      <td>2.330</td>\n",
+       "      <td>3.844</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3539.0</td>\n",
+       "      <td>2963.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[33]</th>\n",
+       "      <td>3.082</td>\n",
+       "      <td>0.461</td>\n",
+       "      <td>2.167</td>\n",
+       "      <td>3.896</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>4221.0</td>\n",
+       "      <td>2697.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[34]</th>\n",
+       "      <td>2.959</td>\n",
+       "      <td>0.531</td>\n",
+       "      <td>1.939</td>\n",
+       "      <td>3.960</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>4874.0</td>\n",
+       "      <td>2358.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[35]</th>\n",
+       "      <td>2.597</td>\n",
+       "      <td>0.633</td>\n",
+       "      <td>1.438</td>\n",
+       "      <td>3.805</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>4870.0</td>\n",
+       "      <td>2547.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[36]</th>\n",
+       "      <td>3.806</td>\n",
+       "      <td>0.554</td>\n",
+       "      <td>2.725</td>\n",
+       "      <td>4.801</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>4346.0</td>\n",
+       "      <td>2605.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[37]</th>\n",
+       "      <td>3.809</td>\n",
+       "      <td>0.669</td>\n",
+       "      <td>2.618</td>\n",
+       "      <td>5.112</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>5117.0</td>\n",
+       "      <td>2597.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears_threshold[38]</th>\n",
+       "      <td>2.597</td>\n",
+       "      <td>0.888</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>4.184</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>5761.0</td>\n",
+       "      <td>2541.0</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>0.084</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.078</td>\n",
+       "      <td>0.089</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>785.0</td>\n",
+       "      <td>1530.0</td>\n",
+       "      <td>1.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  mean     sd  hdi_3%  hdi_97%  mcse_mean   \n",
+       "TotalWorkingYears_threshold[0]  -2.086  0.304  -2.669   -1.541      0.005  \\\n",
+       "TotalWorkingYears_threshold[1]   0.010  0.149  -0.254    0.304      0.004   \n",
+       "TotalWorkingYears_threshold[2]  -0.903  0.201  -1.275   -0.523      0.004   \n",
+       "TotalWorkingYears_threshold[3]  -0.529  0.187  -0.894   -0.194      0.004   \n",
+       "TotalWorkingYears_threshold[4]  -0.038  0.160  -0.340    0.252      0.004   \n",
+       "TotalWorkingYears_threshold[5]   0.419  0.151   0.131    0.699      0.004   \n",
+       "TotalWorkingYears_threshold[6]   0.950  0.142   0.671    1.205      0.004   \n",
+       "TotalWorkingYears_threshold[7]   0.663  0.159   0.363    0.955      0.004   \n",
+       "TotalWorkingYears_threshold[8]   1.062  0.151   0.764    1.336      0.004   \n",
+       "TotalWorkingYears_threshold[9]   1.156  0.157   0.873    1.451      0.004   \n",
+       "TotalWorkingYears_threshold[10]  2.325  0.143   2.063    2.591      0.004   \n",
+       "TotalWorkingYears_threshold[11]  0.730  0.207   0.351    1.127      0.005   \n",
+       "TotalWorkingYears_threshold[12]  1.184  0.194   0.817    1.544      0.005   \n",
+       "TotalWorkingYears_threshold[13]  1.011  0.214   0.637    1.436      0.005   \n",
+       "TotalWorkingYears_threshold[14]  0.974  0.229   0.519    1.377      0.005   \n",
+       "TotalWorkingYears_threshold[15]  1.385  0.214   0.988    1.790      0.005   \n",
+       "TotalWorkingYears_threshold[16]  1.486  0.215   1.076    1.883      0.005   \n",
+       "TotalWorkingYears_threshold[17]  1.538  0.223   1.151    1.981      0.005   \n",
+       "TotalWorkingYears_threshold[18]  1.504  0.246   1.015    1.952      0.006   \n",
+       "TotalWorkingYears_threshold[19]  1.417  0.263   0.882    1.878      0.006   \n",
+       "TotalWorkingYears_threshold[20]  1.897  0.237   1.447    2.332      0.005   \n",
+       "TotalWorkingYears_threshold[21]  2.260  0.243   1.817    2.724      0.006   \n",
+       "TotalWorkingYears_threshold[22]  1.978  0.279   1.470    2.484      0.006   \n",
+       "TotalWorkingYears_threshold[23]  2.245  0.269   1.729    2.746      0.006   \n",
+       "TotalWorkingYears_threshold[24]  2.223  0.295   1.703    2.807      0.006   \n",
+       "TotalWorkingYears_threshold[25]  2.121  0.327   1.509    2.723      0.006   \n",
+       "TotalWorkingYears_threshold[26]  2.327  0.325   1.740    2.950      0.007   \n",
+       "TotalWorkingYears_threshold[27]  1.736  0.416   0.953    2.484      0.008   \n",
+       "TotalWorkingYears_threshold[28]  2.694  0.332   2.036    3.300      0.008   \n",
+       "TotalWorkingYears_threshold[29]  2.537  0.383   1.833    3.248      0.007   \n",
+       "TotalWorkingYears_threshold[30]  2.341  0.431   1.521    3.129      0.007   \n",
+       "TotalWorkingYears_threshold[31]  2.804  0.396   2.076    3.573      0.007   \n",
+       "TotalWorkingYears_threshold[32]  3.073  0.408   2.330    3.844      0.007   \n",
+       "TotalWorkingYears_threshold[33]  3.082  0.461   2.167    3.896      0.007   \n",
+       "TotalWorkingYears_threshold[34]  2.959  0.531   1.939    3.960      0.008   \n",
+       "TotalWorkingYears_threshold[35]  2.597  0.633   1.438    3.805      0.009   \n",
+       "TotalWorkingYears_threshold[36]  3.806  0.554   2.725    4.801      0.008   \n",
+       "TotalWorkingYears_threshold[37]  3.809  0.669   2.618    5.112      0.009   \n",
+       "TotalWorkingYears_threshold[38]  2.597  0.888   0.867    4.184      0.012   \n",
+       "Age                              0.084  0.003   0.078    0.089      0.000   \n",
+       "\n",
+       "                                 mcse_sd  ess_bulk  ess_tail  r_hat  \n",
+       "TotalWorkingYears_threshold[0]     0.003    4099.0    2853.0   1.00  \n",
+       "TotalWorkingYears_threshold[1]     0.003    1677.0    2547.0   1.00  \n",
+       "TotalWorkingYears_threshold[2]     0.003    2099.0    2739.0   1.00  \n",
+       "TotalWorkingYears_threshold[3]     0.003    1870.0    1959.0   1.00  \n",
+       "TotalWorkingYears_threshold[4]     0.003    1735.0    2493.0   1.00  \n",
+       "TotalWorkingYears_threshold[5]     0.003    1455.0    2298.0   1.00  \n",
+       "TotalWorkingYears_threshold[6]     0.003    1250.0    2195.0   1.00  \n",
+       "TotalWorkingYears_threshold[7]     0.003    1386.0    2334.0   1.00  \n",
+       "TotalWorkingYears_threshold[8]     0.003    1259.0    2106.0   1.00  \n",
+       "TotalWorkingYears_threshold[9]     0.003    1314.0    2440.0   1.00  \n",
+       "TotalWorkingYears_threshold[10]    0.003    1109.0    2126.0   1.00  \n",
+       "TotalWorkingYears_threshold[11]    0.003    1984.0    2533.0   1.00  \n",
+       "TotalWorkingYears_threshold[12]    0.003    1658.0    2354.0   1.00  \n",
+       "TotalWorkingYears_threshold[13]    0.003    2068.0    2421.0   1.00  \n",
+       "TotalWorkingYears_threshold[14]    0.004    2081.0    2651.0   1.00  \n",
+       "TotalWorkingYears_threshold[15]    0.004    1712.0    2312.0   1.00  \n",
+       "TotalWorkingYears_threshold[16]    0.004    1738.0    2604.0   1.00  \n",
+       "TotalWorkingYears_threshold[17]    0.004    1886.0    2311.0   1.00  \n",
+       "TotalWorkingYears_threshold[18]    0.004    1739.0    2268.0   1.00  \n",
+       "TotalWorkingYears_threshold[19]    0.004    2220.0    2866.0   1.00  \n",
+       "TotalWorkingYears_threshold[20]    0.004    1874.0    2381.0   1.00  \n",
+       "TotalWorkingYears_threshold[21]    0.004    1926.0    2200.0   1.00  \n",
+       "TotalWorkingYears_threshold[22]    0.004    2066.0    2272.0   1.00  \n",
+       "TotalWorkingYears_threshold[23]    0.004    2183.0    2773.0   1.00  \n",
+       "TotalWorkingYears_threshold[24]    0.004    2419.0    3004.0   1.00  \n",
+       "TotalWorkingYears_threshold[25]    0.004    2661.0    2898.0   1.00  \n",
+       "TotalWorkingYears_threshold[26]    0.005    2351.0    2678.0   1.00  \n",
+       "TotalWorkingYears_threshold[27]    0.006    2749.0    2672.0   1.00  \n",
+       "TotalWorkingYears_threshold[28]    0.005    1912.0    2288.0   1.00  \n",
+       "TotalWorkingYears_threshold[29]    0.005    3087.0    2747.0   1.00  \n",
+       "TotalWorkingYears_threshold[30]    0.005    3526.0    2538.0   1.00  \n",
+       "TotalWorkingYears_threshold[31]    0.005    3354.0    2859.0   1.00  \n",
+       "TotalWorkingYears_threshold[32]    0.005    3539.0    2963.0   1.00  \n",
+       "TotalWorkingYears_threshold[33]    0.005    4221.0    2697.0   1.00  \n",
+       "TotalWorkingYears_threshold[34]    0.005    4874.0    2358.0   1.00  \n",
+       "TotalWorkingYears_threshold[35]    0.007    4870.0    2547.0   1.00  \n",
+       "TotalWorkingYears_threshold[36]    0.006    4346.0    2605.0   1.00  \n",
+       "TotalWorkingYears_threshold[37]    0.007    5117.0    2597.0   1.00  \n",
+       "TotalWorkingYears_threshold[38]    0.008    5761.0    2541.0   1.00  \n",
+       "Age                                0.000     785.0    1530.0   1.01  "
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(sequence_idata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cumprobs = expit_func(sequence_idata.posterior.TotalWorkingYears_threshold).mean((\"chain\", \"draw\"))\n",
+    "cumprobs = np.append(cumprobs, 1)\n",
+    "\n",
+    "plt.figure(figsize=(7, 3))\n",
+    "plt.plot(sorted(attrition.TotalWorkingYears.unique()), cumprobs, marker='o')\n",
+    "plt.ylabel(\"Cumulative probability\")\n",
+    "plt.xlabel(\"Response category\");"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -855,6 +1745,72 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_32825/4080317185.py:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "  ax.set_xticklabels(sequence_model.response_component.response_term.levels);\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sequence_idata_pps = sequence_model.predict(idata=sequence_idata, kind=\"pps\", inplace=False)\n",
+    "\n",
+    "ax = az.plot_ppc(sequence_idata_pps, figsize=(7, 3), textsize=11)\n",
+    "# ax.set_xticks(np.linspace(0.2, 7, 7))\n",
+    "ax.set_xticklabels(sequence_model.response_component.response_term.levels);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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57NmzR79tY2PDL7/8osoTGxvL3bt3q7VeNjY2AMXO9dVXX5V7bNGDy6OzUH300UfF8hblqWgrhZOTEz169ECn07F9+3ays7Px9vZW5XnzzTdJSkrCzs6O7t27F3tVJJCoKq1WS48ePdi9e7fqmgoKCti6dStt2rTB0dFRdUzRehVF4uPjuXjxon5Gpcrcz9LK3LVrF3l5eeXO0lQR5X1mM2fO5Nq1a7z99tvcunWrUt2N/v3vf/Pzzz+r0rZv306TJk2KtTbMnDmTr7/+moCAAFq2bMmoUaPKLX/48OE4OzsTHBxMUlJSiXm+/fZb/WxGGo2m2H3/+uuvuXLliiqttHvSrl07HBwc+Pnnn0v8Lnbv3p0mTZoAhdPPxsbGqoL1goICPv/8c1WZRd2UHg2cjh07RnJyMv379y/3PhSZNGmSfqKCM2fOVHvXMCGEqC3SIiGeSUUz8fj4+NCtWzemTZtGhw4dyM3N5V//+hcff/wxHTt21M92NH78eObPn8+CBQt47bXX+PXXX1m/fn2lFs+qCEtLS9544w2Cg4N57rnnePHFFzlw4IC+e1JZ3NzceO6555g6dSoLFy7EyMiIbdu2FXtABHB2dgZg+fLlDB48GENDQzp16qTvglWSCRMmMGXKFK5evYqbm1uxX1CXLFnCvn37cHNzY+bMmbRr146srCxSU1P55ptviIyMrFDrT1UFBwfj7u5Ov3798Pf3x9jYmPDwcJKSktixY0exX4iPHz/OpEmTGDVqFJcuXeLDDz+kdevW+q4v7du3x87Ojr/97W8oioKFhQV79uzRd+spye7du2nQoAHu7u78+9//Zv78+XTu3LlYf/uqKO8zc3R0ZNCgQURHR/Pqq68WG/NQllatWjFs2DAWLVqElZUVW7duZd++fSxfvrzY2gfvvPMOAQEBfP/998ybN6/M70wRQ0NDoqKiGDBgAD179mTatGn069cPrVbLxYsX+eKLL9izZw83b94ECoPSTZs20b59ezp16sSJEydYuXJlse+PnZ0djRo1Ytu2bTg5OdG4cWNatWpFq1at+Oijjxg8eDADBw7Ey8uL1q1bc+PGDZKTk0lMTNQHCh9++CF79uyhf//+fPjhh/ouYkVTJhsYFP7e1q5dO9577z3CwsIwMDBg8ODBpKamMn/+fKytrZk9e3aF77e5uTmenp5ERETw4osvqmZVE0KIJ0rdjvUWom6dPHlSeffdd5UXXnhBMTY2VrRardKlSxdlwYIFSkZGhj5fdna2MnfuXMXa2lpp1KiR8tprryknT54sddamY8eOqc5TNKvPwzPDKIqivPvuu4pWq1WlpaWlKW+//bZiYWGhmJmZKe+8845y/PjxCs3aFB8fr/Ts2VMxNTVVmjdvrkyaNElJTEwsdmx2drYyadIkpXnz5opGo1HNTvPoNRXJzMxUGjVqpADKJ598UuL9/OOPP5SZM2cqtra2ipGRkWJhYaF069ZN+fDDD5W7d++WeEyRolmbygMo06dPL3HfDz/8oLz++uuKVqtVGjVqpLzyyivKnj17VHmKPqPvvvtOGT9+vGJubq40atRI8fDwUM6ePavK++uvvyru7u5KkyZNlOeee04ZNWqU8vvvvxebZanoszhx4oQydOhQpXHjxkqTJk2Uv/zlL8p//vOfYtdZlVmbyvrMimzatEkBlJ07d5Z9Ex/y4osvKkOGDFG++OILpUOHDoqxsbFiY2NTbIaxh3l5eSkNGjRQLl++XOHzKIqi3Lp1S1m6dKnStWtXpXHjxoqRkZHywgsvKO+8844SFxenz3fz5k1l4sSJSosWLRRTU1Pl1VdfVX744YcS792OHTuU9u3bK0ZGRsXu488//6yMHj1aadGihWJkZKRYWloqr7/+uhIZGakq44cfflB69OihmJiYKJaWlsoHH3ygLF++XAGUW7du6fPl5+cry5cvVxwdHRUjIyPl+eefV9555x3l0qVLqvIq8l0+dOiQAij/7//9v0rdQyGEqE80ivLIKkZCCCGeSP/1X//F0aNHSU1NrbFVonNycrCxseHVV18tcXG3p8WAAQNITU3lt99+q5Hy/fz8iIiI4NKlSzU+hkYIIWqKdG0SQognWHZ2NomJiSQkJBAVFUVoaGiNBBF//PEHZ86cQafT8Z///KdCg6afFHPmzKFLly5YW1tz48YNtm3bxr59+/j000+r/VxHjx7lt99+Izw8nClTpkgQIYR4okkgIYQQT7C0tDTc3Nxo2rQpU6ZMYcaMGTVynq+//hpvb2+srKwIDw8vd8rXJ0l+fj4LFiwgPT0djUbDSy+9xJYtW3jnnXeq/Vw9e/bE1NSUN998k2XLllV7+UIIUZuka5MQQgghhBCi0mT6VyGEEEIIIUSlSSAhhBBCCCGEqDQJJIQQQgghhBCVJoOtKVzF9OrVqzRp0qTYolVCCCGEENVJURTu3LlDq1at9IseCvEkkkACuHr1KtbW1nVdDSGEEEI8Qy5dulRsxXYhniQSSABNmjQBCv9BN23atI5rI4QQQoin2e3bt7G2ttY/fwjxpJJAAvTdmZo2bSqBhBBCCCFqhXSnFk866ZgnhBBCCCGEqDQJJIQQQgghhBCVJoGEEEIIIYQQotJkjIQQQgghxDMkPz+f3Nzcuq6GqKeMjIwwNDSsUF4JJIQQQgghngGKopCens6tW7fquiqinjM3N8fS0rLcCQEkkBBCCCGEeAYUBREtWrTA1NRUZo0SxSiKwv3798nIyADAysqqzPwSSAghhBBCPOXy8/P1QUSzZs3qujqiHmvUqBEAGRkZtGjRosxuTjLYWgghhBDiKVc0JsLU1LSOayKeBEXfk/LG0tRpIJGXl8e8efOwtbWlUaNGtG3bliVLllBQUKDPoygKixYtolWrVjRq1Ii+ffvy73//W1VOdnY2M2bM4Pnnn0er1TJs2DAuX75c25cjhBBCVMnKC2nlvoSoDtKdSVRERb8ndRpILF++nMjISNavX09ycjIrVqxg5cqVhIWF6fOsWLGC0NBQ1q9fz7Fjx7C0tMTd3Z07d+7o8/j6+hIVFcXOnTs5cuQId+/e5c033yQ/P78uLksIIYQQQoinXp2Okfjxxx8ZPnw4Q4YMAcDGxoYdO3Zw/PhxoLA1Ys2aNXz44Ye89dZbAGzevJmWLVuyfft2pkyZQmZmJp9++ilbtmzhjTfeAGDr1q1YW1uzf/9+Bg4cWDcXJ4QQQgghxFOsTgOJV199lcjISH777TccHR35+eefOXLkCGvWrAHgwoULpKenM2DAAP0xJiYmvPbaa8THxzNlyhROnDhBbm6uKk+rVq3o2LEj8fHxJQYS2dnZZGdn67dv375dcxcphBBCCFGPrd73W62da7a7Y6WPSU9PJzAwkK+//porV67QokULXFxc8PX1pX///hUqY9OmTfj6+j51U99mZ2fj7+/Pjh07ePDgAf379yc8PJw2bdrUyvnrtGvTX//6V/7yl7/Qvn17jIyM6NKlC76+vvzlL38BCr84AC1btlQd17JlS/2+9PR0jI2Nee6550rN86jg4GDMzMz0L2tr6+q+NCGEEEII8ZhSU1Pp1q0bsbGxrFixglOnThETE0O/fv2YPn16XVevyqprQcC67t5fp4HEP/7xD7Zu3cr27dtJTExk8+bNhISEsHnzZlW+Rwd8KIpS7iCQsvIEBASQmZmpf126dOnxLkQIIYR4yhw8eLDclxA1zcfHB41GQ0JCAm+//TaOjo506NCBOXPmcPToUX2+0NBQnJ2d0Wq1WFtb4+Pjw927dwE4dOgQ3t7eZGZmotFo0Gg0LFq0CICcnBzmzp1L69at0Wq19OjRg0OHDqnq8Mknn2BtbY2pqSkjR44kNDQUc3NzVZ6IiAjs7OwwNjamXbt2bNmyRbVfo9EQGRnJ8OHD0Wq1LFu2DHt7e0JCQlT5kpKSMDAwICUlpdx7U9S9f9WqVbzxxht06dKFrVu3curUKfbv31/BO/x46rRr0wcffMDf/vY3/vu//xsAZ2dnLl68SHBwMO+++y6WlpZAYavDwwtiZGRk6FspLC0tycnJ4ebNm6pWiYyMDNzc3Eo8r4mJCSYmJjV1WUIIIUS9JkGAeBLcuHGDmJgYAgMD0Wq1xfY//DBvYGDAunXrsLGx4cKFC/j4+DB37lzCw8Nxc3NjzZo1LFiwgDNnzgDQuHFjALy9vUlNTWXnzp20atWKqKgoBg0axKlTp3BwcCAuLo6pU6eyfPlyhg0bxv79+5k/f76qHlFRUcyaNYs1a9bwxhtvsHfvXry9vWnTpg39+vXT51u4cCHBwcGsXr0aQ0NDTExM0Ol0+Pv76/Ns3LiR3r17Y2dnh5eXF6mpqcUCmyJV6d5f3eq0ReL+/fsYGKirYGhoqJ/+1dbWFktLS/bt26ffn5OTw+HDh/VBQrdu3TAyMlLlSUtLIykpqdRAQgghhBBC1G/nzp1DURTat29fbl5fX1/69euHra0tr7/+OkuXLmXXrl0AGBsbY2ZmhkajwdLSEktLSxo3bkxKSgo7duzg888/1z+8+/v78+qrr6LT6QAICwtj8ODB+Pv74+joiI+PD4MHD1adOyQkBC8vL3x8fHB0dGTOnDm89dZbxVobxo4dy4QJE2jbti0vvvgi3t7enDlzhoSEBKCwu9PWrVuZMGECULiq9AsvvFDqNVele391q9MWiaFDhxIYGMgLL7xAhw4d+Ne//kVoaKj+Bmo0Gnx9fQkKCsLBwQEHBweCgoIwNTVl7NixAJiZmTFx4kT8/Pxo1qwZFhYW+Pv74+zsrJ/FSQghhBBqX9CwwnnfJqsGayJEyRRFASq2psHBgwcJCgri119/5fbt2+Tl5ZGVlcW9e/dKbM0ASExMRFEUHB3VA8Czs7P1q3+fOXOGkSNHqva7urqyd+9e/XZycjLvvfeeKk+vXr1Yu3atKq179+6qbSsrK4YMGcLGjRv1ZWZlZTFq1CigcExvVVRkCEB1qdNAIiwsjPnz5+Pj40NGRgatWrViypQpLFiwQJ9n7ty5PHjwAB8fH27evEmPHj347rvvaNKkiT7P6tWradCgAaNHj9aPWN+0aVOZS3oLIYQQQoj6y8HBAY1GQ3JyMiNGjCg138WLF/Hw8GDq1KksXboUCwsLjhw5wsSJE8sc1FxQUIChoSEnTpwo9sxY1PWppIfyogDnYRUZz1tSQDNp0iTGjx/P6tWr0el0jBkzpsKrj1ele391q9OuTU2aNGHNmjVcvHiRBw8ekJKSwrJlyzA2NtbnKRoQk5aWRlZWFocPH6Zjx46qcho2bEhYWBjXr1/n/v377NmzR2ZiEkIIIYR4gllYWDBw4EA2bNjAvXv3iu0vmsr1+PHj5OXlsWrVKl555RUcHR25evWqKq+xsXGxmYy6dOlCfn4+GRkZ2Nvbq15F43Tbt2+v73pUpGi9syJOTk4cOXJElRYfH4+Tk1O51+jh4YFWqyUiIoLo6Gh9r5yKqA/d++u0RUIIIUT9E//5tnLzuI0aVws1qaKDVegO0C+g+ushhHhsRYOlXV1dWbJkCZ06dSIvL499+/YRERFBcnIydnZ25OXlERYWxtChQ4mLiyMyMlJVjo2NDXfv3uXAgQN07twZU1NTHB0dGTduHJ6enqxatYouXbpw7do1YmNjcXZ2xsPDgxkzZtCnTx9CQ0MZOnQosbGxREdHq1obPvjgA0aPHk3Xrl3p378/e/bsYffu3RWaOcnQ0BAvLy8CAgKwt7enZ8+e+n0BAQFcuXKFzz77rMRj60P3fgkkhBBCiGdc3s3sMvdn38zEpK1ZLdVG1LaqLBJXW2xtbUlMTCQwMBA/Pz/S0tJo3rw53bp1IyIiAgAXFxdCQ0NZvnw5AQEB9OnTh+DgYDw9PfXluLm5MXXqVMaMGcP169dZuHAhixYtQqfTsWzZMvz8/Lhy5QrNmjWjZ8+eeHh4AIVjHSIjI1m8eDHz5s1j4MCBzJ49m/Xr1+vLHjFiBGvXrmXlypXMnDkTW1tbdDodffv2rdA1Tpw4kaCgoGKtEWlpafz+++9lHlvX3fs1SkkdvZ4xt2/fxszMjMzMTJo2bVrX1RFCiDolLRK1b+WFtHLzfGBrVW6eIuV9hucyrvPTSy/rt8sLJEbevFViIPHw1Jai4uriuSMrK4sLFy5ga2tLw4YVH2gvips8eTKnT5/mhx9+qJby4uLi6Nu3L5cvXy62CHNdqej3RVokhBAqf4StLz/T/2k+4/0arIkQT6bz59eWuq9t21m1WBMhRHUICQnB3d0drVZLdHQ0mzdvJjw8/LHLzc7O5tKlS8yfP5/Ro0fXmyCiMup0sLUQQgghhBD1WUJCAu7u7jg7OxMZGcm6deuYNGnSY5e7Y8cO2rVrR2ZmJitWrKiGmtY+aZEQQgghatj/3HoRgOcq0IVJCFG/FC1sV928vLzw8vKqkbJriwQSQgghKq0i4yigno+lEEII8VgkkBCiBoWfrFgfSh8XnxquiRBClE7T8FyZ+w3MTwCja6cyQognhgQSQgghxFMqt1FsielNnn+AosgshUKIxyOBhBBCCFEFpc1wlnfzWPHEN18ss6yTqSUvOFXExcazzP2iuNX7ftO/r8/rJAjxJJNZm4QQQgghhBCVJi0SQgghRCky912s6yoIIUS9JYGEEEJUh/JWU67jlZOfaFVZqVoIIUSNk0DiGfBwP9GSSN9RIUqWsOd8hfK5Dm1bwzURQogaVJvBehV+VElPTycwMJCvv/6aK1eu0KJFC1xcXPD19aV///4VKmPTpk34+vpy69atSp+/Pvv444/Zvn07iYmJ3Llzh5s3b2Jubl5r55cxEkIIIYQQol5KTU2lW7duxMbGsmLFCk6dOkVMTAz9+vVj+vTpdV29KsvNza2Wcu7fv8+gQYP4+9//Xi3lVZa0SAghqqy0WWtK03zG+zVUE/EsuJB6oUL5bG1sa7gmQoja4uPjg0ajISEhAa1Wq0/v0KEDEyZM0G+Hhoai0+k4f/48FhYWDB06lBUrVtC4cWMOHTqEt7c3ABqNBoCFCxeyaNEicnJymDdvHtu2bePWrVt07NiR5cuX07dvX33Zn3zyCUuWLOH69esMHDiQ3r17s2TJElXrRkREBCEhIVy6dAlbW1vmzZvH+PHj9fs1Gg0RERFER0ezf/9+/P392bZtG1OnTsXf31+fLykpiU6dOnH27Fns7OzKvT++vr4AHDp0qDK3tdpIi4QQQgghngqr9/2mf4kn340bN4iJiWH69OmqIKLIw114DAwMWLduHUlJSWzevJnY2Fjmzp0LgJubG2vWrKFp06akpaWRlpamf3j39vYmLi6OnTt38ssvvzBq1CgGDRrE2bNnAYiLi2Pq1KnMmjWLkydP4u7uTmBgoKoeUVFRzJo1Cz8/P5KSkpgyZQre3t4cPHhQlW/hwoUMHz6cU6dOMWHCBCZMmIBOp1Pl2bhxI71798bOzg4vLy9VQFMfSYuEEEIIIYSod86dO4eiKLRv377cvEW/zAPY2tqydOlSpk2bRnh4OMbGxpiZmaHRaLC0tNTnS0lJYceOHVy+fJlWrVoB4O/vT0xMDDqdjqCgIMLCwhg8eLA+8HB0dCQ+Pp69e/fqywkJCcHLywsfHx8A5syZw9GjRwkJCaFfv376fGPHjlW1onh7e7NgwQISEhJwdXUlNzeXrVu3snLlSgCsrKwoKCiowp2rPdIiIYQQQggh6h1FUYA/uyOV5eDBg7i7u9O6dWuaNGmCp6cn169f5969e6Uek5iYiKIoODo60rhxY/3r8OHDpKSkAHDmzBlcXV1Vxz26nZycTK9evVRpvXr1Ijk5WZXWvXt31baVlRVDhgxh48aNAOzdu5esrCxGjRoFQHBwMJ99VvZilXVNWiSEEEKIKsg6f6vE9DyyaPBcwxL33bz5U8llZV2hYcPW1VU1IZ4KDg4OaDQakpOTGTFiRKn5Ll68iIeHB1OnTmXp0qVYWFhw5MgRJk6cWOag5oKCAgwNDTlx4gSGhoaqfY0bNwYKg5lHA5miAOdhJeV5NK2k7lmTJk1i/PjxrF69Gp1Ox5gxYzA1NS21zvWNBBJCCCFEDev0xf4y97e+d4EGDTL029+/2aWmqyREvWdhYcHAgQPZsGEDM2fOLPYgfuvWLczNzTl+/Dh5eXmsWrUKA4PCzja7du1S5TU2NiY/P1+V1qVLF/Lz88nIyKB3794l1qF9+/YkJCSo0o4fP67adnJy4siRI3h6eurT4uPjcXJyKvcaPTw80Gq1+oHY33//fbnH1CcSSAghhBDV4HSuNQA5GGFwW/3n9X6euoXC1FTd5UEIUbLw8HDc3NxwdXVlyZIldOrUiby8PPbt20dERATJycnY2dmRl5dHWFgYQ4cOJS4ujsjISFU5NjY23L17lwMHDtC5c2dMTU1xdHRk3LhxeHp6smrVKrp06cK1a9eIjY3F2dkZDw8PZsyYQZ8+fQgNDWXo0KHExsYSHR2tam344IMPGD16NF27dqV///7s2bOH3bt3s39/2T8gABgaGuLl5UVAQAD29vb07NlTvy8gIIArV66U2b0pPT2d9PR0zp07B8CpU6do0qQJL7zwAhYWFpW93ZUmgYQQQgjxhIj/fFtdV0E8jaqwSFxtsbW1JTExkcDAQPz8/EhLS6N58+Z069aNiIgIAFxcXAgNDWX58uUEBATQp08fgoODVS0Ebm5uTJ06lTFjxnD9+nX99K86nY5ly5bh5+fHlStXaNasGT179sTDwwMoHOsQGRnJ4sWLmTdvHgMHDmT27NmsX//n9OcjRoxg7dq1rFy5kpkzZ2Jra4tOp6vwjEsTJ04kKChINRAbIC0tjd9//73MY4vqVqRPnz4A6HQ6vLy8KnT+xyGBhBBCCPGU2GnaXLVd0MC1xHwPtHmY1EaFhKgGVlZWrF+/XvXw/qjZs2cze/ZsVdrD6zhA4VoPRcFHESMjIxYvXqx6GH/U5MmTmTx5smrb3t5elWfatGlMmzat1DJKGldRJC0tjQYNGqgCHyhcjbs8ixYtYtGiReXmqykSSAghhBD/59FFFksbUF3bTqZ+RnhmE3LTS18f4WXLl2u0DgXK/xZLO3/+l2JpbdvOqtF6CFHbQkJCcHd3R6vVEh0dzebNmwkPD3/scrOzs7l06RLz589n9OjRtGzZshpqW7skkBBCiMeUsOc8pJYz487d87gObVs7FRLiEYY56j/3BgX5FGTl1VFthHiyJCQksGLFCu7cuUPbtm1Zt24dkyZNeuxyd+zYwcSJE3FxcWHLli3VUNPaJ4GEEEII8QSIu3kHqwc5pe6v2fYIIZ5dj84AVV28vLxqZRxDTZJAQoh6IPxkxZtIfVx8arAmQoiHFc3EJIQQojgJJIQQQohnUH5u6a0bRQyNjGuhJkKIJ5UEEkIIIercwYMHS91nk3qhFmsihBCioiSQEKzeV/osILPdHWuxJkIIIYQQ4klhUNcVEEIIIYQQQjx5pEVCiEqqzMBoIYR4WuTfzC6WllVwS/++YVvz2quMEKJekEDiKVBW1yQhhBBCCCFqggQSQgghhBDPsNpsaa/KFObp6ekEBgby9ddfc+XKFVq0aIGLiwu+vr7079+/QmVs2rQJX19fbt26Venz11c3btxg4cKFfPfdd1y6dInnn3+eESNGsHTpUszMzGqlDhJICCGEEEKIeik1NZVevXphbm7OihUr6NSpE7m5uXz77bdMnz6d06dP13UVqyQ3NxcjI6PHKuPq1atcvXqVkJAQXnrpJS5evMjUqVO5evUqX3zxRTXVtGwy2FoIIYSoQRnZd3iQe1P1un7vguolhCiZj48PGo2GhIQE3n77bRwdHenQoQNz5szh6NGj+nyhoaE4Ozuj1WqxtrbGx8eHu3fvAnDo0CG8vb3JzMxEo9Gg0WhYtGgRADk5OcydO5fWrVuj1Wrp0aMHhw4dUtXhk08+wdraGlNTU0aOHEloaCjm5uaqPBEREdjZ2WFsbEy7du3YsmWLar9GoyEyMpLhw4ej1WpZtmwZ9vb2hISEqPIlJSVhYGBASkpKufemY8eOfPnllwwdOhQ7Oztef/11AgMD2bNnD3l5eRW8w49HAgkhhBBCCFHv3Lhxg5iYGKZPn45Wqy22/+GHeQMDA9atW0dSUhKbN28mNjaWuXPnAuDm5saaNWto2rQpaWlppKWl4e/vD4C3tzdxcXHs3LmTX375hVGjRjFo0CDOnj0LQFxcHFOnTmXWrFmcPHkSd3d3AgMDVfWIiopi1qxZ+Pn5kZSUxJQpU/D29i62Ps7ChQsZPnw4p06dYsKECUyYMAGdTqfKs3HjRnr37o2dnR1eXl707du3UvcsMzOTpk2b0qBB7XQ6kq5NQohnSsKe83VdBSGEEBVw7tw5FEWhffv25eb19fXVv7e1tWXp0qVMmzaN8PBwjI2NMTMzQ6PRYGlpqc+XkpLCjh07uHz5Mq1atQLA39+fmJgYdDodQUFBhIWFMXjwYH3g4ejoSHx8PHv37tWXExISgpeXFz4+heM/ilpLQkJC6Nevnz7f2LFjmTBhgn7b29ubBQsWkJCQgKurK7m5uWzdupWVK1cCYGVlRUFBQYXv1/Xr11m6dClTpkyp8DGPSwIJIZ5yf4Str+sqCFH/HQwu/G/qSVWyQXbnErMXmLSu4QqJipKZC59eiqIAhd2CynPw4EGCgoL49ddfuX37Nnl5eWRlZXHv3r0SWzMAEhMTURQFR0f14rvZ2dk0a9YMgDNnzjBy5EjVfldXV1UgkZyczHvvvafK06tXL9auXatK6969u2rbysqKIUOGsHHjRn2ZWVlZjBo1CoDg4OByr7vI7du3GTJkCC+99BILFy6s8HGPSwIJIYQQQpRpd6NuJaY31TTWv29wqyEAQbVSI/EscHBwQKPRkJyczIgRI0rNd/HiRTw8PJg6dSpLly7FwsKCI0eOMHHiRHJzc0s9rqCgAENDQ06cOIGhoaFqX+PGhd9tRVGKBTJFAc7DSsrzaFpJAc2kSZMYP348q1evRqfTMWbMGExNTUutc0nu3LnDoEGDaNy4MVFRUY89iLsyZIyEEEIIIYSodywsLBg4cCAbNmzg3r17xfYXTeV6/Phx8vLyWLVqFa+88gqOjo5cvXpVldfY2Jj8/HxVWpcuXcjPzycjIwN7e3vVq6gLVPv27UlISFAdd/z4cdW2k5MTR44cUaXFx8fj5ORU7jV6eHig1WqJiIggOjpa1fWpIm7fvs2AAQMwNjbmq6++omHDhpU6/nFJICGEEEIIIeql8PBw8vPzcXV15csvv+Ts2bMkJyezbt06evbsCYCdnR15eXmEhYVx/vx5tmzZQmRkpKocGxsb7t69y4EDB7h27Rr379/H0dGRcePG4enpye7du7lw4QLHjh1j+fLlfPPNNwDMmDGDb775htDQUM6ePctHH31EdHS0qrXhgw8+YNOmTURGRnL27FlCQ0PZvXu3flxFWQwNDfHy8iIgIAB7e3v9NQEEBATg6elZ6rF37txhwIAB3Lt3j08//ZTbt2+Tnp5Oenp6saCpptR516YrV67w17/+lejoaB48eICjoyOffvop3boVNqMqisLixYv5+OOPuXnzJj169GDDhg106NBBX0Z2djb+/v7s2LGDBw8e0L9/f8LDw2nTpk1dXZYQQgghxBOhKovE1RZbW1sSExMJDAzEz8+PtLQ0mjdvTrdu3YiIiADAxcWF0NBQli9fTkBAAH369CE4OFj1EO7m5sbUqVMZM2YM169fZ+HChSxatAidTseyZcvw8/PjypUrNGvWjJ49e+Lh4QEUjnWIjIxk8eLFzJs3j4EDBzJ79mzWr/9z/OGIESNYu3YtK1euZObMmdja2qLT6So849LEiRMJCgoq1hqRlpbG77//XupxJ06c4KeffgLA3t5ete/ChQvY2NhU6PyPo04DiZs3b9KrVy/69etHdHQ0LVq0ICUlRTWd14oVKwgNDWXTpk04OjqybNky3N3dOXPmDE2aNAEKR+rv2bOHnTt30qxZM/z8/HjzzTdL7PMmhBDPqvjPt9V1FYQQotKsrKxYv3696uH9UbNnz2b27NmqtPHjx6u2IyIi9MFHESMjIxYvXszixYtLLXvy5MlMnjxZtf3og/u0adOYNm1aqWWUNK6iSFpaGg0aNCjW+rBp06ZSjwHo27dvmeXWhjoNJJYvX461tbVqDt2HoydFUVizZg0ffvghb731FgCbN2+mZcuWbN++nSlTppCZmcmnn37Kli1beOONNwDYunUr1tbW7N+/n4EDB9bqNQkhhBBCiKdHSEgI7u7uaLVaoqOj2bx5M+Hh4Y9dbnZ2NpcuXWL+/PmMHj2ali1bVkNta1edBhJfffUVAwcOZNSoURw+fJjWrVvj4+Ojj/ouXLhAeno6AwYM0B9jYmLCa6+9Rnx8PFOmTOHEiRPk5uaq8rRq1YqOHTsSHx9fYiCRnZ1Ndna2fvv27ds1eJVCCFGoMmtYuA5tW4M1qV2PLsokhBBPkoSEBFasWMGdO3do27Yt69atY9KkSY9d7o4dO5g4cSIuLi7FVsJ+UtRpIHH+/HkiIiKYM2cOf//730lISGDmzJmYmJjg6elJeno6QLEIrWXLlly8eBGA9PR0jI2Nee6554rlKTr+UcHBwWU2YQkhRLVLPVJ+HptXa74eQgghKmXXrl01Uq6XlxdeXl41UnZtqdNAoqCggO7duxMUVDjrdJcuXfj3v/9NRESEqp9YRebmfVRZeQICApgzZ45++/bt21hbW1f1MoQQQojKyetZLCm/wET/3jrpz9V3L3W8WiyvEELUB3UaSFhZWfHSSy+p0pycnPjyyy8B9HP4pqenY2Vlpc+TkZGhb6WwtLQkJyeHmzdvqlolMjIycHNzK/G8JiYmmJiYlLhPCCGKqUhrghA16Dx2AGS0er7UPDmmzWurOkIIAdTxOhK9evXizJkzqrTffvuNF198ESic8svS0pJ9+/bp9+fk5HD48GF9kNCtWzeMjIxUedLS0khKSio1kBBCCCGeVjfuPdC/HuTm8SA3j/sF2aqXEEJUhzptkZg9ezZubm4EBQUxevRoEhIS+Pjjj/n444+Bwi5Nvr6+BAUF4eDggIODA0FBQZiamjJ27FgAzMzMmDhxIn5+fjRr1gwLCwv8/f1xdnbWz+IkhBCibsR/vo1LGdfLzWf9knO1nfNC6oVy89ja2Fbb+YQQ4llVp4HEyy+/TFRUFAEBASxZsgRbW1vWrFnDuHHj9Hnmzp3LgwcP8PHx0S9I99133+nXkABYvXo1DRo0YPTo0foF6TZt2iRrSAghRD3U+V5ssbSmqUl1UJOqyecuuZQ8mUeRgqw8/XslrwCFP+d61xiWPcZPCCGeFHW+svWbb77Jm2++Wep+jUbDokWLWLRoUal5GjZsSFhYGGFhYTVQQyGEEEIIIcSj6jyQEEIIIcST6Xb2Xf17g1tZQPF1Q/r161erdRJC1B4JJIQQQgghnmF/hK2vtXM1n/F+pY9JT08nMDCQr7/+mitXrtCiRQtcXFzw9fWlf//+FSpj06ZN+Pr6cuvWrUqfvz6bMmUK+/fv5+rVqzRu3Bg3NzeWL19O+/bta+X8dTprkxBCCCGEEKVJTU2lW7duxMbGsmLFCk6dOkVMTAz9+vVj+vTpdV29KsvNza2Wcrp164ZOpyM5OZlvv/0WRVEYMGAA+fn51VJ+eSSQEEIIIeoBpSBb/8rLu6N/ZWVdIS/vTl1XT4g64ePjg0ajISEhgbfffhtHR0c6dOjAnDlzOHr0qD5faGgozs7OaLVarK2t8fHx4e7dwq53hw4dwtvbm8zMTDQajX78LRQuKzB37lxat26NVqulR48eHDp0SFWHTz75BGtra0xNTRk5ciShoaGYm5ur8kRERGBnZ4exsTHt2rVjy5Ytqv0ajYbIyEiGDx+OVqtl2bJl2NvbExISosqXlJSEgYEBKSkpFbo/7733Hn369MHGxoauXbuybNkyLl26RGpqaoWOf1zStUkIUWsq03xeleZvIYQQT48bN24QExNDYGAgWq222P6HH+YNDAxYt24dNjY2XLhwAR8fH+bOnUt4eDhubm6sWbOGBQsW6Ncva9y4MQDe3t6kpqayc+dOWrVqRVRUFIMGDeLUqVM4ODgQFxfH1KlTWb58OcOGDWP//v3Mnz9fVY+oqChmzZrFmjVreOONN9i7dy/e3t60adNGNUZo4cKFBAcHs3r1agwNDTExMUGn0+Hv76/Ps3HjRnr37o2dnR1eXl6kpqYWC2xKc+/ePXQ6Hba2tlhbW1f0Nj8WaZEQQgghhBD1zrlz51AUpUL9/X19fenXrx+2tra8/vrrLF26lF27dgFgbGyMmZkZGo0GS0tLLC0tady4MSkpKezYsYPPP/9c//Du7+/Pq6++ik6nAyAsLIzBgwfj7++Po6MjPj4+DB48WHXukJAQvLy88PHxwdHRkTlz5vDWW28Va20YO3YsEyZMoG3btrz44ot4e3tz5swZEhISgMLuTlu3bmXChAkAWFlZ8cILL5R77eHh4TRu3JjGjRsTExPDvn37MDY2Lv8GVwMJJIQQQgjxVFu97zfVSzwZFKVw/RWNpvy1Vw4ePIi7uzutW7emSZMmeHp6cv36de7du1fqMYmJiSiKgqOjo/5BvHHjxhw+fFjftejMmTO4urqqjnt0Ozk5mV69eqnSevXqRXJysiqte/fuqm0rKyuGDBnCxo0bAdi7dy9ZWVmMGjUKgODgYD777LNyr33cuHH861//4vDhwzg4ODB69GiysrLKPa46SCAhhBBCCCHqHQcHBzQaTbEH8kddvHgRDw8POnbsyJdffsmJEyfYsGEDUPag5oKCAgwNDTlx4gQnT57Uv5KTk1m7di1QGMw8GsgUBTgPKynPo2kldc+aNGkSO3fu5MGDB+h0OsaMGYOpqWmZ1/soMzMzHBwc6NOnD1988QWnT58mKiqqUmVUlYyREEII8Uz57XJH/rhrrt/Ou/nnL3cPbneugxoJIUpiYWHBwIED2bBhAzNnziz2IH7r1i3Mzc05fvw4eXl5rFq1CgODwt/Ii7o1FTE2Ni42k1GXLl3Iz88nIyOD3r17l1iH9u3b67seFTl+/Lhq28nJiSNHjuDp6alPi4+Px8nJqdxr9PDwQKvVEhERQXR0NN9//325x5RHURSys7Mfu5yKkEBCiCdM+MnwCuf1cfGpwZoIIYqkU/oviA/IqcWaCPF0KRos7erqypIlS+jUqRN5eXns27ePiIgIkpOTsbOzIy8vj7CwMIYOHUpcXByRkZGqcmxsbLh79y4HDhygc+fOmJqa4ujoyLhx4/D09GTVqlV06dKFa9euERsbi7OzMx4eHsyYMYM+ffoQGhrK0KFDiY2NJTo6WtXa8MEHHzB69Gi6du1K//792bNnD7t372b//v3lXp+hoSFeXl4EBARgb29Pz5499fsCAgK4cuVKqd2bzp8/zz/+8Q8GDBhA8+bNuXLlCsuXL6dRo0Z4eHhU8Y5XjgQSQgghRD1je+7Gn+9TUzjc/kUMDO6Rl5VXLG9msyYAXL17FYDc3If+tJsU/ippVGAEQAMDw5qqsniC1edZ8mxtbUlMTCQwMBA/Pz/S0tJo3rw53bp1IyIiAgAXFxdCQ0NZvnw5AQEB9OnTh+DgYFULgZubG1OnTmXMmDFcv36dhQsXsmjRInQ6HcuWLcPPz48rV67QrFkzevbsqX8Q79WrF5GRkSxevJh58+YxcOBAZs+ezfr1f85COGLECNauXcvKlSuZOXMmtra26HQ6+vbtW6FrnDhxIkFBQfpB1kXS0tL4/fffSz2uYcOG/PDDD6xZs4abN2/SsmVL+vTpQ3x8PC1atKjoLX4sGqWkjl7PmNu3b2NmZkZmZiZNmzat6+pUWl0OHJvt7lhn564rlWkRqGs+Lj61umJpdaqpP2wJe85X/qDUI9VfkZLYvKp/6zq0bbUXH//5tmovsyLOZVxXbXe+F1ssT9PmtfNHDwq7Nj330LSRqq5Nl66Ve3w+d8k1MVOlldkikXdLta0xLH/gqEqDHznc/nUMDEzKDCTMjAqDhIcDiSZNMoCqBxKGRhWf+cWgYeF5R2leVKU/PP1lTajK38C6/ttVF88dWVlZXLhwAVtbWxo2bFgr53xaTZ48mdOnT/PDDz9US3lxcXH07duXy5cv07Jly2op83FV9PsiLRJCCCGEEEKUIiQkBHd3d7RaLdHR0WzevJnw8Mf/UTE7O5tLly4xf/58Ro8eXW+CiMqQWZuEEEIIIYQoRUJCAu7u7jg7OxMZGcm6deuYNGnSY5e7Y8cO2rVrR2ZmJitWrKiGmtY+aZEQQgghhBCiFI/OAFVdvLy88PLyqpGya4u0SAghhBBCCCEqTQIJIYQQQgghRKVJ1yYhhBBClCg/t/w1MCozs5MQ4ukigYQQQtQXD08ze/AfJefpF1A7dRFCCCHKIV2bhBBCCCGEEJUmLRJCCCHEE65NbgtMCgoXusss+LM7UtFCdEIIUROq1CLRtm1brl+/Xiz91q1btG1b/auxCiGEEEIIIeqXKrVIpKamkp+fXyw9OzubK1euPHalhBBCCCFE7UjYc77WzuU6tPI/OKenpxMYGMjXX3/NlStXaNGiBS4uLvj6+tK/f/8KlbFp0yZ8fX25detWpc//JFAUBQ8PD2JiYoiKimLEiBG1ct5KBRJfffWV/v23336LmZmZfjs/P58DBw5gY2NTbZUTQgghhBDPrtTUVHr16oW5uTkrVqygU6dO5Obm8u233zJ9+nROnz5d11WsktzcXIyMqq/r4Zo1a9BoNNVWXkVVqmvTiBEjGDFiBBqNhnfffVe/PWLECP77v/+bffv2sWrVqpqqqxBCCCGEeIb4+Pig0WhISEjg7bffxtHRkQ4dOjBnzhyOHj2qzxcaGoqzszNarRZra2t8fHy4e/cuAIcOHcLb25vMzEw0Gg0ajYZFixYBkJOTw9y5c2ndujVarZYePXpw6NAhVR0++eQTrK2tMTU1ZeTIkYSGhmJubq7KExERgZ2dHcbGxrRr144tW7ao9ms0GiIjIxk+fDharZZly5Zhb29PSEiIKl9SUhIGBgakpKRU+B79/PPPhIaGsnHjxgofU10qFUgUFBRQUFDACy+8QEZGhn67oKCA7Oxszpw5w5tvvllTdRVCCCGEEM+IGzduEBMTw/Tp09FqtcX2P/wwb2BgwLp160hKSmLz5s3ExsYyd+5cANzc3FizZg1NmzYlLS2NtLQ0/P39AfD29iYuLo6dO3fyyy+/MGrUKAYNGsTZs2cBiIuLY+rUqcyaNYuTJ0/i7u5OYGCgqh5RUVHMmjULPz8/kpKSmDJlCt7e3hw8eFCVb+HChQwfPpxTp04xYcIEJkyYgE6nU+XZuHEjvXv3xs7ODi8vL/r27VvmPbp//z5/+ctfWL9+PZaWlhW6r9WpSmMkLly4UN31EEIIIUQFaVve1b83bJRLg4Jsino1NFSKj2EU4kl07tw5FEWhffv25eb19fXVv7e1tWXp0qVMmzaN8PBwjI2NMTMzQ6PRqB62U1JS2LFjB5cvX6ZVq1YA+Pv7ExMTg06nIygoiLCwMAYPHqwPPBwdHYmPj2fv3r36ckJCQvDy8sLHxwdA31oSEhJCv3799PnGjh3LhAkT9Nve3t4sWLCAhIQEXF1dyc3NZevWraxcuRIAKysrCgoKyrzu2bNn4+bmxvDhw8u9RzWhytO/HjhwgAMHDuhbJh5WF00rQgghhBDi6aEoCkCF+v4fPHiQoKAgfv31V27fvk1eXh5ZWVncu3evxNYMgMTERBRFwdHRUZWenZ1Ns2bNADhz5gwjR45U7Xd1dVUFEsnJybz33nuqPL169WLt2rWqtO7du6u2raysGDJkCBs3btSXmZWVxahRowAIDg4u85q/+uorYmNj+de//lVmvppUpelfFy9ezIABAzhw4ADXrl3j5s2bqpcQQgghhBCPw8HBAY1GQ3Jycpn5Ll68iIeHBx07duTLL7/kxIkTbNiwASgc1FyagoICDA0NOXHiBCdPntS/kpOT9UGAoijFApmiAOdhJeV5NK2kgGbSpEns3LmTBw8eoNPpGDNmDKampmVeb5HY2FhSUlIwNzenQYMGNGhQ2D7wX//1X+V2iaouVWqRiIyMZNOmTYwfP7666yOEEEKIJ1iB8r+q7fPnfymWp23bWbVVHfEEs7CwYODAgWzYsIGZM2cWexC/desW5ubmHD9+nLy8PFatWoWBQeFv5Lt27VLlNTY2LrZ0QZcuXcjPzycjI4PevXuXWIf27duTkJCgSjt+/Lhq28nJiSNHjuDp6alPi4+Px8nJqdxr9PDwQKvVEhERQXR0NN9//325xxT529/+xqRJk1Rpzs7OrF69mqFDh1a4nMdRpUAiJycHNze36q6LEEIIUe1yj1xUbRcYtOFeRtaf21l5tV0lIUQFhYeH4+bmhqurK0uWLKFTp07k5eWxb98+IiIiSE5Oxs7Ojry8PMLCwhg6dChxcXFERkaqyrGxseHu3bscOHCAzp07Y2pqiqOjI+PGjcPT05NVq1bRpUsXrl27RmxsLM7Oznh4eDBjxgz69OlDaGgoQ4cOJTY2lujoaFVrwwcffMDo0aPp2rUr/fv3Z8+ePezevZv9+/eXe32GhoZ4eXkREBCAvb09PXv21O8LCAjgypUrfPbZZyUea2lpWeIA6xdeeAFbW9uK3uLHUqVAYtKkSWzfvp358+dXd32EEEIIIUQtqsoicbXF1taWxMREAgMD8fPzIy0tjebNm9OtWzciIiIAcHFxITQ0lOXLlxMQEECfPn0IDg5WtRC4ubkxdepUxowZw/Xr11m4cCGLFi1Cp9OxbNky/Pz8uHLlCs2aNaNnz554eHgAhWMdIiMjWbx4MfPmzWPgwIHMnj2b9evX68seMWIEa9euZeXKlcycORNbW1t0Ol2FuxdNnDiRoKAg1UBsgLS0NH7//ffHvIM1q0qBRFZWFh9//DH79++nU6dOxRbUCA0NrZbKCSGEEM+K3HuXSt5hAPnZUKCRlhPxbLKysmL9+vWqh/dHzZ49m9mzZ6vSHu2CHxERoQ8+ihgZGbF48WIWL15catmTJ09m8uTJqm17e3tVnmnTpjFt2rRSyyhpXEWRtLQ0GjRooAp8oHA17soq6zw1oUqBxC+//IKLiwtQuHDGw+piVT0hhKiy1CN1XQMhhBD1WEhICO7u7mi1WqKjo9m8eTPh4eGPXW52djaXLl1i/vz5jB49mpYtW1ZDbWtXlQKJRxfYEEIIIYQQ4mmUkJDAihUruHPnDm3btmXdunXFBjlXxY4dO5g4cSIuLi7FVsJ+UlR5HQkhhBBCCCGedo/OAFVdvLy88PLyqpGya0uVAol+/fqV2YUpNja2yhUSQgghhBBC1H9VCiSKxkcUyc3N5eTJkyQlJfHuu+9WR72EEEIIIYQQ9ViVAonVq1eXmL5o0SLu3r37WBUSQgghnhR/5N0D4AE5dVwTIYSofQbVWdg777zDxo0bq7NIIYQQotplZWXpX9l5OSW+hBBClK1aB1v/+OOPNGzYsDqLFEIIIYRQWb3vt7qughCCKgYSb731lmpbURTS0tI4fvy4rHYthBBCCCHEM6BKgYSZmZlq28DAgHbt2rFkyRIGDBhQLRUTQojKSNhzvq6rIIQQT6T4z7fV2rncRo2rtXOJmlelQEKn01V3PQgODubvf/87s2bNYs2aNUBhS8fixYv5+OOPuXnzJj169GDDhg106NBBf1x2djb+/v7s2LGDBw8e0L9/f8LDw2nTpk2117EuSTOuEEIIIZ5F6enpBAYG8vXXX3PlyhVatGiBi4sLvr6+9O/fv0JlbNq0CV9fX27dulWzla1lffv25fDhw6q0MWPGsHPnzlo5/2MNtj5x4gRbt25l27Zt/Otf/6pyOceOHePjjz+mU6dOqvQVK1YQGhrK+vXrOXbsGJaWlri7u3Pnzh19Hl9fX6Kioti5cydHjhzh7t27vPnmm+Tn51e5PkIIIYQQou6lpqbSrVs3YmNjWbFiBadOnSImJoZ+/foxffr0uq5eleXm5lZbWZMnTyYtLU3/+uijj6qt7PJUKZDIyMjg9ddf5+WXX2bmzJm8//77dOvWjf79+/PHH39Uqqy7d+8ybtw4PvnkE5577jl9uqIorFmzhg8//JC33nqLjh07snnzZu7fv8/27dsByMzM5NNPP2XVqlW88cYbdOnSha1bt3Lq1Cn2799flUsTz6jwk+EVfgkhnizKgzz1K69A9Xpa5CmFL4WSX0I8iXx8fNBoNCQkJPD222/j6OhIhw4dmDNnDkePHtXnCw0NxdnZGa1Wi7W1NT4+PvolCQ4dOoS3tzeZmZloNBo0Gg2LFi0CICcnh7lz59K6dWu0Wi09evTg0KFDqjp88sknWFtbY2pqysiRIwkNDcXc3FyVJyIiAjs7O4yNjWnXrh1btmxR7ddoNERGRjJ8+HC0Wi3Lli3D3t6ekJAQVb6kpCQMDAxISUmp8D0yNTXF0tJS/3p0CEJNqlIgMWPGDG7fvs2///1vbty4wc2bN0lKSuL27dvMnDmzUmVNnz6dIUOG8MYbb6jSL1y4QHp6umrMhYmJCa+99hrx8fFAYYtIbm6uKk+rVq3o2LGjPk9JsrOzuX37tuolhBBCCCHqjxs3bhATE8P06dPRarXF9j/8MG9gYMC6detISkpi8+bNxMbGMnfuXADc3NxYs2YNTZs21f9q7+/vD4C3tzdxcXHs3LmTX375hVGjRjFo0CDOnj0LQFxcHFOnTmXWrFmcPHkSd3d3AgMDVfWIiopi1qxZ+Pn5kZSUxJQpU/D29ubgwYOqfAsXLmT48OGcOnWKCRMmMGHChGLDBTZu3Ejv3r2xs7PDy8uLvn37lnuftm3bxvPPP0+HDh3w9/dX9dypaVUaIxETE8P+/ftxcnLSp7300kts2LChUoOtd+7cSWJiIseOHSu2Lz09HYCWLVuq0lu2bMnFixf1eYyNjVUtGUV5io4vSXBwMIsXL65wPYUQQojqdvglR9V2fk7LkjNq/syXa9K4MK/hn+tcGCgG5CLdecXT59y5cyiKQvv27cvN6+vrq39va2vL0qVLmTZtGuHh4RgbG2NmZoZGo8HS0lKfLyUlhR07dnD58mVatWoFgL+/PzExMeh0OoKCgggLC2Pw4MH6wMPR0ZH4+Hj27t2rLyckJAQvLy98fHwA9K0lISEh9OvXT59v7NixTJgwQb/t7e3NggULSEhIwNXVldzcXLZu3crKlSsBsLKyoqCg7FbTcePGYWtri6WlJUlJSQQEBPDzzz+zb9++cu9ZdahSIFFQUICRkVGxdCMjo3IvuMilS5eYNWsW3333XZlrT2g0GtW2oijF0h5VXp6AgADmzJmj3759+zbW1tYVqrcQQgghhKh5ilLYKa+85z6AgwcPEhQUxK+//srt27fJy8sjKyuLe/fuldiaAZCYmIiiKDg6qoP67OxsmjVrBsCZM2cYOXKkar+rq6sqkEhOTua9995T5enVqxdr165VpXXv3l21bWVlxZAhQ9i4caO+zKysLEaNGgUU/vBdnsmTJ+vfd+zYEQcHB7p3705iYiJdu3Yt9/jHVaVA4vXXX2fWrFns2LFDH8FduXKF2bNnV3j0/IkTJ8jIyKBbt276tPz8fL7//nvWr1/PmTNngMJWBysrK32ejIwMfSuFpaUlOTk53Lx5U9UqkZGRgZubW6nnNjExwcTEpOIXLEQ9Y/P5TxXK98cPT0//byGEEM8WBwcHNBoNycnJjBgxotR8Fy9exMPDg6lTp7J06VIsLCw4cuQIEydOLHNQc0FBAYaGhpw4cQJDQ0PVvsaNC1v/SvpxuijAeVhFfvguKaCZNGkS48ePZ/Xq1eh0OsaMGYOpqWmpdS5P165dMTIy4uzZs7USSFRpjMT69eu5c+cONjY22NnZYW9vj62tLXfu3CEsLKxCZfTv359Tp05x8uRJ/at79+6MGzeOkydP0rZtWywtLVVNMzk5ORw+fFgfJHTr1g0jIyNVnrS0NJKSksoMJIQQQgghRP1mYWHBwIED2bBhA/fu3Su2v2gq1+PHj5OXl8eqVat45ZVXcHR05OrVq6q8xsbGxWb07NKlC/n5+WRkZGBvb696FXWBat++PQkJCarjjh8/rtp2cnLiyJEjqrT4+HjVEIDSeHh4oNVqiYiIIDo6WtX1qSr+/e9/k5ubq/oRviZVqUXC2tqaxMRE9u3bx+nTp1EUhZdeeqnYgOmyNGnShI4dO6rStFotzZo106f7+voSFBSEg4MDDg4OBAUFYWpqytixY4HChfEmTpyIn58fzZo1w8LCAn9/f5ydnStVFyGEEE+23y53LHVfvkmrWqzJs6sgKw+A/AfZqvSsglsANGxrXss1Ek+D8PBw3NzccHV1ZcmSJXTq1Im8vDz27dtHREQEycnJ2NnZkZeXR1hYGEOHDiUuLo7IyEhVOTY2Nty9e5cDBw7QuXNnTE1NcXR0ZNy4cXh6erJq1Sq6dOnCtWvXiI2NxdnZGQ8PD2bMmEGfPn0IDQ1l6NChxMbGEh0drWpt+OCDDxg9ejRdu3alf//+7Nmzh927d1doBlFDQ0O8vLwICAjA3t6enj176vcFBARw5coVPvvssxKPTUlJYdu2bXh4ePD888/z66+/4ufnR5cuXejVq1cV73jlVCqQiI2N5f333+fo0aM0bdoUd3d33N3dgcKpWDt06EBkZCS9e/eulsrNnTuXBw8e4OPjo1+Q7rvvvqNJkyb6PKtXr6ZBgwaMHj1avyDdpk2bijVRCSGEEBVm8IB8csrPJ8RToD6vNm1ra0tiYiKBgYH4+fmRlpZG8+bN6datGxEREQC4uLgQGhrK8uXLCQgIoE+fPgQHB+Pp6akvx83NjalTpzJmzBiuX7/OwoULWbRoETqdjmXLluHn58eVK1do1qwZPXv2xMPDAygc6xAZGcnixYuZN28eAwcOZPbs2axfv15f9ogRI1i7di0rV65k5syZ2NraotPpKjTjEsDEiRMJCgoq1hqRlpbG77//XupxxsbGHDhwgLVr13L37l2sra0ZMmQICxcurLXnYI1SUkevUgwbNox+/foxe/bsEvevW7eOgwcPEhUVVW0VrA23b9/GzMyMzMxMmjZtWtfVKdGTurL1bHfH8jPVA0/a+hAVHSPxsuXLNVyTmtN8xvuVyp+w53zVTpR6pPw8dcC145WSd/QLqHKZ8Z9vq/Kxj+NcxnXVdud7scXyNG3e4rHOUWaLxPkblS/Q4AENDMr/Q5z+f7Ml5ZTTUzjL8M8+zxrDwl8yi8/alFlmGRqNAbkmhROdNHn+zzWbCgwMMKEwXTEo/U+6Bip0TZVlaGSs2n7rwQnVdhuzwi4iD7dItG0767HO+bh/E+v6b1NdPHdkZWVx4cIFbG1ty5zkRpRv8uTJnD59mh9++KFayouLi6Nv375cvny52GyldaWi35dKjZH4+eefGTRoUKn7BwwYwIkTJ0rdL4QQQgghxJMkJCSEn3/+mXPnzhEWFsbmzZt59913H7vc7Oxszp07x/z58xk9enS9CSIqo1Jdm/7zn/+UOO2rvrAGDSq9srUQQognl3XG3nLzaO89qIWaCCFEzUhISGDFihXcuXOHtm3bsm7dOiZNmvTY5e7YsYOJEyfi4uJSbCXsJ0WlAonWrVtz6tQp7O3tS9z/yy+/1NoocSGEEEIIIWrarl27aqRcLy8vvLy8aqTs2lKpQMLDw4MFCxYwePDgYv2lHjx4wMKFC3nzzTertYJCiKo7ll581fjSPMnjKYQQQghR+yoVSMybN4/du3fj6OjI+++/T7t27fQLhWzYsIH8/Hw+/PDDmqqrEEKIp9TtPzLKzfO4A7KFEEJUr0oFEi1btiQ+Pp5p06YREBCgWrp84MCBhIeHP5EDRYQQQojaouQX/u1UCio8aaIQQtRLlV6Q7sUXX+Sbb77h5s2bnDt3DkVRcHBw4LnnnquJ+gkhhBBCCCHqoSqtbA3w3HPP8fLL0qdaCCFqQkJS65J33FWvl+E6tG0t1KZ0N2RGplrR3LgloCHTsHCRPCPNw2tOaEo8RgghalqVAwkhhBBCiPJczkwHwDD1pj7t4sWDqjz9+vWr1ToJIaqHBBJCCCFEOe5S8hpKOcg4B/Hky9x3sdbOZeb+YqWPSU9PJzAwkK+//porV67QokULXFxc8PX1pX///hUqY9OmTfj6+nLr1q1Kn7+++/HHH/nwww/56aefMDIywsXFhejoaBo1alTj55ZAQgghhBBC1Eupqan06tULc3NzVqxYQadOncjNzeXbb79l+vTpnD59uq6rWCW5ubllLvJcUT/++CODBg0iICCAsLAwjI2N+fnnnzEwMKiGWpavds4ihBCixsR/vq1CLyGEeNL4+Pig0WhISEjg7bffxtHRkQ4dOjBnzhyOHj2qzxcaGoqzszNarRZra2t8fHy4e/cuAIcOHcLb25vMzEw0Gg0ajYZFixYBkJOTw9y5c2ndujVarZYePXpw6NAhVR0++eQTrK2tMTU1ZeTIkYSGhmJubq7KExERgZ2dHcbGxrRr167YStUajYbIyEiGDx+OVqtl2bJl2NvbExISosqXlJSEgYEBKSkpFbo/s2fPZubMmfztb3+jQ4cOODg48Pbbb2NiYlKh4x+XtEgIIeqlP8LWV+4AG4+aqYgQQog6cePGDWJiYggMDESr1Rbb//DDvIGBAevWrcPGxoYLFy7g4+PD3LlzCQ8Px83NjTVr1rBgwQLOnDkDQOPGjQHw9vYmNTWVnTt30qpVK6Kiohg0aBCnTp3CwcGBuLg4pk6dyvLlyxk2bBj79+9n/vz5qnpERUUxa9Ys1qxZwxtvvMHevXvx9vamTZs2qvE/CxcuJDg4mNWrV2NoaIiJiQk6nQ5/f399no0bN9K7d2/s7Ozw8vIiNTW1WGBTJCMjg59++olx48bh5uZGSkoK7du3JzAwkFdffbWqt71SpEVCCCGEEELUO0XLDLRv377cvL6+vvTr1w9bW1tef/11li5dyq5duwAwNjbGzMwMjUaDpaUllpaWNG7cmJSUFHbs2MHnn3+uf3j39/fn1VdfRafTARAWFsbgwYPx9/fH0dERHx8fBg8erDp3SEgIXl5e+Pj44OjoyJw5c3jrrbeKtTaMHTuWCRMm0LZtW1588UW8vb05c+YMCQkJQGF3p61btzJhwgQArKyseOGFF0q95vPnC2fxW7RoEZMnTyYmJoauXbvSv39/zp49W8G7/HgkkBBCCCGEEPXOwwsfl+fgwYO4u7vTunVrmjRpgqenJ9evX+fevXulHpOYmIiiKDg6OtK4cWP96/Dhw/quRWfOnMHV1VV13KPbycnJ9OrVS5XWq1cvkpOTVWndu3dXbVtZWTFkyBA2btwIwN69e8nKymLUqFEABAcH89lnn5Va/4KCAgCmTJmCt7c3Xbp0YfXq1bRr105fZk2TQEIIIYQQQtQ7Dg4OaDSaYg/kj7p48SIeHh507NiRL7/8khMnTrBhwwag8Ff+0hQUFGBoaMiJEyc4efKk/pWcnMzatWuBwmDm0UCmKMB5WEl5Hk0rqXvWpEmT2LlzJw8ePECn0zFmzBhMTU3LvN4iVlZWALz00kuqdCcnJ37//fcKlfG4JJAQQgghngglTzWryBS04illYWHBwIED2bBhQ4ktC0VTuR4/fpy8vDxWrVrFK6+8gqOjI1evXlXlNTY2Jj8/X5XWpUsX8vPzycjIwN7eXvWytLQEoH379vquR0WOHz+u2nZycuLIkSOqtPj4eJycnMq9Rg8PD7RaLREREURHR+u7NVWEjY0NrVq10o/7KPLbb7/x4ouVn2a3KiSQEEIIIYQQ9VJ4eDj5+fm4urry5ZdfcvbsWZKTk1m3bh09e/YEwM7Ojry8PMLCwjh//jxbtmwhMjJSVY6NjQ13797lwIEDXLt2jfv37+Po6Mi4cePw9PRk9+7dXLhwgWPHjrF8+XK++eYbAGbMmME333xDaGgoZ8+e5aOPPiI6OlrV2vDBBx+wadMmIiMjOXv2LKGhoezevVs1iLo0hoaGeHl5ERAQgL29vf6aAAICAvD09Cz1WI1GwwcffMC6dev44osvOHfuHPPnz+f06dNMnDixUve5qiSQEEI8vVKPlP8SohbkPbhc7FWQd1v1EkIUZ2trS2JiIv369cPPz4+OHTvi7u7OgQMHiIiIAMDFxYXQ0FCWL19Ox44d2bZtG8HBwapy3NzcmDp1KmPGjKF58+asWLECAJ1Oh6enJ35+frRr145hw4bx008/YW1tDRSOdYiMjCQ0NJTOnTsTExPD7Nmzadiwob7sESNGsHbtWlauXEmHDh346KOP0Ol09O3bt0LXOHHiRHJycoq1RqSlpZXbRcnX15eAgABmz55N586dOXDgAPv27cPOzq5C535cGqWkjl7PmNu3b2NmZkZmZiZNmzat6+qUaPW+3+q6ClUy292xrqtQIeEnw+u6CpVi8/lP1V7my5YvV3uZtelCSdO/Po2Bgo16Sj/XoW3rdI0I7YXPa+1cTZu3KHXfb5c7lrov//yNyp/M4AENDAz1m6WtbH2DnAoVdzeneB1+cOleQs6SNTduCUCmUWF/74YWf3bbUDSgofDXUcWg9D/pGlBdU3UxNDJWbb/14ETJ+Z77c157A81w1b6Hp8isiMf9m1jXf5vq4rkjKyuLCxcuYGtrq3oIFpU3efJkTp8+zQ8//FAt5cXFxdG3b18uX75My5Ytq6XMx1XR74usIyGEEEKIarO7UbcS0w0eeuTQ0JC3yapUuU/qD2riyRcSEoK7uztarZbo6Gg2b95MePjj/wCZnZ3NpUuXmD9/PqNHj643QURlSNcmIYQQQgghSpGQkIC7uzvOzs5ERkaybt06Jk2a9Njl7tixg3bt2pGZmanvavWkkRYJIYR4kjzaXevgP+DCI31obWtnRVMhhHgWFC1sV928vLzw8vKqkbJriwQSQgghhHimPNxNqq7HSwjxJJNAQgghhKhHtJY3i6VpDAsHdTc0KKjt6gghRKkkkBBCCCFKkM6fi1flyKJvQghRjAy2FkIIIYQQQlSaBBJCCCGEEEKISpOuTUIIIcQzIq8gv9w8NbFonRDi6SQtEkIIIYQQQohKkxYJIYQQ9c7FGz2KpZlka/XvC36/9cjeR7efHRoF+L/B4Ab5ZczqpAHFQH4/FMUdPHiw1s7Vr1+/Sh+Tnp5OYGAgX3/9NVeuXKFFixa4uLjg6+tL//79K1TGpk2b8PX15datW5U+f32VmpqKra1tift27drFqFGjarwOEkgIIYQQQoh6KTU1lV69emFubs6KFSvo1KkTubm5fPvtt0yfPp3Tp0/XdRWrJDc3FyMjo8cqw9ramrS0NFXaxx9/zIoVKxg8ePBjlV1R8tOEEEIIIYSol3x8fNBoNCQkJPD222/j6OhIhw4dmDNnDkePHtXnCw0NxdnZGa1Wi7W1NT4+Pty9exeAQ4cO4e3tTWZmJhqNBo1Gw6JFiwDIyclh7ty5tG7dGq1WS48ePTh06JCqDp988gnW1taYmpoycuRIQkNDMTc3V+WJiIjAzs4OY2Nj2rVrx5YtW1T7NRoNkZGRDB8+HK1Wy7Jly7C3tyckJESVLykpCQMDA1JSUsq9N4aGhlhaWqpeUVFRjBkzhsaNG1fwDj8eCSSEEEI8mwwelP4SQtS5GzduEBMTw/Tp09FqtcX2P/wwb2BgwLp160hKSmLz5s3ExsYyd+5cANzc3FizZg1NmzYlLS2NtLQ0/P39AfD29iYuLo6dO3fyyy+/MGrUKAYNGsTZs2cBiIuLY+rUqcyaNYuTJ0/i7u5OYGCgqh5RUVHMmjULPz8/kpKSmDJlCt7e3sW6jC1cuJDhw4dz6tQpJkyYwIQJE9DpdKo8GzdupHfv3tjZ2eHl5UXfvn0rfL9OnDjByZMnmThxYoWPeVzStUkIIYQQQtQ7586dQ1EU2rdvX25eX19f/XtbW1uWLl3KtGnTCA8Px9jYGDMzMzQaDZaWlvp8KSkp7Nixg8uXL9OqVSsA/P39iYmJQafTERQURFhYGIMHD9YHHo6OjsTHx7N37159OSEhIXh5eeHj4wOgby0JCQlRjQkZO3YsEyZM0G97e3uzYMECEhIScHV1JTc3l61bt7Jy5UoArKysKCio+Gr2n376KU5OTri5uVX4mMclLRJCCCGEqLL83JxyX0JUhaIUTiKg0WjKzXvw4EHc3d1p3bo1TZo0wdPTk+vXr3Pv3r1Sj0lMTERRFBwdHWncuLH+dfjwYX3XojNnzuDq6qo67tHt5ORkevXqpUrr1asXycnJqrTu3burtq2srBgyZAgbN24EYO/evWRlZekHSQcHB/PZZ5+Ve+0ADx48YPv27bXaGgESSAghhBBCiHrIwcEBjUZT7IH8URcvXsTDw4OOHTvy5ZdfcuLECTZs2AAUDmouTUFBAYaGhvouQUWv5ORk1q5dCxQGM48GMkUBzsNKyvNoWkndsyZNmsTOnTt58OABOp2OMWPGYGpqWub1luSLL77g/v37eHp6VvrYxyGBhBBCCCGEqHcsLCwYOHAgGzZsKLFloWgq1+PHj5OXl8eqVat45ZVXcHR05OrVq6q8xsbG5OerF2Ts0qUL+fn5ZGRkYG9vr3oVdYFq3749CQkJquOOHz+u2nZycuLIkSOqtPj4eJycnMq9Rg8PD7RaLREREURHR6u6PlXGp59+yrBhw2jevHmVjq8qCSSEEEIIIUS9FB4eTn5+Pq6urnz55ZecPXuW5ORk1q1bR8+ePQGws7MjLy+PsLAwzp8/z5YtW4iMjFSVY2Njw927dzlw4ADXrl3j/v37ODo6Mm7cODw9Pdm9ezcXLlzg2LFjLF++nG+++QaAGTNm8M033xAaGsrZs2f56KOPiI6OVrU2fPDBB2zatInIyEjOnj1LaGgou3fv1o+rKIuhoSFeXl4EBARgb2+vvyaAgICACrUwnDt3ju+//55JkyZV6J5WJwkkhBBCCCFEvWRra0tiYiL9+vXDz8+Pjh074u7uzoEDB4iIiADAxcWF0NBQli9fTseOHdm2bRvBwcGqctzc3Jg6dSpjxoyhefPmrFixAgCdToenpyd+fn60a9eOYcOG8dNPP2FtbQ0UjnWIjIwkNDSUzp07ExMTw+zZs2nYsKG+7BEjRrB27VpWrlxJhw4d+Oijj9DpdBWecWnixInk5OQUa41IS0vj999/L/f4jRs30rp1awYMGFCh81UnjVJSR69nzO3btzEzMyMzM5OmTZvWdXVKtHrfb3VdhSqZ7e5Y11WokPCT4XVdhUqx+fynai/zZcuXq73M2nTBxqN4YuqR4mlPGdeOV4g/9sgfGttXa+382guf10i5Ja5srS19ZWslr+Izm+iVM83rtYd+ast5zN/d7ubcKJb2g0v3EnKC1vJm8TTDwmvPMij7OjU8/srWDQwMy81TWYZGxhg0/HOiSI2mHW+Tpd+uyGrHNfV3sC7+TtXFc0dWVhYXLlzA1tZW9RAsKm/y5MmcPn2aH374oVrKi4uLo2/fvly+fJmWLVtWS5mPq6LfF5n+VQghhBBCiFKEhITg7u6OVqslOjqazZs3Ex7++D9AZmdnc+nSJebPn8/o0aPrTRBRGdK1SQghhChBgVKgfylQ4ksI8fRLSEjA3d0dZ2dnIiMjWbduXbWMR9ixYwft2rUjMzNT39XqSVOnLRLBwcHs3r2b06dP06hRI9zc3Fi+fDnt2rXT51EUhcWLF/Pxxx9z8+ZNevTowYYNG+jQoYM+T3Z2Nv7+/uzYsYMHDx7Qv39/wsPDadOmTV1clhCVVhNdlYQQQgjx+Hbt2lUj5Xp5eeHl5VUjZdeWOg0kDh8+zPTp03n55ZfJy8vjww8/ZMCAAfz666/6uXZXrFhBaGgomzZtwtHRkWXLluHu7s6ZM2do0qQJULia4Z49e9i5cyfNmjXDz8+PN998kxMnTmBoWP19PUXFldWn9UkZPyFEXbl4MancPHkP7tRCTURNam6s7s6gMZQF3IQQT4Y6DSRiYmJU2zqdjhYtWnDixAn69OmDoiisWbOGDz/8kLfeeguAzZs307JlS7Zv386UKVPIzMzk008/ZcuWLbzxxhsAbN26FWtra/bv38/AgQNr/bqEEEI8mw44dygxPSe/7IHdAJlGhQtnNSxnQLUQQtQX9WqwdWZmJlC4AAnAhQsXSE9PV01nZWJiwmuvvUZ8fDxTpkzhxIkT5ObmqvK0atWKjh07Eh8fX2IgkZ2dTXZ2tn779u3bNXVJQojHkJRR8YV1tDY1Vw8hhBBCFFdvBlsrisKcOXN49dVX6dixIwDp6ekAxUaxt2zZUr8vPT0dY2NjnnvuuVLzPCo4OBgzMzP9q2iuYCGEEEIIIUTF1JtA4v333+eXX35hx44dxfY9vHogFAYdj6Y9qqw8AQEBZGZm6l+XLl2qesWFEEIIIYR4BtWLrk0zZszgq6++4vvvv1fNtGRpaQkUtjpYWVnp0zMyMvStFJaWluTk5HDz5k1Vq0RGRgZubm4lns/ExAQTE5OauBRRjzxpi8zVtWPpxyqc90lfvE6Uzjpjb6XyF19mTQghxLOiTlskFEXh/fffZ/fu3cTGxmJra6vab2tri6WlJfv27dOn5eTkcPjwYX2Q0K1bN4yMjFR50tLSSEpKKjWQEEIIUb4b9x6U+xJCCPHsqtMWienTp7N9+3b+93//lyZNmujHNJiZmdGoUSM0Gg2+vr4EBQXh4OCAg4MDQUFBmJqaMnbsWH3eiRMn4ufnR7NmzbCwsMDf3x9nZ2f9LE5CCCHEM08pexG9sjsMi6fZ+fNra+1cbdvOqvQx6enpBAYG8vXXX3PlyhVatGiBi4sLvr6+9O/fv0JlbNq0CV9fX27dulXp89dn6enpfPDBB+zbt487d+7Qrl07/v73v/P222/XyvnrNJCIiIgAoG/fvqp0nU6nX6Bj7ty5PHjwAB8fH/2CdN99951+DQmA1atX06BBA0aPHq1fkG7Tpk2yhoQQQghRDynKGQo4qd8+f/6XEvNV5aFTPF1SU1Pp1asX5ubmrFixgk6dOpGbm8u3337L9OnTOX36dF1XsUpyc3MxMjJ67HLGjx9PZmYmX331Fc8//zzbt29nzJgxHD9+nC5dulRDTctW512bSno9vMqfRqNh0aJFpKWlkZWVxeHDh/WzOhVp2LAhYWFhXL9+nfv377Nnzx6ZiUkIIYQQ4gnn4+ODRqMhISGBt99+G0dHRzp06MCcOXM4evSoPl9oaCjOzs5otVqsra3x8fHh7t27ABw6dAhvb28yMzPRaDT6Z0so7DI/d+5cWrdujVarpUePHhw6dEhVh08++QRra2tMTU0ZOXIkoaGhmJubq/JERERgZ2eHsbEx7dq1Y8uWLar9Go2GyMhIhg8fjlarZdmyZdjb2xMSEqLKl5SUhIGBASkpKRW6Pz/++CMzZszA1dWVtm3bMm/ePMzNzUlMTKzQ8Y+rXgy2FmWvAC2EEEII8ay5ceMGMTExBAYGotVqi+1/+GHewMCAdevWYWNjw4ULF/Dx8WHu3LmEh4fj5ubGmjVrWLBgAWfOnAGgcePGAHh7e5OamsrOnTtp1aoVUVFRDBo0iFOnTuHg4EBcXBxTp05l+fLlDBs2jP379zN//nxVPaKiopg1axZr1qzhjTfeYO/evXh7e9OmTRv69eunz7dw4UKCg4NZvXo1hoaGmJiYoNPp8Pf31+fZuHEjvXv3xs7ODi8vL1JTU4sFNg979dVX+cc//sGQIUMwNzdn165dZGdnF+vtU1MkkBBCCCGEEPXOuXPnUBSF9u3bl5vX19dX/97W1palS5cybdo0wsPDMTY2xszMDI1Go58RFCAlJYUdO3Zw+fJlWrVqBYC/vz8xMTHodDqCgoIICwtj8ODB+od9R0dH4uPj2bv3zxnuQkJC8PLywsfHB0DfWhISEqIKJMaOHcuECRP0297e3ixYsICEhARcXV3Jzc1l69atrFy5EgArKysKCspe6f4f//gHY8aMoVmzZjRo0ABTU1OioqKws7Mr955VBwkkhBBPhXsJJUxfe6vkRSm1jpYlpgshhKg/FKVweoDy1g4DOHjwIEFBQfz666/cvn2bvLw8srKyuHfvXomtGQCJiYkoioKjo6MqPTs7m2bNmgFw5swZRo4cqdrv6uqqCiSSk5N57733VHl69erF2rXqQezdu3dXbVtZWTFkyBA2btyoLzMrK4tRo0YBhQsol2fevHncvHmT/fv38/zzz/PPf/6TUaNG8cMPP+Ds7Fzu8Y9LAgkhhHiCXcloUjwx4+cS87ZucefPDdtXa6hGQghRPRwcHNBoNCQnJzNixIhS8128eBEPDw+mTp3K0qVLsbCw4MiRI0ycOJHc3NxSjysoKMDQ0JATJ04Um6CnqOtTSQscFwU4D6vI4sklBTSTJk1i/PjxrF69Gp1Ox5gxYzA1NS21zg9LSUlh/fr1JCUl0aFDBwA6d+7MDz/8wIYNG4iMjKxQOY9DAgkhxJPp1sW6roGoZdn37unfGzzycKB5qPVfU09n7NO2vFliusYwB4CGhmV3YRDiWWNhYcHAgQPZsGEDM2fOLPYgfuvWLczNzTl+/Dh5eXmsWrUKA4PCeYR27dqlymtsbEx+fr4qrUuXLuTn55ORkUHv3r1LrEP79u1JSEhQpR0/fly17eTkxJEjR/D09NSnxcfH4+TkVO41enh4oNVqiYiIIDo6mu+//77cY4rcv38fQH/NRQwNDcvtElVdJJAQQgghHkNW1n/07/PybErOpKmdSRJLWieivqwPUZCVp9rOf5Ctf59VcIuGbc1ruUbiSVA0WNrV1ZUlS5bQqVMn8vLy2LdvHxERESQnJ2NnZ0deXh5hYWEMHTqUuLi4Yr/G29jYcPfuXQ4cOEDnzp0xNTXF0dGRcePG4enpyapVq+jSpQvXrl0jNjYWZ2dnPDw8mDFjBn369CE0NJShQ4cSGxtLdHS0qrXhgw8+YPTo0XTt2pX+/fuzZ88edu/ezf79+8u9PkNDQ7y8vAgICMDe3p6ePXvq9wUEBHDlyhU+++yzEo9t37499vb2TJkyhZCQEJo1a8Y///lP9u3bp+p6VZPqdPpXIYQQQgghSmNra0tiYiL9+vXDz8+Pjh074u7uzoEDB/Trkbm4uBAaGsry5cvp2LEj27ZtKza+wM3NjalTpzJmzBiaN2/OihUrgMK1yzw9PfHz86Ndu3YMGzaMn376Sb+MQK9evYiMjCQ0NJTOnTsTExPD7Nmzadiwob7sESNGsHbtWlauXEmHDh346KOP0Ol0FZ45aeLEieTk5KgGYgOkpaXx+++/l3qckZER33zzDc2bN2fo0KF06tSJzz77jM2bN+Ph4VGhcz8ujVJSR69nzO3btzEzMyMzM5OmTZvWSR2exelfZ7s7lp/pMYSfDK/R8quTzec/1XUVKuVly5dr5TxJGc1L3/kYXZuelMHWFy8mVWt5FRkjYZ3x569YN+49qNbzV8bFGz3K3G+Qfl+1XaWuTQZlX1/GQ4XmakpuwNegbpE43KXkehdoDErt2qQ1LOyukVUtXZuKl/Fwi0SBYem/Hxbla2BQ/V3DDI2Mi6W99eCE/n0bM8sSWyQeXpCupv5O1vTfopLUxXNHVlYWFy5cwNbWVvUQLCpv8uTJnD59mh9++KFayouLi6Nv375cvnyZli1bVkuZj6ui3xfp2iSEEOKZdl9jUmJ6rqb0QZqVZaAUoCmx49GfHt2vVKlTUvFA4eFSNQV/lqkYPPO/IwpRISEhIbi7u6PVaomOjmbz5s2Ehz/+j5XZ2dlcunSJ+fPnM3r06HoTRFSGdG0SQgghhBCiFAkJCbi7u+Ps7ExkZCTr1q1j0qRJj13ujh07aNeuHZmZmfquVk8aaZEQQghRKyrTXcmA+2XkfHwZBjn6/lDV2fIghHj6PDoDVHXx8vLCy8urRsquLRJICCGEEGWIde5Q5v5SZ2oSQoinnAQSQgghnk5lDabW1N6aDc8b/9/gfsO8sjM+wy5npmOYWnww+sWLBx/aal17FRJCVIiMkRBCCCGEEEJUmgQSQgghhBBCiEqTrk1CCCGEqJeOnr+uf9/whZrp2vTw+hR1saaEEE8yCSSEEEI8EWpxWIMQQogKkEBCCCGEEHp5Bfnl5qmO1a93N+qm2jYo4ZHkynMWALxy88Zjn08IUf0kkBBCCCGEeIatvJBWa+f6wNaq0sekp6cTGBjI119/zZUrV2jRogUuLi74+vrSv3//CpWxadMmfH19uXXrVqXPX5+lpKTg7+/PkSNHyM7OZtCgQYSFhdXaKtkSSAghRB24eDGprqsgqpGp5a3Sd8q0r0JUWWpqKr169cLc3JwVK1bQqVMncnNz+fbbb5k+fTqnT5+u6ypWSW5uLkZGRo9Vxr179xgwYACdO3cmNjYWgPnz5zN06FCOHj2KgUHNz6kkszYJIYQQQoh6ycfHB41GQ0JCAm+//TaOjo506NCBOXPmcPToUX2+0NBQnJ2d0Wq1WFtb4+Pjw927dwE4dOgQ3t7eZGZmotFo0Gg0LFq0CICcnBzmzp1L69at0Wq19OjRg0OHDqnq8Mknn2BtbY2pqSkjR44kNDQUc3NzVZ6IiAjs7OwwNjamXbt2bNmyRbVfo9EQGRnJ8OHD0Wq1LFu2DHt7e0JCQlT5kpKSMDAwICUlpdx7ExcXR2pqKps2bcLZ2RlnZ2d0Oh3Hjh3TBxY1TVokhBCVdiz9WIXyvWz5cg3XRAghxNPqxo0bxMTEEBgYiFarLbb/4Yd5AwMD1q1bh42NDRcuXMDHx4e5c+cSHh6Om5sba9asYcGCBZw5cwaAxo0bA+Dt7U1qaio7d+6kVatWREVFMWjQIE6dOoWDgwNxcXFMnTqV5cuXM2zYMPbv38/8+fNV9YiKimLWrFmsWbOGN954g7179+Lt7U2bNm3o16+fPt/ChQsJDg5m9erVGBoaYmJigk6nw9/fX59n48aN9O7dGzs7O7y8vEhNTS0W2BTJzs5Go9FgYmKiT2vYsCEGBgYcOXKEN954o9L3vLIkkBBC1LqkjOZ1XQUhnk2Kon9rkP/INFiaR7LWQrcIIcpy7tw5FEWhffv25eb19fXVv7e1tWXp0qVMmzaN8PBwjI2NMTMzQ6PRYGlpqc+XkpLCjh07uHz5Mq1atQLA39+fmJgYdDodQUFBhIWFMXjwYP3DvqOjI/Hx8ezdu1dfTkhICF5eXvj4+ADoW0tCQkJUgcTYsWOZMGGCftvb25sFCxaQkJCAq6srubm5bN26lZUrVwJgZWVFQUHp09W98soraLVa/vrXvxIUFISiKPz1r3+loKCAtLTaGfcigYSoMw/P3V0Smc9biMf3kvFx/fumOdl/7si4VfuVEUKISlD+L/DVaDTl5ISDBw8SFBTEr7/+yu3bt8nLyyMrK4t79+6V2JoBkJiYiKIoODqqnzeys7Np1qwZAGfOnGHkyJGq/a6urqpAIjk5mffee0+Vp1evXqxdu1aV1r17d9W2lZUVQ4YMYePGjfoys7KyGDVqFADBwcFlXnPz5s35/PPPmTZtGuvWrcPAwIC//OUvdO3aFUPDx59ZrSIkkBBCCCGEEPWOg4MDGo2G5ORkRowYUWq+ixcv4uHhwdSpU1m6dCkWFhYcOXKEiRMnkpubW+pxBQUFGBoacuLEiWIP3kVdnxRFKRbIKA+17BUpKc+jaSUFNJMmTWL8+PGsXr0anU7HmDFjMDU1LbXOjxowYAApKSlcu3aNBg0aYG5ujqWlJba2thUu43FIICGEEELUsGzD8tdmEEKoWVhYMHDgQDZs2MDMmTOLPYjfunULc3Nzjh8/Tl5eHqtWrdLPVLRr1y5VXmNjY/Lz1f8Ou3TpQn5+PhkZGfTu3bvEOrRv356EhARV2vHjx1XbTk5OHDlyBE9PT31afHw8Tk5O5V6jh4cHWq2WiIgIoqOj+f7778s9piTPP/88ALGxsWRkZDBs2LAqlVNZEkgIUUNsPv+prqsghBBCPNGKBku7urqyZMkSOnXqRF5eHvv27SMiIoLk5GTs7OzIy8sjLCyMoUOHEhcXR2RkpKocGxsb7t69y4EDB+jcuTOmpqY4Ojoybtw4PD09WbVqFV26dOHatWvExsbi7OyMh4cHM2bMoE+fPoSGhjJ06FBiY2OJjo5WtTZ88MEHjB49mq5du9K/f3/27NnD7t272b9/f7nXZ2hoiJeXFwEBAdjb29OzZ0/9voCAAK5cucJnn31W6vE6nQ4nJyeaN2/Ojz/+yKxZs5g9ezbt2rWrwt2uPBlJJYR45tz7Lb1Sr6fRjXsPyn0JUV3yc3PKfWXfv1fX1RT1kK2tLYmJifTr1w8/Pz86duyIu7s7Bw4cICIiAgAXFxdCQ0NZvnw5HTt2ZNu2bcXGF7i5uTF16lTGjBlD8+bNWbFiBVD4IO7p6Ymfnx/t2rVj2LBh/PTTT1hbWwOFYx0iIyMJDQ2lc+fOxMTEMHv2bBo2bKgve8SIEaxdu5aVK1fSoUMHPvroI3Q6HX379q3QNU6cOJGcnBzVQGyAtLQ0fv/99zKPPXPmDCNGjMDJyYklS5bw4YcfFptStiZplJI6ej1jbt++jZmZGZmZmTRt2rTGzlPe4GKh9riDrcNPhldTTapGWiRKn/613Fmbbl2sgdpUndbRsvxMlVRbC9KpBlub/jnY+oZx7c+cdfFGjzL3G6TfL3O/pvTJS9T5ivo6G5QeDGVoCtjn7AxAgab039Ty8ir2cFvWgnSmho2r1LVJeXQapWqmoeqzNjUwqP6BnAWGCiam6q4rV269AMArN2/Q8IVO1X7OR9XWJB+19dzxsKysLC5cuICtra3qIVhU3uTJkzl9+jQ//PBDtZQXFxdH3759uXz5cq2tSF2ein5fpGuTeKLUdXAghFArLzh4FjxvbCmrVwvxFAsJCcHd3R2tVkt0dDSbN28mPPzxn0eys7O5dOkS8+fPZ/To0fUmiKgM6dokhBBCCCFEKRISEnB3d8fZ2ZnIyEjWrVvHpEmTHrvcHTt20K5dOzIzM/VdrZ400iIhhBDPogeZ5edpZPbYpymvu9Jja5BdLCnD4JnvsSuEqEaPzgBVXby8vPDy8qqRsmuLBBJCCPGMuH3fRP8+27D0BmmTBk/XVKW5mpL/1JU1NuJZoDzSKUHzaPylaMrYKYQQ0rVJCCGEEEIIUQXSIiHqrZJmuUq8fR2AnnbNars6QohaUNGZmSrlMSYnLGs2Jj0ZaF2tbmeVvhKxEKJ+kUBCCCGEEHXOIF9DQVYeRnl/Bn4WDc4B4NAwAbNbx/njOeNix/1HeafW6iiEUJNAQgghnhAPrwkhnn4aKteSUtPrTjwsr6D8cTQ1sdaEEKJ+kTESQgghhBBCiEqTFgkhhHjK3CtjRqan3f92cubeIzMMFdTwb2ZVWbW6PirW/vFQgqIptvC1EEJIICGEqDnH0o8VT7x1kZsFrxVLbtVAWws1EkII8aiSJjepKbPdHSt9THp6OoGBgXz99ddcuXKFFi1a4OLigq+vL/37969QGZs2bcLX15dbt25V+vz12ccff8z27dtJTEzkzp073Lx5E3Nzc1WemzdvMnPmTL766isAhg0bRlhYWLF8VSGBhBCiWty837liGQtsarQeNeHeb+kVznvN5FoN1qR2XLzTp/DNg4Z1W5EalJd/F1PLCizKJ4SoU6mpqfTq1Qtzc3NWrFhBp06dyM3N5dtvv2X69OmcPn26rqtYJbm5uRgZGT12Offv32fQoEEMGjSIgICAEvOMHTuWy5cvExMTA8B7773H+PHj2bNnz2OfXwIJISrI5vOf6roKQtSK/IL/6wqUW3KXHcOMB/r3BrXRjeqh1av/KGfV6ke7NVUHU8PGZe7P5uno2lSX8vILP7e8Gpn/t+Ie/mW+Kr+ci+rn4+ODRqMhISEBrfbPlusOHTowYcIE/XZoaCg6nY7z589jYWHB0KFDWbFiBY0bN+bQoUN4e3sDoNEUdtJbuHAhixYtIicnh3nz5rFt2zZu3bpFx44dWb58OX379tWX/cknn7BkyRKuX7/OwIED6d27N0uWLFG1bkRERBASEsKlS5ewtbVl3rx5jB8/Xr9fo9EQERFBdHQ0+/fvx9/fn23btjF16lT8/f31+ZKSkujUqRNnz57Fzs6u3Pvj6+sLwKFDh0rcn5ycTExMDEePHqVHjx766+nZsydnzpyhXbt25Z6jLBJIiCfSjynXy9wv60wIUQ2U0h6Qa3eV4//t5Kx/X1KgUPUxEOVfR0GDBmQbSKBQ175u4gpAbk7xkRp3si6rtvs2aVMrdRI178aNG8TExBAYGKgKIoo83DXHwMCAdevWYWNjw4ULF/Dx8WHu3LmEh4fj5ubGmjVrWLBgAWfOnAGgcePCHwi8vb1JTU1l586dtGrViqioKAYNGsSpU6dwcHAgLi6OqVOnsnz5coYNG8b+/fuZP3++qh5RUVHMmjWLNWvW8MYbb7B37168vb1p06YN/fr10+dbuHAhwcHBrF69GkNDQ0xMTNDpdKpAYuPGjfTu3Rs7Ozu8vLxITU0tNUioiB9//BEzMzN9EAHwyiuvYGZmRnx8vAQSRcLDw1m5ciVpaWl06NCBNWvW0Lt377qulqiAxNv/qOsqiDJUuMuSqNeu3Ksf/z8s7wfnjAbFFyOriVYGKAwSjAyKr0vwsGd6AtOHFvLT5Jc92FoxeGRvDX1m4tly7tw5FEWhffv25eYt+mUewNbWlqVLlzJt2jTCw8MxNjbGzMwMjUaDpaWlPl9KSgo7duzg8uXLtGrVCgB/f39iYmLQ6XQEBQURFhbG4MGD9Q/7jo6OxMfHs3fvXn05ISEheHl54ePjA8CcOXM4evQoISEhqkBi7NixqlYUb29vFixYQEJCAq6uruTm5rJ161ZWrlwJgJWVFQUFj9dKl56eTosWLYqlt2jRgvT0infbLc1TEUj84x//wNfXl/DwcHr16sVHH33E4MGD+fXXX3nhhRfqunpCPFuypN95Tamp2ZgM/8guP9MTJC//bl1X4alUZmhQtFOmdhLVSPm/YLaoO1JZDh48SFBQEL/++iu3b98mLy+PrKws7t27V2JrBkBiYiKKouDoqO7Glp2dTbNmhT0bzpw5w8iRI1X7XV1dVYFEcnIy7733nipPr169WLt2rSqte/fuqm0rKyuGDBnCxo0b9WVmZWUxatQoAIKDg8u97ooo6f4pilKh+1qepyKQCA0NZeLEiUyaNAmANWvW8O233xIREVFtH4J4slS065OMexAVdfVuaoXyGZuU3Z/+YZVdYO5ePVz6pyC/+K9lBiXUU6P5v9aGUi5B83/dh0r6s1bUdalhy4yK1akgp2L5no4/gXr1bQG78hatUxTQPPqFyCveIqWSnw8mjR6zZuJJ4eDggEajITk5mREjRpSa7+LFi3h4eDB16lSWLl2KhYUFR44cYeLEieTmlv6dKigowNDQkBMnTmBoqG5/LOr6VNIDt6IU/7dWUp5H00oKaCZNmsT48eNZvXo1Op2OMWPGYGpqWmqdK8vS0pL//Oc/xdL/+OMPWrZs+djlP/H/F83JyeHEiRP87W9/U6UPGDCA+Pj4Eo/Jzs4mO/vPX+AyMwt/Qb19+3bNVRTIuie/kpUk535WrZ/z8KkrAOT+p/iv563Nn/4/UjfvO5efSa+Sn09O1X7dPlfF48py934ajQ3KnhXDqnHFWi3v55T/cNqhaRLcK56e1bDkOvznQYnJpTNS/6FLu9ez1KyG1x6tb83+/w3gm86F3Q80JTygagwKuF/F7i4FWYU3yuB+VrEgoaSB0AY0INuwAt0B8grIqez3+ylSE52P9J/8w5/1IycqeKgbVLFAohx5mgIeHdvekCTVdpP735Z47B/KmEqdqyafCYrKLumBVPzJwsKCgQMHsmHDBmbOnFnsQfzWrVuYm5tz/Phx8vLyWLVqFQYGhd+pXbt2qfIaGxuTn6/+8nTp0oX8/HwyMjJK7Q7fvn17EhISVGnHj6t/BHJycuLIkSN4enrq0+Lj43Fycir3Gj08PNBqtfqB2N9//325x1RGz549yczM1HefAvjpp5/IzMzEzc3tsct/4gOJa9eukZ+fXyyqatmyZal9v4KDg1m8eHGxdGtr6xqpo6i/dpWf5Sm1s64rIKrF4bqugNqz+w9K1COlfw1XVKqcvz9uRSrgzp07mJmZ1cKZnlxFg6VdXV1ZsmQJnTp1Ii8vj3379hEREUFycjJ2dnbk5eURFhbG0KFDiYuLIzIyUlWOjY0Nd+/e5cCBA3Tu3BlTU1McHR0ZN24cnp6erFq1ii5dunDt2jViY2NxdnbGw8ODGTNm0KdPH0JDQxk6dCixsbFER0erWhs++OADRo8eTdeuXenfvz979uxh9+7d7N+/v9zrMzQ0xMvLi4CAAOzt7enZ888fiAICArhy5QqfffZZqcenp6eTnp7OuXPnADh16hRNmjThhRdewMLCAicnJwYNGsTkyZP56KOPgMLpX998883HHmgNT0EgUaQiTUpFAgICmDNnjn67oKCAixcv4uLiwqVLl2jatGmN1lVUn9u3b2NtbS2f2xNGPrcnj3xmTyb53OonRVG4c+eOfoCvKJ2trS2JiYkEBgbi5+dHWloazZs3p1u3/9/e3cdVVaf7/39tSDQ23qSTQg4qghsdQSEdVEhHMkbE8a5RmK+dCFBODH5LFHSkjumk5qi4vwojoJ1he5NiddQ5ZsGMiZrhGCkxwoSOmjepMB5TyZsUwf37w5/7tMUbtphRvp+Px37UXuuzrvVZa211XetzrbV6kZWVBUBAQABms5l58+aRmprKgAEDmDt3rt0IQXBwMAkJCURFRfHVV1/ZHv9qsViYPXs2ycnJnDhxgjZt2tCvXz8iIiKA6/c6ZGdn8/vf/57/+I//YPDgwUyaNIk//vGPttgjR45k8eLFLFiwgJdffhkvLy8sFovdI2TvZNy4cbzxxht2N2IDVFRUcOzYsTsue6NvNwwYcP09QBaLhZiYGABWr17Nyy+/zC9/+Uvg+gvpvt3/hjBYf+DjatXV1bi6uvLuu+/a3QwzceJESkpK2L69flfsvv76a1q2bElVVZX+sv0B0XH7YdJx++HRMfth0nGTGy5fvszhw4fx8vKiWbMf78smH4T4+Hj27dvHjh077ku8wsJCBg4cyPHjx+/LfQv3Q31/L43vzj0Hubi40KtXLzZv3mw3ffPmzfel9ktEREREHl5paWn8/e9/5+DBg2RkZLBixQpeeOGFBse9cuUKBw8eZPr06URGRjaaJMIRP4rSpsmTJ/P888/Tu3dv+vXrx7Jlyzh27BgJCQnfd9dERERE5AesqKiI+fPnc/78eTp37kx6errtSaENkZuby7hx4wgICGDVqlX3oacP3o8ikbhR7/b6669TUVGBn58fH3zwAR07dqx3jKZNmzJjxgyaNm36HfZU7jcdtx8mHbcfHh2zHyYdN5GGu/kJUPdLTEyM7T6GH6of/D0SIiIiInJnukdCHPHQ3CMhIiIiIiIPnhIJERERERFxmBIJERERERFxmBIJERERERFx2EOVSGRmZtpuGunVq9ddXySyfft2evXqRbNmzejcuXOd163Lg+HIcVu/fj1hYWE8/vjjtGjRgn79+vGXv/zlAfZWwPE/azcUFhbyyCOPEBAQ8N12UG7J0eN25coVXn31VTp27EjTpk3x9vYmJyfnAfVWbnD0uK1evZqePXvi6uqKh4cHsbGxfPXVVw+otyLyY/LQJBJvv/02SUlJvPrqq3z22Wf079+fIUOG3PbV44cPHyYiIoL+/fvz2Wef8corr/Dyyy+zbt26B9zzh5ujx+2jjz4iLCyMDz74gD179hAaGsqwYcP47LPPHnDPH16OHrMbqqqqiI6OZtCgQQ+op/Jt93LcIiMj2bJlC3/605/Yv38/ubm5dO3a9QH2Whw9bh9//DHR0dGMGzeOf/zjH7z77rt8+umn9+WZ+CLy8HloHv/ap08fnnzySbKysmzTunXrxsiRI5k7d26d9r/73e/YuHEj5eXltmkJCQn8/e9/529/+9sD6bM4ftxupXv37kRFRfHaa699V92Ub7nXY/ab3/yGLl264OzszJ///GdKSkoeQG/lBkePW35+Pr/5zW/44osvaN269YPsqnyLo8ctLS2NrKwsDh06ZJuWkZHB/Pnz+fLLLx9In+X7ccfHeW6t37+n90VoqsOLVFZWMmfOHN5//31OnDhB27ZtCQgIICkpqd4Xn5YvX05SUhLnzp1zeP2N2bJly1izZg3FxcWcP3+es2fP0qpVK7s2N/ZdSUkJLi4u9doHevzrt1RXV7Nnzx5++ctf2k3/5S9/yc6dO2+5zN/+9rc67QcPHszu3bu5evXqd9ZX+V/3ctxudu3aNc6fP68TnQfkXo+ZxWLh0KFDzJgx47vuotzCvRy3jRs30rt3b+bPn0/79u0xmUykpKTwzTffPIguC/d23IKDgzl+/DgffPABVquVf/3rX/zXf/0XQ4cOfRBdFnHYkSNH6NWrFwUFBcyfP5/S0lLy8/MJDQ1lwoQJ33f37tn9Ope8dOkS4eHhvPLKK7dtU11dzZgxY/jtb397X9b5bQ9FInH69Glqa2tp166d3fR27dpRWVl5y2UqKytv2b6mpobTp09/Z32V/3Uvx+1mCxcu5OLFi0RGRn4XXZSb3MsxO3DgANOmTWP16tU88sgjD6KbcpN7OW5ffPEFH3/8MWVlZWzYsIFFixbxX//1Xz/of9h/aO7luAUHB7N69WqioqJwcXHB3d2dVq1akZGR8SC6LOKwxMREDAYDRUVFjB49GpPJRPfu3Zk8eTK7du2ytTObzfj7+2M0GvH09CQxMZELFy4AsG3bNmJjY6mqqsJgMGAwGJg5cyZw/SR76tSptG/fHqPRSJ8+fdi2bZtdH9588008PT1xdXVl1KhRmM3mOlf9s7Ky8Pb2xsXFBV9fX1atWmU332AwkJ2dzYgRIzAajcyePRsfHx/S0tLs2pWVleHk5GQ3angnSUlJTJs2jb59+962ze9//3smTZqEv79/vWI64qFIJG4wGAx2361Wa51pd2t/q+ny3XL0uN2Qm5vLzJkzefvtt2nbtu131T25hfoes9raWsaOHcvvf/97TCbTg+qe3IYjf9auXbuGwWBg9erVBAUFERERgdlsZvny5RqVeMAcOW6ff/45L7/8Mq+99hp79uwhPz+fw4cPk5CQ8CC6KuKQM2fOkJ+fz4QJEzAajXXmf/tk3snJifT0dMrKylixYgUFBQVMnToVuJ5AL1q0iBYtWlBRUUFFRQUpKSkAxMbGUlhYyNq1a9m7dy9jxowhPDycAwcOANcfApKQkMDEiRMpKSkhLCyMOXPm2PVjw4YNTJw4keTkZMrKynjxxReJjY1l69atdu1mzJjBiBEjKC0tJS4ujri4OCwWi12bnJwc+vfvj7e3NzExMQwcOLChu/E79VBc/vvJT36Cs7NznSs0p06dqnMl5wZ3d/dbtn/kkUdo06bNd9ZX+V/3ctxuePvttxk3bhzvvvsuzzzzzHfZTfkWR4/Z+fPn2b17N5999hn/9//+X+D6CarVauWRRx7hr3/9K08//fQD6fvD7F7+rHl4eNC+fXtatmxpm9atWzesVivHjx+nS5cu32mf5d6O29y5cwkJCWHKlCkA9OjRA6PRSP/+/Zk9ezYeHh7feb9F6uvgwYNYrdZ6PcQhKSnJ9v9eXl7MmjWL3/72t2RmZuLi4kLLli0xGAy4u7vb2h06dIjc3FyOHz/OE088AUBKSgr5+flYLBbeeOMNMjIyGDJkiC3xMJlM7Ny5k02bNtnipKWlERMTQ2JiIoBttCQtLY3Q0FBbu7FjxxIXF2f7Hhsby2uvvUZRURFBQUFcvXqVt956iwULFgDX/569du3aPey5B+ehGJFwcXGhV69ebN682W765s2bCQ4OvuUy/fr1q9P+r3/9K71796ZJkybfWV/lf93LcYPrIxExMTGsWbNGdb8PmKPHrEWLFpSWllJSUmL7JCQk4OvrS0lJCX369HlQXX+o3cuftZCQEE6ePGkrHQD45z//iZOTEz/96U+/0/7Kdfdy3C5duoSTk/0//c7OzsD/jrqLNBaOVIJs3bqVsLAw2rdvT/PmzYmOjuarr77i4sWLt12muLgYq9WKyWTCzc3N9tm+fbuttGj//v0EBQXZLXfz9/LyckJCQuymhYSE2D2wB6B379523z08PBg6dKjtsdmbNm3i8uXLjBkzBrie+K9cufKu2/59eigSCbieHf7nf/4nOTk5lJeXM2nSJI4dO2Ybzk1NTSU6OtrWPiEhgaNHjzJ58mTKy8vJycnhT3/6ky0jlQfD0eOWm5tLdHQ0CxcupG/fvlRWVlJZWUlVVdX3tQkPHUeOmZOTE35+fnaftm3b0qxZM/z8/G45lC3fDUf/rI0dO5Y2bdoQGxvL559/zkcffcSUKVOIi4vj0Ucf/b4246Hj6HEbNmwY69evJysriy+++ILCwkJefvllgoKCbFdkRRqLLl26YDAY6pyQ3+zo0aNERETg5+fHunXr2LNnD0uWLAHufFPztWvXcHZ2Zs+ePXYXtMrLy1m8eDFw61LBWyXd9SkxvNW/aePHj2ft2rV88803WCwWoqKicHV1veP2NiYPRWkTQFRUFF999RWvv/46FRUV+Pn58cEHH9CxY0cAKioq7J677eXlxQcffMCkSZNYsmQJTzzxBOnp6fz617/+vjbhoeTocVu6dCk1NTVMmDDB7qbPF154geXLlz/o7j+UHD1m0jg4etzc3NzYvHkzL730Er1796ZNmzZERkYye/bs72sTHkqOHreYmBjOnz/PH//4R5KTk2nVqhVPP/008+bN+742QeS2WrduzeDBg1myZAkvv/xynRPxc+fO0apVK3bv3k1NTQ0LFy60jbi98847dm1dXFyora21mxYYGEhtbS2nTp2if//+t+xD165dKSoqspu2e/duu+/dunWzvaPlhp07d9KtW7e7bmNERARGo5GsrCzy8vL46KOP7rpMY/LQvEdCRERE5GH1Q32PxOHDhwkODqZ169a8/vrr9OjRg5qaGjZv3kxWVhbl5eWUlJQQGBjIokWLGDZsGIWFhaSmpnLixAnbexV27txJSEgIH374oe3N7q6urvzbv/0bhYWFLFy4kMDAQE6fPk1BQQH+/v5ERERQWFjIgAEDWLBgAcOGDaOgoIBXX32V2tpazp49C8Cf//xnIiMjSU9PZ9CgQbz33ntMnTqVDz/80HaztMFgYMOGDYwcObLONr766qukpaXRuXNnu9GXG9twp/KmG5UXu3fvJj4+no8++ojmzZvToUMH26Pvjx07xpkzZ9i4cSMLFixgx44dAPj4+ODm5nbLuHqPhIiIiIj8oHl5eVFcXExoaCjJycn4+fkRFhbGli1bbC9iDAgIwGw2M2/ePPz8/Fi9enWdFzIGBweTkJBAVFQUjz/+OPPnzweuv8coOjqa5ORkfH19GT58OJ988gmenp7A9XsdsrOzMZvN9OzZk/z8fCZNmmR3cj1y5EgWL17MggUL6N69O0uXLsVisdT7iUvjxo2jurra7kZsqN8IfnZ2NoGBgcTHxwMwYMAAAgMD2bhxo63Na6+9RmBgIDNmzODChQsEBgYSGBhYZ2TlXmhEQkRERORHrr5XmOXu4uPj2bdvn+3KfkMVFhYycOBAjh8/ftenUj4o9f29PDT3SIiIiIiIOCotLY2wsDCMRiN5eXmsWLGCzMzMBse9cuUKX375JdOnTycyMrLRJBGOUGmTiIiIiMhtFBUVERYWhr+/P9nZ2aSnpzN+/PgGx83NzcXX15eqqipbqdUPjUqbRERERH7kVNokjtDN1iIiIiIi8p1RIiEiIiIiIg5TIiEiIiIiIg5TIiEiAsycOZOAgIAGxzEYDPz5z3++7fwjR45gMBgoKSkBYNu2bRgMBs6dOwfA8uXLadWqVYP7ISIi8l1TIiEiPzgxMTEYDAYMBgNNmjShc+fOpKSkcPHixe+7a3fl6elJRUUFfn5+t5wfFRXFP//5T9v3+5XgiIiI3G96j4SI/CCFh4djsVi4evUqO3bsYPz48Vy8eNH2ptMbrl69SpMmTb6nXtbl7OyMu7v7bec/+uijPProow+wRyIiIvdGIxIi8oPUtGlT3N3d8fT0ZOzYsTz33HP8+c9/tl3Bz8nJoXPnzjRt2hSr1cqxY8cYMWIEbm5utGjRgsjISP71r3/Vibt06VI8PT1xdXVlzJgxtpIjgE8//ZSwsDB+8pOf0LJlS37xi19QXFxcJ0ZFRQVDhgzh0UcfxcvLi3fffdc27+bSppt9u7Rp+fLl/P73v+fvf/+7bQRm+fLlxMXF8atf/cpuuZqaGtzd3cnJyXF8Z4qIiNwDjUiIyI/Co48+ytWrVwE4ePAg77zzDuvWrcPZ2RmAkSNHYjQa2b59OzU1NSQmJhIVFcW2bdtsMW4s99577/H1118zbtw4JkyYwOrVqwE4f/48L7zwAunp6QAsXLiQiIgIDhw4QPPmzW1xpk+fzh/+8AcWL17MqlWr+D//5//g5+dHt27dHNqmqKgoysrKyM/P58MPPwSgZcuWmEwmBgwYQEVFBR4eHgB88MEHXLhwgcjIyHvbgSLy0MosafhbmusrMSDR4WUqKyuZM2cO77//PidOnKBt27YEBASQlJTEoEGD6hVj+fLlJCUl2V0c+jFYtmwZa9asobi4mPPnz3P27Fm7++yOHDnCrFmzKCgooLKykieeeIJ/+7d/49VXX8XFxaXB61ciISI/eEVFRaxZs8b2D0p1dTWrVq3i8ccfB2Dz5s3s3buXw4cP4+npCcCqVavo3r07n376KT//+c+B6y/gWbFiBT/96U8ByMjIYOjQoSxcuBB3d3eefvppu/UuXbqUxx57jO3bt9uNEIwZM8b21tNZs2axefNmMjIyyMx07B/rRx99FDc3Nx555BG7cqjg4GB8fX1ZtWoVU6dOBcBisTBmzBjc3NwcWoeISGN25MgRQkJCaNWqFfPnz6dHjx5cvXqVv/zlL0yYMIF9+/Z93128J/er7PbSpUuEh4cTHh5Oampqnfn79u3j2rVrLF26FB8fH8rKyoiPj+fixYukpaU1eP0qbRKRH6RNmzbh5uZGs2bN6NevHwMGDCAjIwOAjh072pIIgPLycjw9PW1JBMDPfvYzWrVqRXl5uW1ahw4dbEkEQL9+/bh27Rr79+8H4NSpUyQkJGAymWjZsiUtW7bkwoULHDt2zK5v/fr1q/P92+u5H8aPH4/FYrH16/333ycuLu6+rkNE5PuWmJiIwWCgqKiI0aNHYzKZ6N69O5MnT2bXrl22dmazGX9/f4xGI56eniQmJnLhwgXg+tPxYmNjqaqqspWJzpw5E7h+4Wnq1Km0b98eo9FInz597EaqAd58801byeuoUaMwm811nq6XlZWFt7c3Li4utgs932YwGMjOzmbEiBEYjUZmz56Nj49PnZP5srIynJycOHToUL32T1JSEtOmTaNv3763nH/jfsJf/vKXdO7cmeHDh5OSksL69evrFf9ulEiIyA9SaGgoJSUl7N+/n8uXL7N+/Xratm0LgNFotGtrtVoxGAx1Ytxu+g035t34b0xMDHv27GHRokXs3LmTkpIS2rRpQ3V19V37e6f13Ivo6Gi++OIL/va3v/HWW2/RqVMn+vfvf1/XISLyfTpz5gz5+flMmDChzt/rgN3JvJOTE+np6ZSVlbFixQoKCgpsI7bBwcEsWrSIFi1aUFFRQUVFBSkpKQDExsZSWFjI2rVr2bt3L2PGjCE8PJwDBw4AUFhYSEJCAhMnTqSkpISwsDDmzJlj148NGzYwceJEkpOTKSsr48UXXyQ2NpatW7fatZsxYwYjRoygtLSUuLg44uLibBeEbsjJyaF///54e3sTExPDwIEDG7ob66iqqqJ169b3JZYSCRH5QTIajfj4+NCxY8e7Dg//7Gc/49ixY3z55Ze2aZ9//jlVVVV29y0cO3aMkydP2r7/7W9/w8nJCZPJBMCOHTt4+eWXiYiIoHv37jRt2pTTp0/XWd+3r5Ld+N61a9d72k4XFxdqa2vrTG/Tpg0jR47EYrFgsViIjY29p/giIo3VwYMHsVqt9fr7MykpidDQULy8vHj66aeZNWsW77zzDnD979GWLVtiMBhwd3fH3d0dNzc3Dh06RG5uLu+++67t5D0lJYWnnnrKdoKfkZHBkCFDSElJwWQykZiYyJAhQ+zWnZaWRkxMDImJiZhMJiZPnsyzzz5bZ7Rh7NixxMXF0blzZzp27EhsbCz79++nqKgIuF7u9NZbb9lGlz08POjQoUOD9+O3HTp0iIyMDBISEu5LPN0jISI/es888ww9evTgueeeY9GiRbabrX/xi1/Qu3dvW7tmzZrxwgsvkJaWxtdff83LL79MZGSk7f4EHx8fVq1aRe/evfn666+ZMmXKLR/V+u6779K7d2+eeuopVq9eTVFREX/605/uqe+dOnXi8OHDlJSU8NOf/pTmzZvTtGlT4Hp5069+9Stqa2t54YUX7im+iEhjZbVagfqN6G7dupU33niDzz//nK+//pqamhouX77MxYsXbzmaAVBcXIzVarVdLLrhypUrtGnTBoD9+/czatQou/lBQUFs2rTJ9r28vJx///d/t2sTEhLC4sWL7aZ9+98buJ4oDB06lJycHFvMy5cvM2bMGADmzp171+12xMmTJwkPD7e7j6+hNCIhIj96N942/dhjjzFgwACeeeYZOnfuzNtvv23XzsfHh2effZaIiAh++ctf4ufnZ3eDdE5ODmfPniUwMJDnn3+el19+2VZO9W2///3vWbt2LT169GDFihWsXr2an/3sZ/fU91//+teEh4cTGhrK448/Tm5urm3eM888g4eHB4MHD+aJJ564p/giIo1Vly5dMBgMd73H7OjRo0RERODn58e6devYs2cPS5YsAbA9ze9Wrl27hrOzM3v27KGkpMT2KS8vtyUBtyqBvZHgfNut2tw87VYJzfjx41m7di3ffPMNFouFqKgoXF1d77i99+LkyZOEhobSr18/li1bdt/iGqy32hsiItLoXbp0iSeeeIKcnByeffbZ77s7ItKIXb58mcOHD+Pl5UWzZs3s5jXmx78OGTKE0tJS9u/fX+dE/Ny5c7Rq1Yp169bxm9/8hitXruDkdP0a+ezZs5k+fbrtcahr1qzhxRdf5Pz587bl//nPf+Lr68tHH31023vMfvOb33Dx4kXee+8927Tnn3+e9957z/Yo2ZCQELp37253gh4ZGcmlS5dsIxcGg4ENGzYwcuRIu/i1tbV06NCB5ORkfve73/HRRx/VeWBHfWzbto3Q0NA6j38FOHHiBKGhofTq1Yu33nrL9lj0O7nT7+XbVNokIvIDc+3aNSorK1m4cCEtW7Zk+PDh33eXRES+E5mZmQQHBxMUFMTrr79Ojx49qKmpYfPmzWRlZVFeXo63tzc1NTVkZGQwbNgwCgsLyc7OtovTqVMnLly4wJYtW+jZsyeurq6YTCaee+45oqOjWbhwIYGBgZw+fZqCggL8/f2JiIjgpZdeYsCAAZjNZoYNG0ZBQQF5eXl2ow1TpkwhMjKSJ598kkGDBvHee++xfv162/t/7sTZ2ZmYmBhSU1Px8fGxSyJSU1M5ceIEK1euvO3ylZWVVFZWcvDgQQBKS0tp3rw5HTp0oHXr1pw8eZKBAwfSoUMH0tLS+J//+R/bst9+rPi9UmmTiMgPzLFjx2jfvj3vvPMOOTk5PPKIrgmJyI+Tl5cXxcXFhIaGkpycjJ+fH2FhYWzZsoWsrCwAAgICMJvNzJs3Dz8/P1avXl3n/oLg4GASEhKIiori8ccfZ/78+cD1d/BER0eTnJyMr68vw4cP55NPPrE9LjwkJITs7GzMZjM9e/YkPz+fSZMm2V2lHzlyJIsXL2bBggV0796dpUuXYrFY6v3EpXHjxlFdXV3nEd4VFRV1Hi9+s+zsbAIDA4mPjwdgwIABBAYGsnHjRgD++te/cvDgQQoKCvjpT3+Kh4eH7XM/qLRJRERE5EeuvqUqcnfx8fHs27ePHTt23Jd4hYWFDBw4kOPHj9OuXbv7ErOhVNokIiIiItJAaWlphIWFYTQaycvLY8WKFXYP4rhXV65c4csvv2T69OlERkY2miTCESptEhERERG5jaKiIsLCwvD39yc7O5v09PT78vjU3NxcfH19qaqqspVa/dCotElERETkR06lTeKI+v5eNCIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIO05utRURERB5i/5Pxxwe2rsdf+r8OL1NZWcmcOXN4//33OXHiBG3btiUgIICkpCQGDRpUrxjLly8nKSmJc+fOObz+xmzZsmWsWbOG4uJizp8/z9mzZ2nVqpVdm+HDh1NSUsKpU6d47LHHeOaZZ5g3bx5PPPFEg9evEQkRERERaZSOHDlCr169KCgoYP78+ZSWlpKfn09oaCgTJkz4vrt3z65evXpf4ly6dInw8HBeeeWV27YJDQ3lnXfeYf/+/axbt45Dhw4xevTo+7J+JRIiIiIi0iglJiZiMBgoKipi9OjRmEwmunfvzuTJk9m1a5etndlsxt/fH6PRiKenJ4mJiVy4cAGAbdu2ERsbS1VVFQaDAYPBwMyZMwGorq5m6tSptG/fHqPRSJ8+fdi2bZtdH9588008PT1xdXVl1KhRmM3mOlf9s7Ky8Pb2xsXFBV9fX1atWmU332AwkJ2dzYgRIzAajcyePRsfHx/S0tLs2pWVleHk5MShQ4fqtX+SkpKYNm0affv2vW2bSZMm0bdvXzp27EhwcDDTpk1j165d9yWZUSIhIiIiIo3OmTNnyM/PZ8KECRiNxjrzv30y7+TkRHp6OmVlZaxYsYKCggKmTp0KQHBwMIsWLaJFixZUVFRQUVFBSkoKALGxsRQWFrJ27Vr27t3LmDFjCA8P58CBAwAUFhaSkJDAxIkTKSkpISwsjDlz5tj1Y8OGDUycOJHk5GTKysp48cUXiY2NZevWrXbtZsyYwYgRIygtLSUuLo64uDgsFotdm5ycHPr374+3tzcxMTEMHDiwobvRzpkzZ1i9ejXBwcE0adKkwfGUSIiIiIhIo3Pw4EGsVitdu3a9a9ukpCRCQ0Px8vLi6aefZtasWbzzzjsAuLi40LJlSwwGA+7u7ri7u+Pm5sahQ4fIzc3l3XfftZ28p6Sk8NRTT9lO8DMyMhgyZAgpKSmYTCYSExMZMmSI3brT0tKIiYkhMTERk8nE5MmTefbZZ+uMNowdO5a4uDg6d+5Mx44diY2NZf/+/RQVFQHXy53eeust4uLiAPDw8KBDhw4N3o8Av/vd7zAajbRp04Zjx47x3//93/clrhIJEREREWl0rFYrcL0s6G62bt1KWFgY7du3p3nz5kRHR/PVV19x8eLF2y5TXFyM1WrFZDLh5uZm+2zfvt1WWrR//36CgoLslrv5e3l5OSEhIXbTQkJCKC8vt5vWu3dvu+8eHh4MHTqUnJwcADZt2sTly5cZM2YMAHPnzmXlypV33fb6mDJlCp999hl//etfcXZ2Jjo62rZ/G0JPbRIRERGRRqdLly4YDAbKy8sZOXLkbdsdPXqUiIgIEhISmDVrFq1bt+bjjz9m3Lhxd7wP4Nq1azg7O7Nnzx6cnZ3t5rm5uQHXk5mbE5lbnYDfqs3N025VnjV+/Hief/55/t//+39YLBaioqJwdXW9bZ/v1U9+8hN+8pOfYDKZ6NatG56enuzatYt+/fo1KK5GJERERESk0WndujWDBw9myZIltxxZuPEo1927d1NTU8PChQvp27cvJpOJkydP2rV1cXGhtrbWblpgYCC1tbWcOnUKHx8fu4+7uzsAXbt2tZUe3bB792677926dePjjz+2m7Zz5066det2122MiIjAaDSSlZVFXl6erazpu3QjEbpy5UqDY2lEQkREREQapczMTIKDgwkKCuL111+nR48e1NTUsHnzZrKysigvL8fb25uamhoyMjIYNmwYhYWFZGdn28Xp1KkTFy5cYMuWLfTs2RNXV1dMJhPPPfcc0dHRLFy4kMDAQE6fPk1BQQH+/v5ERETw0ksvMWDAAMxmM8OGDaOgoIC8vDy70YYpU6YQGRnJk08+yaBBg3jvvfdYv349H3744V23z9nZmZiYGFJTU/Hx8bEbIUhNTeXEiRN3LG+qrKyksrKSgwcPAlBaWkrz5s3p0KEDrVu3pqioiKKiIp566ikee+wxvvjiC1577TW8vb0bPBoBGpEQERERkUbKy8uL4uJiQkNDSU5Oxs/Pj7CwMLZs2UJWVhYAAQEBmM1m5s2bh5+fH6tXr2bu3Ll2cYKDg0lISCAqKorHH3+c+fPnA2CxWIiOjiY5ORlfX1+GDx/OJ598gqenJ3D9Xofs7GzMZjM9e/YkPz+fSZMm0axZM1vskSNHsnjxYhYsWED37t1ZunQpFoul3k9cGjduHNXV1XVGIyoqKjh27Ngdl83OziYwMJD4+HgABgwYQGBgIBs3bgTg0UcfZf369QwaNAhfX1/i4uLw8/Nj+/btNG3atF79uxOD9X7caSEiIiIijdbly5c5fPgwXl5edifB4rj4+Hj27dvHjh077ku8wsJCBg4cyPHjx2nXrt19idlQ9f29qLRJREREROQ20tLSCAsLw2g0kpeXx4oVK8jMzGxw3CtXrvDll18yffp0IiMjG00S4QiVNomIiIiI3EZRURFhYWH4+/uTnZ1Neno648ePb3Dc3NxcfH19qaqqspVa/dCotElERETkR06lTeKI+v5eNCIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIOUyIhIiIiIiIO05utRURERB5iRe998cDWFTSss8PLVFZWMmfOHN5//31OnDhB27ZtCQgIICkpiUGDBtUrxvLly0lKSuLcuXMOr78xW7ZsGWvWrKG4uJjz589z9uxZWrVqdcu2V65coU+fPvz973/ns88+IyAgoMHr14iEiIiIiDRKR44coVevXhQUFDB//nxKS0vJz88nNDSUCRMmfN/du2dXr169L3EuXbpEeHg4r7zyyl3bTp06lSeeeOK+rPcGJRIiIiIi0iglJiZiMBgoKipi9OjRmEwmunfvzuTJk9m1a5etndlsxt/fH6PRiKenJ4mJiVy4cAGAbdu2ERsbS1VVFQaDAYPBwMyZMwGorq5m6tSptG/fHqPRSJ8+fdi2bZtdH9588008PT1xdXVl1KhRmM3mOlf9s7Ky8Pb2xsXFBV9fX1atWmU332AwkJ2dzYgRIzAajcyePRsfHx/S0tLs2pWVleHk5MShQ4fqtX+SkpKYNm0affv2vWO7vLw8/vrXv9ZZX0MpkRARERGRRufMmTPk5+czYcIEjEZjnfnfPpl3cnIiPT2dsrIyVqxYQUFBAVOnTgUgODiYRYsW0aJFCyoqKqioqCAlJQWA2NhYCgsLWbt2LXv37mXMmDGEh4dz4MABAAoLC0lISGDixImUlJQQFhbGnDlz7PqxYcMGJk6cSHJyMmVlZbz44ovExsaydetWu3YzZsxgxIgRlJaWEhcXR1xcHBaLxa5NTk4O/fv3x9vbm5iYGAYOHNjQ3ci//vUv4uPjWbVqFa6urg2O921KJERERESk0Tl48CBWq5WuXbvetW1SUhKhoaF4eXnx9NNPM2vWLN555x0AXFxcaNmyJQaDAXd3d9zd3XFzc+PQoUPk5uby7rvv2k7eU1JSeOqpp2wn+BkZGQwZMoSUlBRMJhOJiYkMGTLEbt1paWnExMSQmJiIyWRi8uTJPPvss3Wu/o8dO5a4uDg6d+5Mx44diY2NZf/+/RQVFQHXy53eeust4uLiAPDw8KBDhw4N2odWq5WYmBgSEhLo3bt3g2LdihIJEREREWl0rFYrcL0s6G62bt1KWFgY7du3p3nz5kRHR/PVV19x8eLF2y5TXFyM1WrFZDLh5uZm+2zfvt1WWrR//36CgoLslrv5e3l5OSEhIXbTQkJCKC8vt5t284m8h4cHQ4cOJScnB4BNmzZx+fJlxowZA8DcuXNZuXLlXbf9TjIyMvj6669JTU1tUJzbUSIhIiIiIo1Oly5dMBgMdU7Ib3b06FEiIiLw8/Nj3bp17NmzhyVLlgB3vqn52rVrODs7s2fPHkpKSmyf8vJyFi9eDFxPZm5OZG4kON92qzY3T7tVedb48eNZu3Yt33zzDRaLhaioqPtaflRQUMCuXbto2rQpjzzyCD4+PsD1pOaFF15ocHw9/lVEREREGp3WrVszePBglixZwssvv1znRPzcuXO0atWK3bt3U1NTw8KFC3Fyun6N/EZZ0w0uLi7U1tbaTQsMDKS2tpZTp07Rv3//W/aha9euttKjG3bv3m33vVu3bnz88cdER0fbpu3cuZNu3brddRsjIiIwGo1kZWWRl5fHRx99dNdlHJGens7s2bNt30+ePMngwYN5++236dOnT4PjK5EQERERkUYpMzOT4OBggoKCeP311+nRowc1NTVs3ryZrKwsysvL8fb2pqamhoyMDIYNG0ZhYSHZ2dl2cTp16sSFCxfYsmULPXv2xNXVFZPJxHPPPUd0dDQLFy4kMDCQ06dPU1BQgL+/PxEREbz00ksMGDAAs9nMsGHDKCgoIC8vz260YcqUKURGRvLkk08yaNAg3nvvPdavX8+HH3541+1zdnYmJiaG1NRUfHx86Nevn21eamoqJ06cuGN5U2VlJZWVlRw8eBCA0tJSmjdvTocOHWjdunWdeyzc3NwA8Pb25qc//endD8BdqLRJRERERBolLy8viouLCQ0NJTk5GT8/P8LCwtiyZQtZWVkABAQEYDabmTdvHn5+fqxevZq5c+faxQkODiYhIYGoqCgef/xx5s+fD4DFYiE6Oprk5GR8fX0ZPnw4n3zyCZ6ensD1ex2ys7Mxm8307NmT/Px8Jk2aRLNmzWyxR44cyeLFi1mwYAHdu3dn6dKlWCyWej9xady4cVRXV9tusr6hoqKCY8eO3XHZ7OxsAgMDiY+PB2DAgAEEBgaycePGeq27oQzWWxV6iYiIiMiPxuXLlzl8+DBeXl52J8HiuPj4ePbt28eOHTvuS7zCwkIGDhzI8ePHadeu3X2J2VD1/b2otElERERE5DbS0tIICwvDaDSSl5fHihUryMzMbHDcK1eu8OWXXzJ9+nQiIyMbTRLhCJU2iYiIiIjcRlFREWFhYfj7+5OdnU16ejrjx49vcNzc3Fx8fX2pqqqylVr90Ki0SURERORHTqVN4oj6/l40IiEiIiIiIg5TIiEiIiIiIg5TIiEiIiIiIg5TIiEiIiIiIg5TIiEiIiIiIg5TIiEiIiIiIg5TIiEiIiIiIg7Tm61FREREHmI73139wNYVPOY5h5eprKxkzpw5vP/++5w4cYK2bdsSEBBAUlISgwYNqleM5cuXk5SUxLlz5xxef2O2bNky1qxZQ3FxMefPn+fs2bO0atXKrk2nTp04evSo3bTf/e53/OEPf2jw+pVIiIiIiEijdOTIEUJCQmjVqhXz58+nR48eXL16lb/85S9MmDCBffv2fd9dvCdXr16lSZMmDY5z6dIlwsPDCQ8PJzU19bbtXn/9deLj423f3dzcGrxuUGmTiIiIiDRSiYmJGAwGioqKGD16NCaTie7duzN58mR27dpla2c2m/H398doNOLp6UliYiIXLlwAYNu2bcTGxlJVVYXBYMBgMDBz5kwAqqurmTp1Ku3bt8doNNKnTx+2bdtm14c333wTT09PXF1dGTVqFGazuc5V/6ysLLy9vXFxccHX15dVq1bZzTcYDGRnZzNixAiMRiOzZ8/Gx8eHtLQ0u3ZlZWU4OTlx6NCheu2fpKQkpk2bRt++fe/Yrnnz5ri7u9s+SiRERERE5EfrzJkz5OfnM2HCBIxGY5353z6Zd3JyIj09nbKyMlasWEFBQQFTp04FIDg4mEWLFtGiRQsqKiqoqKggJSUFgNjYWAoLC1m7di179+5lzJgxhIeHc+DAAQAKCwtJSEhg4sSJlJSUEBYWxpw5c+z6sWHDBiZOnEhycjJlZWW8+OKLxMbGsnXrVrt2M2bMYMSIEZSWlhIXF0dcXBwWi8WuTU5ODv3798fb25uYmBgGDhzY0N0IwLx582jTpg0BAQHMmTOH6urq+xJXpU0iIiIi0ugcPHgQq9VK165d79o2KSnJ9v9eXl7MmjWL3/72t2RmZuLi4kLLli0xGAy4u7vb2h06dIjc3FyOHz/OE088AUBKSgr5+flYLBbeeOMNMjIyGDJkiC3xMJlM7Ny5k02bNtnipKWlERMTQ2JiIoBttCQtLY3Q0FBbu7FjxxIXF2f7Hhsby2uvvUZRURFBQUFcvXqVt956iwULFgDg4eHBtWvX7mHP2Zs4cSJPPvkkjz32GEVFRaSmpnL48GH+8z//s8GxlUiIiIiISKNjtVqB62VBd7N161beeOMNPv/8c77++mtqamq4fPkyFy9evOVoBkBxcTFWqxWTyWQ3/cqVK7Rp0waA/fv3M2rUKLv5QUFBdolEeXk5//7v/27XJiQkhMWLF9tN6927t913Dw8Phg4dSk5Oji3m5cuXGTNmDABz586963bXx6RJk2z/36NHDx577DFGjx5tG6VoCJU2iYiIiEij06VLFwwGA+Xl5Xdsd/ToUSIiIvDz82PdunXs2bOHJUuWANdvar6da9eu4ezszJ49eygpKbF9ysvLbUmA1Wqtk8jcSHC+7VZtbp52q4Rm/PjxrF27lm+++QaLxUJUVBSurq533N6GunE/xcGDBxscS4mEiIiIiDQ6rVu3ZvDgwSxZsoSLFy/WmX/jUa67d++mpqaGhQsX0rdvX0wmEydPnrRr6+LiQm1trd20wMBAamtrOXXqFD4+PnafGyVQXbt2paioyG653bt3233v1q0bH3/8sd20nTt30q1bt7tuY0REBEajkaysLPLy8uxKn74rn332GXB9RKShVNokIiIiIo1SZmYmwcHBBAUF8frrr9OjRw9qamrYvHkzWVlZlJeX4+3tTU1NDRkZGQwbNozCwkKys7Pt4nTq1IkLFy6wZcsWevbsiaurKyaTieeee47o6GgWLlxIYGAgp0+fpqCgAH9/fyIiInjppZcYMGAAZrOZYcOGUVBQQF5ent1ow5QpU4iMjOTJJ59k0KBBvPfee6xfv54PP/zwrtvn7OxMTEwMqamp+Pj40K9fP9u81NRUTpw4wcqVK2+7fGVlJZWVlbbRhdLSUpo3b06HDh1o3bo1f/vb39i1axehoaG0bNmSTz/9lEmTJjF8+HA6dOjg6OGoQyMSIiIiItIoeXl5UVxcTGhoKMnJyfj5+REWFsaWLVvIysoCICAgALPZzLx58/Dz82P16tV17i8IDg4mISGBqKgoHn/8cebPnw+AxWIhOjqa5ORkfH19GT58OJ988gmenp7A9XsdsrOzMZvN9OzZk/z8fCZNmkSzZs1ssUeOHMnixYtZsGAB3bt3Z+nSpVgslno/cWncuHFUV1fXGY2oqKjg2LFjd1w2OzubwMBA2zsiBgwYQGBgIBs3bgSgadOmvP322wwcOJCf/exnvPbaa8THx5Obm1uvvt2NwXqrQi8RERER+dG4fPkyhw8fxsvLy+4kWBwXHx/Pvn372LFjx32JV1hYyMCBAzl+/Djt2rW7LzEbqr6/F5U2iYiIiIjcRlpaGmFhYRiNRvLy8lixYgWZmZkNjnvlyhW+/PJLpk+fTmRkZKNJIhyh0iYRERERkdsoKioiLCwMf39/srOzSU9PZ/z48Q2Om5ubi6+vL1VVVbZSqx8alTaJiIiI/MiptEkcUd/fi0YkRERERETEYUokRERERETEYUokRERERETEYUokRERERETEYUokRERERETEYUokRERERETEYUokRERERETEYXqztYiIiMhDrGrz0Qe2rpZhHR1eprKykjlz5vD+++9z4sQJ2rZtS0BAAElJSQwaNKheMZYvX05SUhLnzp1zeP2N2bJly1izZg3FxcWcP3+es2fP0qpVqzrt3n//fV5//XX27t2L0WhkwIABrF+/vsHrVyIhIiIiIo3SkSNHCAkJoVWrVsyfP58ePXpw9epV/vKXvzBhwgT27dv3fXfxnly9epUmTZo0OM6lS5cIDw8nPDyc1NTUW7ZZt24d8fHxvPHGGzz99NNYrVZKS0sbvG5QaZOIiIiINFKJiYkYDAaKiooYPXo0JpOJ7t27M3nyZHbt2mVrZzab8ff3x2g04unpSWJiIhcuXABg27ZtxMbGUlVVhcFgwGAwMHPmTACqq6uZOnUq7du3x2g00qdPH7Zt22bXhzfffBNPT09cXV0ZNWoUZrO5zlX/rKwsvL29cXFxwdfXl1WrVtnNNxgMZGdnM2LECIxGI7Nnz8bHx4e0tDS7dmVlZTg5OXHo0KF67Z+kpCSmTZtG3759bzm/pqaGiRMnsmDBAhISEjCZTPj6+jJ69Oh6xb8bJRIiIiIi0uicOXOG/Px8JkyYgNForDP/2yfzTk5OpKenU1ZWxooVKygoKGDq1KkABAcHs2jRIlq0aEFFRQUVFRWkpKQAEBsbS2FhIWvXrmXv3r2MGTOG8PBwDhw4AEBhYSEJCQlMnDiRkpISwsLCmDNnjl0/NmzYwMSJE0lOTqasrIwXX3yR2NhYtm7datduxowZjBgxgtLSUuLi4oiLi8Nisdi1ycnJoX///nh7exMTE8PAgQMbtA+Li4s5ceIETk5OBAYG4uHhwZAhQ/jHP/7RoLg3KJEQERERkUbn4MGDWK1Wunbtete2SUlJhIaG4uXlxdNPP82sWbN45513AHBxcaFly5YYDAbc3d1xd3fHzc2NQ4cOkZuby7vvvms7eU9JSeGpp56yneBnZGQwZMgQUlJSMJlMJCYmMmTIELt1p6WlERMTQ2JiIiaTicmTJ/Pss8/WGW0YO3YscXFxdO7cmY4dOxIbG8v+/fspKioCrpc7vfXWW8TFxQHg4eFBhw4dGrQPv/jiCwBmzpzJf/zHf7Bp0yYee+wxfvGLX3DmzJkGxQYlEiIiIiLSCFmtVuB6WdDdbN26lbCwMNq3b0/z5s2Jjo7mq6++4uLFi7ddpri4GKvVislkws3NzfbZvn27rbRo//79BAUF2S138/fy8nJCQkLspoWEhFBeXm43rXfv3nbfPTw8GDp0KDk5OQBs2rSJy5cvM2bMGADmzp3LypUr77rtd3Lt2jUAXn31VX7961/Tq1cvLBYLBoOBd999t0GxQYmEiIiIiDRCXbp0wWAw1Dkhv9nRo0eJiIjAz8+PdevWsWfPHpYsWQJcv8p/O9euXcPZ2Zk9e/ZQUlJi+5SXl7N48WLgejJzcyJzI8H5tlu1uXnarcqzxo8fz9q1a/nmm2+wWCxERUXh6up6x+11hIeHBwA/+9nPbNOaNm1K586dOXbsWIPjK5EQERERkUandevWDB48mCVLltxyZOHGo1x3795NTU0NCxcupG/fvphMJk6ePGnX1sXFhdraWrtpgYGB1NbWcurUKXx8fOw+7u7uAHTt2tVWenTD7t277b5369aNjz/+2G7azp076dat2123MSIiAqPRSFZWFnl5ebaypvulV69eNG3alP3799umXb16lSNHjtCxo+OP4r2ZHv8qIiIiIo1SZmYmwcHBBAUF8frrr9OjRw9qamrYvHkzWVlZlJeX4+3tTU1NDRkZGQwbNozCwkKys7Pt4nTq1IkLFy6wZcsWevbsiaurKyaTieeee47o6GgWLlxIYGAgp0+fpqCgAH9/fyIiInjppZcYMGAAZrOZYcOGUVBQQF5ent1ow5QpU4iMjOTJJ59k0KBBvPfee6xfv54PP/zwrtvn7OxMTEwMqamp+Pj40K9fP9u81NRUTpw4ccfypsrKSiorKzl48CAApaWlNG/enA4dOtC6dWtatGhBQkICM2bMwNPTk44dO7JgwQIAWwlVQ2hEQkREREQaJS8vL4qLiwkNDSU5ORk/Pz/CwsLYsmULWVlZAAQEBGA2m5k3bx5+fn6sXr2auXPn2sUJDg4mISGBqKgoHn/8cebPnw+AxWIhOjqa5ORkfH19GT58OJ988gmenp7A9XsdsrOzMZvN9OzZk/z8fCZNmkSzZs1ssUeOHMnixYtZsGAB3bt3Z+nSpVgslno/cWncuHFUV1fXGY2oqKi4a/lRdnY2gYGBxMfHAzBgwAACAwPZuHGjrc2CBQv4zW9+w/PPP8/Pf/5zjh49SkFBAY899li9+ncnBuutCr1ERERE5Efj8uXLHD58GC8vL7uTYHFcfHw8+/btY8eOHfclXmFhIQMHDuT48eO0a9fuvsRsqPr+XlTaJCIiIiJyG2lpaYSFhWE0GsnLy2PFihVkZmY2OO6VK1f48ssvmT59OpGRkY0miXCESptERERERG6jqKiIsLAw/P39yc7OJj09nfHjxzc4bm5uLr6+vlRVVdlKrX5oVNokIiIi8iOn0iZxRH1/LxqREBERERERhymREBERERERhymREBERERERhymREBERERERhymREBERERERhymREBERERERhymREBERERERh+nN1iIiIiIPsa1btz6wdYWGhjq8TGVlJXPmzOH999/nxIkTtG3bloCAAJKSkhg0aFC9YixfvpykpCTOnTvn8Pobs2XLlrFmzRqKi4s5f/48Z8+epVWrVrb527Ztu+0+Lyoq4uc//3mD1q8RCRERERFplI4cOUKvXr0oKChg/vz5lJaWkp+fT2hoKBMmTPi+u3fPrl69el/iXLp0ifDwcF555ZVbzg8ODqaiosLuM378eDp16kTv3r0bvH4lEiIiIiLSKCUmJmIwGCgqKmL06NGYTCa6d+/O5MmT2bVrl62d2WzG398fo9GIp6cniYmJXLhwAbh+VT42NpaqqioMBgMGg4GZM2cCUF1dzdSpU2nfvj1Go5E+ffqwbds2uz68+eabeHp64urqyqhRozCbzXZX/QGysrLw9vbGxcUFX19fVq1aZTffYDCQnZ3NiBEjMBqNzJ49Gx8fH9LS0uzalZWV4eTkxKFDh+q1f5KSkpg2bRp9+/a95XwXFxfc3d1tnzZt2rBx40bi4uIwGAz1WsedKJEQERERkUbnzJkz5OfnM2HCBIxGY5353z6Zd3JyIj09nbKyMlasWEFBQQFTp04Frl+VX7RoES1atLBdlU9JSQEgNjaWwsJC1q5dy969exkzZgzh4eEcOHAAgMLCQhISEpg4cSIlJSWEhYUxZ84cu35s2LCBiRMnkpycTFlZGS+++CKxsbF1SsZmzJjBiBEjKC0tJS4ujri4OCwWi12bnJwc+vfvj7e3NzExMQwcOLChu9HOxo0bOX36NDExMfclnu6REBEREZFG5+DBg1itVrp27XrXtklJSbb/9/LyYtasWfz2t78lMzMTFxcXWrZsicFgwN3d3dbu0KFD5Obmcvz4cZ544gkAUlJSyM/Px2Kx8MYbb5CRkcGQIUNsiYfJZGLnzp1s2rTJFictLY2YmBgSExMBbKMlaWlpdvcnjB07lri4ONv32NhYXnvtNYqKiggKCuLq1au89dZbLFiwAAAPDw+uXbt2D3vu9v70pz8xePBgPD0970s8jUiIiIiISKNjtVoB6lWCs3XrVsLCwmjfvj3NmzcnOjqar776iosXL952meLiYqxWKyaTCTc3N9tn+/btttKi/fv3ExQUZLfczd/Ly8sJCQmxmxYSEkJ5ebndtJvvSfDw8GDo0KHk5OQAsGnTJi5fvsyYMWMAmDt3LitXrrzrttfX8ePH+ctf/sK4cePuW0wlEiIiIiLS6HTp0gWDwVDnhPxmR48eJSIiAj8/P9atW8eePXtYsmQJcOebmq9du4azszN79uyhpKTE9ikvL2fx4sXA9WTm5kTmRoLzbbdqc/O0W5VnjR8/nrVr1/LNN99gsViIiorC1dX1jtt7rywWC23atGH48OH3LaYSCRERERFpdFq3bs3gwYNZsmTJLUcWbjzKdffu3dTU1LBw4UL69u2LyWTi5MmTdm1dXFyora21mxYYGEhtbS2nTp3Cx8fH7nOjBKpr164UFRXZLbd792677926dePjjz+2m7Zz5066det2122MiIjAaDSSlZVFXl6eXenT/WS1WrFYLERHR9OkSZP7FleJhIiIiIg0SpmZmdTW1hIUFMS6des4cOAA5eXlpKen069fPwC8vb2pqakhIyODL774glWrVpGdnW0Xp1OnTly4cIEtW7Zw+vRpLl26hMlk4rnnniM6Opr169dz+PBhPv30U+bNm8cHH3wAwEsvvcQHH3yA2WzmwIEDLF26lLy8PLvRhilTprB8+XKys7M5cOAAZrOZ9evX2+6ruBNnZ2diYmJITU3Fx8fHtk0AqampREdH33H5yspKSkpKOHjwIAClpaWUlJRw5swZu3YFBQUcPnz4vpY1gRIJEREREWmkvLy8KC4uJjQ0lOTkZPz8/AgLC2PLli1kZWUBEBAQgNlsZt68efj5+bF69Wrmzp1rFyc4OJiEhASioqJ4/PHHmT9/PoDtKn1ycjK+vr4MHz6cTz75xHYzckhICNnZ2ZjNZnr27El+fj6TJk2iWbNmttgjR45k8eLFLFiwgO7du7N06VIsFku9n7g0btw4qqur64xGVFRUcOzYsTsum52dTWBgIPHx8QAMGDCAwMBANm7caNfuT3/6E8HBwfUaJXGEwXqrQi8RERER+dG4fPkyhw8fxsvLy+4kWBwXHx/Pvn372LFjx32JV1hYyMCBAzl+/Djt2rW7LzEbqr6/Fz3+VURERETkNtLS0ggLC8NoNJKXl8eKFSvIzMxscNwrV67w5ZdfMn36dCIjIxtNEuEIlTaJiIiIiNxGUVERYWFh+Pv7k52dTXp6OuPHj29w3NzcXHx9famqqrKVWv3QqLRJRERE5EdOpU3iiPr+XjQiISIiIiIiDlMiISIiIiIiDlMiISIiIiIiDlMiISIiIiIiDlMiISIiIiIiDlMiISIiIiIiDlMiISIiIiIiDtObrUVEREQeYl98sfiBratz54kOL1NZWcmcOXN4//33OXHiBG3btiUgIICkpCQGDRpUrxjLly8nKSmJc+fOObz+xmzZsmWsWbOG4uJizp8/z9mzZ2nVqpVdm3/+859MmTKFwsJCqqur8ff3Z/bs2YSGhjZ4/RqREBEREZFG6ciRI/Tq1YuCggLmz59PaWkp+fn5hIaGMmHChO+7e/fs6tWr9yXOpUuXCA8P55VXXrltm6FDh1JTU0NBQQF79uwhICCAX/3qV1RWVjZ4/UokRERERKRRSkxMxGAwUFRUxOjRozGZTHTv3p3Jkyeza9cuWzuz2Yy/vz9GoxFPT08SExO5cOECANu2bSM2NpaqqioMBgMGg4GZM2cCUF1dzdSpU2nfvj1Go5E+ffqwbds2uz68+eabeHp64urqyqhRozCbzXWu+mdlZeHt7Y2Liwu+vr6sWrXKbr7BYCA7O5sRI0ZgNBqZPXs2Pj4+pKWl2bUrKyvDycmJQ4cO1Wv/JCUlMW3aNPr27XvL+adPn+bgwYNMmzaNHj160KVLF/7whz9w6dIl/vGPf9RrHXeiREJEREREGp0zZ86Qn5/PhAkTMBqNdeZ/+2TeycmJ9PR0ysrKWLFiBQUFBUydOhWA4OBgFi1aRIsWLaioqKCiooKUlBQAYmNjKSwsZO3atezdu5cxY8YQHh7OgQMHACgsLCQhIYGJEydSUlJCWFgYc+bMsevHhg0bmDhxIsnJyZSVlfHiiy8SGxvL1q1b7drNmDGDESNGUFpaSlxcHHFxcVgsFrs2OTk59O/fH29vb2JiYhg4cGCD9mGbNm3o1q0bK1eu5OLFi9TU1LB06VLatWtHr169GhQbdI+EiIiIiDRCBw8exGq10rVr17u2TUpKsv2/l5cXs2bN4re//S2ZmZm4uLjQsmVLDAYD7u7utnaHDh0iNzeX48eP88QTTwCQkpJCfn4+FouFN954g4yMDIYMGWJLPEwmEzt37mTTpk22OGlpacTExJCYmAhgGy1JS0uzuw9h7NixxMXF2b7Hxsby2muvUVRURFBQEFevXuWtt95iwYIFAHh4eHDt2rV72HP/y2AwsHnzZkaMGEHz5s1xcnKiXbt25Ofn1xlVuRcakRARERGRRsdqtQLXT4bvZuvWrYSFhdG+fXuaN29OdHQ0X331FRcvXrztMsXFxVitVkwmE25ubrbP9u3bbaVF+/fvJygoyG65m7+Xl5cTEhJiNy0kJITy8nK7ab1797b77uHhwdChQ8nJyQFg06ZNXL58mTFjxgAwd+5cVq5ceddtvxOr1UpiYiJt27Zlx44dFBUVMWLECH71q19RUVHRoNigREJEREREGqEuXbpgMBjqnJDf7OjRo0RERODn58e6devYs2cPS5YsAe58U/O1a9dwdnZmz549lJSU2D7l5eUsXnz9SVZWq7VOInMjwfm2W7W5edqtyrPGjx/P2rVr+eabb7BYLERFReHq6nrH7XVEQUEBmzZtYu3atYSEhPDkk0+SmZnJo48+yooVKxocX4mEiIiIiDQ6rVu3ZvDgwSxZsuSWIws3HuW6e/duampqWLhwIX379sVkMnHy5Em7ti4uLtTW1tpNCwwMpLa2llOnTuHj42P3uVEC1bVrV4qKiuyW2717t933bt268fHHH9tN27lzJ926dbvrNkZERGA0GsnKyiIvL8+u9Ol+uHTpEnD9HpJvc3JyanDZFCiREBEREZFGKjMzk9raWoKCgli3bh0HDhygvLyc9PR0+vXrB4C3tzc1NTVkZGTwxRdfsGrVKrKzs+3idOrUiQsXLrBlyxZOnz7NpUuXMJlMPPfcc0RHR7N+/XoOHz7Mp59+yrx58/jggw8AeOmll/jggw8wm80cOHCApUuXkpeXZzfaMGXKFJYvX052djYHDhzAbDazfv16230Vd+Ls7ExMTAypqan4+PjYtgkgNTWV6OjoOy5fWVlJSUkJBw8eBKC0tJSSkhLOnDkDQL9+/Xjsscd44YUX+Pvf/257p8Thw4cZOnRoPY7AnSmREBEREZFGycvLi+LiYkJDQ0lOTsbPz4+wsDC2bNlCVlYWAAEBAZjNZubNm4efnx+rV69m7ty5dnGCg4NJSEggKiqKxx9/nPnz5wNgsViIjo4mOTkZX19fhg8fzieffIKnpydw/V6H7OxszGYzPXv2JD8/n0mTJtGsWTNb7JEjR7J48WIWLFhA9+7dWbp0KRaLpd5PXBo3bhzV1dV1RiMqKio4duzYHZfNzs4mMDCQ+Ph4AAYMGEBgYCAbN24E4Cc/+Qn5+flcuHCBp59+mt69e/Pxxx/z3//93/Ts2bNe/bsTg/VWhV4iIiIi8qNx+fJlDh8+jJeXl91JsDguPj6effv2sWPHjvsSr7CwkIEDB3L8+HHatWt3X2I2VH1/L3r8q4iIiIjIbaSlpREWFobRaCQvL48VK1aQmZnZ4LhXrlzhyy+/ZPr06URGRjaaJMIRKm0SEREREbmNoqIiwsLC8Pf3Jzs7m/T0dMaPH9/guLm5ufj6+lJVVWUrtfqhUWmTiIiIyI+cSpvEEfX9vWhEQkREREREHKZEQkREREREHKZEQkREREREHKZEQkREREREHKZEQkREREREHKZEQkREREREHKZEQkREREREHKY3W4uIiIg8xBYcrnhg65ri5eHwMpWVlcyZM4f333+fEydO0LZtWwICAkhKSmLQoEH1irF8+XKSkpI4d+6cw+tvzJYtW8aaNWsoLi7m/PnznD17llatWtm1KS4u5ne/+x2ffvopzs7O/PrXv8ZsNuPm5tbg9WtEQkREREQapSNHjtCrVy8KCgqYP38+paWl5OfnExoayoQJE77v7t2zq1ev3pc4ly5dIjw8nFdeeeWW80+ePMkzzzyDj48Pn3zyCfn5+fzjH/8gJibmvqxfiYSIiIiINEqJiYkYDAaKiooYPXo0JpOJ7t27M3nyZHbt2mVrZzab8ff3x2g04unpSWJiIhcuXABg27ZtxMbGUlVVhcFgwGAwMHPmTACqq6uZOnUq7du3x2g00qdPH7Zt22bXhzfffBNPT09cXV0ZNWoUZrO5zlX/rKwsvL29cXFxwdfXl1WrVtnNNxgMZGdnM2LECIxGI7Nnz8bHx4e0tDS7dmVlZTg5OXHo0KF67Z+kpCSmTZtG3759bzl/06ZNNGnShCVLluDr68vPf/5zlixZwrp16zh48GC91nEnSiREREREpNE5c+YM+fn5TJgwAaPRWGf+t0/mnZycSE9Pp6ysjBUrVlBQUMDUqVMBCA4OZtGiRbRo0YKKigoqKipISUkBIDY2lsLCQtauXcvevXsZM2YM4eHhHDhwAIDCwkISEhKYOHEiJSUlhIWFMWfOHLt+bNiwgYkTJ5KcnExZWRkvvvgisbGxbN261a7djBkzGDFiBKWlpcTFxREXF4fFYrFrk5OTQ//+/fH29iYmJoaBAwc2aB9euXIFFxcXnJz+95T/0UcfBeDjjz9uUGxQIiEiIiIijdDBgwexWq107dr1rm2TkpIIDQ3Fy8uLp59+mlmzZvHOO+8A4OLiQsuWLTEYDLi7u+Pu7o6bmxuHDh0iNzeXd99913bynpKSwlNPPWU7wc/IyGDIkCGkpKRgMplITExkyJAhdutOS0sjJiaGxMRETCYTkydP5tlnn60z2jB27Fji4uLo3LkzHTt2JDY2lv3791NUVARcL3d66623iIuLA8DDw4MOHTo0aB8+/fTTVFZWsmDBAqqrqzl79qytDKqiouH3xiiREBEREZFGx2q1AtfLgu5m69athIWF0b59e5o3b050dDRfffUVFy9evO0yxcXFWK1WTCYTbm5uts/27dttpUX79+8nKCjIbrmbv5eXlxMSEmI3LSQkhPLycrtpvXv3tvvu4eHB0KFDycnJAa6XIV2+fJkxY8YAMHfuXFauXHnXbb+T7t27s2LFChYuXIirqyvu7u507tyZdu3a4ezs3KDYoERCRERERBqhLl26YDAY6pyQ3+zo0aNERETg5+fHunXr2LNnD0uWLAHufFPztWvXcHZ2Zs+ePZSUlNg+5eXlLF68GLiezNycyNxIcL7tVm1unnar8qzx48ezdu1avvnmGywWC1FRUbi6ut5xex01duxYKisrOXHiBF999RUzZ87kf/7nf/Dy8mpwbCUSIiIiItLotG7dmsGDB7NkyZJbjizceJTr7t27qampYeHChfTt2xeTycTJkyft2rq4uFBbW2s3LTAwkNraWk6dOoWPj4/dx93dHYCuXbvaSo9u2L17t933bt261bnfYOfOnXTr1u2u2xgREYHRaCQrK4u8vDxbWdN3oV27dri5ufH222/TrFkzwsLCGhxT75EQERERkUYpMzOT4OBggoKCeP311+nRowc1NTVs3ryZrKwsysvL8fb2pqamhoyMDIYNG0ZhYSHZ2dl2cTp16sSFCxfYsmULPXv2xNXVFZPJxHPPPUd0dDQLFy4kMDCQ06dPU1BQgL+/PxEREbz00ksMGDAAs9nMsGHDKCgoIC8vz260YcqUKURGRvLkk08yaNAg3nvvPdavX8+HH3541+1zdnYmJiaG1NRUfHx86Nevn21eamoqJ06cuGN5U2VlJZWVlbYnMJWWltK8eXM6dOhA69atAfjjH/9IcHAwbm5ubN68mSlTpvCHP/yhzpOn7olVRERERH7UvvnmG+vnn39u/eabb77vrjjs5MmT1gkTJlg7duxodXFxsbZv3946fPhw69atW21tzGaz1cPDw/roo49aBw8ebF25cqUVsJ49e9bWJiEhwdqmTRsrYJ0xY4bVarVaq6urra+99pq1U6dO1iZNmljd3d2to0aNsu7du9e23LJly6zt27e3Pvroo9aRI0daZ8+ebXV3d7frY2ZmprVz587WJk2aWE0mk3XlypV28wHrhg0bbrl9hw4dsgLW+fPn201/4YUXrL/4xS/uuG9mzJhhBep8LBaLrc3zzz9vbd26tdXFxcXao0ePOn27lfr+Xgz//8aJiIiIyI/U5cuXOXz4MF5eXjRr1uz77s4PWnx8PPv27WPHjh33JV5hYSEDBw7k+PHjtGvX7r7EbKj6/l5U2iQiIiIichtpaWmEhYVhNBrJy8tjxYoVZGZmNjjulStX+PLLL5k+fTqRkZGNJolwhG62FhERERG5jaKiIsLCwvD39yc7O5v09HTGjx/f4Li5ubn4+vpSVVXF/Pnz70NPHzyVNomIiIj8yKm0SRxR39+LRiRERERERMRhSiREREREHhIqRJH6qO/vRImEiIiIyI9ckyZNALh06dL33BP5IbjxO7nxu7kdPbVJRERE5EfO2dmZVq1acerUKQBcXV3tXqomAtdHIi5dusSpU6do1aoVzs7Od2yvm61FREREHgJWq5XKykrOnTv3fXdFGrlWrVrh7u5+12RTiYSIiIjIQ6S2tparV69+392QRqpJkyZ3HYm4QYmEiIiIiIg4TDdbi4iIiIiIw5RIiIiIiIiIw5RIiIiIiIiIw5RIiIiIiIiIw5RIiIiIiIiIw5RIiIiIiIiIw5RIiIiIiIiIw/4/nPMbSGLX4jEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3))\n",
+    "for i in range(20):\n",
+    "    outcome = expit_func(sequence_idata.posterior.TotalWorkingYears_threshold).sel(TotalWorkingYears_threshold_dim=i).to_numpy().flatten()\n",
+    "    ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n",
+    "ax.set_xlabel(\"Probability\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Cumulative Probability by Category\")\n",
+    "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 3dffcce108214c3788d1d5fc6d76b7977b26f75b Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Mon, 18 Sep 2023 20:38:06 +0200
Subject: [PATCH 05/13] code review changes

---
 docs/notebooks/ordinal_regression.ipynb | 1410 +++++++++++------------
 1 file changed, 653 insertions(+), 757 deletions(-)

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index c68d25655..560d273fd 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
@@ -32,9 +23,19 @@
    "source": [
     "# Ordinal Regression\n",
     "\n",
-    "In some scenarios, the response variable is discrete, like a count, and ordered. For example, a rating from 1 to 5. The result is a set of **ordered categories**. Ordered category data presents three challenges when modelling:\n",
+    "In some scenarios, the response variable is discrete, like a count, and ordered. Common examples of such data come from questionnaires where the respondent is asked to rate a product, service, or experience on a scale. This scale is often referred to as a [Likert scale](https://en.wikipedia.org/wiki/Likert_scale). For example, a five-level Likert scale could be:\n",
+    "\n",
+    "- 1 = Strongly disagree\n",
+    "- 2 = Disagree\n",
+    "- 3 = Neither agree nor disagree\n",
+    "- 4 = Agree\n",
+    "- 5 = Strongly agree\n",
     "\n",
-    "1. Unlike a count, the differences in the values are not necessarily equidistant. For example, it may be much harder for a restuarant to go from 4 to 5 stars than from 2 to 3 stars. \n",
+    "The result is a set of **ordered categories** where each category has an associated numeric value (1-5). However, you can't compute a meaningful difference between the categories. Moreover, the response variable can also be a discrete count, and ordered. For example, a restaurant can be rated on a scale of 1-5 stars where 1 is the worst and 5 is the best. Yes, you can compute the difference between 1 and 2 stars, but it is often treated as ordinal in an applied setting.\n",
+    "\n",
+    "Ordinal data presents three challenges when modelling:\n",
+    "\n",
+    "1. Unlike a count, the differences in the values are not necessarily equidistant or meaningful. For example, computing the difference between \"Strongly disagree\" and \"Disagree\". Or, in the case of the restaurant rating, it may be much harder for a restuarant to go from 4 to 5 stars than from 2 to 3 stars. \n",
     "2. The distribution of ordinal responses may be nonnormal, particularly if very low or high values are infrequently chosen.\n",
     "3. The variances of the unobserved variables that underlie the observed ordered category may differ between the category, time points, etc. \n",
     "\n",
@@ -47,15 +48,15 @@
    "source": [
     "## Cumulative model\n",
     "\n",
-    "A cumulative model assumes that the observed ordinal variable $Y$ originates from the \"categorization\" of a latent continuous variable $\\hat{Y}$. To model the categorization process, the model assumes that there are $K$ thresholds (cutpoints) $\\tau_k$ that partition $\\hat{Y}$ into $K+1$ observable, ordered categories. Additionally, if we assume $\\hat{Y}$ to have a certain distribution (e.g., Normal) with a cumulative distribution function $F$, the probability of $Y$ being equal to category $k$ is\n",
+    "A cumulative model assumes that the observed ordinal variable $Y$ originates from the \"categorization\" of a latent continuous variable $Z$. To model the categorization process, the model assumes that there are $K$ thresholds (cutpoints) $\\tau_k$ that partition $Z$ into $K+1$ observable, ordered categories. Additionally, if we assume $Z$ to have a certain distribution (e.g., Normal) with a cumulative distribution function $F$, the probability of $Y$ being equal to category $k$ is\n",
     "\n",
     "$$Pr(Y = k) = F(\\tau_k) - F(\\tau_{k-1})$$\n",
     "\n",
-    "where each $F(\\tau)$ is a cumulative probability. For example, suppose we have 3 categories and we are interested in the probability of $k=3$, and have thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$. Additionally, if we assume $\\hat{Y}$ to be normally distributed with $\\sigma = 1$ then\n",
+    "where each $F(\\tau)$ is a cumulative probability. For example, suppose we have 3 categories and we are interested in the probability of $Y=3$, and have three thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$ for three categories. Additionally, if we assume $Z$ to be normally distributed with $\\sigma = 1$ then\n",
     "\n",
     "$$Pr(Y = 3) = \\Phi(\\tau_2) - \\Phi(\\tau_1) - \\Phi(\\tau_0)$$\n",
     "\n",
-    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$'s that depends on the number of $k$ as the prior for the thresholds. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
+    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of $k$ as the prior for the thresholds. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
     "\n",
     "Lastly, as each $F(\\tau)$ implies a cumulative probability for each $k$, the largest response value always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 3$ response values, we only need $K − 1 = 2$ intercepts."
    ]
@@ -77,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -101,7 +102,7 @@
        "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,19 +118,19 @@
    "source": [
     "### Intercept only model\n",
     "\n",
-    "Before we fit a model with predictors, let's attempt to recover the parameters of an ordered distribution with an intercepts only model to get a feel for the cumulative link function. First, to compare the intercepts only model, we compute the empirical log-cumulative-odds of the categories directly from the data below. "
+    "Before we fit a model with predictors, let's attempt to recover the parameters of an ordered distribution using a model with only the thresholds to get a feel for the cumulative link function. Traditionally, in Bambi if we wanted to recover the parameters of the likelihood, we would use an intercept only model and write the formula as `response ~ 1` where `1` indicates to include the intercept. However, in the case of ordinal regression, the thresholds \"take the place\" of the intercept. Thus, we can write the formula as `response ~ 0` to indicate that we do not want to include an intercept. To fit a cumulative ordinal model, we pass `family=\"cumulative\"`. To compare the thresholds only model, we compute the empirical log-cumulative-odds of the categories directly from the data below. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_32825/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_9466/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
       "  logit_func = lambda x: np.log(x / (1 - x))\n"
      ]
     },
@@ -140,7 +141,7 @@
        "        1.76938091,         nan])"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -155,15 +156,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
-      "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/distributions/transforms.py:56: FutureWarning: univariate_ordered has been deprecated, use ordered instead.\n",
+      "  warnings.warn(f\"{name} has been deprecated, use ordered instead.\", FutureWarning)\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
@@ -218,30 +219,30 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
       "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
      ]
     }
    ],
    "source": [
-    "model = bmb.Model(\"response ~ 1\", data=trolly, family=\"cumulative\")\n",
-    "idata = model.fit()"
+    "model = bmb.Model(\"response ~ 0\", data=trolly, family=\"cumulative\")\n",
+    "idata = model.fit(random_seed=1234)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -279,74 +280,74 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>response_threshold[0]</th>\n",
-       "      <td>-1.922</td>\n",
+       "      <td>-1.916</td>\n",
        "      <td>0.030</td>\n",
-       "      <td>-1.979</td>\n",
-       "      <td>-1.865</td>\n",
+       "      <td>-1.971</td>\n",
+       "      <td>-1.857</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4234.0</td>\n",
-       "      <td>2969.0</td>\n",
+       "      <td>4461.0</td>\n",
+       "      <td>2908.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[1]</th>\n",
-       "      <td>-1.269</td>\n",
+       "      <td>-1.266</td>\n",
        "      <td>0.024</td>\n",
-       "      <td>-1.316</td>\n",
-       "      <td>-1.225</td>\n",
+       "      <td>-1.312</td>\n",
+       "      <td>-1.222</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5204.0</td>\n",
-       "      <td>3592.0</td>\n",
+       "      <td>5137.0</td>\n",
+       "      <td>3794.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[2]</th>\n",
-       "      <td>-0.719</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>-0.757</td>\n",
-       "      <td>-0.678</td>\n",
+       "      <td>-0.718</td>\n",
+       "      <td>0.021</td>\n",
+       "      <td>-0.755</td>\n",
+       "      <td>-0.674</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5581.0</td>\n",
-       "      <td>3758.0</td>\n",
+       "      <td>5348.0</td>\n",
+       "      <td>3578.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[3]</th>\n",
-       "      <td>0.249</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>0.212</td>\n",
-       "      <td>0.287</td>\n",
+       "      <td>0.248</td>\n",
+       "      <td>0.021</td>\n",
+       "      <td>0.208</td>\n",
+       "      <td>0.285</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5138.0</td>\n",
-       "      <td>3361.0</td>\n",
+       "      <td>5505.0</td>\n",
+       "      <td>3678.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[4]</th>\n",
-       "      <td>0.893</td>\n",
+       "      <td>0.891</td>\n",
        "      <td>0.022</td>\n",
-       "      <td>0.852</td>\n",
-       "      <td>0.935</td>\n",
+       "      <td>0.848</td>\n",
+       "      <td>0.931</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5173.0</td>\n",
-       "      <td>3303.0</td>\n",
+       "      <td>5037.0</td>\n",
+       "      <td>3301.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[5]</th>\n",
-       "      <td>1.775</td>\n",
+       "      <td>1.771</td>\n",
        "      <td>0.029</td>\n",
-       "      <td>1.717</td>\n",
-       "      <td>1.825</td>\n",
+       "      <td>1.718</td>\n",
+       "      <td>1.826</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5318.0</td>\n",
-       "      <td>3331.0</td>\n",
+       "      <td>5572.0</td>\n",
+       "      <td>3543.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -355,23 +356,23 @@
       ],
       "text/plain": [
        "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "response_threshold[0] -1.922  0.030  -1.979   -1.865        0.0      0.0  \\\n",
-       "response_threshold[1] -1.269  0.024  -1.316   -1.225        0.0      0.0   \n",
-       "response_threshold[2] -0.719  0.022  -0.757   -0.678        0.0      0.0   \n",
-       "response_threshold[3]  0.249  0.020   0.212    0.287        0.0      0.0   \n",
-       "response_threshold[4]  0.893  0.022   0.852    0.935        0.0      0.0   \n",
-       "response_threshold[5]  1.775  0.029   1.717    1.825        0.0      0.0   \n",
+       "response_threshold[0] -1.916  0.030  -1.971   -1.857        0.0      0.0  \\\n",
+       "response_threshold[1] -1.266  0.024  -1.312   -1.222        0.0      0.0   \n",
+       "response_threshold[2] -0.718  0.021  -0.755   -0.674        0.0      0.0   \n",
+       "response_threshold[3]  0.248  0.021   0.208    0.285        0.0      0.0   \n",
+       "response_threshold[4]  0.891  0.022   0.848    0.931        0.0      0.0   \n",
+       "response_threshold[5]  1.771  0.029   1.718    1.826        0.0      0.0   \n",
        "\n",
        "                       ess_bulk  ess_tail  r_hat  \n",
-       "response_threshold[0]    4234.0    2969.0    1.0  \n",
-       "response_threshold[1]    5204.0    3592.0    1.0  \n",
-       "response_threshold[2]    5581.0    3758.0    1.0  \n",
-       "response_threshold[3]    5138.0    3361.0    1.0  \n",
-       "response_threshold[4]    5173.0    3303.0    1.0  \n",
-       "response_threshold[5]    5318.0    3331.0    1.0  "
+       "response_threshold[0]    4461.0    2908.0    1.0  \n",
+       "response_threshold[1]    5137.0    3794.0    1.0  \n",
+       "response_threshold[2]    5348.0    3578.0    1.0  \n",
+       "response_threshold[3]    5505.0    3678.0    1.0  \n",
+       "response_threshold[4]    5037.0    3301.0    1.0  \n",
+       "response_threshold[5]    5572.0    3543.0    1.0  "
       ]
      },
-     "execution_count": 111,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -391,12 +392,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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rV67E0KFDUVFRgerqagwcOPCOk9YmJiZixowZRs1ORERNk0YjsPd8PpYlZWLX2Tzc7GsKcrPDyKggPB3pBycFuyjp3ph0TBlQc3ftVkKIWm03paWlYeLEiXj33XfRv39/KJVKvP766xgzZgwWL15823OmTp2KyZMna7dLSkrg7+9vvDdARESNXmlFFTYcu4TlB7JwIb9c2/5wKw8kRAehZ6gHpOyipPukV1E2efJkvPfee7C3t9cpcG7ns88+0+uF3d3dIZPJat0Vy8vLq3X37KbExETExMTg9ddfBwC0b98e9vb26NGjB2bNmgUfn9rLS8jlcsjlcr0yERER3erC1TIsP5CF9ccuoUxV00XpILfC0539MDIqEC08HEyckBoTvYqy48ePo6qqSvt9Xeq6w3U7NjY26Ny5M3bu3IknnnhC275z504MGjTotudcv34dVla6kW8u7aTH8wpERER3pdEI7P4zD0uTsvD7n1e17S097JEQHYQnO/nBQW7yjiZqhPT6W7Vr167bfn+/Jk+ejBEjRiAyMhJRUVFYuHAhsrOzMWbMGAA1XY85OTlYvnw5ACAuLg4vvvgiFixYoO2+fOWVV9ClSxf4+voaLRcRETU9xTeqsO7oRXx3MAtZBdcBABIJ8EhrTyREB6F7iLtBNx+IDHVfpf7FixchkUjg5+d3T+cPHToUBQUFmDlzJpRKJcLDw7F161btOptKpRLZ2dna40eNGoXS0lLMmzcPr776Kpo1a4bevXvjo48+up+3QURETdi5K6VYdiATG5NzcL2yZhJ0R4UVhkb6Y2RUEALc7EyckJoKveYpu1V1dTVmzJiBuXPnoqysZj4WBwcHTJgwAdOmTYO1tXk/dcJ5yoiISK0R+PXMFSw7kIn95/9eFeYBLwckRAfhiY7NYWfDLkoyDqPOU3ar8ePHY9OmTZg9ezaioqIAAAcOHMD06dORn5+Pr7/++t5TExER1aNr1yux9khNF+WlohsAAKkE6NvGCwnRQYhq4cYuSjIZg++UOTs7Y82aNYiNjdVp//nnn/HMM8+guLjYqAGNjXfKiIianj9yS7AsKRObjuegokoDAGhmZ42hD/pjRLdA+Lmwi5LqT73dKVMoFAgKCqrVHhQUBBsbG0MvR0REVC+q1RrsTLuCpUmZOJRRqG0P83HCqOhADOrQHAprmQkTEukyuCgbN24c3nvvPSxZskQ7/5dKpcL777+P8ePHGz0gERGRIQrLK7H6cDZWHszC5eIKAIBMKsGAtt5IiA7Cg0Eu7KIks6RXUfbkk0/qbP/yyy/w8/NDREQEAODEiROorKzEI488YvyEREREejiVU4xlSZn48cRlVFbXdFG62ttgWBd/PNctED7OtiZOSHRnehVlzs7OOttPPfWUzjaXLSIiIlOoUmuw7VQuliVl4mhWkba9XXNnJEQH4fH2PuyiJIuhV1G2ZMmS+s5BRESkt6ulqpouykNZuFKiAgBYSSWIbeeDUdFB6BTQjF2UZHE4CQsREVmMExevYVlSJv53UolKdU0XpbuDHMO7BuDZrgHwclKYOCHRvbunomz9+vX4/vvvkZ2djcrKSp19ycnJRglGREQEAJXVGmxNVWJpUiZSLl7Ttnfwb4ZR0UGIbecNuRW7KMnyGVyUzZ07F2+//TYSEhLw448/4vnnn0d6ejqOHDmCcePG1UdGIiJqgvJKKrDyUDZWHspGfllNF6W1TIK49r5IiA5ChH8z0wYkMjKDi7L58+dj4cKFGDZsGJYtW4Y33ngDLVq0wLvvvovCwsK7X4CIiJostUbgcEYh8kor4OmoQJdgV8ikf4/9EkIgObumi3JrqhLVmpr5zb2c5Hi2ayCGdQmAh6PcVPGJ6pXBRVl2djaio6MBALa2tigtLQUAjBgxAt26dcO8efOMm5CIiBqFbaeUmLElDcq/5g4DAB9nBabFtcHDrTzxv5NKLEvKRGrO3yvDRAa6ICE6CAPCvWEtk5oiNlGDMbgo8/b2RkFBAQIDAxEYGIiDBw8iIiICGRkZMHDFJiIiaiK2nVJi7Ipk/PN/CWVxBcasSIaD3AplqmoAgI2VFAMjfDEqOgjhzZ1rX4yokTK4KOvduze2bNmCTp064YUXXsCkSZOwfv16HD16tNYks0RERGqNwIwtabUKsluVqarh7STHiKggPPOgP9wc2EVJTY/BRdnChQuh0dQ8hjxmzBi4urpi3759iIuLw5gxY4wekIiILNvhjEKdLsu6fDIkAt1DPRogEZF5Mrgok0qlkEr/7tePj49HfHy8UUMREVHjce5KqV7HFZRX3v0gokbsnuYpKyoqwuLFi3HmzBlIJBKEhYXh+eefh6urq7HzERGRBRJC4PjFa1iyPxM/nbys1zmejpz4lZo2gx9l2bNnD4KDgzF37lwUFRWhsLAQc+fORXBwMPbs2VMfGYmIyEJUVmvww/EcDP5qP56cn4QtJy5DIwAbWd1LHklQ8xRml2D+Yk9Nm8F3ysaNG4f4+HgsWLAAMlnNDMpqtRovvfQSxo0bh1OnThk9JBERmberpSqsOpSNFYeycLW0ZqJXG5kUAzvUPEV5qeg6xq6oWfHl1gH/N0u1aXFtdOYrI2qKJMLAeSxsbW2RkpKCVq1a6bSfPXsWHTp0wI0bN4wa0NhKSkrg7OyM4uJiODk5mToOEZFFO5VTjG/3Z+B/J/5ei9LTUY4R3QIxrGsA3G95ivJO85QNCPdp8OxEDUXf2sPgO2WdOnXCmTNnahVlZ86cQYcOHQwOSkRElqVarcH201ewNCkDRzKLtO0d/Jvh+ZggxIb7wMaq9uiYAeE+6NvG+44z+hM1ZXoVZSdPntR+P3HiRLz88ss4f/48unXrBgA4ePAgvvrqK3z44Yf1k5KIiEyuqLwSq49k47sDWdq7XVZSCR5r74NR0UHoGOBy12vIpBJEtXSr76hEFkmv7kupVAqJRHLXGfslEgnUarXRwtUHdl8SERnmj9wSLEvKxKbjOaioqumidLO3wbNdA/Bst0B4OfGpSaI7MWr3ZUZGhtGCERGR+VNrBH49cwVLkzKRlF6gbW/r64TnY4LxeHsfKKxlJkxI1PjoVZQFBgbWdw4iIjIDxTeqsO7oRSw7kImLhTUPbkklwIBwb4yKDsaDQS6QSDgGjKg+3NPksenp6ZgzZ47O5LEvv/wyWrZsaex8RETUANKvlmFZUibWH7uE65U1w1Ccba0xrEsARkQFonkzWxMnJGr8DC7Ktm/fjoEDB6JDhw6IiYmBEAJJSUlo27YttmzZgr59+9ZHTiIiMjKNRmDPuatYuj8Te/68qm1/wMsBz8cEY3CH5rC1YRclUUMxeJ6yjh07on///rWetHzzzTexY8cOJCcnGzWgsXGgPxE1dWWqamxMvoSlSZm4cLUcACCRAI+09sLzMUGIbunGLkoiI9K39jC4KFMoFEhNTUVoaKhO+59//on27dujoqKijjPNA4syImqqsguuY9mBTHx/5CJKVdUAAEe5FYZE+iMhOhCBbvYmTkjUONXb5LEeHh5ISUmpVZSlpKTA09PT8KRERFRvhBA4kF6Ab/dn4tc/ruDmr+Et3O2REB2Epzr7wUF+T8OLicjIDP5JfPHFF/Hvf/8bFy5cQHR0NCQSCfbt24ePPvoIr776an1kJCIiA92oVOOHlBws3Z+Js1dKte0PPeCB52OC0DPUA1LOpE9kVgzuvhRCYM6cOfj0009x+fJlAICvry9ef/11TJw40ezHIbD7kogas8vXbmD5gSysOZKNa9erAAB2NjI81ckPCdFBCPF0MHFCoqanXrovq6ursXLlSgwbNgyTJk1CaWnNb1+Ojo73l5aIiO6ZEAJHs4qwZH8Gtp++ArWm5ndtf1dbJEQFYUikP5xtrU2ckojuxqCizMrKCmPHjsWZM2cAsBgjIjIlVbUaW04osWR/Bk5fLtG2R7Vww/MxQXgkzIuLfRNZEIPHlHXt2hXHjx/nLP9ERCaSV1KBFQezsOpwNvLLKgEAcispnujYHKNigtDam0MziCyRwUXZSy+9hFdffRWXLl1C586dYW+v+wh1+/btjRaOiIj+lnLxGpbsz8BPJ5Wo/quL0sdZgRFRgRj2YABc7G1MnJCI7ofBA/2lUmnti0gkEEJAIpFArVYbLVx94EB/IrIkVWoNtqYqsTQpE8ezr2nbIwNd8HxMMPq19YK1rPa/y0RkPuptnrKMjIz7CvZP8+fPx8cffwylUom2bdtizpw56NGjR53Hq1QqzJw5EytWrEBubi78/Pzw9ttvY/To0UbNRURkSgVlKqw6lI0Vh7JwpUQFALCRSfF4hA+ejw5GOz9nEyckImMzuCgz5liytWvX4pVXXsH8+fMRExODb775BrGxsUhLS0NAQMBtz4mPj8eVK1ewePFihISEIC8vD9XV1UbLRERkSqcvF2Pp/kz8eOIyKqs1AAAPRzme6xqI4V0D4OEoN3FCIqovBndfAsDZs2fx5Zdf4syZM5BIJGjdujUmTJiAVq1aGXSdrl27olOnTliwYIG2LSwsDIMHD0ZiYmKt47dt24ZnnnkGFy5cgKurq6GxAbD7kojMT7Vag51pV7AkKROHMwq17RF+zng+JhiPtvOBjRW7KIkslb61h8E/5evXr0d4eDiOHTuGiIgItG/fHsnJyQgPD8e6dev0vk5lZSWOHTuGfv366bT369cPSUlJtz1n8+bNiIyMxOzZs9G8eXM88MADeO2113Djxg1D3wYRkcldu16Jr/eko+fHuzF2ZTIOZxTCSipBXIQvNoyNxg/jYjC4Y3MWZERNhMHdl2+88QamTp2KmTNn6rRPmzYNU6ZMwZAhQ/S6Tn5+PtRqNby8vHTavby8kJube9tzLly4gH379kGhUGDTpk3Iz8/HSy+9hMLCQnz77be3PUelUkGlUmm3S0pKbnscEVFD+fNKKZYmZWJj8iVUVNV0Ubra22B4lwA81y0Q3s4KEyckIlMwuCjLzc3FyJEja7U/99xz+Pjjjw0O8M9lmW4+xXk7Go0GEokEK1euhLNzzSDXzz77DE8//TS++uor2Nra1jonMTERM2bMMDgXEZExaTQCv/2Rh6VJmdh3Pl/b3trbEaNjgjGwgy8U1jITJiQiUzO4KHv44Yexd+9ehISE6LTv27fvjk9N/pO7uztkMlmtu2J5eXm17p7d5OPjg+bNm2sLMqBmDJoQApcuXUJoaGitc6ZOnYrJkydrt0tKSuDv7693TiKi+1FaUYV1Ry9h2YFMZBVcBwBIJUC/Nt4YFROErsGuZr9mMBE1DIOLsoEDB2LKlCk4duwYunXrBgA4ePAg1q1bhxkzZmDz5s06x9bFxsYGnTt3xs6dO/HEE09o23fu3IlBgwbd9pyYmBisW7cOZWVlcHCoWVT3zz//hFQqhZ+f323PkcvlkMv5tBIRNayM/HIsS8rEuqMXUV5ZM3+jk8IKw/7qovR3tTNxQiIyN0aZPPa2F9ZjItm1a9dixIgR+PrrrxEVFYWFCxdi0aJFOH36NAIDAzF16lTk5ORg+fLlAICysjKEhYWhW7dumDFjBvLz8/Gvf/0LPXv2xKJFi/TKxacviai+CCGw91w+luzPwK6zV7XtIZ4OGBUdhCc7NYedjcG/CxORhau3yWM1Gs19BbvV0KFDUVBQgJkzZ0KpVCI8PBxbt27VzoWmVCqRnZ2tPd7BwQE7d+7EhAkTEBkZCTc3N8THx2PWrFlGy0REdDtqjcDhjELklVbA01GBLsGu2sW+y1XV2Hg8B0v3ZyD9ajkAQCIBerfyxKiYIHQPcWcXJRHd1T3NU2bJeKeMiAy17ZQSM7akQVlcoW3zcVZgXK8QZBWUY82RiyitqJnE2kFuhac7+2FUdBCC3O3ruiQRNSH1dqeMiKgp2XZKibErkvHP316VxRX4zw+ntNtBbnZIiA7C05394KiwbtiQRNQosCgjIqqDWiMwY0tarYLsVnIrKb4a3hG9W3tBKmUXJRHdO04TTURUh8MZhTpdlrejqtbAXm7NgoyI7huLMiKiOlwpuXNBdlNeqX7HERHdyT0VZenp6fjPf/6DYcOGIS8vD0DNYuGnT582ajgiIlO5fO0G/rv3gl7HejpyWSQiun8GF2V79uxBu3btcOjQIWzcuBFlZWUAgJMnT2LatGlGD0hE1JCEENiYfAn95/yOU5fvvFauBDVPYXYJdm2YcETUqBlclL355puYNWsWdu7cCRsbG217r169cODAAaOGIyJqSAVlKoxZcQyTvz+B0opqRPg3w/SBbSBBTQF2q5vb0+LaaOcrIyK6HwY/fZmamopVq1bVavfw8EBBQYFRQhERNbQdp3Px1qZU5JdVwkoqwSt9QjGmZ0tYyaTwdlLUmqfM21mBaXFtMCDcx4SpiagxMbgoa9asGZRKJYKDg3Xajx8/jubNmxstGBFRQyipqMLMLWlYf+wSAOABLwd8Ft8B4c2dtccMCPdB3zbedc7oT0RkDAYXZcOHD8eUKVOwbt06SCQSaDQa7N+/H6+99hpGjhxZHxmJiOpFUno+Xl93EjnXbkAiAf7dowUm9X0ACmtZrWNlUgmiWrqZICURNRUGL7NUVVWFUaNGYc2aNRBCwMrKCmq1GsOHD8fSpUshk9X+x8yccJklIqqoUmP2trP4dn8GAMDf1RafDunAAftEVC/0rT3uee3L9PR0HD9+HBqNBh07dkRoaOg9h21ILMqImrYTF69h8vcp2oXDh3UJwNuPhcFBzgVOiKh+1Nval3v27EHPnj3RsmVLtGzZ8r5CEhE1lCq1BvN+O495u85DrRHwcJRj9lPt0au1p6mjEREBuIeirG/fvvD29sbw4cPx3HPPITw8vD5yEREZzbkrpZj8/Qmk5hQDAB5v74P3BoXDxd7mLmcSETUcg+cpu3z5Mt544w3s3bsX7du3R/v27TF79mxcunSpPvIREd0zjUbgv3sv4LEv9yE1pxjOttaYO6wj5g3vxIKMiMzOPY8pA4CMjAysWrUKq1evxh9//IGHHnoIv/32mzHzGR3HlBE1DRcLr+O1dSdwKKMQANDzAQ/Mfro9vJy4JBIRNax6H+h/k1qtxs8//4x33nkHJ0+ehFqtvp/L1TsWZUSNmxAC3x+9iJlb0lBeqYadjQxvPxaG4V0CIJFwXjEianj1NtD/pv3792PlypVYv349KioqMHDgQHzwwQf3ejkiovuWV1qBqRtS8esfeQCAyEAXfBofgUA3exMnIyK6O4OLsrfeegurV6/G5cuX0adPH8yZMweDBw+GnZ1dfeQjItLL1lQl3t6UiqLrVbCRSTG53wN4sUcLzrpPRBbD4KJs9+7deO211zB06FC4u7vXRyYiIr0VX6/CtM2n8EPKZQBAmI8TPh8agdbeHJ5ARJbF4KIsKSmpPnIQERns9z+v4o31J5FbUgGpBBj7cEu8/MgDsLEy+MFyIiKT06so27x5M2JjY2FtbY3Nmzff8diBAwcaJRgRUV2uV1Yjcesf+O5gFgAg2N0enwyJQOdAFxMnIyK6d3o9fSmVSpGbmwtPT09IpXX/BiqRSPj0JRHVq2NZhXj1+xPILLgOAEiICsSU2Naws+EySURknoz69KVGo7nt90REDUVVrcYXv5zD13vSoRGAt5MCHw9pjx6hHqaORkRkFAYPvFi+fDlUKlWt9srKSixfvtwooYiIbnVGWYJB8/Zj/u6aguzJjs2xfdJDLMiIqFExePJYmUwGpVIJT0/dRXwLCgrg6enJ7ksiMhq1RmDh7xfw2c6zqFILuNhZ44Mn2iG2nY+poxER6a3eJo8VQtx2VuxLly7B2dnZ0MsREd1WZn45Xl13AseyigAAfcI8kfhke3g4yk2cjIiofuhdlHXs2BESiQQSiQSPPPIIrKz+PlWtViMjIwMDBgyol5BE1HQIIbDyUDbe/+kMblSp4SC3wrtxbTCksx+XSSKiRk3vomzw4MEAgJSUFPTv3x8ODg7afTY2NggKCsJTTz1l9IBE1HTkFlfgjQ0n8fufVwEA3Vq44uOnI+DvyhVDiKjx07somzZtGgAgKCgIQ4cOhUKhqLdQRNS0CCGw+cRlvPPDKZRUVENuJcUbA1rj+eggSLlMEhE1EQaPKUtISKiPHETURBWWV+KdH07hp1QlAKC9nzM+i49AiKejiZMRETUsg4sytVqNzz//HN9//z2ys7NRWVmps7+wsNBo4YiocfvtjyuYsiEVV0tVkEklmNA7BON6hcBaxmWSiKjpMfhfvhkzZuCzzz5DfHw8iouLMXnyZDz55JOQSqWYPn16PUQkosamTFWNNzecxOilR3G1VIUQTwdseikar/R5gAUZETVZBs9T1rJlS8ydOxePPfYYHB0dkZKSom07ePAgVq1aVV9ZjYLzlBGZ1qELBXh13QlcKroBiQQYHROM1/u3gsJaZupoRET1ot7mKcvNzUW7du0AAA4ODiguLgYAPP7443jnnXfuMS4RNXYVVWp8uuMs/rsvA0IAzZvZ4pMhEYhq6WbqaEREZsHgfgI/Pz8olTUDckNCQrBjxw4AwJEjRyCXc1JHIqrtVE4x4r7ch0V7awqy+Eg/bHulBwsyIqJbGHyn7IknnsCvv/6Krl274uWXX8awYcOwePFiZGdnY9KkSfWRkYgsVLVag/m70zH313Oo1gi4O8jx4ZPt0KeNl6mjERGZHYPHlP3TwYMHkZSUhJCQEAwcONDg8+fPn4+PP/4YSqUSbdu2xZw5c9CjR4+7nrd//3707NkT4eHhSElJ0fv1OKaMqGGkXy3D5O9P4MTFawCA2HBvvP9EO7ja25g2GBFRA9O39rjvoux+rF27FiNGjMD8+fMRExODb775Bv/973+RlpaGgICAOs8rLi5Gp06dEBISgitXrrAoIzIjGo3AsgOZ+PDnP6Cq1sBRYYX3BoVjUAdfLpNERE2SUYuyzZs36/3Chtwt69q1Kzp16oQFCxZo28LCwjB48GAkJibWed4zzzyD0NBQyGQy/PDDDyzKiMxEzrUbeO37EzhwoQAA0CPUHbOfbg8fZ1sTJyMiMh2jPn15c93Lu5FIJFCr1XodW1lZiWPHjuHNN9/Uae/Xrx+SkpLqPG/JkiVIT0/HihUrMGvWLL1ei4jqlxACG5JzMGPzaZSqqqGwluLtR8PwXLdA3h0jItKTXkWZRqMx+gvn5+dDrVbDy0t3wK+Xlxdyc3Nve865c+fw5ptvYu/evbCy0u8ZBZVKBZVKpd0uKSm599BEVEt+mQpTN6ZiZ9oVAEDHgGb4LL4Dgt3tTZyMiMiyGPz0pbH987doIcRtf7NWq9UYPnw4ZsyYgQceeEDv6ycmJmLGjBn3nZOIatt2Khdvb0pFQXklrGUSvNLnAfzfQy1gxVn5iYgMZvBA/5kzZ95x/7vvvqvXdSorK2FnZ4d169bhiSee0La//PLLSElJwZ49e3SOv3btGlxcXCCT/T3rt0ajgRACMpkMO3bsQO/evWu9zu3ulPn7+3NMGdF9KKmowvTNp7ExOQcA0NrbEZ/Fd0AbX/5MERH9U73N6L9p0yad7aqqKmRkZMDKygotW7bUuyizsbFB586dsXPnTp2ibOfOnRg0aFCt452cnJCamqrTNn/+fPz2229Yv349goODb/s6crmck9oSGdH+8/l4fd0JXC6ugFQC/PuhlpjUNxRyKy6TRER0Pwwuyo4fP16rraSkBKNGjdIprvQxefJkjBgxApGRkYiKisLChQuRnZ2NMWPGAACmTp2KnJwcLF++HFKpFOHh4Trne3p6QqFQ1GonIuO7UanGR9v+wNKkTABAoJsdPh0SgcggV9MGIyJqJIwypszJyQkzZ87E448/jhEjRuh93tChQ1FQUICZM2dCqVQiPDwcW7duRWBgIABAqVQiOzvbGBGJ6D4czy7Cq9+fwIX8cgDAs10D8NajYbCXm3xYKhFRo2G0yWP37duHuLg4FBUVGeNy9YbzlBHpr7Jagy9/O4evdp2HRgBeTnJ89FR7PNzK09TRiIgsRr2NKZs7d67OthACSqUS3333HQYMGGB4UiIyS2dzSzH5+xScvlwzjczACF/MHNQWzey4TBIRUX0wuCj7/PPPdbalUik8PDyQkJCAqVOnGi0YEZmGWiOweN8FfLL9T1SqNWhmZ41Zg8PxeHtfU0cjImrUDC7KMjIy6iMHEZmB7ILreG3dCRzOLAQA9GrlgY+eag9PJ4WJkxERNX4cpUtEEEJgzZGLeO9/abheqYa9jQzvPN4GQx/05zJJREQNxOCirKKiAl9++SV27dqFvLy8WkswJScnGy0cEdW/vJIKTNlwErvOXgUAdAlyxafxEfB3tTNxMiKipsXgomz06NHYuXMnnn76aXTp0oW/RRNZsP+dvIz//HAK165XwUYmxev9W2F092DIpPy5JiJqaAYXZT/99BO2bt2KmJiY+shDREak1ggczihEXmkFPB0V6BLsCplUgmvXK/HOj6ex5cRlAEBbXyd8PrQDHvByNHFiIqKmy+CirHnz5nB05D/cROZu2yklZmxJg7K4Qtvm46zAU52a4/ujl5BXqoJMKsG4h1tifO9Q2FhxEXEiIlMy+F/hTz/9FFOmTEFWVlZ95CEiI9h2SomxK5J1CjIAUBZXYN6udOSVqtDC3R4bxkZjcr9WLMiIiMyAwXfKIiMjUVFRgRYtWsDOzg7W1tY6+wsLC40WjogMp9YIzNiShjst1WFnI8Pm8d3hoOAD2ERE5sLgf5GHDRuGnJwcfPDBB/Dy8uJAfyIzczijsNYdsn+6XqlGak4xolq6NVAqIiK6G4OLsqSkJBw4cAARERH1kYeI7kN2wXWsPZKt17F5pXcu3IiIqGEZXJS1bt0aN27cqI8sRGQgIQT+yC3F9tO52H76Cs4oS/Q+19ORs/QTEZkTg4uyDz/8EK+++iref/99tGvXrtaYsjutfk5E90+jEUjOLtIWYtmF17X7ZFIJHgx0QZqyBCUV1bc9XwLA27lmegwiIjIfBhdlAwYMAAA88sgjOu1CCEgkEqjVauMkIyKtymoNDlwowPbTudiZdgVXS1XafTZWUjwU6o5+bb3RJ8wLrvY22qcvAegM+L85AnRaXBtOEEtEZGYMLsp27dpVHzmI6B/KVdXY8+dVbD+di9/+yEPpLXe+HOVW6B3mif5tvdHzAQ/Yy3V/lAeE+2DBc51qzVPm7azAtLg2GBDu02Dvg4iI9CMRQtzpyflGp6SkBM7OziguLmZXK5mdovJK/HLmCrafvoK9565CVf332rLuDnL0beOF/m29EN3SXa+5xeqa0Z+IiBqOvrWHwXfKfv/99zvuf+ihhwy9JFGTdvnaDez4a3zY4cxCqDV//54U4GqH/m290L+tNzoGuBhcUMmkEk57QURkIQwuyh5++OFabbfOVcYxZUR3dz6vDNtP52LH6VycuFSssy/Mx0lbiLX2duRcgERETYTBRVlRUZHOdlVVFY4fP4533nkH77//vtGCETUmQgicvFT81xOTuUi/Wq7dJ5EAnQNc0L+tN/q39UaAm50JkxIRkakYXJQ5OzvXauvbty/kcjkmTZqEY8eOGSUYkaWrVmtwOLMQ20/lYkfaFZ0B99YyCaJbuqN/W2/0aePJOcOIiMjwoqwuHh4eOHv2rLEuR2SRKqrU2HsuH9tP5+LXM1dQdL1Ku8/ORoaHW3mgf1tv9GrtCSeF9R2uRERETY3BRdnJkyd1toUQUCqV+PDDD7n0EjVJxTeqsOuPPGw/nYs9f17F9cq/x1W62FmjT1jN+LDuoe5QWMtMmJSIiMyZwUVZhw4dIJFI8M+ZNLp164Zvv/3WaMGIzFleaQV2ptVMXXEgPR9V6r9/HnydFejX1hv92nqhS5ArrGR3n7qCiIjI4KIsIyNDZ1sqlcLDwwMKBcfEUOOWVVCuXdooObsIt/5eEuLpoH1isl1zZz4xSUREBjO4KAsMDKyPHERmRwiBM8pS7ROTf+SW6uyP8G+mLcRaejiYKCURETUWehdlv/32G8aPH4+DBw/Wmo22uLgY0dHR+Prrr9GjRw+jhyRqKOqbi32fysX2tFxcLLyh3SeTStA12BX9/+qa9HG2NWFSIiJqbPQuyubMmYMXX3zxtssDODs74//+7//w2WefsSgji1NZrUFSej62n76CnWlXkF/292LfcispHnqg5onJR1p7wsXexoRJiYioMdO7KDtx4gQ++uijOvf369cPn3zyiVFCEdW3clU1dp+tWex71x95KFXdsti3wgqPtP5rse9WHrCzMdrMMURERHXS+3+bK1euwNq67nmVrKyscPXqVaOEIqoPhTcX+z6Vi73n81F5y2LfHo5y9GtTMz6sWws3vRb7JiIiMia9i7LmzZsjNTUVISEht91/8uRJ+Pj4GC0YkTHkaBf7zsXhjELcstY3At3s/lrayAsd/V0gNXCxbyIiImPSuyh79NFH8e677yI2NrbW9Bc3btzAtGnT8Pjjjxs9IJGhzueVYtupmqkrUnN0F/tu4+NUU4iFe6GVFxf7JiIi8yER/5wFtg5XrlxBp06dIJPJMH78eLRq1QoSiQRnzpzBV199BbVajeTkZHh5edV35vtSUlICZ2dnFBcX3/ahBTIPao3A4YxC5JVWwNNRgS7BrpDVcSdLCIETtyz2feEfi30/GOiKfn9NXeHvysW+iYioYelbe+h9p8zLywtJSUkYO3Yspk6dqp3RXyKRoH///pg/f77ZF2RkGbadUmLGljSdBbx9nBWYFtcGA8Jrusir1BoczijE9tO52HH6CnJLdBf7jgn5a7HvMC94OMob/D0QEREZSu87ZbcqKirC+fPnIYRAaGgoXFxc6iNbveCdMvO27ZQSY1ck459/KW/eI/u/ni1wtbQSv/5xBdduWezb3kaGh/96YrJXKw84crFvIiIyE/rWHvdUlFkyFmXmS60R6P7Rbzp3yO7E1d4GfcJqCrGYEC72TURE5sno3ZdE9e1wRqFeBdmAcC+Mig5GZKALF/smIqJGg0UZmVx2wXXsPX8V3x+5qNfxseE+6NbCrZ5TERERNSyT32aYP38+goODoVAo0LlzZ+zdu7fOYzdu3Ii+ffvCw8MDTk5OiIqKwvbt2xswLRnDteuV2JqqxNSNqXho9i489PEuvL3pFE5cKr77yQA8HRV3P4iIiMjCmPRO2dq1a/HKK69g/vz5iImJwTfffIPY2FikpaUhICCg1vG///47+vbtiw8++ADNmjXDkiVLEBcXh0OHDqFjx44meAekD1W1GseyirD/fD72ncvHyZxi3DqS0UoqQacAF0SHuOG7A1koLK+sNdAfqBns7+1cMz0GERFRY2PSgf5du3ZFp06dsGDBAm1bWFgYBg8ejMTERL2u0bZtWwwdOhTvvvuuXsdzoH/9E0Lg7JVS7DuXj73n8nE4oxA3qtQ6x4R6OqB7qDu6h7ijaws3OMhrfj+4+fQlAJ3C7ObTlwue66SdFoOIiMgSmP1A/8rKShw7dgxvvvmmTnu/fv2QlJSk1zU0Gg1KS0vh6lr3nROVSgWVSqXdLikpubfAdEe5xRXYdz4f+85dxb7zBcgvU+nsd3eQo3uIG7qHeqB7iDu8nW/fBTkg3AcLnutUa54y73/MU0ZERNTYmKwoy8/Ph1qtrjXhrJeXF3Jzc/W6xqeffory8nLEx8fXeUxiYiJmzJhxX1mptjJVNQ5dKMDec/nYfz4f5/LKdPYrrKXoGuyGHqHu6B7qbtCSRgPCfdC3jbfeM/oTERE1BiZ/+vKf/1ELIfT6z3v16tWYPn06fvzxR3h6etZ53NSpUzF58mTtdklJCfz9/e89cBNVrdbgxKVi7biw5OwiVN+yurdEArRv7vxXl6QHOgU2g9zq3ucNk0kliGrJJyyJiKjpMFlR5u7uDplMVuuuWF5e3l2Xa1q7di1eeOEFrFu3Dn369LnjsXK5HHI5l9kxlBACmQXXse/cVew9l48DFwpQWlGtc0yAq512XFh0Szc0s7MxUVoiIiLLZ7KizMbGBp07d8bOnTvxxBNPaNt37tyJQYMG1Xne6tWrMXr0aKxevRqPPfZYQ0RtMgrLK7V3wvadz0fOtRs6+51trRHd0g3dQ93RI8QDAW5c3JuIiMhYTNp9OXnyZIwYMQKRkZGIiorCwoULkZ2djTFjxgCo6XrMycnB8uXLAdQUZCNHjsQXX3yBbt26ae+y2drawtnZ2WTvw1JVVKlxNLMIe89fxf7z+Th9uURnqgprmQSdA13Q46/B+eHNnTmui4iIqJ6YtCgbOnQoCgoKMHPmTCiVSoSHh2Pr1q0IDAwEACiVSmRnZ2uP/+abb1BdXY1x48Zh3Lhx2vaEhAQsXbq0oeNbHI1GIE1ZUnM37HzNVBWqao3OMa29HdE9xB0xoe7oGuwKOxuTDzskIiJqErggeSN3+dqNmvnCzucj6Xw+CsordfZ7OckRE+KOHqHuiAlx52z5RERERmb285RR/SitqMKB9IK/5gzLx4X8cp39djYydGvhhu5/FWIhng56T1VBRERE9YdFmYWrUmuQcvGadnB+ysVrUN8yVYVUAkT4N0OPEHd0D/VAB/9msLEy+ZKnRERE9A8syiyMEALpV8v/mjk/HwcvFKJMpTtVRbC7fc24sBB3RLV0g7OttYnSEhERkb5YlFmA/DIV9p/P186ef+vyQwDgYmeN6BD3v+6GucPPhVNVEBERWRoWZWboRqUahzMLtRO3/pFbqrPfxkqKB4Nc0D3EAz1C3dHGxwlSTlVBRERk0ViUGZlaIwxes1GtETh9uVg7OP9oZhEq1bpTVbTxcdKuI/lgkCsU1ve+hBERERGZHxZlRrTtlBIztqTpdC/6OCswLa4NBoT76Bx7sfC6tgjbn56Pa9erdPb7OCvQ/a/uyJgQd7g7cKkoIiKixoxFmZFsO6XE2BXJ+Oekb7nFFRi7IhmfxEfA3kaGvX89JZlVcF3nOAe5Fbq1cNPeDWvhbs+pKoiIiJoQFmVGoNYIzNiSVqsgA6Bte/X7EzrtMqkEHf2b1awjGeqOCL9msJJxqgoiIqKmikWZERzOKKz1ROTt+Dor0K+tN2JC3NGthSscFZyqgoiIiGqwKDOCvNK7F2QAMCW2NQZ1aF7PaYiIiMgSsb/MCPRdL5LrShIREVFdWJQZQZdgV/g4K1DXsHwJap6m7BLs2pCxiIiIyIKwKDMCmVSCaXFtAKBWYXZze1pcm7vOV0ZERERNF4syIxkQ7oMFz3WCt7NuF6W3swILnutUa54yIiIioltxoL8RDQj3Qd823gbP6E9ERETEoszIZFIJolq6mToGERERWRh2XxIRERGZARZlRERERGaARRkRERGRGWhyY8qEqFmNsqSkxMRJiIiIqCm4WXPcrEHq0uSKstLSUgCAv7+/iZMQERFRU1JaWgpnZ+c690vE3cq2Rkaj0eDy5ctwdHSERFI/U1WUlJTA398fFy9ehJOTU728RlPAz9E4+DkaDz9L4+DnaBz8HI2jIT5HIQRKS0vh6+sLqbTukWNN7k6ZVCqFn59fg7yWk5MTf1CMgJ+jcfBzNB5+lsbBz9E4+DkaR31/jne6Q3YTB/oTERERmQEWZURERERmgEVZPZDL5Zg2bRrkcrmpo1g0fo7Gwc/RePhZGgc/R+Pg52gc5vQ5NrmB/kRERETmiHfKiIiIiMwAizIiIiIiM8CijIiIiMgMsCgjIiIiMgMsyozo999/R1xcHHx9fSGRSPDDDz+YOpJFSkxMxIMPPghHR0d4enpi8ODBOHv2rKljWZwFCxagffv22gkRo6Ki8PPPP5s6lsVLTEyERCLBK6+8YuooFmX69OmQSCQ6X97e3qaOZbFycnLw3HPPwc3NDXZ2dujQoQOOHTtm6lgWJSgoqNbfSYlEgnHjxpksE4syIyovL0dERATmzZtn6igWbc+ePRg3bhwOHjyInTt3orq6Gv369UN5ebmpo1kUPz8/fPjhhzh69CiOHj2K3r17Y9CgQTh9+rSpo1msI0eOYOHChWjfvr2po1iktm3bQqlUar9SU1NNHckiFRUVISYmBtbW1vj555+RlpaGTz/9FM2aNTN1NIty5MgRnb+PO3fuBAAMGTLEZJma3DJL9Sk2NhaxsbGmjmHxtm3bprO9ZMkSeHp64tixY3jooYdMlMryxMXF6Wy///77WLBgAQ4ePIi2bduaKJXlKisrw7PPPotFixZh1qxZpo5jkaysrHh3zAg++ugj+Pv7Y8mSJdq2oKAg0wWyUB4eHjrbH374IVq2bImePXuaKBHvlJEFKC4uBgC4urqaOInlUqvVWLNmDcrLyxEVFWXqOBZp3LhxeOyxx9CnTx9TR7FY586dg6+vL4KDg/HMM8/gwoULpo5kkTZv3ozIyEgMGTIEnp6e6NixIxYtWmTqWBatsrISK1aswOjRoyGRSEyWg0UZmTUhBCZPnozu3bsjPDzc1HEsTmpqKhwcHCCXyzFmzBhs2rQJbdq0MXUsi7NmzRokJycjMTHR1FEsVteuXbF8+XJs374dixYtQm5uLqKjo1FQUGDqaBbnwoULWLBgAUJDQ7F9+3aMGTMGEydOxPLly00dzWL98MMPuHbtGkaNGmXSHOy+JLM2fvx4nDx5Evv27TN1FIvUqlUrpKSk4Nq1a9iwYQMSEhKwZ88eFmYGuHjxIl5++WXs2LEDCoXC1HEs1q1DO9q1a4eoqCi0bNkSy5Ytw+TJk02YzPJoNBpERkbigw8+AAB07NgRp0+fxoIFCzBy5EgTp7NMixcvRmxsLHx9fU2ag3fKyGxNmDABmzdvxq5du+Dn52fqOBbJxsYGISEhiIyMRGJiIiIiIvDFF1+YOpZFOXbsGPLy8tC5c2dYWVnBysoKe/bswdy5c2FlZQW1Wm3qiBbJ3t4e7dq1w7lz50wdxeL4+PjU+sUqLCwM2dnZJkpk2bKysvDLL7/gX//6l6mj8E4ZmR8hBCZMmIBNmzZh9+7dCA4ONnWkRkMIAZVKZeoYFuWRRx6p9ZTg888/j9atW2PKlCmQyWQmSmbZVCoVzpw5gx49epg6isWJiYmpNU3Qn3/+icDAQBMlsmw3HyZ77LHHTB2FRZkxlZWV4fz589rtjIwMpKSkwNXVFQEBASZMZlnGjRuHVatW4ccff4SjoyNyc3MBAM7OzrC1tTVxOsvx1ltvITY2Fv7+/igtLcWaNWuwe/fuWk+30p05OjrWGs9ob28PNzc3jnM0wGuvvYa4uDgEBAQgLy8Ps2bNQklJCRISEkwdzeJMmjQJ0dHR+OCDDxAfH4/Dhw9j4cKFWLhwoamjWRyNRoMlS5YgISEBVlZmUBIJMppdu3YJALW+EhISTB3NotzuMwQglixZYupoFmX06NEiMDBQ2NjYCA8PD/HII4+IHTt2mDpWo9CzZ0/x8ssvmzqGRRk6dKjw8fER1tbWwtfXVzz55JPi9OnTpo5lsbZs2SLCw8OFXC4XrVu3FgsXLjR1JIu0fft2AUCcPXvW1FGEEEJIhBDCNOUgEREREd3Egf5EREREZoBFGREREZEZYFFGREREZAZYlBERERGZARZlRERERGaARRkRERGRGWBRRkRERGQGWJQRERERmQEWZUSkl1GjRkEikUAikcDKygoBAQEYO3YsioqKTB3N4o0aNQqDBw82dQwiMjEWZUSktwEDBkCpVCIzMxP//e9/sWXLFrz00kumjkVGJIRAdXW1qWMQNUksyohIb3K5HN7e3vDz80O/fv0wdOhQ7NixQ+eYJUuWICwsDAqFAq1bt8b8+fO1+yorKzF+/Hj4+PhAoVAgKCgIiYmJ2v0SiQQLFixAbGwsbG1tERwcjHXr1ulcPzU1Fb1794atrS3c3Nzw73//G2VlZdr9N+86ffLJJ/Dx8YGbmxvGjRuHqqoq7THz589HaGgoFAoFvLy88PTTT2v3CSEwe/ZstGjRAra2toiIiMD69evv+LmoVCq88cYb8Pf3h1wuR2hoKBYvXgwAUKvVeOGFFxAcHAxbW1u0atUKX3zxhfbc6dOnY9myZfjxxx+1dyJ3794NAMjJycHQoUPh4uICNzc3DBo0CJmZmdpzq6urMXHiRDRr1gxubm6YMmUKEhISdO66qVQqTJw4EZ6enlAoFOjevTuOHDmi3b97925IJBJs374dkZGRkMvl+O677yCVSnH06FGd9/nll18iMDAQXJ2PqJ6YdOVNIrIYCQkJYtCgQdrt9PR00aZNG+Hl5aVtW7hwofDx8REbNmwQFy5cEBs2bBCurq5i6dKlQgghPv74Y+Hv7y9+//13kZmZKfbu3StWrVqlPR+AcHNzE4sWLRJnz54V//nPf4RMJhNpaWlCCCHKy8u1i1mnpqaKX3/9VQQHB4uEhASdnE5OTmLMmDHizJkzYsuWLcLOzk67YPORI0eETCYTq1atEpmZmSI5OVl88cUX2vPfeust0bp1a7Ft2zaRnp4ulixZIuRyudi9e3edn018fLzw9/cXGzduFOnp6eKXX34Ra9asEUIIUVlZKd59911x+PBhceHCBbFixQphZ2cn1q5dK4QQorS0VMTHx4sBAwYIpVIplEqlUKlUory8XISGhorRo0eLkydPirS0NDF8+HDRqlUroVKphBBCzJo1S7i6uoqNGzeKM2fOiDFjxggnJyedP6eJEycKX19fsXXrVnH69GmRkJAgXFxcREFBgRBCiF27dgkAon379mLHjh3i/PnzIj8/X/Tt21e89NJLOu+zY8eO4t13373zXxQiumcsyohILwkJCUImkwl7e3uhUCgEAAFAfPbZZ9pj/P39dYosIYR47733RFRUlBBCiAkTJojevXsLjUZz29cAIMaMGaPT1rVrVzF27FghRE3R5+LiIsrKyrT7f/rpJyGVSkVubq42Z2BgoKiurtYeM2TIEDF06FAhhBAbNmwQTk5OoqSkpNbrl5WVCYVCIZKSknTaX3jhBTFs2LDbZj579qwAIHbu3Hnb/bfz0ksviaeeekq7/c+CVwghFi9eLFq1aqXzWalUKmFrayu2b98uhBDCy8tLfPzxx9r91dXVIiAgQHutsrIyYW1tLVauXKk9prKyUvj6+orZs2cLIf4uyn744Qed11+7dq1wcXERFRUVQgghUlJShEQiERkZGXq/TyIyDLsviUhvvXr1QkpKCg4dOoQJEyagf//+mDBhAgDg6tWruHjxIl544QU4ODhov2bNmoX09HQANV2LKSkpaNWqFSZOnFir6xMAoqKiam2fOXMGAHDmzBlERETA3t5euz8mJgYajQZnz57VtrVt2xYymUy77ePjg7y8PABA3759ERgYiBYtWmDEiBFYuXIlrl+/DgBIS0tDRUUF+vbtq/Meli9frn0P/5SSkgKZTIaePXvW+bl9/fXXiIyMhIeHBxwcHLBo0SJkZ2fX/UEDOHbsGM6fPw9HR0dtDldXV1RUVCA9PR3FxcW4cuUKunTpoj1HJpOhc+fO2u309HRUVVUhJiZG22ZtbY0uXbpoP9ObIiMjdbYHDx4MKysrbNq0CQDw7bffolevXggKCrpjbiK6d1amDkBElsPe3h4hISEAgLlz56JXr16YMWMG3nvvPWg0GgDAokWL0LVrV53zbhZInTp1QkZGBn7++Wf88ssviI+PR58+fe46ZksikQCoGe918/u6jgFqCo9/7ruZz9HREcnJydi9ezd27NiBd999F9OnT8eRI0e0x/z0009o3ry5zjXkcvltX9fW1vaO2b///ntMmjQJn376KaKiouDo6IiPP/4Yhw4duuN5Go0GnTt3xsqVK2vt8/Dw0HlvtxK3jPe6+f3tjvln262FLgDY2NhgxIgRWLJkCZ588kmsWrUKc+bMuWNmIro/vFNGRPds2rRp+OSTT3D58mV4eXmhefPmuHDhAkJCQnS+goODtec4OTlh6NChWLRoEdauXYsNGzagsLBQu//gwYM6r3Hw4EG0bt0aANCmTRukpKSgvLxcu3///v2QSqV44IEH9M5tZWWFPn36YPbs2Th58iQyMzPx22+/oU2bNpDL5cjOzq71Hvz9/W97rXbt2kGj0WDPnj233b93715ER0fjpZdeQseOHRESElLrrpuNjQ3UarVOW6dOnXDu3Dl4enrWyuLs7AxnZ2d4eXnh8OHD2nPUajWOHz+u3Q4JCYGNjQ327dunbauqqsLRo0cRFhZ218/pX//6F3755RfMnz8fVVVVePLJJ+96DhHdO94pI6J79vDDD6Nt27b44IMPMG/ePEyfPh0TJ06Ek5MTYmNjoVKpcPToURQVFWHy5Mn4/PPP4ePjgw4dOkAqlWLdunXw9vZGs2bNtNdct24dIiMj0b17d6xcuRKHDx/WPsn47LPPYtq0aUhISMD06dNx9epVTJgwASNGjICXl5demf/3v//hwoULeOihh+Di4oKtW7dCo9GgVatWcHR0xGuvvYZJkyZBo9Gge/fuKCkpQVJSEhwcHJCQkFDrekFBQUhISMDo0aMxd+5cREREICsrC3l5eYiPj0dISAiWL1+O7du3Izg4GN999x2OHDmiU6gGBQVh+/btOHv2LNzc3ODs7Ixnn30WH3/8MQYNGoSZM2fCz88P2dnZ2LhxI15//XX4+flhwoQJSExMREhICFq3bo0vv/wSRUVF2rtg9vb2GDt2LF5//XW4uroiICAAs2fPxvXr1/HCCy/c9bMKCwtDt27dMGXKFIwePfqudwWJ6D6ZdkgbEVmK2w1GF0KIlStXChsbG5Gdna3d7tChg7CxsREuLi7ioYceEhs3bhRC1AzU79Chg7C3txdOTk7ikUceEcnJydprARBfffWV6Nu3r5DL5SIwMFCsXr1a5/VOnjwpevXqJRQKhXB1dRUvvviiKC0tvWPOl19+WfTs2VMIIcTevXtFz549hYuLi7C1tRXt27fXPgkphBAajUZ88cUXolWrVsLa2lp4eHiI/v37iz179tT52dy4cUNMmjRJ+Pj4CBsbGxESEiK+/fZbIYQQFRUVYtSoUcLZ2Vk0a9ZMjB07Vrz55psiIiJCe35eXp7o27evcHBwEADErl27hBBCKJVKMXLkSOHu7i7kcrlo0aKFePHFF0VxcbEQQoiqqioxfvx44eTkJFxcXMSUKVPEkCFDxDPPPKOTbcKECdprxMTEiMOHD2v33xzoX1RUdNv3tnjxYgFA5xwiqh8SITjhDBGZB4lEgk2bNnF2+3uk0WgQFhaG+Ph4vPfee0a55vvvv481a9YgNTXVKNcjorqx+5KIyEJlZWVhx44d6NmzJ1QqFebNm4eMjAwMHz78vq9dVlaGM2fO4MsvvzRagUdEd8aB/kREFkoqlWLp0qV48MEHERMTg9TUVPzyyy96DeK/m/Hjx6N79+7o2bMnRo8ebYS0RHQ37L4kIiIiMgO8U0ZERERkBliUEREREZkBFmVEREREZoBFGREREZEZYFFGREREZAZYlBERERGZARZlRERERGaARRkRERGRGWBRRkRERGQG/h+96MkWBrIMDwAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -413,17 +414,53 @@
     "plt.figure(figsize=(7, 3))\n",
     "plt.plot(sorted(trolly.response.unique()), cumprobs, marker='o')\n",
     "plt.ylabel(\"Cumulative probability\")\n",
-    "plt.xlabel(\"Response category\");"
+    "plt.xlabel(\"Response category\")\n",
+    "plt.title(\"Cumulative probabilities of response categories\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can take the derivative of the cumulative probabilities to get the posterior probabilities for each category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# derivative\n",
+    "ddx = np.diff(cumprobs)\n",
+    "probs = np.insert(ddx, 0, cumprobs[0])\n",
+    "\n",
+    "plt.figure(figsize=(7, 3))\n",
+    "plt.bar(sorted(trolly.response.unique()), probs)\n",
+    "plt.ylabel(\"Probability\")\n",
+    "plt.xlabel(\"Response category\")\n",
+    "plt.title(\"Posterior Probability of each response category\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -447,7 +484,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Notice in both plots above, the jump in cumulative probability from category 3 to 4. Additionally, the estimates of the coefficients is precise for each category. Now that we have an understanding how the cumulative link function is applied to produce ordered cumulative outcomes, we will add predictors to the model. "
+    "Notice in the plots above, the jump in probability from category 3 to 4. Additionally, the estimates of the coefficients is precise for each category. Now that we have an understanding how the cumulative link function is applied to produce ordered cumulative outcomes, we will add predictors to the model. "
    ]
   },
   {
@@ -466,12 +503,12 @@
     "\n",
     "The same predictor term $\\eta$ is subtracted from each threshold because if we decrease the log-cumulative-odds of every outcome value $k$ below the maximum, this shifts probability mass upwards towards higher outcome values. Thus, positive $\\beta$ values correspond to increasing $x$, which is associated with an increase in the mean response $Y$. The parameters to be estimated from the model are the thresholds $\\tau$ and the predictor terms $\\eta$ coefficients.  \n",
     "\n",
-    "To add predictors for ordinal models in Bambi, we continue to use the familiar syntax."
+    "To add predictors for ordinal models in Bambi, we continue to use the formula interface."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -480,6 +517,8 @@
      "text": [
       "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
       "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/distributions/transforms.py:56: FutureWarning: univariate_ordered has been deprecated, use ordered instead.\n",
+      "  warnings.warn(f\"{name} has been deprecated, use ordered instead.\", FutureWarning)\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
@@ -519,7 +558,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 01:19&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 01:11&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -536,7 +575,9 @@
      "text": [
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 79 seconds.\n"
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 71 seconds.\n"
      ]
     }
    ],
@@ -546,19 +587,19 @@
     "    data=trolly, \n",
     "    family=\"cumulative\"\n",
     ")\n",
-    "idata = model.fit()"
+    "idata = model.fit(random_seed=1234)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the summary dataframe below, we only select the predictor variables as the cutpoints are not of interest at the moment."
+    "In the summary dataframe below, we only select the predictor variables as the thresholds are not of interest at the moment."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -596,62 +637,62 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>action[1]</th>\n",
-       "      <td>-0.464</td>\n",
-       "      <td>0.057</td>\n",
-       "      <td>-0.574</td>\n",
-       "      <td>-0.357</td>\n",
+       "      <td>-0.463</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>-0.565</td>\n",
+       "      <td>-0.359</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2537.0</td>\n",
-       "      <td>2856.0</td>\n",
+       "      <td>2662.0</td>\n",
+       "      <td>3107.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>intention[1]</th>\n",
-       "      <td>-0.274</td>\n",
-       "      <td>0.059</td>\n",
-       "      <td>-0.386</td>\n",
-       "      <td>-0.169</td>\n",
+       "      <td>-0.273</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>-0.161</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2705.0</td>\n",
-       "      <td>2756.0</td>\n",
+       "      <td>2326.0</td>\n",
+       "      <td>2681.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact[1]</th>\n",
-       "      <td>-0.324</td>\n",
-       "      <td>0.072</td>\n",
-       "      <td>-0.456</td>\n",
-       "      <td>-0.190</td>\n",
+       "      <td>-0.322</td>\n",
+       "      <td>0.070</td>\n",
+       "      <td>-0.453</td>\n",
+       "      <td>-0.195</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2918.0</td>\n",
-       "      <td>2525.0</td>\n",
+       "      <td>2894.0</td>\n",
+       "      <td>2920.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>action:intention[1, 1]</th>\n",
-       "      <td>-0.458</td>\n",
-       "      <td>0.084</td>\n",
-       "      <td>-0.611</td>\n",
-       "      <td>-0.296</td>\n",
+       "      <td>-0.457</td>\n",
+       "      <td>0.081</td>\n",
+       "      <td>-0.597</td>\n",
+       "      <td>-0.302</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2895.0</td>\n",
-       "      <td>2917.0</td>\n",
+       "      <td>2432.0</td>\n",
+       "      <td>2892.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact:intention[1, 1]</th>\n",
-       "      <td>-1.286</td>\n",
-       "      <td>0.099</td>\n",
-       "      <td>-1.473</td>\n",
-       "      <td>-1.104</td>\n",
+       "      <td>-1.284</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>-1.464</td>\n",
+       "      <td>-1.095</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>3275.0</td>\n",
-       "      <td>3357.0</td>\n",
+       "      <td>2760.0</td>\n",
+       "      <td>2875.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -660,21 +701,21 @@
       ],
       "text/plain": [
        "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "action[1]               -0.464  0.057  -0.574   -0.357      0.001    0.001  \\\n",
-       "intention[1]            -0.274  0.059  -0.386   -0.169      0.001    0.001   \n",
-       "contact[1]              -0.324  0.072  -0.456   -0.190      0.001    0.001   \n",
-       "action:intention[1, 1]  -0.458  0.084  -0.611   -0.296      0.002    0.001   \n",
-       "contact:intention[1, 1] -1.286  0.099  -1.473   -1.104      0.002    0.001   \n",
+       "action[1]               -0.463  0.055  -0.565   -0.359      0.001    0.001  \\\n",
+       "intention[1]            -0.273  0.058  -0.375   -0.161      0.001    0.001   \n",
+       "contact[1]              -0.322  0.070  -0.453   -0.195      0.001    0.001   \n",
+       "action:intention[1, 1]  -0.457  0.081  -0.597   -0.302      0.002    0.001   \n",
+       "contact:intention[1, 1] -1.284  0.100  -1.464   -1.095      0.002    0.001   \n",
        "\n",
        "                         ess_bulk  ess_tail  r_hat  \n",
-       "action[1]                  2537.0    2856.0    1.0  \n",
-       "intention[1]               2705.0    2756.0    1.0  \n",
-       "contact[1]                 2918.0    2525.0    1.0  \n",
-       "action:intention[1, 1]     2895.0    2917.0    1.0  \n",
-       "contact:intention[1, 1]    3275.0    3357.0    1.0  "
+       "action[1]                  2662.0    3107.0    1.0  \n",
+       "intention[1]               2326.0    2681.0    1.0  \n",
+       "contact[1]                 2894.0    2920.0    1.0  \n",
+       "action:intention[1, 1]     2432.0    2892.0    1.0  \n",
+       "contact:intention[1, 1]    2760.0    2875.0    1.0  "
       ]
      },
-     "execution_count": 115,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -696,12 +737,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -730,12 +771,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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i4uKQm5tbq3G1bdsWAErt68cff6x025KLkpef1vTll1+WqltSp6qjCx06dEDv3r0RGRmJXbt2IS8vDx4eHpI6I0eORFpaGiwtLdGzZ89Sn6okCTUll8vRu3dv7N27V3JMxcXF2LFjB1q3bg0bGxvJNiXvYyiRmJiIa9euKZ88VJ3+LK/Nb775BoWFhZU+zagqKvudzZ49G3fv3sXYsWPx8OHDak0B+vPPP3Hq1ClJ2a5du9C4ceNSowSzZ8/Gzz//DH9/fxgZGcHV1bXS9keNGgVbW1sEBQUhLS2tzDq//PKL8qk/MpmsVL///PPPuHXrlqSsvD5p164drK2tcerUqTLPxZ49e6Jx48YAnj+CNS4uTpKIFxcX49tvv5W0WTJ16OWk6Pjx40hPT8fgwYMr7YcSXl5eypv+z58/X+vTtYiIagNHEuitVPLEGm9vb9jZ2eGTTz5Bp06dUFBQgJMnT2LTpk3o3Lmz8qlAH3/8MRYtWoTFixejf//+OHv2LDZs2FCtFz9VhbGxMYYMGYKgoCA0adIEbdq0wcGDB5VThiri4OCAJk2aYPr06ViyZAk0NTWxc+fOUhd/AGBrawsAWLFiBYYPHw51dXV06dJFOS2qLJ6enpg2bRpu374NBweHUt98fv7554iNjYWDgwNmz56Ndu3a4dmzZ7h69Sr279+PiIiIKo3a1FRQUBAcHR0xcOBA+Pn5QUtLC2FhYUhLS8Pu3btLfbN74sQJeHl5wdXVFTdu3MCnn36KVq1aKaejtG/fHpaWlli4cCGEEGjatCn27dunnGpTlr1790JDQwOOjo74888/sWjRInTt2rXU/PaaqOx3ZmNjg2HDhiEmJgbvv/9+qXsMKmJqaooPPvgAAQEBMDExwY4dOxAbG4sVK1aUerb/xIkT4e/vj//+97/47LPPKjxnSqirqyM6OhpDhw6Fvb09PvnkEwwcOBByuRzXrl3Dd999h3379uHBgwcAniecUVFRaN++Pbp06YLk5GSsWrWq1PljaWkJXV1d7Ny5Ex06dECjRo1gamoKU1NTfPnllxg+fDicnJzg7u6OVq1a4f79+0hPT0dKSooyCfj000+xb98+DB48GJ9++qly2lbJY4PV1J5/l9auXTv84x//QGhoKNTU1DB8+HBcvXoVixYtgpmZGebOnVvl/jY0NMSkSZMQHh6ONm3aSJ4+RkTUYKj2vmki1UpNTRWTJ08W77zzjtDS0hJyuVx0795dLF68WGRlZSnr5eXliQULFggzMzOhq6sr+vfvL1JTU8t9utHx48cl+yl5+s2LT1ARQojJkycLuVwuKVMoFGLs2LGiadOmwsDAQEycOFGcOHGiSk83SkxMFPb29kJPT0+0aNFCeHl5iZSUlFLb5uXlCS8vL9GiRQshk8kkT3F5+ZhKZGdnC11dXQFAbN68ucz+vHPnjpg9e7YwNzcXmpqaomnTpsLOzk58+umnIjc3t8xtSpQ83agyAMSMGTPKXHfkyBExaNAgIZfLha6urnjvvffEvn37JHVKfke//vqr+Pjjj4WhoaHQ1dUVzs7O4uLFi5K6Z8+eFY6OjqJx48aiSZMmwtXVVVy/fr3U04hKfhfJycnCxcVFNGrUSDRu3Fj8/e9/F3/99Vep46zJ040q+p2ViIqKEgDEnj17Ku7EF7Rp00aMGDFCfPfdd6JTp05CS0tLtG3bttSTuF7k7u4uNDQ0xM2bN6u8HyGEePjwofjiiy9Ejx49RKNGjYSmpqZ45513xMSJE0VCQoKy3oMHD8SUKVNEy5YthZ6ennj//ffFkSNHyuy73bt3i/bt2wtNTc1S/Xjq1Cnh5uYmWrZsKTQ1NYWxsbEYNGiQiIiIkLRx5MgR0bt3b6GtrS2MjY3F/PnzxYoVKwQA8fDhQ2W9oqIisWLFCmFjYyM0NTVF8+bNxcSJE8WNGzck7VXlXD506JAAIP79739Xqw+JiOqLTIiX3sBDRESvpY8++gjHjh3D1atX6+ztxfn5+Wjbti3ef//9Ml9M9qYYOnQorl69igsXLtRJ+76+vggPD8eNGzfq/J4VIqKa4HQjIqLXWF5eHlJSUpCUlITo6GiEhITUSYJw584dnD9/HpGRkfjrr7+qdAPy62LevHno3r07zMzMcP/+fezcuROxsbHYsmVLre/r2LFjuHDhAsLCwjBt2jQmCETUYDFJICJ6jSkUCjg4OEBfXx/Tpk3DrFmz6mQ/P//8Mzw8PGBiYoKwsLBKH3v6OikqKsLixYuRmZkJmUyGjh07Yvv27Zg4cWKt78ve3h56enoYOXIkli1bVuvtExHVFk43IiIiIiIiCT4ClYiIiIiIJFSaJBQWFuKzzz6Dubk5dHV1YWFhgc8//xzFxcXKOkIIBAQEwNTUFLq6uhgwYAD+/PNPSTt5eXmYNWsWmjdvDrlcjg8++AA3b96s78MhIiIiInojqDRJWLFiBSIiIrBhwwakp6dj5cqVWLVqFUJDQ5V1Vq5ciZCQEGzYsAHHjx+HsbExHB0d8ejRI2UdHx8fREdHY8+ePTh69Chyc3MxcuRIFBUVqeKwiIiIiIheayq9J2HkyJEwMjKSPEHio48+gp6eHrZv3w4hBExNTeHj44N//vOfAJ6PGhgZGWHFihWYNm0asrOz0aJFC2zfvh3jxo0DANy+fRtmZmbYv38/nJycKo2juLgYt2/fRuPGjUu9cImIiIioNgkh8OjRI5iamipf2EfU0Kj06Ubvv/8+IiIicOHCBdjY2ODUqVM4evQo1q5dCwDIyMhAZmYmhg4dqtxGW1sb/fv3R2JiIqZNm4bk5GQUFBRI6piamqJz585ITEwsM0nIy8tDXl6ecvnWrVvo2LFj3R0oERER0Utu3LhRp2+iJ3oVKk0S/vnPfyI7Oxvt27eHuro6ioqKsHz5cvz9738HAGRmZgIAjIyMJNsZGRnh2rVryjpaWlpo0qRJqTol278sKCgIS5cuLVV+48YN6Ovrv/JxEREREZUnJycHZmZmaNy4sapDISqXSpOEr7/+Gjt27MCuXbvQqVMnpKamwsfHB6amppg8ebKy3stTgIQQlU4LqqiOv78/5s2bp1wu+ceqr6/PJIGIiIjqBac4U0Om0iRh/vz5WLhwIf72t78BAGxtbXHt2jUEBQVh8uTJMDY2BvB8tMDExES5XVZWlnJ0wdjYGPn5+Xjw4IFkNCErKwsODg5l7ldbWxva2tp1dVhERERERK81ld4t8+TJk1I37KirqysfgWpubg5jY2PExsYq1+fn5+Pw4cPKBMDOzg6ampqSOgqFAmlpaeUmCUREREREVD6VjiS4uLhg+fLleOedd9CpUyecPHkSISEh8PT0BPB8GM7HxweBgYGwtraGtbU1AgMDoaenh/HjxwMADAwMMGXKFPj6+qJZs2Zo2rQp/Pz8YGtriyFDhqjy8IiIiIiIXksqTRJCQ0OxaNEieHt7IysrC6amppg2bRoWL16srLNgwQI8ffoU3t7eePDgAXr37o1ff/1VcrPPmjVroKGhATc3Nzx9+hSDBw9GVFQU1NXVVXFYRERERA2SEAKFhYV8l9RbSl1dHRoaGlW6H0al70loKHJycmBgYIDs7GzeuExERER1SlXXHfn5+VAoFHjy5Em97ZMaHj09PZiYmEBLS6vCeiodSSAiIiKiuldcXIyMjAyoq6vD1NQUWlpafLrSW0YIgfz8fNy5cwcZGRmwtrau8GV+TBKIiIiI3nD5+fkoLi6GmZkZ9PT0VB0OqYiuri40NTVx7do15OfnQ0dHp9y6fBc4ERER0Vuiom+O6e1Q1XOAZwoRERG9MRK/3YnEb3eqOgyi1x6TBCIiIiIikmCSQEREREREErxxmYiIiOgttib2Qr3ta66jTbW3yczMxPLly/Hzzz/j1q1baNmyJbp16wYfHx8MHjy4Sm1ERUXBx8cHDx8+rPb+G7K8vDz4+flh9+7dyneFhYWFoXXr1q/cNkcSiIiIiKhBunr1Kuzs7BAXF4eVK1fizJkzOHDgAAYOHIgZM2aoOrwaKygoqJV2fHx8EB0djT179uDo0aPIzc3FyJEja+VleUwSiIiI6I1TcgMzb2J+vXl7e0MmkyEpKQljx46FjY0NOnXqhHnz5uHYsWPKeiEhIbC1tYVcLoeZmRm8vb2Rm5sLADh06BA8PDyQnZ0NmUwGmUyGgIAAAM8fDbtgwQK0atUKcrkcvXv3xqFDhyQxbN68Wfno2DFjxiAkJASGhoaSOuHh4bC0tISWlhbatWuH7du3S9bLZDJERERg1KhRkMvlWLZsGaysrBAcHCypl5aWBjU1NVy+fLnSvsnOzsaWLVuwevVqDBkyBN27d8eOHTtw5swZ/Pbbb1Xs4fIxSSAiIiKiBuf+/fs4cOAAZsyYAblcXmr9ixfqampqWL9+PdLS0rBt2zbExcVhwYIFAAAHBwesXbsW+vr6UCgUUCgU8PPzAwB4eHggISEBe/bswenTp+Hq6ophw4bh4sWLAICEhARMnz4dc+bMQWpqKhwdHbF8+XJJHNHR0ZgzZw58fX2RlpaGadOmwcPDA/Hx8ZJ6S5YswahRo3DmzBl4enrC09MTkZGRkjpbt25F3759YWlpCXd3dwwYMKDc/klOTkZBQQGGDh2qLDM1NUXnzp2RmJhYeQdXgvckEBEREVGDc+nSJQgh0L59+0rr+vj4KH82NzfHF198gU8++QRhYWHQ0tKCgYEBZDIZjI2NlfUuX76M3bt34+bNmzA1NQUA+Pn54cCBA4iMjERgYCBCQ0MxfPhwZVJhY2ODxMRE/PTTT8p2goOD4e7uDm9vbwBQjnIEBwdj4MCBynrjx4+Hp6enctnDwwOLFy9GUlISevXqhYKCAuzYsQOrVq0CAJiYmKC4uLjcY87MzISWlhaaNGkiKTcyMkJmZmalfVYZjiQQERERUYMjhADwfKpOZeLj4+Ho6IhWrVqhcePGmDRpEu7du4fHjx+Xu01KSgqEELCxsUGjRo2Un8OHDyun+5w/fx69evWSbPfycnp6Ovr06SMp69OnD9LT0yVlPXv2lCybmJhgxIgR2Lp1KwDgp59+wrNnz+Dq6goACAoKwldffVXpsb9MCFGlPqsMkwQiIiIianCsra0hk8lKXWy/7Nq1a3B2dkbnzp3x/fffIzk5GRs3bgRQ8Q3CxcXFUFdXR3JyMlJTU5Wf9PR0rFu3DkDZF9wlycuLyqrzcllZU6a8vLywZ88ePH36FJGRkRg3bhz09PQqPN4SxsbGyM/Px4MHDyTlWVlZMDIyqlIbFWGSQEREREQNTtOmTeHk5ISNGzeWOSJQ8jjTEydOoLCwEKtXr8Z7770HGxsb3L59W1JXS0ur1BN/unfvjqKiImRlZcHKykryKZmW1L59eyQlJUm2O3HihGS5Q4cOOHr0qKQsMTERHTp0qPQYnZ2dIZfLER4ejpiYGMl0pMrY2dlBU1MTsbGxyjKFQoG0tDQ4ODhUuZ3yMEloAOrz+cREREREr4uwsDAUFRWhV69e+P7773Hx4kWkp6dj/fr1sLe3BwBYWlqisLAQoaGhuHLlCrZv346IiAhJO23btkVubi4OHjyIu3fv4smTJ7CxscGECRMwadIk7N27FxkZGTh+/DhWrFiB/fv3AwBmzZqF/fv3IyQkBBcvXsSXX36JmJgYySjB/PnzERUVhYiICFy8eBEhISHYu3ev8j6Giqirq8Pd3R3+/v6wsrJSHhMA+Pv7Y9KkSeVua2BggClTpsDX1xcHDx7EyZMnMXHiRNja2mLIkCHV6ucyCRLZ2dkCgMjOzlbJ/kN+Pa+S/b6tNp7cKDae3KjqMIiIqA4kfLOj1KehUcV1x9OnT8XZs2fF06dP622fteX27dtixowZok2bNkJLS0u0atVKfPDBByI+Pl5ZJyQkRJiYmAhdXV3h5OQkvvrqKwFAPHjwQFln+vTpolmzZgKAWLJkiRBCiPz8fLF48WLRtm1boampKYyNjcWYMWPE6dOnldtt2rRJtGrVSujq6orRo0eLZcuWCWNjY0mMYWFhwsLCQmhqagobGxvx1VdfSdYDENHR0WUe3+XLlwUAsXLlSkn55MmTRf/+/Svsm6dPn4qZM2eKpk2bCl1dXTFy5Ehx/fr1Srepyrkg+/+Bv9VycnJgYGCA7Oxs6Ovr1/v+18ReqNEbCKlmwlLDAADe3bxVHAkREdW2st6L4OA6QQWRlE8V1x3Pnj1DRkYGzM3NoaOjUy/7fFNNnToV586dw5EjR2qlvYSEBAwYMAA3b96slXsJKlPVc4GPQCUiIiIiKkdwcDAcHR0hl8sRExODbdu2ISws7JXbzcvLw40bN7Bo0SK4ubnVS4JQHbwngYiIiIioHElJSXB0dIStrS0iIiKwfv16eHl5vXK7u3fvRrt27ZCdnY2VK1fWQqS1iyMJRERERETl+Oabb+qkXXd3d7i7u9dJ27WBSQIREb2xkvZdUf7cy8VChZEQEb1eON2IiIiIiIgkmCQQEREREZEEpxtRxeKDgIH+qo6CSGXuhG6QLLeYNVNFkRAREdUfJgkNBN+VQERUt168PwHgPQpERBXhdCMiIiIiIpLgSAIRERHR2yw+qP72xSnMrw2OJBARVcOd0A3KDxER1b3MzEzMmjULFhYW0NbWhpmZGVxcXHDw4MEqtxEVFQVDQ8O6C1JFNm3ahAEDBkBfXx8ymQwPHz6stbaZJBARERFRg3T16lXY2dkhLi4OK1euxJkzZ3DgwAEMHDgQM2bMUHV4NVZQUFAr7Tx58gTDhg3Dv/71r1pp70VMEoiIiIioQfL29oZMJkNSUhLGjh0LGxsbdOrUCfPmzcOxY8eU9UJCQmBrawu5XA4zMzN4e3sjNzcXAHDo0CF4eHggOzsbMpkMMpkMAQEBAID8/HwsWLAArVq1glwuR+/evXHo0CFJDJs3b4aZmRn09PQwZswYhISElBqVCA8Ph6WlJbS0tNCuXTts375dsl4mkyEiIgKjRo2CXC7HsmXLYGVlheDgYEm9tLQ0qKmp4fLly1XqHx8fHyxcuBDvvfdelepXB5MEIiIiImpw7t+/jwMHDmDGjBmQy+Wl1r94oa6mpob169cjLS0N27ZtQ1xcHBYsWAAAcHBwwNq1a6Gvrw+FQgGFQgE/Pz8AgIeHBxISErBnzx6cPn0arq6uGDZsGC5evAgASEhIwPTp0zFnzhykpqbC0dERy5cvl8QRHR2NOXPmwNfXF2lpaZg2bRo8PDwQHx8vqbdkyRKMGjUKZ86cgaenJzw9PREZGSmps3XrVvTt2xeWlpZwd3fHgAEDXrUba4w3LhMRERFRg3Pp0iUIIdC+fftK6/r4+Ch/Njc3xxdffIFPPvkEYWFh0NLSgoGBAWQyGYyNjZX1Ll++jN27d+PmzZswNTUFAPj5+eHAgQOIjIxEYGAgQkNDMXz4cGVSYWNjg8TERPz000/KdoKDg+Hu7g5vb28AUI5yBAcHY+DAgcp648ePh6enp3LZw8MDixcvRlJSEnr16oWCggLs2LEDq1atAgCYmJiguLi4Bj1XOziSQEREREQNjhACwPOpOpWJj4+Ho6MjWrVqhcaNG2PSpEm4d+8eHj9+XO42KSkpEELAxsYGjRo1Un4OHz6snO5z/vx59OrVS7Ldy8vp6eno06ePpKxPnz5IT0+XlPXs2VOybGJighEjRmDr1q0AgJ9++gnPnj2Dq6srACAoKAhfffVVpcdeVziSQG+tsNQw5c/e3bxVGAkRERG9zNraGjKZDOnp6Rg9enS59a5duwZnZ2dMnz4dX3zxBZo2bYqjR49iypQpFd4gXFxcDHV1dSQnJ0NdXV2yrlGjRgCeJyovJyklycuLyqrzcllZU6a8vLzw8ccfY82aNYiMjMS4ceOgp6dXbsz1iSMJVLn4oPp9hjIRERG99Zo2bQonJyds3LixzBGBksd9njhxAoWFhVi9ejXee+892NjY4Pbt25K6WlpaKCoqkpR1794dRUVFyMrKgpWVleRTMi2pffv2SEpKkmx34sQJyXKHDh1w9OhRSVliYiI6dOhQ6TE6OztDLpcjPDwcMTExkulIqsYkgYiIiIgapLCwMBQVFaFXr174/vvvcfHiRaSnp2P9+vWwt7cHAFhaWqKwsBChoaG4cuUKtm/fjoiICEk7bdu2RW5uLg4ePIi7d+/iyZMnsLGxwYQJEzBp0iTs3bsXGRkZOH78OFasWIH9+/cDAGbNmoX9+/cjJCQEFy9exJdffomYmBjJKMH8+fMRFRWFiIgIXLx4ESEhIdi7d6/yPoaKqKurw93dHf7+/rCyslIeEwD4+/tj0qRJFW6fmZmJ1NRUXLp0CQBw5swZpKam4v79+1Xr4ApwuhERERHR26wBvwXZ3NwcKSkpWL58OXx9faFQKNCiRQvY2dkhPDwcANCtWzeEhIRgxYoV8Pf3R79+/RAUFCS5wHZwcMD06dMxbtw43Lt3D0uWLEFAQAAiIyOxbNky+Pr64tatW2jWrBns7e3h7OwM4Pm9BREREVi6dCk+++wzODk5Ye7cudiw4X8v1Bw9ejTWrVuHVatWYfbs2TA3N0dkZGSVn0w0ZcoUBAYGlhpFUCgUuH79eoXblsRWol+/fgCAyMhIuLu7V2n/5ZGJsiZWvWVycnJgYGCA7Oxs6Ovr1/v+18ReAADMdbSp931X6sVpRg34j0h1vHgvQgnek0DlqejNyi1mzazHSKiqkvZdqVK9Xi4WdRwJqULitztLlTm4TlBBJOVTxXXHs2fPkJGRAXNzc+jo6NTLPt9UU6dOxblz53DkyJFaaS8hIQEDBgzAzZs3YWRkVCttVqSq5wJHEoiIiIiIyhEcHAxHR0fI5XLExMRg27ZtCAsr/YVjdeXl5eHGjRtYtGgR3Nzc6iVBqA7ek0BEREREVI6kpCQ4OjrC1tYWERERWL9+Pby8vF653d27d6Ndu3bIzs7GypUrayHS2sWRBCIiIiKicnzzzTd10q67u/sr3zdQlziSQEREREREEkwSiIiIiIhIgkkCERERERFJMEkgIiIiIiIJ3rhMRFRDL75Dge9MICKiNwlHEoiIiIiISELlIwm3bt3CP//5T8TExODp06ewsbHBli1bYGdnBwAQQmDp0qXYtGkTHjx4gN69e2Pjxo3o1KmTso28vDz4+flh9+7dePr0KQYPHoywsDC0bt1aVYdFRERE9FoIS331F4NVlXc373rbF70alY4kPHjwAH369IGmpiZiYmJw9uxZrF69GoaGhso6K1euREhICDZs2IDjx4/D2NgYjo6OePTokbKOj48PoqOjsWfPHhw9ehS5ubkYOXIkioqKVHBUNbcm9oKqQ3ijhaWG1esfQiIiInp1mZmZmDVrFiwsLKCtrQ0zMzO4uLjg4MGDVW4jKipKcn35Jrh//z5mzZqFdu3aQU9PD++88w5mz56N7OzsWmlfpSMJK1asgJmZGSIjI5Vlbdu2Vf4shMDatWvx6aef4sMPPwQAbNu2DUZGRti1axemTZuG7OxsbNmyBdu3b8eQIUMAADt27ICZmRl+++03ODk51esxEREREVHtuHr1Kvr06QNDQ0OsXLkSXbp0QUFBAX755RfMmDED586dU3WINVJQUABNTc1XauP27du4ffs2goOD0bFjR1y7dg3Tp0/H7du38d13371yjCodSfjxxx/Rs2dPuLq6omXLlujevTs2b96sXJ+RkYHMzEwMHTpUWaatrY3+/fsjMTERAJCcnIyCggJJHVNTU3Tu3FlZ52V5eXnIycmRfFSFowdEREREZfP29oZMJkNSUhLGjh0LGxsbdOrUCfPmzcOxY8eU9UJCQmBrawu5XA4zMzN4e3sjNzcXAHDo0CF4eHggOzsbMpkMMpkMAQEBAID8/HwsWLAArVq1glwuR+/evXHo0CFJDJs3b4aZmRn09PQwZswYhISElBqVCA8Ph6WlJbS0tNCuXTts375dsl4mkyEiIgKjRo2CXC7HsmXLYGVlheDgYEm9tLQ0qKmp4fLly5X2TefOnfH999/DxcUFlpaWGDRoEJYvX459+/ahsLCwij1cPpUmCVeuXEF4eDisra3xyy+/YPr06Zg9eza++uorAM+HlwDAyMhIsp2RkZFyXWZmJrS0tNCkSZNy67wsKCgIBgYGyo+ZmVltHxoRERERvYL79+/jwIEDmDFjBuRyean1L16oq6mpYf369UhLS8O2bdsQFxeHBQsWAAAcHBywdu1a6OvrQ6FQQKFQwM/PDwDg4eGBhIQE7NmzB6dPn4arqyuGDRuGixcvAgASEhIwffp0zJkzB6mpqXB0dMTy5cslcURHR2POnDnw9fVFWloapk2bBg8PD8THx0vqLVmyBKNGjcKZM2fg6ekJT09PyWwaANi6dSv69u0LS0tLuLu7Y8CAAdXqs+zsbOjr60ND49UnC6k0SSguLkaPHj0QGBiI7t27Y9q0aZg6dSrCw8Ml9WQymWRZCFGq7GUV1fH390d2drbyc+PGjVc7ECIiIiKqVZcuXYIQAu3bt6+0ro+PDwYOHAhzc3MMGjQIX3zxBb755hsAgJaWFgwMDCCTyWBsbAxjY2M0atQIly9fxu7du/Htt98qL8z9/Pzw/vvvKy/eQ0NDMXz4cPj5+cHGxgbe3t4YPny4ZN/BwcFwd3eHt7c3bGxsMG/ePHz44YelRgnGjx8PT09PWFhYoE2bNvDw8MD58+eRlJQE4PkUpB07dsDT0xMAYGJignfeeafK/XXv3j188cUXmDZtWpW3qYhKkwQTExN07NhRUtahQwdcv34dAGBsbAwApUYEsrKylKMLxsbGyM/Px4MHD8qt8zJtbW3o6+tLPkRERETUcAghAJT+srgs8fHxcHR0RKtWrdC4cWNMmjQJ9+7dw+PHj8vdJiUlBUII2NjYoFGjRsrP4cOHldN9zp8/j169ekm2e3k5PT0dffr0kZT16dMH6enpkrKePXtKlk1MTDBixAhs3boVAPDTTz/h2bNncHV1BfB85kvJ7JrK5OTkYMSIEejYsSOWLFlSpW0qo9IkoU+fPjh//ryk7MKFC2jTpg0AwNzcHMbGxoiNjVWuz8/Px+HDh+Hg4AAAsLOzg6ampqSOQqFAWlqasg4RERERvV6sra0hk8lKXWy/7Nq1a3B2dlbO0U9OTsbGjRsBPP92vjzFxcVQV1dHcnIyUlNTlZ/09HSsW7cOQNkzU0qSlxdVZdZLWVOmvLy8sGfPHjx9+hSRkZEYN24c9PT0Kjzelz169AjDhg1Do0aNEB0d/co3RJdQ6dON5s6dCwcHBwQGBsLNzQ1JSUnYtGkTNm3aBOB5h/v4+CAwMBDW1tawtrZGYGAg9PT0MH78eACAgYEBpkyZAl9fXzRr1gxNmzaFn58fbG1tlU87IiIiojdX4rc7VR0C1YGmTZvCyckJGzduxOzZs0tdZD98+BCGhoY4ceIECgsLsXr1aqipPf/+u2SqUQktLa1Sj8bv3r07ioqKkJWVhb59+5YZQ/v27ZXTgUqcOHFCstyhQwccPXoUkyZNUpYlJiaiQ4cOlR6js7Mz5HI5wsPDERMTg//+97+VbvOinJwcODk5QVtbGz/++CN0dHSqtX1FVJokvPvuu4iOjoa/vz8+//xzmJubY+3atZgwYYKyzoIFC/D06VN4e3srX6b266+/onHjxso6a9asgYaGBtzc3JQvU4uKioK6uroqDouIiIiIakFYWBgcHBzQq1cvfP755+jSpQsKCwsRGxuL8PBwpKenw9LSEoWFhQgNDYWLiwsSEhIQEREhaadt27bIzc3FwYMH0bVrV+jp6cHGxgYTJkzApEmTsHr1anTv3h13795FXFwcbG1t4ezsjFmzZqFfv34ICQmBi4sL4uLiEBMTIxklmD9/Ptzc3NCjRw8MHjwY+/btw969e/Hbb79Venzq6upwd3eHv78/rKysYG9vr1zn7++PW7dulTvl6NGjRxg6dCiePHmCHTt2SJ7Y2aJFi1e+Dlb5G5dHjhyJkSNHlru+5DFVJY+qKouOjg5CQ0MRGhpaBxG+peKDVB0BERER1YOG/BZkc3NzpKSkYPny5fD19YVCoUCLFi1gZ2enfNBNt27dEBISghUrVsDf3x/9+vVDUFCQ5Jt9BwcHTJ8+HePGjcO9e/ewZMkSBAQEIDIyEsuWLYOvry9u3bqFZs2awd7eHs7OzgCeT42PiIjA0qVL8dlnn8HJyQlz587Fhg0blG2PHj0a69atw6pVqzB79myYm5sjMjKyyk8mmjJlCgIDA5U3LJdQKBTK+3TLkpycjD/++AMAYGVlJVmXkZEhefdYTchEWROr3jI5OTkwMDBQPjaqPr38noS5jjb1uv9ylZUkDPSv/zhqUUVvW27IfyBJte6Ebqi8EoAWs2bWcSRUVUn7rlSpXi8XizqOhOpLZdONHFwnVLi+vqniuuPZs2fIyMiAubl5rU5JeRtNnToV586dw5EjR2qlvYSEBAwYMAA3b94s96E7tamq54LKRxKIiIiIiBqq4OBgODo6Qi6XIyYmBtu2bUNYWPlfPFZVXl4ebty4gUWLFsHNza1eEoTqUOnTjYiIiIiIGrKkpCQ4OjrC1tYWERERWL9+Pby8vF653d27d6Ndu3bIzs7GypUrayHS2sWRBCIiInqjlUxHamjTjuj18PKTkmqLu7s73N3d66Tt2sCRBCIiIiIikmCSQEREREREEkwSiIiIiIhIgkkCERERERFJMEkgIiIiIiIJJglERERERCTBR6ASERERvcWq+mb52sC3078+OJJARERERA1WZmYmZs2aBQsLC2hra8PMzAwuLi44ePBglduIioqCoaFh3QWpItOmTYOlpSV0dXXRokULjBo1CufOnauVtpkkEBEREVGDdPXqVdjZ2SEuLg4rV67EmTNncODAAQwcOBAzZsxQdXg1VlBQUCvt2NnZITIyEunp6fjll18ghMDQoUNRVFT0ym0zSSACEJYahrDUMFWHQURERC/w9vaGTCZDUlISxo4dCxsbG3Tq1Anz5s3DsWPHlPVCQkJga2sLuVwOMzMzeHt7Izc3FwBw6NAheHh4IDs7GzKZDDKZDAEBAQCA/Px8LFiwAK1atYJcLkfv3r1x6NAhSQybN2+GmZkZ9PT0MGbMGISEhJQalQgPD4elpSW0tLTQrl07bN++XbJeJpMhIiICo0aNglwux7Jly2BlZYXg4GBJvbS0NKipqeHy5ctV6p9//OMf6NevH9q2bYsePXpg2bJluHHjBq5evVql7SvCJIGqLj5I1REQERHRW+L+/fs4cOAAZsyYAblcXmr9ixfqampqWL9+PdLS0rBt2zbExcVhwYIFAAAHBwesXbsW+vr6UCgUUCgU8PPzAwB4eHggISEBe/bswenTp+Hq6ophw4bh4sWLAICEhARMnz4dc+bMQWpqKhwdHbF8+XJJHNHR0ZgzZw58fX2RlpaGadOmwcPDA/Hx8ZJ6S5YswahRo3DmzBl4enrC09MTkZGRkjpbt25F3759YWlpCXd3dwwYMKDK/fX48WNERkbC3NwcZmZmVd6uPEwSiIiIiKjBuXTpEoQQaN++faV1fXx8MHDgQJibm2PQoEH44osv8M033wAAtLS0YGBgAJlMBmNjYxgbG6NRo0a4fPkydu/ejW+//VZ5Ye7n54f3339fefEeGhqK4cOHw8/PDzY2NvD29sbw4cMl+w4ODoa7uzu8vb1hY2ODefPm4cMPPyw1SjB+/Hh4enrCwsICbdq0gYeHB86fP4+kpCQAz6cg7dixA56engAAExMTvPPOO5Uee1hYGBo1aoRGjRrhwIEDiI2NhZaWVuUdXAkmCURERETU4AghADyfqlOZ+Ph4ODo6olWrVmjcuDEmTZqEe/fu4fHjx+Vuk5KSAiEEbGxslBfZjRo1wuHDh5XTfc6fP49evXpJtnt5OT09HX369JGU9enTB+np6ZKynj17SpZNTEwwYsQIbN26FQDw008/4dmzZ3B1dQUABAUF4auvvqr02CdMmICTJ0/i8OHDsLa2hpubG549e1bpdpVhkkBEREREDY61tTVkMlmpi+2XXbt2Dc7OzujcuTO+//57JCcnY+PGjQAqvkG4uLgY6urqSE5ORmpqqvKTnp6OdevWAXieqLycpJQkLy8qq87LZWVNmfLy8sKePXvw9OlTREZGYty4cdDT06vweF9mYGAAa2tr9OvXD9999x3OnTuH6OjoarVRFiYJRERERNTgNG3aFE5OTti4cWOZIwIPHz4EAJw4cQKFhYVYvXo13nvvPdjY2OD27duSulpaWqWe+NO9e3cUFRUhKysLVlZWko+xsTEAoH379srpQCVOnDghWe7QoQOOHj0qKUtMTESHDh0qPUZnZ2fI5XKEh4cjJiZGOdXoVQghkJeX98rt8GVqREQvqc8XCxERUfnCwsLg4OCAXr164fPPP0eXLl1QWFiI2NhYhIeHIz09HZaWligsLERoaChcXFyQkJCAiIgISTtt27ZFbm4uDh48iK5du0JPTw82NjaYMGECJk2ahNWrV6N79+64e/cu4uLiYGtrC2dnZ8yaNQv9+vVDSEgIXFxcEBcXh5iYGMkowfz58+Hm5oYePXpg8ODB2LdvH/bu3Yvffvut0uNTV1eHu7s7/P39YWVlBXt7e+U6f39/3Lp1q9wpR1euXMHXX3+NoUOHokWLFrh16xZWrFgBXV1dODs717DH/4dJAhEREdFbrCG/Bdnc3BwpKSlYvnw5fH19oVAo0KJFC9jZ2SE8PBwA0K1bN4SEhGDFihXw9/dHv379EBQUhEmTJinbcXBwwPTp0zFu3Djcu3cPS5YsQUBAACIjI7Fs2TL4+vri1q1baNasGezt7ZUX2X369EFERASWLl2Kzz77DE5OTpg7dy42bPjfl0mjR4/GunXrsGrVKsyePRvm5uaIjIys8pOJpkyZgsDAwFKjCAqFAtevXy93Ox0dHRw5cgRr167FgwcPYGRkhH79+iExMREtW7asaheXSybKmlj1lsnJyYGBgQGys7Ohr69fr/teE3tBsjzX0aZe91+u8h53OtC/fuOoRVV5D4J3N+96iIQaupqMJDTk/8m+bZL2XalSvV4uFnUcCdWXxG93Vqmeg+uEOo6kalRx3fHs2TNkZGTA3NwcOjo69bLPN9XUqVNx7tw5HDlypFbaS0hIwIABA3Dz5k0YGRnVSpsVqeq5wJEEIiIiIqJyBAcHw9HREXK5HDExMdi2bRvCwl79Bax5eXm4ceMGFi1aBDc3t3pJEKqDNy4TEREREZUjKSkJjo6OsLW1RUREBNavXw8vL69Xbnf37t1o164dsrOzsXLlylqItHZxJIGIiN5KL05L4tQjIipPyUvZapu7uzvc3d3rpO3awJEEIiIiIiKSYJJAREREREQSTBKIiIiIiEiCSQIREREREUkwSSAiIiIiIgkmCUREREREJFGjR6BaWFjg+PHjaNasmaT84cOH6NGjB65cqdrbLomIiIhItar6lvLawMcNvz5qNJJw9epVFBUVlSrPy8vDrVu3XjkoIiIiIiIAyMzMxKxZs2BhYQFtbW2YmZnBxcUFBw8erHIbUVFRMDQ0rLsgVUwIgeHDh0Mmk+GHH36olTarNZLw448/Kn/+5ZdfYGBgoFwuKirCwYMH0bZt21oJjKg2hKW++mvTiYiISDWuXr2KPn36wNDQECtXrkSXLl1QUFCAX375BTNmzMC5c+dUHWKNFBQUQFNTs9baW7t2LWQyWa21B1RzJGH06NEYPXo0ZDIZJk+erFwePXo0/va3vyE2NharV6+u1QCJiIiI6O3k7e0NmUyGpKQkjB07FjY2NujUqRPmzZuHY8eOKeuFhITA1tYWcrkcZmZm8Pb2Rm5uLgDg0KFD8PDwQHZ2NmQyGWQyGQICAgAA+fn5WLBgAVq1agW5XI7evXvj0KFDkhg2b94MMzMz6OnpYcyYMQgJCSk1KhEeHg5LS0toaWmhXbt22L59u2S9TCZDREQERo0aBblcjmXLlsHKygrBwcGSemlpaVBTU8Ply5er3EenTp1CSEgItm7dWuVtqqJaSUJxcTGKi4vxzjvvICsrS7lcXFyMvLw8nD9/HiNHjqzVAImIiIjo7XP//n0cOHAAM2bMgFwuL7X+xQt1NTU1rF+/Hmlpadi2bRvi4uKwYMECAICDgwPWrl0LfX19KBQKKBQK+Pn5AQA8PDyQkJCAPXv24PTp03B1dcWwYcNw8eJFAEBCQgKmT5+OOXPmIDU1FY6Ojli+fLkkjujoaMyZMwe+vr5IS0vDtGnT4OHhgfj4eEm9JUuWYNSoUThz5gw8PT3h6emJyMhISZ2tW7eib9++sLS0hLu7OwYMGFBhHz158gR///vfsWHDBhgbG1epX6uqRjcuZ2Rk1GoQREREREQvunTpEoQQaN++faV1fXx8lD+bm5vjiy++wCeffIKwsDBoaWnBwMAAMplMciF9+fJl7N69Gzdv3oSpqSkAwM/PDwcOHEBkZCQCAwMRGhqK4cOHK5MKGxsbJCYm4qefflK2ExwcDHd3d3h7ewOAcpQjODgYAwcOVNYbP348PD09lcseHh5YvHgxkpKS0KtXLxQUFGDHjh1YtWoVAMDExATFxcUVHvfcuXPh4OCAUaNGVdpH1VWjJAEADh48iIMHDypHFF5U28MdRERE5anPJ7MQUf0RQgBAlebax8fHIzAwEGfPnkVOTg4KCwvx7NkzPH78uMxRCABISUmBEAI2NjaS8ry8POUTPM+fP48xY8ZI1vfq1UuSJKSnp+Mf//iHpE6fPn2wbt06SVnPnj0lyyYmJhgxYgS2bt2qbPPZs2dwdXUFAAQFBVV4zD/++CPi4uJw8uTJCuvVVI2ebrR06VIMHToUBw8exN27d/HgwQPJh4iIiIjoVVhbW0MmkyE9Pb3CeteuXYOzszM6d+6M77//HsnJydi4cSOA5zcIl6e4uBjq6upITk5Gamqq8pOenq68wBdClEpSSpKXF5VV5+WyspIVLy8v7NmzB0+fPkVkZCTGjRsHPT29Co+3RFxcHC5fvgxDQ0NoaGhAQ+P5d/8fffRRpdOUqqJGIwkRERGIiorCxx9//MoBUAMUX3HmSkRERFTXmjZtCicnJ2zcuBGzZ88udZH98OFDGBoa4sSJEygsLMTq1auhpvb8++9vvvlGUldLS6vU4/u7d++OoqIiZGVloW/fvmXG0L59eyQlJUnKTpw4IVnu0KEDjh49ikmTJinLEhMT0aFDh0qP0dnZGXK5HOHh4YiJicF///vfSrcpsXDhQnh5eUnKbG1tsWbNGri4uFS5nfLUKEnIz8+Hg4PDK++ciIiIiKg8YWFhcHBwQK9evfD555+jS5cuKCwsRGxsLMLDw5Geng5LS0sUFhYiNDQULi4uSEhIQEREhKSdtm3bIjc3FwcPHkTXrl2hp6cHGxsbTJgwAZMmTcLq1avRvXt33L17F3FxcbC1tYWzszNmzZqFfv36ISQkBC4uLoiLi0NMTIxklGD+/Plwc3NDjx49MHjwYOzbtw979+7Fb7/9Vunxqaurw93dHf7+/rCysoK9vb1ynb+/P27duoWvvvqqzG2NjY3LvFn5nXfegbm5eVW7uFw1ShK8vLywa9cuLFq06JUDICJ6E9wJ3aD8ucWsmSqMhIioehryW5DNzc2RkpKC5cuXw9fXFwqFAi1atICdnR3Cw8MBAN26dUNISAhWrFgBf39/9OvXD0FBQZJv9h0cHDB9+nSMGzcO9+7dw5IlSxAQEIDIyEgsW7YMvr6+uHXrFpo1awZ7e3s4OzsDeH5vQUREBJYuXYrPPvsMTk5OmDt3LjZs+N/f/NGjR2PdunVYtWoVZs+eDXNzc0RGRlZ5ys+UKVMQGBgouakZABQKBa5fv/6KPVhzMlHWxKpKzJkzB1999RW6dOmCLl26lHoZREhISK0FWB9ycnJgYGCA7Oxs6Ovr1+u+18RekCzPdbQpp2Y9qmi60UD/+oujFlT3ZWre3bzrKBJ6nbx4wV8TTBLqV23cuNyQL5Koconf7qxSPQfXCXUcSdWo4rrj2bNnyMjIgLm5OXR0dOpln2+qqVOn4ty5czhy5EittJeQkIABAwbg5s2bMDIyqpU2K1LVc6FGIwmnT59Gt27dADx/6cOLavttb0REREREqhIcHAxHR0fI5XLExMRg27ZtCAur3peQZcnLy8ONGzewaNEiuLm51UuCUB01ShJefjkEEREREdGbKCkpCStXrsSjR49gYWGB9evXl7phuCZ2796NKVOmoFu3bqXe0NwQ1Pg9CUREREREb7qXn5RUW9zd3eHu7l4nbdeGGr0nYeDAgRg0aFC5n5oICgqCTCaTvDFPCIGAgACYmppCV1cXAwYMwJ9//inZLi8vD7NmzULz5s0hl8vxwQcf4ObNmzWKgYiIiIiIapgkdOvWDV27dlV+OnbsiPz8fKSkpMDW1rba7R0/fhybNm1Cly5dJOUrV65ESEgINmzYgOPHj8PY2BiOjo549OiRso6Pjw+io6OxZ88eHD16FLm5uRg5cmSpZ+ESEREREVHV1Gi60Zo1a8osDwgIQG5ubrXays3NxYQJE7B582YsW7ZMWS6EwNq1a/Hpp5/iww8/BABs27YNRkZG2LVrF6ZNm4bs7Gxs2bIF27dvx5AhQwAAO3bsgJmZGX777Tc4OTnV5PCIiIiIiN5qNRpJKM/EiROxdevWam0zY8YMjBgxQnmRXyIjIwOZmZkYOnSoskxbWxv9+/dHYmIiACA5ORkFBQWSOqampujcubOyTlny8vKQk5Mj+RAREdHrJfHbnVV+/CkRVU+t3rj8+++/V+vZu3v27EFKSgqOHz9eal1mZiYAlHoclJGREa5du6aso6WlhSZNmpSqU7J9WYKCgrB06dIqx0lERERE9DapUZJQMv2nhBACCoUCJ06cqPJbmG/cuIE5c+bg119/rTCxePm9C0KISt/FUFkdf39/zJs3T7mck5MDMzOzKsVNRERERPSmq1GSYGBgIFlWU1NDu3bt8Pnnn0um/lQkOTkZWVlZsLOzU5YVFRXhv//9LzZs2IDz588DeD5aYGJioqyTlZWlHF0wNjZGfn4+Hjx4IBlNyMrKgoODQ7n71tbWhra2dpXiJCIiInqT1eeUrYby1muqXI3uSYiMjJR8tmzZgn//+99VThAAYPDgwThz5gxSU1OVn549e2LChAlITU2FhYUFjI2NERsbq9wmPz8fhw8fViYAdnZ20NTUlNRRKBRIS0urMEkgIiIiotdDZmYmZs2aBQsLC2hra8PMzAwuLi44ePBglduIioqCoaFh3QWpIgMGDIBMJpN8/va3v9VK2690T0JycjLS09Mhk8nQsWNHdO/evcrbNm7cGJ07d5aUyeVyNGvWTFnu4+ODwMBAWFtbw9raGoGBgdDT08P48eMBPB/RmDJlCnx9fdGsWTM0bdoUfn5+sLW1LXUjdEO0JvZCmWVzHW1UEA0RERFRw3L16lX06dMHhoaGWLlyJbp06YKCggL88ssvmDFjBs6dO6fqEGukoKAAmpqatdLW1KlT8fnnnyuXdXV1a6XdGo0kZGVlYdCgQXj33Xcxe/ZszJw5E3Z2dhg8eDDu3LlTK4EBwIIFC+Dj4wNvb2/07NkTt27dwq+//orGjRsr66xZswajR4+Gm5sb+vTpAz09Pezbtw/q6uq1FgcRERER1T9vb2/IZDIkJSVh7NixsLGxQadOnTBv3jwcO3ZMWS8kJAS2traQy+UwMzODt7e38rH8hw4dgoeHB7Kzs5XftgcEBAB4PktlwYIFaNWqFeRyOXr37o1Dhw5JYti8eTPMzMygp6eHMWPGICQkpNSoRHh4OCwtLaGlpYV27dph+/btkvUymQwREREYNWoU5HI5li1bBisrKwQHB0vqpaWlQU1NDZcvX65yH+np6cHY2Fj5efm2gJqqUZIwa9Ys5OTk4M8//8T9+/fx4MEDpKWlIScnB7Nnz65xMIcOHcLatWuVyyW/RIVCgWfPnuHw4cOlRh90dHQQGhqKe/fu4cmTJ9i3bx9vQiYiIiJ6zd2/fx8HDhzAjBkzIJfLS61/8UJdTU0N69evR1paGrZt24a4uDgsWLAAAODg4IC1a9dCX18fCoUCCoUCfn5+AAAPDw8kJCRgz549OH36NFxdXTFs2DBcvHgRAJCQkIDp06djzpw5SE1NhaOjI5YvXy6JIzo6GnPmzIGvry/S0tIwbdo0eHh4ID4+XlJvyZIlGDVqFM6cOQNPT094enoiMjJSUmfr1q3o27cvLC0t4e7ujgEDBlTaTzt37kTz5s3RqVMn+Pn5SV46/CpqNN3owIED+O2339ChQwdlWceOHbFx48Zq3ZdARERERFSWS5cuQQiB9u3bV1rXx8dH+bO5uTm++OILfPLJJwgLC4OWlhYMDAwgk8lgbGysrHf58mXs3r0bN2/ehKmpKQDAz88PBw4cQGRkJAIDAxEaGorhw4crkwobGxskJibip59+UrYTHBwMd3d3eHt7A4BylCM4OBgDBw5U1hs/fjw8PT2Vyx4eHli8eDGSkpLQq1cvFBQUYMeOHVi1ahUAwMTEBMXFxRUe94QJE2Bubg5jY2OkpaXB398fp06dktyvW1M1ShKKi4vLnEelqalZ6cEQEREREVVGCAGg9OPwyxIfH4/AwECcPXsWOTk5KCwsxLNnz/D48eMyRyEAICUlBUII2NhI7wXNy8tDs2bNAADnz5/HmDFjJOt79eolSRLS09Pxj3/8Q1KnT58+WLdunaSsZ8+ekmUTExOMGDECW7duVbb57NkzuLq6Anj+Xq/KTJ06Vflz586dYW1tjZ49eyIlJQU9evSodPuK1Gi60aBBgzBnzhzcvn1bWXbr1i3MnTsXgwcPfqWAiIiIiIisra0hk8mQnp5eYb1r167B2dkZnTt3xvfff4/k5GRs3LgRwPMbhMtTXFwMdXV1JCcnS562mZ6errzAL+vdWyXJy4uq8l6vspIVLy8v7NmzB0+fPkVkZCTGjRsHPT29Co+3Ij169ICmpqZyutSrqFGSsGHDBjx69Aht27aFpaUlrKysYG5ujkePHiE0NPSVgyIiIiKit1vTpk3h5OSEjRs34vHjx6XWP3z4EABw4sQJFBYWYvXq1XjvvfdgY2Mj+SIbALS0tFBUVCQp6969O4qKipCVlQUrKyvJp2RaUvv27ZGUlCTZ7sSJE5LlDh064OjRo5KyxMREybT88jg7O0MulyM8PBwxMTGS6Ug18eeff6KgoEDyjrGaqtF0IzMzM6SkpCA2Nhbnzp2DEAIdO3Z8LR47SkRERESvh7CwMDg4OKBXr174/PPP0aVLFxQWFiI2Nhbh4eFIT0+HpaUlCgsLERoaChcXFyQkJCAiIkLSTtu2bZGbm4uDBw+ia9eu0NPTg42NDSZMmIBJkyZh9erV6N69O+7evYu4uDjY2trC2dkZs2bNQr9+/RASEgIXFxfExcUhJiZGMkowf/58uLm5oUePHhg8eDD27duHvXv34rfffqv0+NTV1eHu7g5/f39YWVnB3t5euc7f3x+3bt3CV199Vea2ly9fxs6dO+Hs7IzmzZvj7Nmz8PX1Rffu3dGnT58a9vj/VCtJiIuLw8yZM3Hs2DHo6+vD0dERjo6OAIDs7Gx06tQJERER6Nu37ysHRkRERER1ryG/Bdnc3BwpKSlYvnw5fH19oVAo0KJFC9jZ2SE8PBwA0K1bN4SEhGDFihXw9/dHv379EBQUhEmTJinbcXBwwPTp0zFu3Djcu3cPS5YsQUBAACIjI7Fs2TL4+vri1q1baNasGezt7eHs7Azg+b0FERERWLp0KT777DM4OTlh7ty52LBhg7Lt0aNHY926dVi1ahVmz54Nc3NzREZGVunJRAAwZcoUBAYGlhpFUCgUuH79ernbaWlp4eDBg1i3bh1yc3NhZmaGESNGYMmSJbXyKgCZKGtiVTk++OADDBw4EHPnzi1z/fr16xEfH4/o6OhXDqw+5eTkwMDAANnZ2dDX16+3/Zb1MjUAqn+ZWnwFN8oM9K+/OGpBWGpYtep7d/Ouo0jodXIndEPllSrQYtbMWoqEqiJp35VXbqOXi0UtREL1LfHbndWq31AuhlVx3fHs2TNkZGTA3NwcOjo69bLPN9XUqVNx7tw5HDlypFbaS0hIwIABA3Dz5k0YGRnVSpsVqeq5UK17Ek6dOoVhw4aVu37o0KFITk6uTpNERERERA1WcHAwTp06hUuXLiE0NBTbtm3D5MmTX7ndvLw8XLp0CYsWLYKbm1u9JAjVUa3pRn/99VeFr5DW0NCo1TcuUwNUMsrwmo0oEBERvTjy0FBGFajhS0pKwsqVK/Ho0SNYWFhg/fr18PLyeuV2d+/ejSlTpqBbt26l3tDcEFQrSWjVqhXOnDkDKyurMtefPn26Vu6mJiIiIiJqCL755ps6adfd3R3u7u510nZtqFaS4OzsjMWLF2P48OGl5jA9ffoUS5YswciRI2s1QCKi+vCq9yEQERG9SaqVJHz22WfYu3cvbGxsMHPmTLRr1075kouNGzeiqKgIn376aV3FSkRERESvoBrPq6E3VFXPgWolCUZGRkhMTMQnn3wCf39/yeuynZycEBYW1uBuuiAiIqrMi09I4pOO6E1Uck/pkydPoKurq+JoSJWePHkCABXeZwzU4GVqbdq0wf79+/HgwQNcunQJQghYW1ujSZMmNYuUiIiIiOqUuro6DA0NkZWVBQDQ09OTvBCM3nxCCDx58gRZWVkwNDSs9F0KNXrjMgA0adIE7777bk03JyIiIqJ6ZGxsDADKRIHeToaGhspzoSI1ThKIiIiI6PUhk8lgYmKCli1boqCgQNXhkApoampW+W3MTBKIiIiI3iLq6upVvlCkt1e13rhMRERERERvPiYJREREREQkwSSBiIiIiIgkmCQQvSAsNQxhqWGqDoOIiIhIpZgkEBERERGRBJMEIiIiIiKSYJJAREREREQSTBKIiIiIiEiCSQIREREREUkwSSAiIiIiIgkmCUREREREJKGh6gCIiIiqK2nfFVWHQET0RuNIAhERERERSTBJICIiIiIiCSYJREREREQkwSSBiIiIiIgkeOMy/U98kKojICIiIqIGgEkCEVEtuxO6Qflzi1kzVRgJERFRzXC6ERERERERSXAkgd5IYalhqg6BiIiI6LXFkQQiIiIiIpJgkkBERERERBJMEoiIiIiISIJJAhERERERSTBJICIiIiIiCSYJREREREQkwSSBiIiIiIgkmCQQEREREZEEkwQiIiIiIpJQaZIQFBSEd999F40bN0bLli0xevRonD9/XlJHCIGAgACYmppCV1cXAwYMwJ9//impk5eXh1mzZqF58+aQy+X44IMPcPPmzfo8FCIiIiKiN4ZKk4TDhw9jxowZOHbsGGJjY1FYWIihQ4fi8ePHyjorV65ESEgINmzYgOPHj8PY2BiOjo549OiRso6Pjw+io6OxZ88eHD16FLm5uRg5ciSKiopUcVhERERERK81DVXu/MCBA5LlyMhItGzZEsnJyejXrx+EEFi7di0+/fRTfPjhhwCAbdu2wcjICLt27cK0adOQnZ2NLVu2YPv27RgyZAgAYMeOHTAzM8Nvv/0GJyenej8uIiIiqhuJ3+5UdQhEb4UGdU9CdnY2AKBp06YAgIyMDGRmZmLo0KHKOtra2ujfvz8SExMBAMnJySgoKJDUMTU1RefOnZV1XpaXl4ecnBzJh4iIiIiInmswSYIQAvPmzcP777+Pzp07AwAyMzMBAEZGRpK6RkZGynWZmZnQ0tJCkyZNyq3zsqCgIBgYGCg/ZmZmtX04r2RN7AVVh0BEREREb7EGkyTMnDkTp0+fxu7du0utk8lkkmUhRKmyl1VUx9/fH9nZ2crPjRs3ah44EREREdEbpkEkCbNmzcKPP/6I+Ph4tG7dWllubGwMAKVGBLKyspSjC8bGxsjPz8eDBw/KrfMybW1t6OvrSz5ERERERPScSpMEIQRmzpyJvXv3Ii4uDubm5pL15ubmMDY2RmxsrLIsPz8fhw8fhoODAwDAzs4OmpqakjoKhQJpaWnKOkREREREVHUqfbrRjBkzsGvXLvznP/9B48aNlSMGBgYG0NXVhUwmg4+PDwIDA2FtbQ1ra2sEBgZCT08P48ePV9adMmUKfH190axZMzRt2hR+fn6wtbVVPu2I6kB80PP/DvRXbRxEREREVOtUmiSEh4cDAAYMGCApj4yMhLu7OwBgwYIFePr0Kby9vfHgwQP07t0bv/76Kxo3bqysv2bNGmhoaMDNzQ1Pnz7F4MGDERUVBXV19fo6FHrDhKWGKX/27uatwkiIiIiI6p9KkwQhRKV1ZDIZAgICEBAQUG4dHR0dhIaGIjQ0tBajIyIiIiJ6OzWIG5eJiIiIiKjhYJKgInwXAhERERE1VEwSiIiIiIhIgkkCERERERFJMEkgIiIiIiIJlT7diIhIle6EblB1CERERA0SRxKIiIiIiEiCIwlERET01kn8dqfyZwfXCSqMhKhh4kgCERERERFJMEkgIiIiIiIJTjciIiJ6QdK+K8qfe7lYqDASIiLV4UgCERERERFJMEkgIiIiIiIJJglERERERCTBexKIiOrQyy9sazFrpooief29eK8AERHVLY4kEBERERGRBJMEIiIiIiKSYJJAREREREQSTBKIiIiIiEiCNy7TGyUsNUzVIRARERG99jiSQEREREREEkwSiIiIiIhIgtONCIgPUnUERERERNSAcCSBXg0TDCIiIqI3DpMEIiIiIiKS4HQjIiIiavASv92p6hCI3iocSSAiIiIiIgkmCUSVCEsN4/sXiIiI6K3CJKGBWhN7QdUhEBEREdFbikkCERERERFJMElQAY4SEBERNRyJ3+7kjdFEL2GSQEREREREEkwSiIiIiIhIgu9JIKK3yp3QDQ1m/y1mzVRhJEREROXjSAIREREREUkwSSAiIiIiIgkmCW+7+CBVR0BEREREDQzvSSAiIipH0r4rkuVeLhYqioSIqH4xSSAiogbr5Yt0IiKqH5xuREREREREEhxJoDdCWGqYqkMgIiIiemNwJIFeXXwQb4AmIiIieoNwJKEBWxN7AXMdbVQdBhERkUokfrtT1SEQvbWYJBDRG03Vb1gmIiJ6HXG6ERERERERSXAkoZ6tib2g6hCohkpujvbu5q3iSIhIVV58JCvfmUBEbzImCfTa4hON6HX34lSoFrNmqjCShoPvRSBVevEeCAfXCSqMhEj13pjpRmFhYTA3N4eOjg7s7Oxw5MgRVYdUK+ps5IFPJCIiogYo8dudyg8Rqc4bMZLw9ddfw8fHB2FhYejTpw++/PJLDB8+HGfPnsU777yj6vDeHvFBwEB/VUdBxJuViYiIXtEbMZIQEhKCKVOmwMvLCx06dMDatWthZmaG8PBwVYdGb6Cw1DBOdSKqRUn7rig/RETUMLz2Iwn5+flITk7GwoULJeVDhw5FYmJimdvk5eUhLy9PuZydnQ0AyMnJqbtAAWyMu1Sj7YJ+SMGMQVa1F8h/V9deWy+r4z4EgM2nN9f5Pqqirs8Xqp67EV+qOoRXov2WnU8nYjJUHcIr49+AV/dH9NeqDqFcdfn7LWlbCFFn+yB6Va99knD37l0UFRXByMhIUm5kZITMzMwytwkKCsLSpUtLlZuZmdVJjLXhX6oOoMo+V3UA9cYPfqoOgd4k/1yg6giI6EXu/6jzXTx69AgGBgZ1vh+imnjtk4QSMplMsiyEKFVWwt/fH/PmzVMuFxcX4/79+2jWrFm527xucnJyYGZmhhs3bkBfX1/V4agM++E59sNz7Af2QQn2w3Psh+fqux+EEHj06BFMTU3rfF9ENfXaJwnNmzeHurp6qVGDrKysUqMLJbS1taGtrS0pMzQ0rKsQVUpfX/+t/sNfgv3wHPvhOfYD+6AE++E59sNz9dkPHEGghu61v3FZS0sLdnZ2iI2NlZTHxsbCwcFBRVEREREREb2+XvuRBACYN28ePv74Y/Ts2RP29vbYtGkTrl+/junTp6s6NCIiIiKi184bkSSMGzcO9+7dw+effw6FQoHOnTtj//79aNOmjapDUxltbW0sWbKk1LSqtw374Tn2w3PsB/ZBCfbDc+yH59gPRKXJBJ+/RUREREREL3jt70kgIiIiIqLaxSSBiIiIiIgkmCQQEREREZEEkwQiIiIiIpJgkvAaCwsLg7m5OXR0dGBnZ4cjR46UW1ehUGD8+PFo164d1NTU4OPjU3+B1rHq9MPevXvh6OiIFi1aQF9fH/b29vjll1/qMdq6U51+OHr0KPr06YNmzZpBV1cX7du3x5o1a+ox2rpRnT54UUJCAjQ0NNCtW7e6DbCeVKcfDh06BJlMVupz7ty5eoy4blT3fMjLy8Onn36KNm3aQFtbG5aWlti6dWs9RVt3qtMP7u7uZZ4PnTp1qseIa191z4WdO3eia9eu0NPTg4mJCTw8PHDv3r16ipaogRD0WtqzZ4/Q1NQUmzdvFmfPnhVz5swRcrlcXLt2rcz6GRkZYvbs2WLbtm2iW7duYs6cOfUbcB2pbj/MmTNHrFixQiQlJYkLFy4If39/oampKVJSUuo58tpV3X5ISUkRu3btEmlpaSIjI0Ns375d6OnpiS+//LKeI6891e2DEg8fPhQWFhZi6NChomvXrvUTbB2qbj/Ex8cLAOL8+fNCoVAoP4WFhfUcee2qyfnwwQcfiN69e4vY2FiRkZEh/vjjD5GQkFCPUde+6vbDw4cPJefBjRs3RNOmTcWSJUvqN/BaVN0+OHLkiFBTUxPr1q0TV65cEUeOHBGdOnUSo0ePrufIiVSLScJrqlevXmL69OmSsvbt24uFCxdWum3//v3fmCThVfqhRMeOHcXSpUtrO7R6VRv9MGbMGDFx4sTaDq3e1LQPxo0bJz777DOxZMmSNyJJqG4/lCQJDx48qIfo6k91+yEmJkYYGBiIe/fu1Ud49eZV/zZER0cLmUwmrl69Whfh1Yvq9sGqVauEhYWFpGz9+vWidevWdRYjUUPE6Uavofz8fCQnJ2Po0KGS8qFDhyIxMVFFUdW/2uiH4uJiPHr0CE2bNq2LEOtFbfTDyZMnkZiYiP79+9dFiHWupn0QGRmJy5cvY8mSJXUdYr14lXOhe/fuMDExweDBgxEfH1+XYda5mvTDjz/+iJ49e2LlypVo1aoVbGxs4Ofnh6dPn9ZHyHWiNv42bNmyBUOGDHltX05akz5wcHDAzZs3sX//fggh8Ndff+G7777DiBEj6iNkogbjjXjj8tvm7t27KCoqgpGRkaTcyMgImZmZKoqq/tVGP6xevRqPHz+Gm5tbXYRYL16lH1q3bo07d+6gsLAQAQEB8PLyqstQ60xN+uDixYtYuHAhjhw5Ag2NN+NPYU36wcTEBJs2bYKdnR3y8vKwfft2DB48GIcOHUK/fv3qI+xaV5N+uHLlCo4ePQodHR1ER0fj7t278Pb2xv3791/b+xJe9W+kQqFATEwMdu3aVVch1rma9IGDgwN27tyJcePG4dmzZygsLMQHH3yA0NDQ+giZqMF4M/7P+JaSyWSSZSFEqbK3QU37Yffu3QgICMB//vMftGzZsq7Cqzc16YcjR44gNzcXx44dw8KFC2FlZYW///3vdRlmnapqHxQVFWH8+PFYunQpbGxs6iu8elOdc6Fdu3Zo166dctne3h43btxAcHDwa5sklKhOPxQXF0Mmk2Hnzp0wMDAAAISEhGDs2LHYuHEjdHV16zzeulLTv5FRUVEwNDTE6NGj6yiy+lOdPjh79ixmz56NxYsXw8nJCQqFAvPnz8f06dOxZcuW+giXqEFgkvAaat68OdTV1Ut9C5KVlVXq25I32av0w9dff40pU6bg22+/xZAhQ+oyzDr3Kv1gbm4OALC1tcVff/2FgICA1zJJqG4fPHr0CCdOnMDJkycxc+ZMAM8vEoUQ0NDQwK+//opBgwbVS+y1qbb+Nrz33nvYsWNHbYdXb2rSDyYmJmjVqpUyQQCADh06QAiBmzdvwtrauk5jrguvcj4IIbB161Z8/PHH0NLSqssw61RN+iAoKAh9+vTB/PnzAQBdunSBXC5H3759sWzZMpiYmNR53EQNAe9JeA1paWnBzs4OsbGxkvLY2Fg4ODioKKr6V9N+2L17N9zd3bFr1643Yo5pbZ0PQgjk5eXVdnj1orp9oK+vjzNnziA1NVX5mT59Otq1a4fU1FT07t27vkKvVbV1Lpw8efK1vhCqST/06dMHt2/fRm5urrLswoULUFNTQ+vWres03rryKufD4cOHcenSJUyZMqUuQ6xzNemDJ0+eQE1Nenmkrq4O4PnfSaK3hirulqZXV/JIty1btoizZ88KHx8fIZfLlU+gWLhwofj4448l25w8eVKcPHlS2NnZifHjx4uTJ0+KP//8UxXh15rq9sOuXbuEhoaG2Lhxo+Qxfw8fPlTVIdSK6vbDhg0bxI8//iguXLggLly4ILZu3Sr09fXFp59+qqpDeGU1+Tfxojfl6UbV7Yc1a9aI6OhoceHCBZGWliYWLlwoAIjvv/9eVYdQK6rbD48ePRKtW7cWY8eOFX/++ac4fPiwsLa2Fl5eXqo6hFpR038XEydOFL17967vcOtEdfsgMjJSaGhoiLCwMHH58mVx9OhR0bNnT9GrVy9VHQKRSjBJeI1t3LhRtGnTRmhpaYkePXqIw4cPK9dNnjxZ9O/fX1IfQKlPmzZt6jfoOlCdfujfv3+Z/TB58uT6D7yWVacf1q9fLzp16iT09PSEvr6+6N69uwgLCxNFRUUqiLz2VPffxIvelCRBiOr1w4oVK4SlpaXQ0dERTZo0Ee+//774+eefVRB17avu+ZCeni6GDBkidHV1RevWrcW8efPEkydP6jnq2lfdfnj48KHQ1dUVmzZtqudI6051+2D9+vWiY8eOQldXV5iYmIgJEyaImzdv1nPURKolE4JjZ0RERERE9D+8J4GIiIiIiCSYJBARERERkQSTBCIiIiIikmCSQEREREREEkwSiIiIiIhIgkkCERERERFJMEkgIiIiIiIJJglERERERCTBJIGICEBAQAC6dev2yu3IZDL88MMP5a6/evUqZDIZUlNTAQCHDh2CTCbDw4cPAQBRUVEwNDR85TiIiIheBZMEInrtuLu7QyaTQSaTQVNTExYWFvDz88Pjx49VHVqlzMzMoFAo0Llz5zLXjxs3DhcuXFAu11byQkREVB0aqg6AiKgmhg0bhsjISBQUFODIkSPw8vLC48ePER4eLqlXUFAATU1NFUVZmrq6OoyNjctdr6urC11d3XqMiIiIqDSOJBDRa0lbWxvGxsYwMzPD+PHjMWHCBPzwww/Kb963bt0KCwsLaGtrQwiB69evY9SoUWjUqBH09fXh5uaGv/76q1S7X375JczMzKCnpwdXV1flNCAAOH78OBwdHdG8eXMYGBigf//+SElJKdWGQqHA8OHDoaurC3Nzc3z77bfKdS9PN3rZi9ONoqKisHTpUpw6dUo5chIVFQVPT0+MHDlSsl1hYSGMjY2xdevW6ncmERHRS5gkENEbQVdXFwUFBQCAS5cu4ZtvvsH333+vvBgfPXo07t+/j8OHDyM2NhaXL1/GuHHjJG2UbLdv3z4cOHAAqampmDFjhnL9o0ePMHnyZBw5cgTHjh2DtbU1nJ2d8ejRI0k7ixYtwkcffYRTp05h4sSJ+Pvf/4709PRqH9O4cePg6+uLTp06QaFQQKFQYNy4cfDy8sKBAwegUCiUdffv34/c3Fy4ublVez9EREQv43QjInrtJSUlYdeuXRg8eDAAID8/H9u3b0eLFi0AALGxsTh9+jQyMjJgZmYGANi+fTs6deqE48eP49133wUAPHv2DNu2bUPr1q0BAKGhoRgxYgRWr14NY2NjDBo0SLLfL7/8Ek2aNMHhw4cl3+y7urrCy8sLAPDFF18gNjYWoaGhCAsLq9Zx6erqolGjRtDQ0JBMUXJwcEC7du2wfft2LFiwAAAQGRkJV1dXNGrUqFr7ICIiKgtHEojotfTTTz+hUaNG0NHRgb29Pfr164fQ0FAAQJs2bZQJAgCkp6fDzMxMmSAAQMeOHWFoaCj5hv+dd95RJggAYG9vj+LiYpw/fx4AkJWVhenTp8PGxgYGBgYwMDBAbm4url+/LonN3t6+1HJNRhIq4uXlhcjISGVcP//8Mzw9PWt1H0RE9PbiSAIRvZYGDhyI8PBwaGpqwtTUVHJzslwul9QVQkAmk5Vqo7zyEiXrSv7r7u6OO3fuYO3atWjTpg20tbVhb2+P/Pz8SuOtaD81MWnSJCxcuBC///47fv/9d7Rt2xZ9+/at1X0QEdHbiyMJRPRaksvlsLKyQps2bSp9elHHjh1x/fp13LhxQ1l29uxZZGdno0OHDsqy69ev4/bt28rl33//HWpqarCxsQEAHDlyBLNnz4azszM6deoEbW1t3L17t9T+jh07Vmq5ffv2NTpOLS0tFBUVlSpv1qwZRo8ejcjISERGRsLDw6NG7RMREZWFIwlE9MYbMmQIunTpggkTJmDt2rUoLCyEt7c3+vfvj549eyrr6ejoYPLkyQgODkZOTg5mz54NNzc35f0AVlZW2L59O3r27ImcnBzMnz+/zMeVfvvtt+jZsyfef/997Ny5E0lJSdiyZUuNYm/bti0yMjKQmpqK1q1bo3HjxtDW1gbwfMrRyJEjUVRUhMmTJ9eofSIiorJwJIGI3nglb0Fu0qQJ+vXrhyFDhsDCwgJff/21pJ6VlRU+/PBDODs7Y+jQoejcubPkZuOtW7fiwYMH6N69Oz7++GPMnj0bLVu2LLW/pUuXYs+ePejSpQu2bduGnTt3omPHjjWK/aOPPsKwYcMwcOBAtGjRArt371auGzJkCExMTODk5ARTU9MatU9ERFQWmRBCqDoIIiKqvidPnsDU1BRbt27Fhx9+qOpwiIjoDcLpRkREr5ni4mJkZmZi9erVMDAwwAcffKDqkIiI6A3DJIGI6DVz/fp1mJubo3Xr1oiKioKGBv+UExFR7eJ0IyIiIiIikuCNy0REREREJMEkgYiIiIiIJJgkEBERERGRBJMEIiIiIiKSYJJAREREREQSTBKIiIiIiEiCSQIREREREUkwSSAiIiIiIon/B4XJ+F+e0WHPAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -766,12 +807,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -795,20 +836,6 @@
     "Looking at the observed and posterior predictive mean, the model captures the observed frequencies of the categories well. "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# does not work because predictions are a vector of probabilities\n",
-    "bmb.interpret.predictions(\n",
-    "    model=model,\n",
-    "    idata=idata,\n",
-    "    covariates=\"action\"\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -834,11 +861,11 @@
     "\n",
     "For some ordinal variables, the assumption of a **single** underlying continuous variable (as in cumulative models) may not be appropriate. If the response can be understood as being the result of a sequential process, such that a higher response category is possible only after all lower categories are achieved, then a sequential model may be more appropriate than a cumulative model.\n",
     "\n",
-    "Sequential models assume that for **every** category $k$ there is latent continuous variable $\\hat{Y_k}$ that determines the transition between categories $k$ and $k+1$. Now, a threshold $\\tau$ belongs to each latent process. If there are 3 categories, then there are 3 latent processes. If $\\hat{Y}_k$ is greater than the threshold $\\tau_k$, the sequential process continues, otherwise it stops at category $k$. As with the cumulative model, we still assume a distribution for $\\hat{Y}_k$ with a cumulative distribution function $F$.\n",
+    "Sequential models assume that for **every** category $k$ there is a latent continuous variable $Z$ that determines the transition between categories $k$ and $k+1$. Now, a threshold $\\tau$ belongs to each latent process. If there are 3 categories, then there are 3 latent processes. If $Z_k$ is greater than the threshold $\\tau_k$, the sequential process continues, otherwise it stops at category $k$. As with the cumulative model, we still assume a distribution for $Z_k$ with a cumulative distribution function $F$.\n",
     "\n",
-    "As an example, lets suppose we are interested in modeling the probability a boxer makes it to round 3. This implies that the particular boxer in question survived round 1 $\\hat{Y}_1 > \\tau_1$ , 2 $\\hat{Y}_2 > \\tau_2$, and 3 $\\hat{Y}_3 > \\tau_3$. This can be written as \n",
+    "As an example, lets suppose we are interested in modeling the probability a boxer makes it to round 3. This implies that the particular boxer in question survived round 1 $Z_1 > \\tau_1$ , 2 $Z_2 > \\tau_2$, and 3 $Z_3 > \\tau_3$. This can be written as \n",
     "\n",
-    "$$Pr(Y = 3) = (1 - Pr(\\hat{Y_1})(1 - Pr(\\hat{Y_2}))(1 - Pr(\\hat{Y_3})$$\n",
+    "$$Pr(Y = 3) = (1 - Pr(Z_1))(1 - Pr(Z_2))(1 - Pr(Z_3))$$\n",
     "\n",
     "As in the cumulative model above, if we assume $Y$ to be normally distributed with the thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$ and cumulative distribution function $\\Phi$ then\n",
     "\n",
@@ -862,27 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "attrition = pd.read_csv(\"data/hr_employee_attrition.tsv.txt\", sep=\"\\t\")\n",
-    "attrition = attrition[attrition[\"Attrition\"] == \"No\"]\n",
-    "attrition[\"YearsAtCompany\"] = pd.Categorical(attrition[\"YearsAtCompany\"], ordered=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.scatter(attrition.TotalWorkingYears, attrition.YearsAtCompany, alpha=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -908,130 +915,75 @@
        "      <th></th>\n",
        "      <th>YearsAtCompany</th>\n",
        "      <th>Age</th>\n",
-       "      <th>TotalWorkingYears</th>\n",
-       "      <th>YearsInCurrentRole</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>6</td>\n",
-       "      <td>41</td>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>10</td>\n",
        "      <td>49</td>\n",
-       "      <td>10</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>37</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>8</td>\n",
        "      <td>33</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>2</td>\n",
        "      <td>27</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1465</th>\n",
-       "      <td>5</td>\n",
-       "      <td>36</td>\n",
-       "      <td>17</td>\n",
-       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1466</th>\n",
-       "      <td>7</td>\n",
-       "      <td>39</td>\n",
-       "      <td>9</td>\n",
+       "      <th>5</th>\n",
        "      <td>7</td>\n",
+       "      <td>32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1467</th>\n",
-       "      <td>6</td>\n",
-       "      <td>27</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1468</th>\n",
-       "      <td>9</td>\n",
-       "      <td>49</td>\n",
-       "      <td>17</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1469</th>\n",
-       "      <td>4</td>\n",
-       "      <td>34</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>59</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1470 rows × 4 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      YearsAtCompany  Age TotalWorkingYears  YearsInCurrentRole\n",
-       "0                  6   41                 8                   4\n",
-       "1                 10   49                10                   7\n",
-       "2                  0   37                 7                   0\n",
-       "3                  8   33                 8                   7\n",
-       "4                  2   27                 6                   2\n",
-       "...              ...  ...               ...                 ...\n",
-       "1465               5   36                17                   2\n",
-       "1466               7   39                 9                   7\n",
-       "1467               6   27                 6                   2\n",
-       "1468               9   49                17                   6\n",
-       "1469               4   34                 6                   3\n",
-       "\n",
-       "[1470 rows x 4 columns]"
+       "  YearsAtCompany  Age\n",
+       "1             10   49\n",
+       "3              8   33\n",
+       "4              2   27\n",
+       "5              7   32\n",
+       "6              1   59"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "attrition"
+    "attrition = pd.read_csv(\"data/hr_employee_attrition.tsv.txt\", sep=\"\\t\")\n",
+    "attrition = attrition[attrition[\"Attrition\"] == \"No\"]\n",
+    "attrition[\"YearsAtCompany\"] = pd.Categorical(attrition[\"YearsAtCompany\"], ordered=True)\n",
+    "attrition[[\"YearsAtCompany\", \"Age\"]].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fitting a sequential model is similar to fitting a cumulative model. The only difference is that we pass `family=\"sratio\"` to the `bambi.Model` constructor. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
-      "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
@@ -1071,7 +1023,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 03:57&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 03:24&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1086,18 +1038,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 238 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 205 seconds.\n"
      ]
     }
    ],
    "source": [
-    "sequence_model = bmb.Model(\"YearsAtCompany ~ TotalWorkingYears\", data=attrition, family=\"sratio\")\n",
-    "sequence_idata = sequence_model.fit()"
+    "sequence_model = bmb.Model(\"YearsAtCompany ~ 0 + TotalWorkingYears\", data=attrition, family=\"sratio\")\n",
+    "sequence_idata = sequence_model.fit(random_seed=1234)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1134,576 +1086,520 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[0]</th>\n",
-       "      <td>-2.086</td>\n",
-       "      <td>0.304</td>\n",
-       "      <td>-2.669</td>\n",
-       "      <td>-1.541</td>\n",
-       "      <td>0.005</td>\n",
+       "      <th>YearsAtCompany_threshold[0]</th>\n",
+       "      <td>-2.571</td>\n",
+       "      <td>0.197</td>\n",
+       "      <td>-2.930</td>\n",
+       "      <td>-2.195</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>4099.0</td>\n",
-       "      <td>2853.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>5509.0</td>\n",
+       "      <td>2243.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[1]</th>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.149</td>\n",
-       "      <td>-0.254</td>\n",
-       "      <td>0.304</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>1677.0</td>\n",
-       "      <td>2547.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[1]</th>\n",
+       "      <td>-1.054</td>\n",
+       "      <td>0.109</td>\n",
+       "      <td>-1.248</td>\n",
+       "      <td>-0.838</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>3092.0</td>\n",
+       "      <td>2725.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[2]</th>\n",
-       "      <td>-0.903</td>\n",
-       "      <td>0.201</td>\n",
-       "      <td>-1.275</td>\n",
-       "      <td>-0.523</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>2099.0</td>\n",
-       "      <td>2739.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[2]</th>\n",
+       "      <td>-1.018</td>\n",
+       "      <td>0.116</td>\n",
+       "      <td>-1.252</td>\n",
+       "      <td>-0.812</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>3545.0</td>\n",
+       "      <td>2514.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[3]</th>\n",
-       "      <td>-0.529</td>\n",
-       "      <td>0.187</td>\n",
-       "      <td>-0.894</td>\n",
-       "      <td>-0.194</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>1870.0</td>\n",
-       "      <td>1959.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[3]</th>\n",
+       "      <td>-0.757</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>-0.970</td>\n",
+       "      <td>-0.538</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>3458.0</td>\n",
+       "      <td>2916.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[4]</th>\n",
-       "      <td>-0.038</td>\n",
-       "      <td>0.160</td>\n",
-       "      <td>-0.340</td>\n",
-       "      <td>0.252</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>1735.0</td>\n",
-       "      <td>2493.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[4]</th>\n",
+       "      <td>-0.754</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>-0.969</td>\n",
+       "      <td>-0.520</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2900.0</td>\n",
+       "      <td>2993.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[5]</th>\n",
-       "      <td>0.419</td>\n",
-       "      <td>0.151</td>\n",
-       "      <td>0.131</td>\n",
-       "      <td>0.699</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>1455.0</td>\n",
-       "      <td>2298.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[5]</th>\n",
+       "      <td>0.263</td>\n",
+       "      <td>0.105</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>0.442</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2262.0</td>\n",
+       "      <td>2599.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[6]</th>\n",
-       "      <td>0.950</td>\n",
-       "      <td>0.142</td>\n",
-       "      <td>0.671</td>\n",
-       "      <td>1.205</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>1250.0</td>\n",
-       "      <td>2195.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[6]</th>\n",
+       "      <td>-0.501</td>\n",
+       "      <td>0.143</td>\n",
+       "      <td>-0.772</td>\n",
+       "      <td>-0.238</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>3734.0</td>\n",
+       "      <td>2865.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[7]</th>\n",
-       "      <td>0.663</td>\n",
-       "      <td>0.159</td>\n",
-       "      <td>0.363</td>\n",
-       "      <td>0.955</td>\n",
-       "      <td>0.004</td>\n",
+       "      <th>YearsAtCompany_threshold[7]</th>\n",
+       "      <td>-0.079</td>\n",
+       "      <td>0.140</td>\n",
+       "      <td>-0.336</td>\n",
+       "      <td>0.189</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>1386.0</td>\n",
-       "      <td>2334.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>3139.0</td>\n",
+       "      <td>2944.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[8]</th>\n",
-       "      <td>1.062</td>\n",
-       "      <td>0.151</td>\n",
-       "      <td>0.764</td>\n",
-       "      <td>1.336</td>\n",
-       "      <td>0.004</td>\n",
+       "      <th>YearsAtCompany_threshold[8]</th>\n",
+       "      <td>0.040</td>\n",
+       "      <td>0.150</td>\n",
+       "      <td>-0.226</td>\n",
+       "      <td>0.333</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>1259.0</td>\n",
-       "      <td>2106.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2926.0</td>\n",
+       "      <td>2874.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[9]</th>\n",
-       "      <td>1.156</td>\n",
-       "      <td>0.157</td>\n",
-       "      <td>0.873</td>\n",
-       "      <td>1.451</td>\n",
-       "      <td>0.004</td>\n",
+       "      <th>YearsAtCompany_threshold[9]</th>\n",
+       "      <td>0.401</td>\n",
+       "      <td>0.150</td>\n",
+       "      <td>0.132</td>\n",
+       "      <td>0.692</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>1314.0</td>\n",
-       "      <td>2440.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2979.0</td>\n",
+       "      <td>2550.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[10]</th>\n",
-       "      <td>2.325</td>\n",
-       "      <td>0.143</td>\n",
-       "      <td>2.063</td>\n",
-       "      <td>2.591</td>\n",
-       "      <td>0.004</td>\n",
+       "      <th>YearsAtCompany_threshold[10]</th>\n",
+       "      <td>1.290</td>\n",
+       "      <td>0.150</td>\n",
+       "      <td>0.997</td>\n",
+       "      <td>1.556</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>1109.0</td>\n",
-       "      <td>2126.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2092.0</td>\n",
+       "      <td>2430.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[11]</th>\n",
-       "      <td>0.730</td>\n",
-       "      <td>0.207</td>\n",
-       "      <td>0.351</td>\n",
-       "      <td>1.127</td>\n",
-       "      <td>0.005</td>\n",
+       "      <th>YearsAtCompany_threshold[11]</th>\n",
+       "      <td>0.451</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>0.857</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>1984.0</td>\n",
-       "      <td>2533.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>2714.0</td>\n",
+       "      <td>2847.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[12]</th>\n",
-       "      <td>1.184</td>\n",
-       "      <td>0.194</td>\n",
-       "      <td>0.817</td>\n",
-       "      <td>1.544</td>\n",
+       "      <th>YearsAtCompany_threshold[12]</th>\n",
+       "      <td>-0.117</td>\n",
+       "      <td>0.294</td>\n",
+       "      <td>-0.666</td>\n",
+       "      <td>0.436</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>1658.0</td>\n",
-       "      <td>2354.0</td>\n",
-       "      <td>1.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[13]</th>\n",
-       "      <td>1.011</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>0.637</td>\n",
-       "      <td>1.436</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>2068.0</td>\n",
-       "      <td>2421.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>4272.0</td>\n",
+       "      <td>2223.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[14]</th>\n",
-       "      <td>0.974</td>\n",
-       "      <td>0.229</td>\n",
-       "      <td>0.519</td>\n",
-       "      <td>1.377</td>\n",
+       "      <th>YearsAtCompany_threshold[13]</th>\n",
+       "      <td>0.533</td>\n",
+       "      <td>0.253</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.984</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>2081.0</td>\n",
-       "      <td>2651.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>2965.0</td>\n",
+       "      <td>2717.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[15]</th>\n",
-       "      <td>1.385</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>0.988</td>\n",
-       "      <td>1.790</td>\n",
+       "      <th>YearsAtCompany_threshold[14]</th>\n",
+       "      <td>0.410</td>\n",
+       "      <td>0.287</td>\n",
+       "      <td>-0.144</td>\n",
+       "      <td>0.936</td>\n",
        "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>1712.0</td>\n",
-       "      <td>2312.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>3090.0</td>\n",
+       "      <td>2611.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[16]</th>\n",
-       "      <td>1.486</td>\n",
-       "      <td>0.215</td>\n",
-       "      <td>1.076</td>\n",
-       "      <td>1.883</td>\n",
+       "      <th>YearsAtCompany_threshold[15]</th>\n",
+       "      <td>0.825</td>\n",
+       "      <td>0.266</td>\n",
+       "      <td>0.314</td>\n",
+       "      <td>1.311</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>1738.0</td>\n",
-       "      <td>2604.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>3271.0</td>\n",
+       "      <td>2917.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[17]</th>\n",
-       "      <td>1.538</td>\n",
-       "      <td>0.223</td>\n",
-       "      <td>1.151</td>\n",
-       "      <td>1.981</td>\n",
+       "      <th>YearsAtCompany_threshold[16]</th>\n",
+       "      <td>0.468</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>-0.170</td>\n",
+       "      <td>1.111</td>\n",
        "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>1886.0</td>\n",
-       "      <td>2311.0</td>\n",
-       "      <td>1.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[18]</th>\n",
-       "      <td>1.504</td>\n",
-       "      <td>0.246</td>\n",
-       "      <td>1.015</td>\n",
-       "      <td>1.952</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>1739.0</td>\n",
-       "      <td>2268.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>3911.0</td>\n",
+       "      <td>2940.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[19]</th>\n",
-       "      <td>1.417</td>\n",
-       "      <td>0.263</td>\n",
-       "      <td>0.882</td>\n",
-       "      <td>1.878</td>\n",
+       "      <th>YearsAtCompany_threshold[17]</th>\n",
+       "      <td>0.289</td>\n",
+       "      <td>0.383</td>\n",
+       "      <td>-0.436</td>\n",
+       "      <td>0.964</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>2220.0</td>\n",
-       "      <td>2866.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3805.0</td>\n",
+       "      <td>2814.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[20]</th>\n",
-       "      <td>1.897</td>\n",
-       "      <td>0.237</td>\n",
-       "      <td>1.447</td>\n",
-       "      <td>2.332</td>\n",
+       "      <th>YearsAtCompany_threshold[18]</th>\n",
+       "      <td>0.868</td>\n",
+       "      <td>0.330</td>\n",
+       "      <td>0.234</td>\n",
+       "      <td>1.472</td>\n",
        "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>1874.0</td>\n",
-       "      <td>2381.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>3684.0</td>\n",
+       "      <td>2862.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[21]</th>\n",
-       "      <td>2.260</td>\n",
-       "      <td>0.243</td>\n",
-       "      <td>1.817</td>\n",
-       "      <td>2.724</td>\n",
+       "      <th>YearsAtCompany_threshold[19]</th>\n",
+       "      <td>0.865</td>\n",
+       "      <td>0.346</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>1.498</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>1926.0</td>\n",
-       "      <td>2200.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>3837.0</td>\n",
+       "      <td>2669.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[22]</th>\n",
-       "      <td>1.978</td>\n",
-       "      <td>0.279</td>\n",
-       "      <td>1.470</td>\n",
-       "      <td>2.484</td>\n",
-       "      <td>0.006</td>\n",
+       "      <th>YearsAtCompany_threshold[20]</th>\n",
+       "      <td>2.277</td>\n",
+       "      <td>0.274</td>\n",
+       "      <td>1.741</td>\n",
+       "      <td>2.766</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2066.0</td>\n",
-       "      <td>2272.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>2832.0</td>\n",
+       "      <td>2810.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[23]</th>\n",
-       "      <td>2.245</td>\n",
-       "      <td>0.269</td>\n",
-       "      <td>1.729</td>\n",
-       "      <td>2.746</td>\n",
+       "      <th>YearsAtCompany_threshold[21]</th>\n",
+       "      <td>1.940</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>1.242</td>\n",
+       "      <td>2.560</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2183.0</td>\n",
-       "      <td>2773.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>3193.0</td>\n",
+       "      <td>2981.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[24]</th>\n",
-       "      <td>2.223</td>\n",
-       "      <td>0.295</td>\n",
-       "      <td>1.703</td>\n",
-       "      <td>2.807</td>\n",
+       "      <th>YearsAtCompany_threshold[22]</th>\n",
+       "      <td>2.558</td>\n",
+       "      <td>0.353</td>\n",
+       "      <td>1.916</td>\n",
+       "      <td>3.236</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2419.0</td>\n",
-       "      <td>3004.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>3331.0</td>\n",
+       "      <td>2546.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[25]</th>\n",
-       "      <td>2.121</td>\n",
-       "      <td>0.327</td>\n",
-       "      <td>1.509</td>\n",
-       "      <td>2.723</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>2661.0</td>\n",
-       "      <td>2898.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[23]</th>\n",
+       "      <td>0.423</td>\n",
+       "      <td>0.685</td>\n",
+       "      <td>-0.860</td>\n",
+       "      <td>1.655</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>5871.0</td>\n",
+       "      <td>2751.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[26]</th>\n",
-       "      <td>2.327</td>\n",
-       "      <td>0.325</td>\n",
-       "      <td>1.740</td>\n",
-       "      <td>2.950</td>\n",
-       "      <td>0.007</td>\n",
+       "      <th>YearsAtCompany_threshold[24]</th>\n",
+       "      <td>1.843</td>\n",
+       "      <td>0.513</td>\n",
+       "      <td>0.802</td>\n",
+       "      <td>2.709</td>\n",
+       "      <td>0.008</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>2351.0</td>\n",
-       "      <td>2678.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>4798.0</td>\n",
+       "      <td>2686.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[27]</th>\n",
-       "      <td>1.736</td>\n",
-       "      <td>0.416</td>\n",
-       "      <td>0.953</td>\n",
-       "      <td>2.484</td>\n",
+       "      <th>YearsAtCompany_threshold[25]</th>\n",
+       "      <td>1.869</td>\n",
+       "      <td>0.553</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>2.873</td>\n",
        "      <td>0.008</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>2749.0</td>\n",
-       "      <td>2672.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>5084.0</td>\n",
+       "      <td>2838.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[28]</th>\n",
-       "      <td>2.694</td>\n",
-       "      <td>0.332</td>\n",
-       "      <td>2.036</td>\n",
-       "      <td>3.300</td>\n",
+       "      <th>YearsAtCompany_threshold[26]</th>\n",
+       "      <td>2.139</td>\n",
+       "      <td>0.558</td>\n",
+       "      <td>1.132</td>\n",
+       "      <td>3.231</td>\n",
        "      <td>0.008</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>1912.0</td>\n",
-       "      <td>2288.0</td>\n",
-       "      <td>1.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[29]</th>\n",
-       "      <td>2.537</td>\n",
-       "      <td>0.383</td>\n",
-       "      <td>1.833</td>\n",
-       "      <td>3.248</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3087.0</td>\n",
-       "      <td>2747.0</td>\n",
-       "      <td>1.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[30]</th>\n",
-       "      <td>2.341</td>\n",
-       "      <td>0.431</td>\n",
-       "      <td>1.521</td>\n",
-       "      <td>3.129</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3526.0</td>\n",
-       "      <td>2538.0</td>\n",
-       "      <td>1.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[31]</th>\n",
-       "      <td>2.804</td>\n",
-       "      <td>0.396</td>\n",
-       "      <td>2.076</td>\n",
-       "      <td>3.573</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3354.0</td>\n",
-       "      <td>2859.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>4867.0</td>\n",
+       "      <td>2851.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[32]</th>\n",
-       "      <td>3.073</td>\n",
-       "      <td>0.408</td>\n",
-       "      <td>2.330</td>\n",
-       "      <td>3.844</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3539.0</td>\n",
-       "      <td>2963.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <th>YearsAtCompany_threshold[27]</th>\n",
+       "      <td>1.688</td>\n",
+       "      <td>0.688</td>\n",
+       "      <td>0.377</td>\n",
+       "      <td>2.902</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>6452.0</td>\n",
+       "      <td>2998.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[33]</th>\n",
-       "      <td>3.082</td>\n",
-       "      <td>0.461</td>\n",
-       "      <td>2.167</td>\n",
-       "      <td>3.896</td>\n",
+       "      <th>YearsAtCompany_threshold[28]</th>\n",
+       "      <td>1.852</td>\n",
+       "      <td>0.687</td>\n",
+       "      <td>0.621</td>\n",
+       "      <td>3.164</td>\n",
+       "      <td>0.009</td>\n",
        "      <td>0.007</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>4221.0</td>\n",
-       "      <td>2697.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>5523.0</td>\n",
+       "      <td>3164.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[34]</th>\n",
-       "      <td>2.959</td>\n",
-       "      <td>0.531</td>\n",
-       "      <td>1.939</td>\n",
-       "      <td>3.960</td>\n",
+       "      <th>YearsAtCompany_threshold[29]</th>\n",
+       "      <td>1.490</td>\n",
+       "      <td>0.751</td>\n",
+       "      <td>0.184</td>\n",
+       "      <td>2.979</td>\n",
+       "      <td>0.011</td>\n",
        "      <td>0.008</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>4874.0</td>\n",
-       "      <td>2358.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>5018.0</td>\n",
+       "      <td>2984.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[35]</th>\n",
-       "      <td>2.597</td>\n",
-       "      <td>0.633</td>\n",
-       "      <td>1.438</td>\n",
-       "      <td>3.805</td>\n",
+       "      <th>YearsAtCompany_threshold[30]</th>\n",
+       "      <td>2.132</td>\n",
+       "      <td>0.694</td>\n",
+       "      <td>0.899</td>\n",
+       "      <td>3.500</td>\n",
        "      <td>0.009</td>\n",
        "      <td>0.007</td>\n",
-       "      <td>4870.0</td>\n",
-       "      <td>2547.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>5767.0</td>\n",
+       "      <td>2584.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[36]</th>\n",
-       "      <td>3.806</td>\n",
-       "      <td>0.554</td>\n",
-       "      <td>2.725</td>\n",
-       "      <td>4.801</td>\n",
-       "      <td>0.008</td>\n",
+       "      <th>YearsAtCompany_threshold[31]</th>\n",
+       "      <td>2.351</td>\n",
+       "      <td>0.699</td>\n",
+       "      <td>1.085</td>\n",
+       "      <td>3.722</td>\n",
+       "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>4346.0</td>\n",
-       "      <td>2605.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>6991.0</td>\n",
+       "      <td>2987.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[37]</th>\n",
-       "      <td>3.809</td>\n",
-       "      <td>0.669</td>\n",
-       "      <td>2.618</td>\n",
-       "      <td>5.112</td>\n",
+       "      <th>YearsAtCompany_threshold[32]</th>\n",
+       "      <td>3.486</td>\n",
+       "      <td>0.648</td>\n",
+       "      <td>2.305</td>\n",
+       "      <td>4.714</td>\n",
        "      <td>0.009</td>\n",
        "      <td>0.007</td>\n",
-       "      <td>5117.0</td>\n",
-       "      <td>2597.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>4981.0</td>\n",
+       "      <td>3083.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YearsAtCompany_threshold[33]</th>\n",
+       "      <td>2.421</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.949</td>\n",
+       "      <td>4.105</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>5710.0</td>\n",
+       "      <td>2938.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>TotalWorkingYears_threshold[38]</th>\n",
-       "      <td>2.597</td>\n",
-       "      <td>0.888</td>\n",
-       "      <td>0.867</td>\n",
-       "      <td>4.184</td>\n",
+       "      <th>YearsAtCompany_threshold[34]</th>\n",
+       "      <td>3.333</td>\n",
+       "      <td>0.860</td>\n",
+       "      <td>1.647</td>\n",
+       "      <td>4.913</td>\n",
        "      <td>0.012</td>\n",
        "      <td>0.008</td>\n",
-       "      <td>5761.0</td>\n",
-       "      <td>2541.0</td>\n",
-       "      <td>1.00</td>\n",
+       "      <td>5747.0</td>\n",
+       "      <td>2698.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Age</th>\n",
-       "      <td>0.084</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>0.078</td>\n",
-       "      <td>0.089</td>\n",
+       "      <th>TotalWorkingYears</th>\n",
+       "      <td>0.130</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>0.119</td>\n",
+       "      <td>0.140</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>785.0</td>\n",
-       "      <td>1530.0</td>\n",
-       "      <td>1.01</td>\n",
+       "      <td>968.0</td>\n",
+       "      <td>1511.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                  mean     sd  hdi_3%  hdi_97%  mcse_mean   \n",
-       "TotalWorkingYears_threshold[0]  -2.086  0.304  -2.669   -1.541      0.005  \\\n",
-       "TotalWorkingYears_threshold[1]   0.010  0.149  -0.254    0.304      0.004   \n",
-       "TotalWorkingYears_threshold[2]  -0.903  0.201  -1.275   -0.523      0.004   \n",
-       "TotalWorkingYears_threshold[3]  -0.529  0.187  -0.894   -0.194      0.004   \n",
-       "TotalWorkingYears_threshold[4]  -0.038  0.160  -0.340    0.252      0.004   \n",
-       "TotalWorkingYears_threshold[5]   0.419  0.151   0.131    0.699      0.004   \n",
-       "TotalWorkingYears_threshold[6]   0.950  0.142   0.671    1.205      0.004   \n",
-       "TotalWorkingYears_threshold[7]   0.663  0.159   0.363    0.955      0.004   \n",
-       "TotalWorkingYears_threshold[8]   1.062  0.151   0.764    1.336      0.004   \n",
-       "TotalWorkingYears_threshold[9]   1.156  0.157   0.873    1.451      0.004   \n",
-       "TotalWorkingYears_threshold[10]  2.325  0.143   2.063    2.591      0.004   \n",
-       "TotalWorkingYears_threshold[11]  0.730  0.207   0.351    1.127      0.005   \n",
-       "TotalWorkingYears_threshold[12]  1.184  0.194   0.817    1.544      0.005   \n",
-       "TotalWorkingYears_threshold[13]  1.011  0.214   0.637    1.436      0.005   \n",
-       "TotalWorkingYears_threshold[14]  0.974  0.229   0.519    1.377      0.005   \n",
-       "TotalWorkingYears_threshold[15]  1.385  0.214   0.988    1.790      0.005   \n",
-       "TotalWorkingYears_threshold[16]  1.486  0.215   1.076    1.883      0.005   \n",
-       "TotalWorkingYears_threshold[17]  1.538  0.223   1.151    1.981      0.005   \n",
-       "TotalWorkingYears_threshold[18]  1.504  0.246   1.015    1.952      0.006   \n",
-       "TotalWorkingYears_threshold[19]  1.417  0.263   0.882    1.878      0.006   \n",
-       "TotalWorkingYears_threshold[20]  1.897  0.237   1.447    2.332      0.005   \n",
-       "TotalWorkingYears_threshold[21]  2.260  0.243   1.817    2.724      0.006   \n",
-       "TotalWorkingYears_threshold[22]  1.978  0.279   1.470    2.484      0.006   \n",
-       "TotalWorkingYears_threshold[23]  2.245  0.269   1.729    2.746      0.006   \n",
-       "TotalWorkingYears_threshold[24]  2.223  0.295   1.703    2.807      0.006   \n",
-       "TotalWorkingYears_threshold[25]  2.121  0.327   1.509    2.723      0.006   \n",
-       "TotalWorkingYears_threshold[26]  2.327  0.325   1.740    2.950      0.007   \n",
-       "TotalWorkingYears_threshold[27]  1.736  0.416   0.953    2.484      0.008   \n",
-       "TotalWorkingYears_threshold[28]  2.694  0.332   2.036    3.300      0.008   \n",
-       "TotalWorkingYears_threshold[29]  2.537  0.383   1.833    3.248      0.007   \n",
-       "TotalWorkingYears_threshold[30]  2.341  0.431   1.521    3.129      0.007   \n",
-       "TotalWorkingYears_threshold[31]  2.804  0.396   2.076    3.573      0.007   \n",
-       "TotalWorkingYears_threshold[32]  3.073  0.408   2.330    3.844      0.007   \n",
-       "TotalWorkingYears_threshold[33]  3.082  0.461   2.167    3.896      0.007   \n",
-       "TotalWorkingYears_threshold[34]  2.959  0.531   1.939    3.960      0.008   \n",
-       "TotalWorkingYears_threshold[35]  2.597  0.633   1.438    3.805      0.009   \n",
-       "TotalWorkingYears_threshold[36]  3.806  0.554   2.725    4.801      0.008   \n",
-       "TotalWorkingYears_threshold[37]  3.809  0.669   2.618    5.112      0.009   \n",
-       "TotalWorkingYears_threshold[38]  2.597  0.888   0.867    4.184      0.012   \n",
-       "Age                              0.084  0.003   0.078    0.089      0.000   \n",
+       "                               mean     sd  hdi_3%  hdi_97%  mcse_mean   \n",
+       "YearsAtCompany_threshold[0]  -2.571  0.197  -2.930   -2.195      0.003  \\\n",
+       "YearsAtCompany_threshold[1]  -1.054  0.109  -1.248   -0.838      0.002   \n",
+       "YearsAtCompany_threshold[2]  -1.018  0.116  -1.252   -0.812      0.002   \n",
+       "YearsAtCompany_threshold[3]  -0.757  0.117  -0.970   -0.538      0.002   \n",
+       "YearsAtCompany_threshold[4]  -0.754  0.122  -0.969   -0.520      0.002   \n",
+       "YearsAtCompany_threshold[5]   0.263  0.105   0.050    0.442      0.002   \n",
+       "YearsAtCompany_threshold[6]  -0.501  0.143  -0.772   -0.238      0.002   \n",
+       "YearsAtCompany_threshold[7]  -0.079  0.140  -0.336    0.189      0.003   \n",
+       "YearsAtCompany_threshold[8]   0.040  0.150  -0.226    0.333      0.003   \n",
+       "YearsAtCompany_threshold[9]   0.401  0.150   0.132    0.692      0.003   \n",
+       "YearsAtCompany_threshold[10]  1.290  0.150   0.997    1.556      0.003   \n",
+       "YearsAtCompany_threshold[11]  0.451  0.214   0.055    0.857      0.004   \n",
+       "YearsAtCompany_threshold[12] -0.117  0.294  -0.666    0.436      0.005   \n",
+       "YearsAtCompany_threshold[13]  0.533  0.253   0.037    0.984      0.005   \n",
+       "YearsAtCompany_threshold[14]  0.410  0.287  -0.144    0.936      0.005   \n",
+       "YearsAtCompany_threshold[15]  0.825  0.266   0.314    1.311      0.005   \n",
+       "YearsAtCompany_threshold[16]  0.468  0.340  -0.170    1.111      0.005   \n",
+       "YearsAtCompany_threshold[17]  0.289  0.383  -0.436    0.964      0.006   \n",
+       "YearsAtCompany_threshold[18]  0.868  0.330   0.234    1.472      0.005   \n",
+       "YearsAtCompany_threshold[19]  0.865  0.346   0.214    1.498      0.006   \n",
+       "YearsAtCompany_threshold[20]  2.277  0.274   1.741    2.766      0.005   \n",
+       "YearsAtCompany_threshold[21]  1.940  0.348   1.242    2.560      0.006   \n",
+       "YearsAtCompany_threshold[22]  2.558  0.353   1.916    3.236      0.006   \n",
+       "YearsAtCompany_threshold[23]  0.423  0.685  -0.860    1.655      0.009   \n",
+       "YearsAtCompany_threshold[24]  1.843  0.513   0.802    2.709      0.008   \n",
+       "YearsAtCompany_threshold[25]  1.869  0.553   0.818    2.873      0.008   \n",
+       "YearsAtCompany_threshold[26]  2.139  0.558   1.132    3.231      0.008   \n",
+       "YearsAtCompany_threshold[27]  1.688  0.688   0.377    2.902      0.009   \n",
+       "YearsAtCompany_threshold[28]  1.852  0.687   0.621    3.164      0.009   \n",
+       "YearsAtCompany_threshold[29]  1.490  0.751   0.184    2.979      0.011   \n",
+       "YearsAtCompany_threshold[30]  2.132  0.694   0.899    3.500      0.009   \n",
+       "YearsAtCompany_threshold[31]  2.351  0.699   1.085    3.722      0.009   \n",
+       "YearsAtCompany_threshold[32]  3.486  0.648   2.305    4.714      0.009   \n",
+       "YearsAtCompany_threshold[33]  2.421  0.841   0.949    4.105      0.011   \n",
+       "YearsAtCompany_threshold[34]  3.333  0.860   1.647    4.913      0.012   \n",
+       "TotalWorkingYears             0.130  0.006   0.119    0.140      0.000   \n",
        "\n",
-       "                                 mcse_sd  ess_bulk  ess_tail  r_hat  \n",
-       "TotalWorkingYears_threshold[0]     0.003    4099.0    2853.0   1.00  \n",
-       "TotalWorkingYears_threshold[1]     0.003    1677.0    2547.0   1.00  \n",
-       "TotalWorkingYears_threshold[2]     0.003    2099.0    2739.0   1.00  \n",
-       "TotalWorkingYears_threshold[3]     0.003    1870.0    1959.0   1.00  \n",
-       "TotalWorkingYears_threshold[4]     0.003    1735.0    2493.0   1.00  \n",
-       "TotalWorkingYears_threshold[5]     0.003    1455.0    2298.0   1.00  \n",
-       "TotalWorkingYears_threshold[6]     0.003    1250.0    2195.0   1.00  \n",
-       "TotalWorkingYears_threshold[7]     0.003    1386.0    2334.0   1.00  \n",
-       "TotalWorkingYears_threshold[8]     0.003    1259.0    2106.0   1.00  \n",
-       "TotalWorkingYears_threshold[9]     0.003    1314.0    2440.0   1.00  \n",
-       "TotalWorkingYears_threshold[10]    0.003    1109.0    2126.0   1.00  \n",
-       "TotalWorkingYears_threshold[11]    0.003    1984.0    2533.0   1.00  \n",
-       "TotalWorkingYears_threshold[12]    0.003    1658.0    2354.0   1.00  \n",
-       "TotalWorkingYears_threshold[13]    0.003    2068.0    2421.0   1.00  \n",
-       "TotalWorkingYears_threshold[14]    0.004    2081.0    2651.0   1.00  \n",
-       "TotalWorkingYears_threshold[15]    0.004    1712.0    2312.0   1.00  \n",
-       "TotalWorkingYears_threshold[16]    0.004    1738.0    2604.0   1.00  \n",
-       "TotalWorkingYears_threshold[17]    0.004    1886.0    2311.0   1.00  \n",
-       "TotalWorkingYears_threshold[18]    0.004    1739.0    2268.0   1.00  \n",
-       "TotalWorkingYears_threshold[19]    0.004    2220.0    2866.0   1.00  \n",
-       "TotalWorkingYears_threshold[20]    0.004    1874.0    2381.0   1.00  \n",
-       "TotalWorkingYears_threshold[21]    0.004    1926.0    2200.0   1.00  \n",
-       "TotalWorkingYears_threshold[22]    0.004    2066.0    2272.0   1.00  \n",
-       "TotalWorkingYears_threshold[23]    0.004    2183.0    2773.0   1.00  \n",
-       "TotalWorkingYears_threshold[24]    0.004    2419.0    3004.0   1.00  \n",
-       "TotalWorkingYears_threshold[25]    0.004    2661.0    2898.0   1.00  \n",
-       "TotalWorkingYears_threshold[26]    0.005    2351.0    2678.0   1.00  \n",
-       "TotalWorkingYears_threshold[27]    0.006    2749.0    2672.0   1.00  \n",
-       "TotalWorkingYears_threshold[28]    0.005    1912.0    2288.0   1.00  \n",
-       "TotalWorkingYears_threshold[29]    0.005    3087.0    2747.0   1.00  \n",
-       "TotalWorkingYears_threshold[30]    0.005    3526.0    2538.0   1.00  \n",
-       "TotalWorkingYears_threshold[31]    0.005    3354.0    2859.0   1.00  \n",
-       "TotalWorkingYears_threshold[32]    0.005    3539.0    2963.0   1.00  \n",
-       "TotalWorkingYears_threshold[33]    0.005    4221.0    2697.0   1.00  \n",
-       "TotalWorkingYears_threshold[34]    0.005    4874.0    2358.0   1.00  \n",
-       "TotalWorkingYears_threshold[35]    0.007    4870.0    2547.0   1.00  \n",
-       "TotalWorkingYears_threshold[36]    0.006    4346.0    2605.0   1.00  \n",
-       "TotalWorkingYears_threshold[37]    0.007    5117.0    2597.0   1.00  \n",
-       "TotalWorkingYears_threshold[38]    0.008    5761.0    2541.0   1.00  \n",
-       "Age                                0.000     785.0    1530.0   1.01  "
+       "                              mcse_sd  ess_bulk  ess_tail  r_hat  \n",
+       "YearsAtCompany_threshold[0]     0.002    5509.0    2243.0    1.0  \n",
+       "YearsAtCompany_threshold[1]     0.001    3092.0    2725.0    1.0  \n",
+       "YearsAtCompany_threshold[2]     0.001    3545.0    2514.0    1.0  \n",
+       "YearsAtCompany_threshold[3]     0.001    3458.0    2916.0    1.0  \n",
+       "YearsAtCompany_threshold[4]     0.002    2900.0    2993.0    1.0  \n",
+       "YearsAtCompany_threshold[5]     0.002    2262.0    2599.0    1.0  \n",
+       "YearsAtCompany_threshold[6]     0.002    3734.0    2865.0    1.0  \n",
+       "YearsAtCompany_threshold[7]     0.002    3139.0    2944.0    1.0  \n",
+       "YearsAtCompany_threshold[8]     0.002    2926.0    2874.0    1.0  \n",
+       "YearsAtCompany_threshold[9]     0.002    2979.0    2550.0    1.0  \n",
+       "YearsAtCompany_threshold[10]    0.002    2092.0    2430.0    1.0  \n",
+       "YearsAtCompany_threshold[11]    0.003    2714.0    2847.0    1.0  \n",
+       "YearsAtCompany_threshold[12]    0.005    4272.0    2223.0    1.0  \n",
+       "YearsAtCompany_threshold[13]    0.003    2965.0    2717.0    1.0  \n",
+       "YearsAtCompany_threshold[14]    0.004    3090.0    2611.0    1.0  \n",
+       "YearsAtCompany_threshold[15]    0.003    3271.0    2917.0    1.0  \n",
+       "YearsAtCompany_threshold[16]    0.004    3911.0    2940.0    1.0  \n",
+       "YearsAtCompany_threshold[17]    0.005    3805.0    2814.0    1.0  \n",
+       "YearsAtCompany_threshold[18]    0.004    3684.0    2862.0    1.0  \n",
+       "YearsAtCompany_threshold[19]    0.004    3837.0    2669.0    1.0  \n",
+       "YearsAtCompany_threshold[20]    0.004    2832.0    2810.0    1.0  \n",
+       "YearsAtCompany_threshold[21]    0.004    3193.0    2981.0    1.0  \n",
+       "YearsAtCompany_threshold[22]    0.004    3331.0    2546.0    1.0  \n",
+       "YearsAtCompany_threshold[23]    0.010    5871.0    2751.0    1.0  \n",
+       "YearsAtCompany_threshold[24]    0.005    4798.0    2686.0    1.0  \n",
+       "YearsAtCompany_threshold[25]    0.006    5084.0    2838.0    1.0  \n",
+       "YearsAtCompany_threshold[26]    0.006    4867.0    2851.0    1.0  \n",
+       "YearsAtCompany_threshold[27]    0.006    6452.0    2998.0    1.0  \n",
+       "YearsAtCompany_threshold[28]    0.007    5523.0    3164.0    1.0  \n",
+       "YearsAtCompany_threshold[29]    0.008    5018.0    2984.0    1.0  \n",
+       "YearsAtCompany_threshold[30]    0.007    5767.0    2584.0    1.0  \n",
+       "YearsAtCompany_threshold[31]    0.006    6991.0    2987.0    1.0  \n",
+       "YearsAtCompany_threshold[32]    0.007    4981.0    3083.0    1.0  \n",
+       "YearsAtCompany_threshold[33]    0.008    5710.0    2938.0    1.0  \n",
+       "YearsAtCompany_threshold[34]    0.008    5747.0    2698.0    1.0  \n",
+       "TotalWorkingYears               0.000     968.0    1511.0    1.0  "
       ]
      },
-     "execution_count": 142,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1712,14 +1608,23 @@
     "az.summary(sequence_idata)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**THIS IS WRONG. CUMULATIVE PROBS. CANNOT DECREASE**\n",
+    "\n",
+    "The coefficients are still on the logits scale, so we need to apply the inverse of the logit function to transform back to cumulative probabilities. Below, we plot the cumulative probabilities for each category."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -1729,38 +1634,40 @@
     }
    ],
    "source": [
-    "cumprobs = expit_func(sequence_idata.posterior.TotalWorkingYears_threshold).mean((\"chain\", \"draw\"))\n",
+    "cumprobs = expit_func(sequence_idata.posterior.YearsAtCompany_threshold).mean((\"chain\", \"draw\"))\n",
     "cumprobs = np.append(cumprobs, 1)\n",
     "\n",
     "plt.figure(figsize=(7, 3))\n",
-    "plt.plot(sorted(attrition.TotalWorkingYears.unique()), cumprobs, marker='o')\n",
+    "plt.plot(sorted(attrition.YearsAtCompany.unique()), cumprobs, marker='o')\n",
     "plt.ylabel(\"Cumulative probability\")\n",
     "plt.xlabel(\"Response category\");"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "### Posterior predictive samples\n",
+    "\n",
+    "Again, using the posterior predictive samples, we can visualize the model fit against the observed data. In the case of the sequential model, the model does an alright job of capturing the observed frequencies of the categories. For pedagogical purposes, this fit is sufficient."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_32825/4080317185.py:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_9466/1303656969.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  ax.set_xticklabels(sequence_model.response_component.response_term.levels);\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -1772,8 +1679,7 @@
    "source": [
     "sequence_idata_pps = sequence_model.predict(idata=sequence_idata, kind=\"pps\", inplace=False)\n",
     "\n",
-    "ax = az.plot_ppc(sequence_idata_pps, figsize=(7, 3), textsize=11)\n",
-    "# ax.set_xticks(np.linspace(0.2, 7, 7))\n",
+    "az.plot_ppc(sequence_idata_pps, figsize=(7, 3), textsize=11)\n",
     "ax.set_xticklabels(sequence_model.response_component.response_term.levels);"
    ]
   },
@@ -1781,42 +1687,32 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Fri Sep 15 2023\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.11.0\n",
+      "IPython version      : 8.13.2\n",
+      "\n",
+      "bambi     : 0.13.0.dev0\n",
+      "arviz     : 0.15.1\n",
+      "numpy     : 1.24.2\n",
+      "pandas    : 2.0.1\n",
+      "matplotlib: 3.7.1\n",
+      "\n",
+      "Watermark: 2.3.1\n",
+      "\n"
+     ]
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(7, 3))\n",
-    "for i in range(20):\n",
-    "    outcome = expit_func(sequence_idata.posterior.TotalWorkingYears_threshold).sel(TotalWorkingYears_threshold_dim=i).to_numpy().flatten()\n",
-    "    ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n",
-    "ax.set_xlabel(\"Probability\")\n",
-    "ax.set_ylabel(\"Count\")\n",
-    "ax.set_title(\"Cumulative Probability by Category\")\n",
-    "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");"
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1835,7 +1731,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.0"
   },
   "orig_nbformat": 4
  },

From f7a182634052679f035e44f6b09aa2ace6980e19 Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Tue, 19 Sep 2023 20:49:50 +0200
Subject: [PATCH 06/13] remove intercept in models

---
 docs/notebooks/ordinal_regression.ipynb | 938 +++++++++++++-----------
 1 file changed, 498 insertions(+), 440 deletions(-)

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index 560d273fd..faf2e09a8 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -2,9 +2,17 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    }
+   ],
    "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
@@ -56,7 +64,7 @@
     "\n",
     "$$Pr(Y = 3) = \\Phi(\\tau_2) - \\Phi(\\tau_1) - \\Phi(\\tau_0)$$\n",
     "\n",
-    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of $k$ as the prior for the thresholds. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
+    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of categories as the prior for the thresholds. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
     "\n",
     "Lastly, as each $F(\\tau)$ implies a cumulative probability for each $k$, the largest response value always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 3$ response values, we only need $K − 1 = 2$ intercepts."
    ]
@@ -78,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +110,7 @@
        "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,14 +131,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_9466/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_30380/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
       "  logit_func = lambda x: np.log(x / (1 - x))\n"
      ]
     },
@@ -141,7 +149,7 @@
        "        1.76938091,         nan])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,13 +164,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/formulae/terms/variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/distributions/transforms.py:56: FutureWarning: univariate_ordered has been deprecated, use ordered instead.\n",
       "  warnings.warn(f\"{name} has been deprecated, use ordered instead.\", FutureWarning)\n",
       "Auto-assigning NUTS sampler...\n",
@@ -204,7 +214,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -219,18 +229,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
       "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
      ]
     }
@@ -240,11 +250,58 @@
     "idata = model.fit(random_seed=1234)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, the components of the model are outputed. Notice how the thresholds are a grid of six values ranging from -2 to 2. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "       Formula: response ~ 0\n",
+       "        Family: cumulative\n",
+       "          Link: p = logit\n",
+       "  Observations: 9930\n",
+       "        Priors: \n",
+       "    target = p\n",
+       "        \n",
+       "        \n",
+       "        Auxiliary parameters\n",
+       "            threshold ~ Normal(mu: [-2.  -1.2 -0.4  0.4  1.2  2. ], sigma: 1.0, transform: ordered)\n",
+       "------\n",
+       "* To see a plot of the priors call the .plot_priors() method.\n",
+       "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/xarray/core/concat.py:546: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
+      "  common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -280,74 +337,74 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>response_threshold[0]</th>\n",
-       "      <td>-1.916</td>\n",
+       "      <td>-1.917</td>\n",
        "      <td>0.030</td>\n",
-       "      <td>-1.971</td>\n",
-       "      <td>-1.857</td>\n",
+       "      <td>-1.974</td>\n",
+       "      <td>-1.863</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4461.0</td>\n",
-       "      <td>2908.0</td>\n",
+       "      <td>4097.0</td>\n",
+       "      <td>3163.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[1]</th>\n",
-       "      <td>-1.266</td>\n",
+       "      <td>-1.267</td>\n",
        "      <td>0.024</td>\n",
        "      <td>-1.312</td>\n",
-       "      <td>-1.222</td>\n",
+       "      <td>-1.220</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5137.0</td>\n",
-       "      <td>3794.0</td>\n",
+       "      <td>5391.0</td>\n",
+       "      <td>3302.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[2]</th>\n",
-       "      <td>-0.718</td>\n",
+       "      <td>-0.719</td>\n",
        "      <td>0.021</td>\n",
-       "      <td>-0.755</td>\n",
-       "      <td>-0.674</td>\n",
+       "      <td>-0.760</td>\n",
+       "      <td>-0.681</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5348.0</td>\n",
-       "      <td>3578.0</td>\n",
+       "      <td>5439.0</td>\n",
+       "      <td>3698.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[3]</th>\n",
        "      <td>0.248</td>\n",
-       "      <td>0.021</td>\n",
-       "      <td>0.208</td>\n",
-       "      <td>0.285</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>0.211</td>\n",
+       "      <td>0.287</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5505.0</td>\n",
-       "      <td>3678.0</td>\n",
+       "      <td>5416.0</td>\n",
+       "      <td>3644.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[4]</th>\n",
-       "      <td>0.891</td>\n",
+       "      <td>0.890</td>\n",
        "      <td>0.022</td>\n",
-       "      <td>0.848</td>\n",
-       "      <td>0.931</td>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.930</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5037.0</td>\n",
-       "      <td>3301.0</td>\n",
+       "      <td>4966.0</td>\n",
+       "      <td>3439.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>response_threshold[5]</th>\n",
-       "      <td>1.771</td>\n",
-       "      <td>0.029</td>\n",
-       "      <td>1.718</td>\n",
-       "      <td>1.826</td>\n",
+       "      <td>1.770</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>1.721</td>\n",
+       "      <td>1.823</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5572.0</td>\n",
-       "      <td>3543.0</td>\n",
+       "      <td>4785.0</td>\n",
+       "      <td>3368.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -355,24 +412,24 @@
        "</div>"
       ],
       "text/plain": [
-       "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "response_threshold[0] -1.916  0.030  -1.971   -1.857        0.0      0.0  \\\n",
-       "response_threshold[1] -1.266  0.024  -1.312   -1.222        0.0      0.0   \n",
-       "response_threshold[2] -0.718  0.021  -0.755   -0.674        0.0      0.0   \n",
-       "response_threshold[3]  0.248  0.021   0.208    0.285        0.0      0.0   \n",
-       "response_threshold[4]  0.891  0.022   0.848    0.931        0.0      0.0   \n",
-       "response_threshold[5]  1.771  0.029   1.718    1.826        0.0      0.0   \n",
+       "                        mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "response_threshold[0] -1.917  0.030  -1.974   -1.863        0.0      0.0   \n",
+       "response_threshold[1] -1.267  0.024  -1.312   -1.220        0.0      0.0   \n",
+       "response_threshold[2] -0.719  0.021  -0.760   -0.681        0.0      0.0   \n",
+       "response_threshold[3]  0.248  0.020   0.211    0.287        0.0      0.0   \n",
+       "response_threshold[4]  0.890  0.022   0.847    0.930        0.0      0.0   \n",
+       "response_threshold[5]  1.770  0.027   1.721    1.823        0.0      0.0   \n",
        "\n",
        "                       ess_bulk  ess_tail  r_hat  \n",
-       "response_threshold[0]    4461.0    2908.0    1.0  \n",
-       "response_threshold[1]    5137.0    3794.0    1.0  \n",
-       "response_threshold[2]    5348.0    3578.0    1.0  \n",
-       "response_threshold[3]    5505.0    3678.0    1.0  \n",
-       "response_threshold[4]    5037.0    3301.0    1.0  \n",
-       "response_threshold[5]    5572.0    3543.0    1.0  "
+       "response_threshold[0]    4097.0    3163.0    1.0  \n",
+       "response_threshold[1]    5391.0    3302.0    1.0  \n",
+       "response_threshold[2]    5439.0    3698.0    1.0  \n",
+       "response_threshold[3]    5416.0    3644.0    1.0  \n",
+       "response_threshold[4]    4966.0    3439.0    1.0  \n",
+       "response_threshold[5]    4785.0    3368.0    1.0  "
       ]
      },
-     "execution_count": 32,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,12 +449,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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2VigUOHPmjEadPXv2IC8vT69xlX1BPXiu+0dNVabsC+rBL8jvv/++XN2yOto+PfPz80Pbtm0RHR2N1atXo7CwECNGjNCo07t3b5w7dw4+Pj4ICgoq96NNUhYXF4fTp09rlK1evRpWVlZo1arVQ/cNDg6GmZkZVq5cqVF+48YN7NmzB127di23z6pVq8qdC4B6RFhF91QIgaVLl1YYQ25ubrn3avXq1ZBKpejYseND49fGg09dKmNubo7OnTvj1KlTaN68eYXvR0VJoq2tLV588UWMHTsWWVlZDx0l2bVrV8THx5drVl6xYgUkEgk6d+6s8/VV9/U8+P7//PPPKCkpUb//Xbp0AYByn6ljx47h/PnzFX6mdFFRfI97rWXKugssXry40jr6+K7o3bs3hBC4efNmhTE2a9YMANC2bVvIZDKsW7dOY//Dhw+X67pRFZ+p6vhdIO3xSRlVKDg4GIsXL8aYMWPQunVrjB49Gv7+/iguLsapU6ewZMkSBAQEoE+fPgCAYcOG4cMPP8S0adPQqVMnxMfH45tvvoGNjY1e43JxccFzzz2HuXPnol69evDy8sKff/6pblZ4mJCQENSrVw+jRo3C9OnTYWJiglWrVpVLcgCovzDnzZuHsLAwGBkZoXnz5upm1oqMHDkSb775Jm7duoWQkBA0btxYY/usWbOwa9cuhISEYMKECWjcuDEKCgqQmJiIrVu34rvvvnvkU0k3Nzf07dsXM2bMgKurK1auXIldu3Zh3rx5j/wr1dbWFh9++CGmTp2K4cOHY/DgwcjMzMTMmTMhl8vLjVgzNTXFZ599hry8PDzzzDPq0ZdhYWFo3749gNI560xNTTF48GC89957KCgowOLFi3H79u0KY7C3t8fo0aORlJSERo0aYevWrVi6dClGjx4NT0/Ph8avjWbNmmHt2rVYt24dGjRoALlcrn4vH/TVV1+hffv26NChA0aPHg2FQoHc3FxcvnwZW7ZsUffP6dOnDwICAhAUFARHR0dcu3YNX375Jby8vCodzQaUTgi7YsUK9OrVC7NmzYKXlxf++OMPLFq0CKNHj9YYVacPVXE9GzduhLGxMbp166YefRkYGKju/9S4cWO88cYb+PrrryGVShEWFqYefenh4YFJkybpfB3axKfttVakQ4cOGDZsGGbPno3U1FT07t0bMpkMp06dgrm5OcaPH6+X74rQ0FC88cYbGDFiBI4fP46OHTvCwsICycnJOHjwIJo1a4bRo0fDzs4OUVFR6u+0AQMG4MaNG5g5cyZcXV01RnRX1Weqqn8XSAeGHGVANV9sbKyIiIgQnp6ewtTUVFhYWIiWLVuKadOmibS0NHW9wsJC8d577wkPDw9hZmYmOnXqJGJjYysdfXns2DGN85SN9kpPT9coj4iIEBYWFhplycnJ4sUXXxR2dnbCxsZGDB06VBw/flyr0ZcxMTEiODhYmJubC0dHR/Haa6+JkydPltu3sLBQvPbaa8LR0VFIJBIBQCQkJAghyo++LJOdnS3MzMwEALF06dIK72d6erqYMGGC8Pb2FiYmJsLOzk60bt1afPDBByIvL6/CfcqUjdDbsGGD8Pf3F6ampkKhUJQbsVo2UnH9+vUVHuf//u//RPPmzYWpqamwsbER/fr1Kzfys+y+nzlzRjz77LPCzMxM2NnZidGjR5eLc8uWLSIwMFDI5XJRv3598e6774pt27aVGy3ZqVMn4e/vL/bt2yeCgoKETCYTrq6uYurUqaK4uFjjmHjM0ZeJiYmie/fuwsrKSgAQXl5eQoiKR5yVlY8cOVLUr19fmJiYCEdHRxESEiJmz56trvPZZ5+JkJAQ4eDgIExNTYWnp6d49dVXRWJiYoX3937Xrl0TQ4YMEfb29sLExEQ0btxYfPrpp0KpVGrU03X05dixYyvcpq/rKbu3J06cEH369BGWlpbCyspKDB48WKSmpmqcU6lUinnz5olGjRoJExMT4eDgIIYOHSquX7+uUa/s/X9QRESE+n3SNj5tr7UySqVSfPHFFyIgIED9exAcHCy2bNmirqOP7wohhPjxxx9F27ZthYWFhTAzMxM+Pj5i+PDh4vjx4+o6KpVKzJ49W7i7uwtTU1PRvHlz8fvvv4vAwEAxYMAAjdi1+UyVfd4//fTTctduqN8F0o5ECCGqNw0kosehUCgQEBCA33//3dCh0FNuxowZmDlzJtLT05+o7xs9voSEBDRp0gTTp0/H1KlTDR0OVRM2XxIRERnQ6dOnsWbNGoSEhMDa2hoXL17E/PnzYW1tjVdffdXQ4VE1YlJGRERkQBYWFjh+/Dh++OEH3LlzBzY2Nnj22WcxZ86cCqfKoKcXmy+JiIiIagBOiUFERERUAzApIyIiIqoBmJQRERER1QB1rqO/SqXCrVu3YGVlpdUSFERERERPQgiB3NxcuLm5aUwI/KA6l5TdunULHh4ehg6DiIiI6pjr168/dOWWOpeUWVlZASi9MdbW1gaOhoiIiJ52OTk58PDwUOcglalzSVlZk6W1tTWTMiIiIqo2j+o2xY7+RERERDUAkzIiIiKiGqDONV8SERERAYBSJXA0IQtpuQVwspKjjbcdjKSGm5nBoE/K/vrrL/Tp0wdubm6QSCT49ddfH7nP/v370bp1a8jlcjRo0ADfffdd1QdKRERET5Xt55LRft4eDF56GG+tjcXgpYfRft4ebD+XbLCYDJqU5efnIzAwEN98841W9RMSEvD888+jQ4cOOHXqFKZOnYoJEybgl19+qeJIiYiI6Gmx/VwyRq88ieTsAo3ylOwCjF550mCJmUGbL8PCwhAWFqZ1/e+++w6enp748ssvAQB+fn44fvw4FixYgBdeeKGKoiQiIqKnhVIlMHNLPEQF2wQACYCZW+LRralLtTdl1qqO/ocOHUL37t01ynr06IHjx4+juLi4wn0KCwuRk5Oj8UNERER109GErHJPyO4nACRnF+BoQlb1BfWvWpWUpaSkwNnZWaPM2dkZJSUlyMjIqHCfuXPnwsbGRv3D2fyJiIjqrpTse1rVS8utPHGrKrUqKQPKT7wmhKiwvMyUKVOQnZ2t/rl+/XqVx0hEREQ1y+38IizedwVz/jivVX0nK3kVR1RerZoSw8XFBSkpKRplaWlpMDY2hr29fYX7yGQyyGSy6giPiIiIapi4W9lYHpOI32JvobBEBQCQSABRUacylPYpc7EpnR6jutWqpCw4OBhbtmzRKNu5cyeCgoJgYmJioKiIiIioJilWqrAzLhXLYhJwLPG2utzfzRoRIQrIjaV4a20sAGh0+C9rc5vep6lB5iszaFKWl5eHy5cvq18nJCQgNjYWdnZ28PT0xJQpU3Dz5k2sWLECADBq1Ch88803iIqKwuuvv45Dhw7hhx9+wJo1awx1CURERFRDZOYVYs3RJKw8nISUnNI+YcZSCXoGuCAyRIHWXvXU3Z1MjaWYuSVeo9O/i40c0/s0Rc8AV4PELxGisgd4VW/fvn3o3LlzufKIiAgsW7YMkZGRSExMxL59+9Tb9u/fj0mTJiEuLg5ubm54//33MWrUKK3PmZOTAxsbG2RnZ3NBciIioqfAmRt3sCwmEb+fTkaRsrSJ0sHSFEPaeGJIWy+42FTcP6y6ZvTXNvcwaFJmCEzKiIiIar+iEhW2nUvG8phEnEy6oy4PdLdBRIgCvZq7QmZsZLgA76Nt7lGr+pQRERFR3ZaWW4DVR5Kw6kgS0nMLAQAmRhL0auaKiBAFWnrWM3CEj49JGREREdV4p5JuY3lMIv44m4xiZWkjn6OVDK+09cSQtp4GmcJC35iUERERUY1UWKLEH2dKmyhP38hWl7fytEVEiAJhAa4wNa51U65WikkZERER1Sgp2QVYdeQa1hxNQkZeEQDA1EiK3oGuiAxRoLm7rWEDrCJMyoiIiMjghBA4ce02lsUkYvu5FJSoSpsoXazlGNrOEy+38YSD5dM9GTyTMiIiIjKYgmIlNp++heUxiYi7laMub6OwQ0SIAt39nWFi9PQ0UT4MkzIiIiKqdrfu3MPKw6VNlLfvFgMAZMZS9GvhhogQBfzdbAwcYfVjUkZERETVQgiBIwlZWB6TiJ3xqVD+20RZ39YMQ9t54eVnPFDPwtTAURoOkzIiIiKqUveKlPg19iaWxyTiQkquurxdAztEhnjjOT8nGNeRJsqHYVJGREREVeJ61l2sPHwNa49dR/a90iZKuYkUA1q6IyLEC01cuLLO/ZiUERERkd4IIRBzJRPLYhLx5/lU/NtCCQ87Mwxvp0B4kAdszE0MG2QNxaSMiIiInlh+YQk2nSptoryUlqcub+/rgIgQBbo0caqSxb6fJkzKiIiI6LFdy8zHikPX8PPx68gtKAEAmJsa4YVWpU2Uvk5WBo6w9mBSRkRERDpRqQQOXs7AsphE7L2YBvFvE6WXvTmGByvwUpA7rOVsotQVkzIiIiLSSl5hCX45cQPLDyXianq+urxTI0dEhijQqZEjpGyifGxMyoiIiOihrqbnYcWha9hw4gbyCkubKC1lxnixtTuGB3uhgaOlgSN8OjApIyIionJUKoH9/6RjWUwi9v+Tri5v4GiBiGAFXmjtDksZ0wh94t0kIiIitZyCYqw/fgM/HUpEYuZdAIBEAnRp7ISIEAXa+zqwibKKMCkjIiIiXErNxfJDidh48ibuFikBAFZyY4QHeWB4sBe87C0MHOHTj0kZERFRHaVUCey5kIblMYk4eDlDXd7QyRIRIQoMaFkfFmyirDa800RERHVM9t1irDuehJ8OX8P1rHsAAKkE6OrnjBEhCgT72EMiYRNlddM5KXv22WcxcuRIvPTSSzAzM6uKmIiIiKgKXEjJwfKYRGw6dRMFxSoAgI2ZCV5+xgND23nBw87cwBHWbTonZa1bt8Z7772H8ePHIzw8HK+++iratWtXFbERERHREypRqrD7fCqWxSTi8NUsdXkTFytEhijQr0V9mJkaGTBCKiMRomweXu0plUr8/vvviI6OxtatW+Hr64uRI0di2LBhcHZ2roo49SYnJwc2NjbIzs6GtTVXpyciotpHqRI4mpCFtNwCOFnJ0cbbrty6kln5RVh7LAmrDifh5p3SJkojqQTdmzojIkSBtt52bKKsJtrmHo+VlN0vPT0d33//PebMmQOlUonnn38eEyZMQJcuXZ7ksFWGSRkREdVm288lY+aWeCRnF6jLXG3kmN6nKXoGuCLuVjaWxyTit9hbKCwpbaKsZ26CwW08MbSdF9xs2fWoummbezxRR/+jR48iOjoaa9asgZOTEyIjI5GcnIw+ffpg9OjRWLBgwZMcnoiIiO6z/VwyRq88iQefpqRkF2DUypPwcbTAlfuWP/J3s0ZkiAJ9At0gN2ETZU2n85OytLQ0/PTTT4iOjsalS5fQp08fvPbaa+jRo4f6Meju3bvRv39/5OXlVUnQT4JPyoiIqDZSqgTaz9uj8YSsIkYSIKyZKyJDFGjtVY9NlDVAlT0pc3d3h4+PD0aOHInIyEg4OjqWq9OmTRs888wzuh6aiIiIKnE0IeuRCRkALBzcCr2au1ZDRKRvOidlf/75Jzp06PDQOtbW1ti7d+9jB0VERESakv/trP8oJSpVFUdCVUWq6w7Tp0/HnTt3ypXn5OTU2M79REREtVVabgG+3P0PZv4er1V9Jyt5FUdEVUXnJ2X79+9HUVFRufKCggIcOHBAL0ERERHVdaeSbmNZTCK2nk1GsbK0+7dUAqgq6QkuAeBiUzo9BtVOWidlZ86cAQAIIRAfH4+UlBT1NqVSie3bt6N+/fr6j5CIiKiOKCxR4o8zyVgek4jTN7LV5a08bRERooBUIsGENacAQGMEZllX/ul9mpabr4xqD62TshYtWkAikUAikVTYTGlmZoavv/5ar8ERERHVBSnZBVh15BrWHE1CRl5pa5SpkRS9A0tHUTZ3t1XXNTGSlJunzOW+ecqo9tI6KUtISIAQAg0aNMDRo0c1Rl2amprCyckJRkacA4WIiEgbQgicuHYb0TGJ2HEuBSX/tku6WMsxtJ0nXm7jCQdLWbn9ega4oltTl0fO6E+1j9ZJmZeXFwBAxVEdREREj62gWInNp29heUwi4m7lqMvbKOwQEaJAd39nmBg9fByekVSCYB/7qg6VqplWSdnmzZsRFhYGExMTbN68+aF1+/btq5fAiIiIniY379zDysPXsPZoEm7fLQYAyIyl6NfCDREhCvi72Rg4QjI0rWb0l0qlSElJgZOTE6TSyrN3iUQCpVKp1wD1jTP6ExFRdRFC4EhCFpbHJGJHXIp65KSbjRzDghV4+RkP1LMwNWyQVOX0OqP//U2WbL4kIiJ6uHtFSvwaexPLYxJxISVXXd6ugR0iQxR4zs8Zxo9ooqS6x+CfiEWLFsHb2xtyuRytW7d+5Fxnq1atQmBgIMzNzeHq6ooRI0YgMzOzmqIlIiKq3PWsu5i79Tzazf0TUzaexYWUXMhNpBjcxhPbJ3bA2jeC0TPAlQkZVUirJ2ULFy7U+oATJkzQuu66deswceJELFq0CKGhofj+++8RFhaG+Ph4eHp6lqt/8OBBDB8+HF988QX69OmDmzdvYtSoUXjttdewadMmrc9LRESkL0IIxFzJxLKYRPx5PlXdROlezwzDg70QHuQBW3M2UdKjadWnzNvbW7uDSSS4evWq1idv27YtWrVqhcWLF6vL/Pz80L9/f8ydO7dc/QULFmDx4sW4cuWKuuzrr7/G/Pnzcf369QrPUVhYiMLCQvXrnJwceHh4sE8ZERE9kfzCEmw6VdpEeSktT13e3tcBESEKdGnixGkqCICe+5QlJCToLbAyRUVFOHHiBCZPnqxR3r17d8TExFS4T0hICD744ANs3boVYWFhSEtLw4YNG9CrV69KzzN37lzMnDlTr7ETEVHddS0zHysOXcPPx68jt6AEAGBuaoSBreojIliBhs5WBo6Qaiud177Ul4yMDCiVSjg7O2uUOzs7ayzhdL+QkBCsWrUKgwYNQkFBAUpKStC3b9+HriQwZcoUREVFqV+XPSkjIiLSlkolcPByBpbFJGLvxTSUtTF52ZtjeLACL7Z2h42ZiWGDpFpPq6QsKioKH330ESwsLDQSnIp8/vnnOgUgkWg+2hVClCsrEx8fjwkTJmDatGno0aMHkpOT8e6772LUqFH44YcfKtxHJpNBJis/IzIREdGj5BWW4JcTN7D8UCKupueryzs1ckRkiAKdGjlCyiZK0hOtkrJTp06huLhY/e/KVJZMVcTBwQFGRkblnoqlpaWVe3pWZu7cuQgNDcW7774LAGjevDksLCzQoUMHzJ49G66uXPOLiIie3NX0PKw4dA0bTtxAXmFpE6WlzBgvtnbHsGAv+DhaGjhCehpplZTt3bu3wn8/CVNTU7Ru3Rq7du3CgAED1OW7du1Cv379Ktzn7t27MDbWDLlsvU0txisQERFVSqUS2P9POpbFJGL/P+nq8gaOFogIVmBgq/qwkrOJkqrOE/Upu379OiQSCdzd3R9r/6ioKAwbNgxBQUEIDg7GkiVLkJSUhFGjRgEo7Q928+ZNrFixAgDQp08fvP7661i8eLG6+XLixIlo06YN3NzcnuRSiIiojsopKMb64zfw06FEJGbeBQBIJEDnxk6ICFGgg68DmyipWuiclJWUlGDmzJlYuHAh8vJKhwBbWlpi/PjxmD59OkxMtP8rYtCgQcjMzMSsWbOQnJyMgIAAbN26Vb34eXJyMpKSktT1IyMjkZubi2+++QZvv/02bG1t0aVLF8ybN0/XyyAiojrucloulsdcwy8nb+BuUekSgVZyY4QHeWBYOy8oHCwMHCHVNVrNU3a/UaNGYdOmTZg1axaCg4MBAIcOHcKMGTPQr18/fPfdd1USqL5w7UsiorpLqRLYcyENy2MScfByhrq8oZMlIkIUGNCyPixkBpuYgJ5S2uYeOidlNjY2WLt2LcLCwjTKt23bhpdffhnZ2dmPF3E1YVJGRFT3ZN8txrrjSfjp8DVcz7oHoLSJ8jk/Z0SGKBDiY6/TYDUiXeh18tj7yeVyKBSKcuUKhQKmplxGgoiIao6LKblYFpOIX0/dxL3i0iZKGzMTDHqmtInSw87cwBES/UfnpGzs2LH46KOPEB0drZ7/q7CwEHPmzMG4ceP0HiAREZEuSpQq7D6fimUxiTh8NUtd3sTFChEhCvRvUR9mpkYGjJCoYlolZQMHDtR4vXv3bri7uyMwMBAAcPr0aRQVFaFr1676j5CIiEgLt/OLsPbYdaw8fA0375Q2UUolQA9/F0SEKNDW245NlFSjaZWU2djYaLx+4YUXNF5z2SIiIjKUuFvZWB6TiN9ib6GwRAUAqGdugpfbeGJoOy/UtzUzcIRE2tEqKYuOjq7qOIiIiLRWrFRhZ1wqlsUk4FjibXW5v5s1IkIU6BvoBrkJmyipduG4XyIiqjUy8wqx5mgSVh5OQkpOAQDASCpBzwAXRIYoEORVj02UVGs9VlK2YcMG/Pzzz0hKSkJRUZHGtpMnT+olMCIiojJnb2RjWUwitpy+hSJlaROlvYUphrT1xCttveBiIzdwhERPTuekbOHChfjggw8QERGB3377DSNGjMCVK1dw7NgxjB07tipiJCKiOqioRIVt55KxPCYRJ5PuqMubu9sgIliBXs1d2URJTxWdk7JFixZhyZIlGDx4MJYvX4733nsPDRo0wLRp05CVlfXoAxARUZ2lVAkcTchCWm4BnKzkaONtB6MH1pVMyy3A6iNJWHUkCem5hQAAEyMJnm/miogQBVp62LKJkp5KOidlSUlJCAkJAQCYmZkhNzcXADBs2DC0a9cO33zzjX4jJCKip8L2c8mYuSUeydkF6jJXGzmm92mKngGuOJV0G8tjEvHH2WQUK0sXm3G0kuGVtp4Y0sYTTtZsoqSnm85JmYuLCzIzM+Hl5QUvLy8cPnwYgYGBSEhIgI4rNhERUR2x/VwyRq88iQf/L5GSXYBRK0/Cy94c1zLvqstbetoiMkSBsABXmBpLqzdYIgPROSnr0qULtmzZglatWuHVV1/FpEmTsGHDBhw/frzcJLNERERKlcDMLfHlEjIA6rJrmXdhIpWgT6AbIkIUCPSwrcYIiWoGnZOyJUuWQKUqHfkyatQo2NnZ4eDBg+jTpw9GjRql9wCJiKh2O5qQpdFkWZmvh7REzwDXaoiIqGbSOSmTSqWQSv97lBweHo7w8HC9BkVERE+PW3fuProSoJ6Nn6iueqx5ym7fvo0ffvgB58+fh0QigZ+fH0aMGAE7Ozt9x0dERLVUWm4BVh1OQvTfiVrVd7JiR36q23TuPbl//354e3tj4cKFuH37NrKysrBw4UJ4e3tj//79VREjERHVImdu3MGkdbEI/WQPvvrzEnIKiiF9yAwWEpSOwmzjzT/sqW7T+UnZ2LFjER4ejsWLF8PIqHTSPqVSiTFjxmDs2LE4d+6c3oMkIqKarVipwvZzKYj+O0FjoteWnrYYEeoNCYAJa04BgEaH/7JcbXqfpuXmKyOqa3ROyq5cuYJffvlFnZABgJGREaKiorBixQq9BkdERDVbZl4h1h67jp8OXVOvRWliJEGvZq6IDPVGi/tGUZoYScrNU+Zy3zxlRHWdzklZq1atcP78eTRu3Fij/Pz582jRooW+4iIiohos/lYOlsUk4NfYWyj6t4O+g6UphrT1wtC2FU/02jPAFd2aujxyRn+iukqrpOzMmTPqf0+YMAFvvfUWLl++jHbt2gEADh8+jG+//RaffPJJ1URJREQGV6JUYff5VET/nYgjCf8tqxdQ3xojQrzRO9AVMuOHr0VpJJUg2Me+qkMlqpUkQotp+KVSKSQSySNn7JdIJFAqlXoLrirk5OTAxsYG2dnZsLa2NnQ4REQ13p27RVh37DpWHLqGm3fuAShNrnoGuGBkqAKtPOtxLUqih9A299DqSVlCQoLeAiMiotrhn9RcLItJxMaTN1BQXNpEWc/cBIPbeGJYsBdcbcwMHCHR00WrpMzLy6uq4yAiohpApRLYcyENy2IScfByhrq8iYsVRoQq0K9FfchNHt5ESUSP57Emj71y5Qq+/PJLjclj33rrLfj4+Og7PiIiqgY5BcVYf/wGVhxKVC8MLpUA3Zo6IzLEG+0a2LGJkqiK6ZyU7dixA3379kWLFi0QGhoKIQRiYmLg7++PLVu2oFu3blURJxERVYGr6XlYHpOIDSduIL+otE+wtdwYL7fxxLB2XvCwMzdwhER1h1Yd/e/XsmVL9OjRo9xIy8mTJ2Pnzp04efKkXgPUN3b0J6K6TqUS+OtSOpbFJGLfxXR1ua+TJSJDFBjYqj7MTR+rIYWIKqBt7qFzUiaXy3H27Fk0bNhQo/yff/5B8+bNUVBQUMmeNQOTMiKqq/ILS/DLyRtYFpOIq+n5AACJBOjS2AmRoQq093VgEyVRFdDr6Mv7OTo6IjY2tlxSFhsbCycnJ90jJSKiKpWUeRfLDyXi5+PXkVtQAgCwlBnjpSB3RAQroHCwMHCERAQ8RlL2+uuv44033sDVq1cREhICiUSCgwcPYt68eXj77berIkYiItKREAKHrmTix78T8eeFVJS1iXg7WCAi2AsvBnnAUsYmSqKaROfmSyEEvvzyS3z22We4desWAMDNzQ3vvvsuJkyYUOMffbP5koieZveKlPg19iaW/Z2Ii6m56vIODR0wMtQbnRo5QspljYiqVZU0X5aUlGDVqlUYPHgwJk2ahNzc0l94KyurJ4uWiIieyM079/DToWtYeywJd+4WAwDMTY3wQit3RIR4wdeJ39NENZ1OSZmxsTFGjx6N8+fPA2AyRkRkSEIIHEu8jWUxCdgRlwqlqrThw72eGSJDFHgpyAM2ZiYGjpKItKVzh4K2bdvi1KlTnOWfiMhACoqV2HL6FpbFJCLuVo66PMTHHpEhCnT1c4YRmyiJah2dk7IxY8bg7bffxo0bN9C6dWtYWGiO2mnevLnegiMiov+k5hRg5eFrWH0kCZn5RQAAmbEUA1vVR0SIAk1c2E+WqDbTuaO/VCotfxCJBEIISCQSKJVKvQVXFdjRn4hqm5NJt7Hs70RsPZuMkn+bKN1s5BgWrMDLz3ignoWpgSMkooepsnnKEhISnigwIiJ6tKISFbaeTUZ0TCJOX7+jLm+jsENkqALdmzrD2Kj8H8lEVHvpnJTpuy/ZokWL8OmnnyI5ORn+/v748ssv0aFDh0rrFxYWYtasWVi5ciVSUlLg7u6ODz74ACNHjtRrXEREhpCeW4jVR5Kw8sg1pOcWAgBMjaTo28INkSEKBNS3MXCERFRVHmvmwIsXL+Lrr7/G+fPnIZFI0KRJE4wfPx6NGzfW6Tjr1q3DxIkTsWjRIoSGhuL7779HWFgY4uPj4enpWeE+4eHhSE1NxQ8//ABfX1+kpaWhpKTkcS6DiKjGOHczGz/+nYDfTyejSKkCADhZyTC0nReGtPWEg6XMwBESUVXTuU/Zhg0bMHjwYAQFBSE4OBgAcPjwYRw7dgyrV6/GSy+9pPWx2rZti1atWmHx4sXqMj8/P/Tv3x9z584tV3/79u14+eWXcfXqVdjZ2ekSthr7lBFRTVGsVGFHXAqW/Z2I49duq8tbeNhiRKgCYQGuMDVmEyVRbVdlC5I3aNAAQ4cOxaxZszTKp0+fjp9++glXr17V6jhFRUUwNzfH+vXrMWDAAHX5W2+9hdjYWOzfv7/cPmPGjME///yDoKAg/PTTT7CwsEDfvn3x0UcfwczMrMLzFBYWorCwUP06JycHHh4eTMqIyGCy8ouw5mgSVh6+huTsAgCAsVSCXs1dERmiQEvPegaOkIj0qco6+qekpGD48OHlyocOHYpPP/1U6+NkZGRAqVTC2dlZo9zZ2RkpKSkV7nP16lUcPHgQcrkcmzZtQkZGBsaMGYOsrCz8+OOPFe4zd+5czJw5U+u4iIiqyvnkHCz7OxG/xt5EYUlpE6W9hSleaeuJV9p5wdlabuAIiciQdE7Knn32WRw4cAC+vr4a5QcPHnxoB/3KPLhWZtnUGhVRqVSQSCRYtWoVbGxKO7t+/vnnePHFF/Htt99W+LRsypQpiIqKUr8ue1JGRFQdlCqB3edTEf13Ag5fzVKXB9S3xogQb/Rq7gq5iZEBIySimkLnpKxv3754//33ceLECbRr1w5AaZ+y9evXY+bMmdi8ebNG3co4ODjAyMio3FOxtLS0ck/Pyri6uqJ+/frqhAwo7YMmhMCNGzfQsGHDcvvIZDLIZOwgS0TVK/tuMdYdT8KKQ9dw4/Y9AICRVIKe/i4YEapAa696lf4BSkR1k14mj63wwFpMJNu2bVu0bt0aixYtUpc1bdoU/fr1q7Cj/5IlSzBx4kSkpaXB0tISAPDbb79h4MCByMvLq7Rf2f3Y0Z+IqtLltFxE/52IjSdv4l5x6XegrbkJBrfxxLB2XnCzffT3FBE9XaqsT5lKpXqiwO4XFRWFYcOGqUdyLlmyBElJSRg1ahSA0qbHmzdvYsWKFQCAIUOG4KOPPsKIESMwc+ZMZGRk4N1338XIkSO1SsiIiB6XUiVwNCELabkFcLKSo423nXp9SZVKYN8/aYj+OxEHLmWo92niYoURoQr0a1GfTZRE9EiPNU+ZvgwaNAiZmZmYNWsWkpOTERAQgK1bt6onqE1OTkZSUpK6vqWlJXbt2oXx48cjKCgI9vb2CA8Px+zZsw11CURUB2w/l4yZW+LVIyUBwNVGjvd6NMade8VYHpOIxMy7AACJBOjm54zIUAWCG9iziZKItKZz82Vtx+ZLItLF9nPJGL3yJB71RWklN8bLz3hgeLACHnbm1RIbEdUOVdZ8SURUVyhVAjO3xD80ITOSSjCtT1O82ModFjJ+pRLR4+NU0URElTiakKXRZFkRpUqgkZMVEzIiemJMyoiIKpGW+/CETNd6REQP81hJ2ZUrV/C///0PgwcPRlpaGoDSdSnj4uL0GhwRkaHkFBTjl5M3tKrrZMWZ+InoyemclO3fvx/NmjXDkSNHsHHjRuTl5QEAzpw5g+nTp+s9QCKi6hZzJQNhXx7AX/9kPLSeBKWjMNt421VPYET0VNM5KZs8eTJmz56NXbt2wdTUVF3euXNnHDp0SK/BERFVp4JiJWZuicOQpUdw8849eNqZ453ujSBBaQJ2v7LX0/s0Vc9XRkT0JHTumXr27FmsXr26XLmjoyMyMzP1EhQRUXU7ff0Oon6OxZX0fADAkLae+OB5P1jIjOHrZFlunjIXGzmm92mKngGuhgqZiJ4yOidltra2SE5Ohre3t0b5qVOnUL9+fb0FRkRUHYqVKnz95yV8u+8KlCoBJysZ5r3YHJ0bO6nr9AxwRbemLpXO6E9EpA86J2VDhgzB+++/j/Xr10MikUClUuHvv//GO++8g+HDh1dFjEREVeKf1FxE/RyLczdzAAB9A90wq58/bM1Ny9U1kkoQ7GNf3SESUR2i84z+xcXFiIyMxNq1ayGEgLGxMZRKJYYMGYJly5bByKhmr+/GGf2JSKkS+PFgAj7deRFFJSrYmpvgo34B6BPoZujQiOgppG3u8djLLF25cgWnTp2CSqVCy5Yt0bBhw8cOtjoxKSOq265n3cXb60/jaEIWAKBzY0fMe6E5nKw5rQURVY0qW2Zp//796NSpE3x8fODj4/NEQRIRVRchBNYdu46Pfo9HfpESFqZG+F/vpnj5GQ8uGk5ENYLOSVm3bt3g4uKCIUOGYOjQoQgICKiKuIiI9CYtpwCTN57Fngulk123UdhhwUuB8LTnwuFEVHPoPE/ZrVu38N577+HAgQNo3rw5mjdvjvnz5+PGDe1mviYiqk5/nElG9y//wp4LaTA1kmLq802w5o12TMiIqMZ57D5lAJCQkIDVq1djzZo1uHDhAjp27Ig9e/boMz69Y58yorrhzt0iTPstDptP3wIA+LtZ44tBLdDI2crAkRFRXVPlHf3LKJVKbNu2DR9++CHOnDkDpVL5JIerckzKiJ5++/9Jx3sbTiM1pxBGUgnGPuuDcV0awtT4sZb7JSJ6IlXW0b/M33//jVWrVmHDhg0oKChA37598fHHHz/u4YiInlh+YQk+3noeq44kAQAaOFrg8/AWaOFha9jAiIi0oHNSNnXqVKxZswa3bt3Cc889hy+//BL9+/eHuTn7ZxCR4RxPzMLb60/jWuZdAMCIUAXe69EEZqY1e+5EIqIyOidl+/btwzvvvINBgwbBwcGhKmIiItJaYYkSn+/6B0v+ugohADcbORa8FIgQX34/EVHtonNSFhMTUxVxEBHpLO5WNt7++TQupOQCAF5s7Y5pfZrCWm5i4MiIiHSnVVK2efNmhIWFwcTEBJs3b35o3b59++olMCKiypQoVfj+r6v4cvc/KFYK2FuYYu7AZuju72Lo0IiIHptWoy+lUilSUlLg5OQEqbTy0UsSiYSjL4moSiVk5CPq51icSroDAOjh74w5A5rBwVJm2MCIiCqh19GXKpWqwn8TEVUXlUpg5ZFr+HjreRQUq2AlM8aMvv4Y2Ko+l0kioqeCzpP2rFixAoWFheXKi4qKsGLFCr0ERUR0v1t37mH4j0cx7bc4FBSrEOprjx2TOuKF1u5MyIjoqaHz5LFGRkZITk6Gk5OTRnlmZiacnJzYfElEeiOEwKZTNzF9cxxyC0ogN5FiSpgfhrXzglTKZIyIaocqmzxWCFHhX6Y3btyAjY2NrocjIqpQZl4hPth0DtvjUgAALTxs8Xl4IBo4Who4MiKiqqF1UtayZUtIJBJIJBJ07doVxsb/7apUKpGQkICePXtWSZBEVLfsik/FlI1nkJFXBGOpBBOfa4hRnXxgbMRlkojo6aV1Uta/f38AQGxsLHr06AFLy//+WjU1NYVCocALL7yg9wCJqO7ILSjGrC3xWH/iBgCgsbMVPgsPREB9PoUnoqef1knZ9OnTAQAKhQKDBg2CXC6vsqCIqO6JuZKBd9efwc079yCRAG90bICobo0gM+YySURUN+jcpywiIqIq4iCiOqqgWIn52y/ix78TAACedub4LDwQzyjsDBwZEVH10jkpUyqV+OKLL/Dzzz8jKSkJRUVFGtuzsrL0FhwRPd1OX7+DqJ9jcSU9HwAwpK0nPnjeDxYynb+aiIhqPZ17zc6cOROff/45wsPDkZ2djaioKAwcOBBSqRQzZsyoghCJ6GlTrFTh813/YODiGFxJz4eTlQzRI57BxwOaMSEjojpL53nKfHx8sHDhQvTq1QtWVlaIjY1Vlx0+fBirV6+uqlj1gvOUERnWpdRcTPo5Fudu5gAA+ga6YVY/f9iamxo4MiKiqlFl85SlpKSgWbNmAABLS0tkZ2cDAHr37o0PP/zwMcMloqedUiXw48EEfLrzIopKVLA1N8FH/QLQJ9DN0KEREdUIOidl7u7uSE5OhqenJ3x9fbFz5060atUKx44dg0zGBYGJqLzrWXfx9vrTOJpQ2ue0c2NHzHuhOZysOYqbiKiMzknZgAED8Oeff6Jt27Z46623MHjwYPzwww9ISkrCpEmTqiJGIqqlhBD4+fh1zNoSj/wiJSxMjfC/3k3x8jMeXLOSiOgBOvcpe9Dhw4cRExMDX19f9O3bV19xVRn2KSOqHmk5BZi88Sz2XEgDALRR2GHBS4HwtDc3cGRERNVL29zjidcsadeuHaKioh47IVu0aBG8vb0hl8vRunVrHDhwQKv9/v77bxgbG6NFixaPdV4iqjp/nElG9y//wp4LaTA1kmLq802w5o12TMiIiB5Cq+bLzZs3a31AXZKzdevWYeLEiVi0aBFCQ0Px/fffIywsDPHx8fD09Kx0v+zsbAwfPhxdu3ZFamqq1ucjoqqVfbcY0zafw2+xtwAA/m7W+GJQCzRytjJwZERENZ9WzZdSqXYP1CQSCZRKpdYnb9u2LVq1aoXFixery/z8/NC/f3/MnTu30v1efvllNGzYEEZGRvj1118RGxur9TnZfElUNfb/k473NpxGak4hjKQSjH3WB+O6NISpMRcRJ6K6Ta9TYqhUKr0FVqaoqAgnTpzA5MmTNcq7d++OmJiYSveLjo7GlStXsHLlSsyePfuR5yksLERhYaH6dU5OzuMHTUTl5BeW4OOt57HqSBIAoIGjBT4Pb4EWHraGDYyIqJYx2NTZGRkZUCqVcHZ21ih3dnZGSkpKhftcunQJkydPxoEDB2BsrF3oc+fOxcyZM584XiIq73hiFt5efxrXMu8CAEaEKvBejyYwM+Ui4kREutI5KZs1a9ZDt0+bNk2n4z04LF4IUeFQeaVSiSFDhmDmzJlo1KiR1sefMmUKoqKi1K9zcnLg4eGhU4xEpKmwRIkvdl3Ckr+uQCUANxs5FrwUiBBfB0OHRkRUa+mclG3atEnjdXFxMRISEmBsbAwfHx+tkzIHBwcYGRmVeyqWlpZW7ukZAOTm5uL48eM4deoUxo0bB6C0WVUIAWNjY+zcuRNdunQpt59MJuOktkR6FH8rB1E/x+JCSi4A4MXW7pjWpyms5SYGjoyIqHbTOSk7depUubKcnBxERkZiwIABWh/H1NQUrVu3xq5duzT227VrF/r161euvrW1Nc6ePatRtmjRIuzZswcbNmyAt7e3DldBRLoqUarw/V9X8eXuf1CsFLC3MMXcgc3Q3d/F0KERET0V9NKnzNraGrNmzULv3r0xbNgwrfeLiorCsGHDEBQUhODgYCxZsgRJSUkYNWoUgNKmx5s3b2LFihWQSqUICAjQ2N/JyQlyubxcORHpV0JGPqJ+jsWppDsAgB7+zpgzoBkcLPkUmohIX/TW0f/OnTvqxcm1NWjQIGRmZmLWrFlITk5GQEAAtm7dCi8vLwBAcnIykpKS9BUiEelIpRJYeeQa5m69gHvFSljJjDGjrz8GtqrPZZKIiPRM52WWFi5cqPFaCIHk5GT89NNP6NixI9asWaPXAPWN85QRaSc5+x7e23AGBy5lAABCfe3x6YuBcLM1M3BkRES1i17nKbvfF198ofFaKpXC0dERERERmDJliu6RElGNIoTAr7E3Me23OOQWlEBuIsWUMD8Ma+cFqZRPx4iIqorOSVlCQkJVxEFENUBmXiE+2HQO2+NKR0W38LDF5+GBaOBoaeDIiIiefgabPJaIapZd8amYsvEMMvKKYCyVYOJzDTGqkw+MjbhMEhFRddA5KSsoKMDXX3+NvXv3Ii0trdwSTCdPntRbcERU9XILijFrSzzWn7gBAGjsbIXPwgMRUN/GwJEREdUtOidlI0eOxK5du/Diiy+iTZs2HIFFVIvFXMnAu+vP4Oade5BIgDc6NkBUt0aQGXOZJCKi6qZzUvbHH39g69atCA0NrYp4iEiPlCqBowlZSMstgJOVHG287WAklaCgWIn52y/ix79L+4h62pnjs/BAPKOwM3DERER1l85JWf369WFlZVUVsRCRHm0/l4yZW+KRnF2gLnO1kSMiWIH1J67jSno+AGBIW0988LwfLGTsYkpEZEg6z1O2bds2LFy4EN999516ktfahPOUUV2w/VwyRq88iYf9cjtZyTDvxebo3Nip2uIiIqqLqmyesqCgIBQUFKBBgwYwNzeHiYnmIsRZWVm6R0tEeqNUCczcEv/QhExuIsW2tzrAnsskERHVGDonZYMHD8bNmzfx8ccfw9nZmR39iWqYowlZGk2WFSkoVuGf1DwEMykjIqoxdE7KYmJicOjQIQQGBlZFPET0BLLvFuOPs7e0qpuW+/DEjYiIqpfOSVmTJk1w7969qoiFiB5DSnYBdsanYGdcKg5fzUSJSrtuok5W8iqOjIiIdKFzUvbJJ5/g7bffxpw5c9CsWbNyfcrYeZ6o6l1Jz8OOuBTsiEvF6et3NLY1drbEzTsFyCssqXBfCQAXm9LpMYiIqObQOSnr2bMnAKBr164a5UIISCQSKJVK/URGRGpCCJy9ma1OxC6n5am3SSRAK8966OHvjO5NXaBwsFCPvgSg0eG/rAfo9D5NYcTFxYmIahSdk7K9e/dWRRxE9IASpQpHE7OwMy4VO+NScOu+zvsmRhIE+zigh78zujV1LtcU2TPAFYuHtio3T5mLjRzT+zRFzwDXarsOIiLSjs7zlNV2nKeMarKCYiX++icdO+JS8eeFVNy5W6zeZm5qhGcbO6KHvws6N3GCtdzkIUcqVdmM/kREVH2qbJ6yv/7666HbO3bsqOshieq07HvF2HMhFTvOpWL/P+m4V/xfFwA7C1M85+eEHv4uCPV1gNxEtzUpjaQSBPvY6ztkIiKqAjonZc8++2y5svvnKmOfMqJHS80pwM740mbJQ1c0R0zWtzVDd39n9PB3QZBXPRgbSQ0YKRERVRedk7Lbt29rvC4uLsapU6fw4YcfYs6cOXoLjOhpk5CR/29H/RScSrqjsa2RsyV6+Lugh78L/N2sOSkzEVEdpHNSZmNjU66sW7dukMlkmDRpEk6cOKGXwIhqOyEEzt3Mwc740kTsn9Q8je0tPW3ViZi3g4WBoiQioppC56SsMo6Ojrh48aK+DkdUK5UoVTiWeBs74lKwKz4VN+/8N9Gy8b/9u7r7u6B7U2c4W3PyViIi+o/OSdmZM2c0XgshkJycjE8++YRLL1GdVFCsxMFLGdgRl4Ld51Nx+74Rk2YmmiMmbcwePWKSiIjqJp2TshYtWkAikeDBmTTatWuHH3/8UW+BEdVkOQXF2HshDTviUrDvYjruFv03wMXW3ATP+ZV21O/QUPcRk0REVDfpnJQlJCRovJZKpXB0dIRczqYYerql/TtickdcCg5fzUSx8r8/TNxs5KXNkv7OaKOw44hJIiLSmc5JmZeXV1XEQVQjJd4/YvL6Hdz/gNjXyRI9/p26oll9G46YJCKiJ6J1UrZnzx6MGzcOhw8fLjcbbXZ2NkJCQvDdd9+hQ4cOeg+SqLoIIRB3Kwc7/11j8mJqrsb2Fh6lIya7+zvDx9HSQFESEdHTSOuk7Msvv8Trr79e4fIANjY2ePPNN/H5558zKaNaR6kSOJ6YhR1xqdgZn4IbtzVHTLZrYP/vGpMucLFhMz0REVUNrZOy06dPY968eZVu7969OxYsWKCXoIiqWkGxEn9fzsDOuFTsPp+KzPwi9Ta5iRSdGpWOmOzaxBk25hwxSUREVU/rpCw1NRUmJpX/z8nY2Bjp6el6CYqoKpSNmNwZl4p9F9OQf9+ISRszE3T9d43Jjg0dYWbKEZNERFS9tE7K6tevj7Nnz8LX17fC7WfOnIGrq6veAiPSh/TcQuz6d8RkzJUMjRGTLtZy9RqTbbztYMIRk0REZEBaJ2XPP/88pk2bhrCwsHLTX9y7dw/Tp09H79699R4gka6SMu+qR0yeSLqtMWLSx9FCvbRRc3eOmCQioppDIh6cBbYSqampaNWqFYyMjDBu3Dg0btwYEokE58+fx7fffgulUomTJ0/C2dm5qmN+Ijk5ObCxsUF2dnaFgxaoZlCqBI4mZCEttwBOVnK08baDkbTiBEoIgfjknNKO+nEpuJCiOWIy0N0G3f9NxHydOGKSiIiql7a5h9ZPypydnRETE4PRo0djypQp6hn9JRIJevTogUWLFtX4hIxqh+3nkjFzSzySswvUZa42ckzv0xQ9A0qbyJUqgRPXSteY3BmfgutZ/42YNJJK0NbbDj38XdCtqTPcbM2q/RqIiIh0pfWTsvvdvn0bly9fhhACDRs2RL169aoitirBJ2U12/ZzyRi98iQe/FCWPSMb09kHmXlF2H0+FRl5/42YlBlL0VE9YtIJ9SxMqy1mIiKih9E293ispKw2Y1JWcylVAu3n7dF4QvYw1nJjPOfnjO7+LujYyAHmpjovUEFERFTl9N58SVTVjiZkaZWQdfNzRkSIAm0bcMQkERE9PZiUkcHdvHMPBy+lY+3R61rV7x3oivYNHao4KiIioupl8McMixYtgre3N+RyOVq3bo0DBw5UWnfjxo3o1q0bHB0dYW1tjeDgYOzYsaMaoyV9yL5XjB1xKfjw13PovGAfQj/Zg/d/OYtT1+9otb+TFZc6IiKip49Bn5StW7cOEydOxKJFixAaGorvv/8eYWFhiI+Ph6enZ7n6f/31F7p164aPP/4Ytra2iI6ORp8+fXDkyBG0bNnSAFdA2igqUSH2+h0cvJSOA5czcPr6Haju68loJJUg0N0GIb4OWHMkCVn5ReU6+gOlnf1dbEqnxyAiInraGLSjf9u2bdGqVSssXrxYXebn54f+/ftj7ty5Wh3D398fgwYNwrRp07Sqz47+VU8IgctpeThwKQMHL2fgyNVMjSWNAKCBgwXaN3RAe18HtPOxh7W8dAmvstGXADQSs7LRl4uHtlJPi0FERFQb1PiO/kVFRThx4gQmT56sUd69e3fExMRodQyVSoXc3FzY2VX+5KSwsBCFhYXq1zk5OY8XMD1UWm4B/r6cgQOXMvD35Qyk5hRqbLezMEWorwM6+DogtKED6lcyd1jPAFcsHtqq3DxlLg/MU0ZERPS0MVhSlpGRAaVSWW7CWWdnZ6SkpGh1jM8++wz5+fkIDw+vtM7cuXMxc+bMJ4qVyrtbVIIjCVk4+G8S9uAs+jJjKdp426G9rwNCfR3Q1NUa0kpm5H9QzwBXdGvqovWM/kRERE8Dg4++fHDtQSGEVusRrlmzBjNmzMBvv/0GJyenSutNmTIFUVFR6tc5OTnw8PB4/IDrKKVK4OzNbBy8lI6DlzNw8todFClV6u0SCeDvZo32vo5o7+uAIEU9yE2MHvt8RlIJgn3s9RE6ERFRrWCwpMzBwQFGRkblnoqlpaU9crmmdevW4dVXX8X69evx3HPPPbSuTCaDTCZ74njromuZ+ermyJgrmci+V6yxvb6tGTo0LH0SFurrADvOok9ERPTYDJaUmZqaonXr1ti1axcGDBigLt+1axf69etX6X5r1qzByJEjsWbNGvTq1as6Qq0z7twtwt+XM3HwcgYOXk7XWE8SAKzkxgjxsUf7hqVPwxT25lo91SQiIqJHM2jzZVRUFIYNG4agoCAEBwdjyZIlSEpKwqhRowCUNj3evHkTK1asAFCakA0fPhxfffUV2rVrp37KZmZmBhsbG4NdR21VWKLEicTb/yZhGTh7Mxv3j8U1lkrQyqse2vs6oH1DBzSvbwNjzqBPRERUJQyalA0aNAiZmZmYNWsWkpOTERAQgK1bt8LLywsAkJycjKSkJHX977//HiUlJRg7dizGjh2rLo+IiMCyZcuqO/xaRwiB88m5paMkL2fgaEImCopVGnUaOVuWjpJs6IC23vawkBm82yEREVGdwAXJn3LJ2fdw8N/5wv6+nIGMvCKN7Y5WstInYf8+DXO25mz5RERE+lTj5ymjqpFbUIwjV7Nw8HIGDlxKx5X0fI3tZiZGaNugdKqKDg0d0cjZkv3CiIiIagAmZbVcsVKFMzfulM6efykDsdfvoOS+NYykEqCZuy06/PskrJVnPZgas18YERFRTcOkrJYRQuBqRj4OXiqdPf/w1UzkFZZo1PGyN//3SZgDghs4wMbcxEDREhERkbaYlNUCGXmF+Ptyhnr2/Fv3LT8EALbmJgj1cVCvJelhZ26gSImIiOhxMSmrgQqKlTiaUNov7OClDMQna67XaWokRZCiHto3dEAHX0c0dbPmEkRERES1HJMyPVOqhM5rNqpUAnG3cnDgcjr+vpyBY4m3UVSiOVWFn6s1Ovz7JOwZhR3MTB9/CSMiIiKqeZiU6dH2c8mYuSUeyfc1L7rayDG9T1P0DHDVqHs96676SVjMlQzcvqu5hJGrjVw9TUWIjwMcrbhUFBER0dOMSZmebD+XjNErT+LBSd9SsgsweuVJLAgPhIWpkXotycTMuxr1LGXGaNfAXr2WpI+jBaeqICIiqkOYlOmBUiUwc0t8uYQMgLrs7Z9Pa5QbSSVo6WGr7pwf6GELEy5hREREVGcxKdODowlZGk2WlXGzkaO7vwtCfR3QroEdrOScqoKIiIhKMSnTg7TcRydkAPB+WBP0a1G/iqMhIiKi2ojtZXrgZKXdepHa1iMiIqK6h0mZHrTxtoOrjRyVdcuXoHQ0ZRtvu+oMi4iIiGoRJmV6YCSVYHqfpgBQLjErez29T1NO8EpERESVYlKmJz0DXLF4aCu42Gg2UbrYyLF4aKty85QRERER3Y8d/fWoZ4ArujV10XlGfyIiIiImZXpmJJUg2Mfe0GEQERFRLcPmSyIiIqIagEkZERERUQ3ApIyIiIioBqhzfcqEKF2NMicnx8CREBERUV1QlnOU5SCVqXNJWW5uLgDAw8PDwJEQERFRXZKbmwsbG5tKt0vEo9K2p4xKpcKtW7dgZWUFiaRqpqrIycmBh4cHrl+/Dmtr6yo5R13A+6gfvI/6w3upH7yP+sH7qB/VcR+FEMjNzYWbmxuk0sp7jtW5J2VSqRTu7u7Vci5ra2v+ougB76N+8D7qD++lfvA+6gfvo35U9X182BOyMuzoT0RERFQDMCkjIiIiqgGYlFUBmUyG6dOnQyaTGTqUWo33UT94H/WH91I/eB/1g/dRP2rSfaxzHf2JiIiIaiI+KSMiIiKqAZiUEREREdUATMqIiIiIagAmZUREREQ1AJMyPfrrr7/Qp08fuLm5QSKR4NdffzV0SLXS3Llz8cwzz8DKygpOTk7o378/Ll68aOiwap3FixejefPm6gkRg4ODsW3bNkOHVevNnTsXEokEEydONHQotcqMGTMgkUg0flxcXAwdVq118+ZNDB06FPb29jA3N0eLFi1w4sQJQ4dVqygUinKfSYlEgrFjxxosJiZlepSfn4/AwEB88803hg6lVtu/fz/Gjh2Lw4cPY9euXSgpKUH37t2Rn59v6NBqFXd3d3zyySc4fvw4jh8/ji5duqBfv36Ii4szdGi11rFjx7BkyRI0b97c0KHUSv7+/khOTlb/nD171tAh1Uq3b99GaGgoTExMsG3bNsTHx+Ozzz6Dra2toUOrVY4dO6bxedy1axcA4KWXXjJYTHVumaWqFBYWhrCwMEOHUett375d43V0dDScnJxw4sQJdOzY0UBR1T59+vTReD1nzhwsXrwYhw8fhr+/v4Giqr3y8vLwyiuvYOnSpZg9e7ahw6mVjI2N+XRMD+bNmwcPDw9ER0eryxQKheECqqUcHR01Xn/yySfw8fFBp06dDBQRn5RRLZCdnQ0AsLOzM3AktZdSqcTatWuRn5+P4OBgQ4dTK40dOxa9evXCc889Z+hQaq1Lly7Bzc0N3t7eePnll3H16lVDh1Qrbd68GUFBQXjppZfg5OSEli1bYunSpYYOq1YrKirCypUrMXLkSEgkEoPFwaSMajQhBKKiotC+fXsEBAQYOpxa5+zZs7C0tIRMJsOoUaOwadMmNG3a1NBh1Tpr167FyZMnMXfuXEOHUmu1bdsWK1aswI4dO7B06VKkpKQgJCQEmZmZhg6t1rl69SoWL16Mhg0bYseOHRg1ahQmTJiAFStWGDq0WuvXX3/FnTt3EBkZadA42HxJNdq4ceNw5swZHDx40NCh1EqNGzdGbGws7ty5g19++QURERHYv38/EzMdXL9+HW+99RZ27twJuVxu6HBqrfu7djRr1gzBwcHw8fHB8uXLERUVZcDIah+VSoWgoCB8/PHHAICWLVsiLi4OixcvxvDhww0cXe30ww8/ICwsDG5ubgaNg0/KqMYaP348Nm/ejL1798Ld3d3Q4dRKpqam8PX1RVBQEObOnYvAwEB89dVXhg6rVjlx4gTS0tLQunVrGBsbw9jYGPv378fChQthbGwMpVJp6BBrJQsLCzRr1gyXLl0ydCi1jqura7k/rPz8/JCUlGSgiGq3a9euYffu3XjttdcMHQqflFHNI4TA+PHjsWnTJuzbtw/e3t6GDumpIYRAYWGhocOoVbp27VpulOCIESPQpEkTvP/++zAyMjJQZLVbYWEhzp8/jw4dOhg6lFonNDS03DRB//zzD7y8vAwUUe1WNpisV69ehg6FSZk+5eXl4fLly+rXCQkJiI2NhZ2dHTw9PQ0YWe0yduxYrF69Gr/99husrKyQkpICALCxsYGZmZmBo6s9pk6dirCwMHh4eCA3Nxdr167Fvn37yo1upYezsrIq15/RwsIC9vb27Oeog3feeQd9+vSBp6cn0tLSMHv2bOTk5CAiIsLQodU6kyZNQkhICD7++GOEh4fj6NGjWLJkCZYsWWLo0GodlUqF6OhoREREwNi4BqREgvRm7969AkC5n4iICEOHVqtUdA8BiOjoaEOHVquMHDlSeHl5CVNTU+Ho6Ci6du0qdu7caeiwngqdOnUSb731lqHDqFUGDRokXF1dhYmJiXBzcxMDBw4UcXFxhg6r1tqyZYsICAgQMplMNGnSRCxZssTQIdVKO3bsEADExYsXDR2KEEIIiRBCGCYdJCIiIqIy7OhPREREVAMwKSMiIiKqAZiUEREREdUATMqIiIiIagAmZUREREQ1AJMyIiIiohqASRkRERFRDcCkjIiIiKgGYFJGREREVAMwKSMirURGRkIikUAikcDY2Bienp4YPXo0bt++bejQar3IyEj079/f0GEQkYExKSMirfXs2RPJyclITEzE//3f/2HLli0YM2aMocMiPRJCoKSkxNBhENVJTMqISGsymQwuLi5wd3dH9+7dMWjQIOzcuVOjTnR0NPz8/CCXy9GkSRMsWrRIva2oqAjjxo2Dq6sr5HI5FAoF5s6dq94ukUiwePFihIWFwczMDN7e3li/fr3G8c+ePYsuXbrAzMwM9vb2eOONN5CXl6feXvbUacGCBXB1dYW9vT3Gjh2L4uJidZ1FixahYcOGkMvlcHZ2xosvvqjeJoTA/Pnz0aBBA5iZmSEwMBAbNmx46H0pLCzEe++9Bw8PD8hkMjRs2BA//PADAECpVOLVV1+Ft7c3zMzM0LhxY3z11VfqfWfMmIHly5fjt99+Uz+J3LdvHwDg5s2bGDRoEOrVqwd7e3v069cPiYmJ6n1LSkowYcIE2Nrawt7eHu+//z4iIiI0nroVFhZiwoQJcHJyglwuR/v27XHs2DH19n379kEikWDHjh0ICgqCTCbDTz/9BKlUiuPHj2tc59dffw0vLy9wyWSiKmLQ5dCJqNaIiIgQ/fr1U7++cuWKaNq0qXB2dlaXLVmyRLi6uopffvlFXL16Vfzyyy/Czs5OLFu2TAghxKeffio8PDzEX3/9JRITE8WBAwfE6tWr1fsDEPb29mLp0qXi4sWL4n//+58wMjIS8fHxQggh8vPzhZubmxg4cKA4e/as+PPPP4W3t7eIiIjQiNPa2lqMGjVKnD9/XmzZskWYm5uLJUuWCCGEOHbsmDAyMhKrV68WiYmJ4uTJk+Krr75S7z916lTRpEkTsX37dnHlyhURHR0tZDKZ2LdvX6X3Jjw8XHh4eIiNGzeKK1euiN27d4u1a9cKIYQoKioS06ZNE0ePHhVXr14VK1euFObm5mLdunVCCCFyc3NFeHi46Nmzp0hOThbJycmisLBQ5Ofni4YNG4qRI0eKM2fOiPj4eDFkyBDRuHFjUVhYKIQQYvbs2cLOzk5s3LhRnD9/XowaNUpYW1trvE8TJkwQbm5uYuvWrSIuLk5ERESIevXqiczMTCGEEHv37hUARPPmzcXOnTvF5cuXRUZGhujWrZsYM2aMxnW2bNlSTJs27eEfFCJ6bEzKiEgrERERwsjISFhYWAi5XC4ACADi888/V9fx8PDQSLKEEOKjjz4SwcHBQgghxo8fL7p06SJUKlWF5wAgRo0apVHWtm1bMXr0aCFEadJXr149kZeXp97+xx9/CKlUKlJSUtRxenl5iZKSEnWdl156SQwaNEgIIcQvv/wirK2tRU5OTrnz5+XlCblcLmJiYjTKX331VTF48OAKY7548aIAIHbt2lXh9oqMGTNGvPDCC+rXDya8Qgjxww8/iMaNG2vcq8LCQmFmZiZ27NghhBDC2dlZfPrpp+rtJSUlwtPTU32svLw8YWJiIlatWqWuU1RUJNzc3MT8+fOFEP8lZb/++qvG+detWyfq1asnCgoKhBBCxMbGColEIhISErS+TiLSDZsviUhrnTt3RmxsLI4cOYLx48ejR48eGD9+PAAgPT0d169fx6uvvgpLS0v1z+zZs3HlyhUApU2LsbGxaNy4MSZMmFCu6RMAgoODy70+f/48AOD8+fMIDAyEhYWFentoaChUKhUuXryoLvP394eRkZH6taurK9LS0gAA3bp1g5eXFxo0aIBhw4Zh1apVuHv3LgAgPj4eBQUF6Natm8Y1rFixQn0ND4qNjYWRkRE6depU6X377rvvEBQUBEdHR1haWmLp0qVISkqq/EYDOHHiBC5fvgwrKyt1HHZ2digoKMCVK1eQnZ2N1NRUtGnTRr2PkZERWrdurX595coVFBcXIzQ0VF1mYmKCNm3aqO9pmaCgII3X/fv3h7GxMTZt2gQA+PHHH9G5c2coFIqHxk1Ej8/Y0AEQUe1hYWEBX19fAMDChQvRuXNnzJw5Ex999BFUKhUAYOnSpWjbtq3GfmUJUqtWrZCQkIBt27Zh9+7dCA8Px3PPPffIPlsSiQRAaX+vsn9XVgcoTTwe3FYWn5WVFU6ePIl9+/Zh586dmDZtGmbMmIFjx46p6/zxxx+oX7++xjFkMlmF5zUzM3to7D///DMmTZqEzz77DMHBwbCyssKnn36KI0eOPHQ/lUqF1q1bY9WqVeW2OTo6alzb/cR9/b3K/l1RnQfL7k90AcDU1BTDhg1DdHQ0Bg4ciNWrV+PLL798aMxE9GT4pIyIHtv06dOxYMEC3Lp1C87Ozqhfvz6uXr0KX19fjR9vb2/1PtbW1hg0aBCWLl2KdevW4ZdffkFWVpZ6++HDhzXOcfjwYTRp0gQA0LRpU8TGxiI/P1+9/e+//4ZUKkWjRo20jtvY2BjPPfcc5s+fjzNnziAxMRF79uxB06ZNIZPJkJSUVO4aPDw8KjxWs2bNoFKpsH///gq3HzhwACEhIRgzZgxatmwJX1/fck/dTE1NoVQqNcpatWqFS5cuwcnJqVwsNjY2sLGxgbOzM44ePareR6lU4tSpU+rXvr6+MDU1xcGDB9VlxcXFOH78OPz8/B55n1577TXs3r0bixYtQnFxMQYOHPjIfYjo8fFJGRE9tmeffRb+/v74+OOP8c0332DGjBmYMGECrK2tERYWhsLCQhw/fhy3b99GVFQUvvjiC7i6uqJFixaQSqVYv349XFxcYGtrqz7m+vXrERQUhPbt22PVqlU4evSoeiTjK6+8gunTpyMiIgIzZsxAeno6xo8fj2HDhsHZ2VmrmH///XdcvXoVHTt2RL169bB161aoVCo0btwYVlZWeOeddzBp0iSoVCq0b98eOTk5iImJgaWlJSIiIsodT6FQICIiAiNHjsTChQsRGBiIa9euIS0tDeHh4fD19cWKFSuwY8cOeHt746effsKxY8c0ElWFQoEdO3bg4sWLsLe3h42NDV555RV8+umn6NevH2bNmgV3d3ckJSVh48aNePfdd+Hu7o7x48dj7ty58PX1RZMmTfD111/j9u3b6qdgFhYWGD16NN59913Y2dnB09MT8+fPx927d/Hqq68+8l75+fmhXbt2eP/99zFy5MhHPhUkoidk2C5tRFRbVNQZXQghVq1aJUxNTUVSUpL6dYsWLYSpqamoV6+e6Nixo9i4caMQorSjfosWLYSFhYWwtrYWXbt2FSdPnlQfC4D49ttvRbdu3YRMJhNeXl5izZo1Guc7c+aM6Ny5s5DL5cLOzk68/vrrIjc396FxvvXWW6JTp05CCCEOHDggOnXqJOrVqyfMzMxE8+bN1SMhhRBCpVKJr776SjRu3FiYmJgIR0dH0aNHD7F///5K7829e/fEpEmThKurqzA1NRW+vr7ixx9/FEIIUVBQICIjI4WNjY2wtbUVo0ePFpMnTxaBgYHq/dPS0kS3bt2EpaWlACD27t0rhBAiOTlZDB8+XDg4OAiZTCYaNGggXn/9dZGdnS2EEKK4uFiMGzdOWFtbi3r16on3339fvPTSS+Lll1/WiG38+PHqY4SGhoqjR4+qt5d19L99+3aF1/bDDz8IABr7EFHVkAjBCWeIqGaQSCTYtGkTZ7d/TCqVCn5+fggPD8dHH32kl2POmTMHa9euxdmzZ/VyPCKqHJsviYhqqWvXrmHnzp3o1KkTCgsL8c033yAhIQFDhgx54mPn5eXh/Pnz+Prrr/WW4BHRw7GjPxFRLSWVSrFs2TI888wzCA0NxdmzZ7F7926tOvE/yrhx49C+fXt06tQJI0eO1EO0RPQobL4kIiIiqgH4pIyIiIioBmBSRkRERFQDMCkjIiIiqgGYlBERERHVAEzKiIiIiGoAJmVERERENQCTMiIiIqIagEkZERERUQ3w/z/3ce96n4JoAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -427,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -455,12 +512,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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u+ot+1uX/rer8KS0txf379yXlhoaGkuXyKTnl50VN4qvpsVamfMpQTf+OpaSkoH///gDKnuyWlJSEo0eP4uuvv5bEXZ2axnv37l2oqKhU+HtnZGQkWa7L721lddXV1TFx4kRs2rQJDx48wO3bt7F582Z4e3tXes8ZEb1+eE8C1QtlZWX07dsXu3fvxo0bN9C6desXbqOuro6CgoIK5fV98aehoQEAFfZVkwvsxMRE3Lp1C/v375ePHgCQX9i/DE9PTyxZsgTx8fFwd3fH9u3b4evrC2VlZXmdt956Cx07dsSCBQsqbaP8f/rV0dDQQNeuXV9Y7/lvx5s3bw4lJSVkZWVVqHvr1i15fM/Kzs6uUDc7OxvW1tYAgPT0dPz111+IiYnBuHHj5HWqe1pMdnY2WrVqJV8uLi7G3bt3K1xQ1jc9PT2MGzcO//d//wd/f39ER0dj9OjR8nsrXqSqvgCkF8MffvghOnTogFWrVqFZs2Y4fvw4NmzYUOM4W7RoIf/5Ojo6ws7ODr169cL06dOxc+dOAGU/J01NTfnc/uc9+3McNmwYhg0bhoKCAhw5cgTBwcEYPXo0zM3N4ejo+MLjKz+28v9Wdf4oKSlJRq5q6kXx1eZYn9e1a1cYGBjgP//5D4KDg184YhQfHw9VVVXs3LlT/ncGKLu/p6ZqGq+hoSGKi4tx7949SaLw/M+hLr+3VR3nF198gX//+9+IiorC06dPUVxcjEmTJtX42Ijo1caRBKo3AQEBEEJgwoQJKCwsrLC+qKgIO3bskC+bm5vj5MmTkjqJiYnIz8+v17jKnyLy/L62b9/+wm3L/+f5/Ddn33//fYW6z3/r+SJ2dnbo3r07oqOjsWnTJhQUFMDT01NSZ8iQIUhPT4eVlRW6du1a4VOTJKGutLW10b17d2zdulVyTKWlpdiwYQNat24NGxsbyTYbN26ULCcnJ+Pq1avyJw/Vpj+ranPz5s0oLi5+4dOMauJFP7OpU6fizp07GDlyJB48eFDjKUBA2TSvv/76S1K2adMm6OjoVBglmDp1Kn799VcEBATAyMgIo0aNquWR/E+PHj0wduxY/Prrr/IbsocMGYLMzEwYGhpWeh49+6Sdcurq6ujVqxcWLVoEABWeXBQXFyd5gs/Vq1eRnJws/7nY2tqiVatW2LRpk6Teo0ePsGXLFvkTj+qqqvjqcqzlVFVV8a9//Qtnz57Fd999V2mdnJwcJCUlASg7n1VUVCSJ/ZMnT7B+/fpK463sPKtpvOVfUvz444+S7ePj4yXLdfm9rYqJiQlGjRqF8PBwREZGYujQofLRFiJ6/XEkgeqNo6MjIiIi4OPjAwcHB3zxxRdo3749ioqKcOLECaxZswYdOnTA0KFDAQCff/455syZg7lz56JXr144c+YMVq1aVWEazMsyNjZGv379EBwcjObNm6NNmzbYu3evfMpQdZycnNC8eXNMmjQJgYGBUFVVxcaNGytc/AGAvb09AGDRokUYNGgQlJWV0bFjR/m0qMp4eXlh4sSJuHXrFpycnGBraytZ/+233yIhIQFOTk6YOnUqbG1t8fTpU1y5cgW7du1CZGRkjUZt6io4OBguLi7o06cP/P39oaamhvDwcKSnpyMuLq7CN5DHjh2Dt7c3Ro0ahevXr+Prr79Gq1at4OPjAwBo164drKysMHv2bAghYGBggB07dsin2lRm69atUFFRgYuLi/zpRu+++26Fudl18aKfmY2NDQYOHIjdu3fjgw8+qHCPQXVMTU3x0UcfISgoCCYmJtiwYQMSEhKwaNGiChfHn332GQICAvDf//4X33zzTbXnTE189913+PHHHzFnzhz88ccf8PX1xZYtW9CzZ09Mnz4dHTt2RGlpKa5du4bff/8dfn5+6N69O+bOnYsbN26gb9++aN26NR48eIAVK1ZI7sMpl5OTgxEjRmDChAnIzc1FYGAgNDQ05E8kUlJSwuLFizFmzBgMGTIEEydOREFBAZYsWYIHDx7g3//+d62Pqybx1fRYqzJz5kxkZGQgMDAQKSkpGD16NMzMzJCbm4v//ve/WLNmDebNmwdnZ2cMHjwYoaGhGD16NP75z3/i7t27CAkJqXQ6jr29PeLj4/Hjjz/C0tISGhoasLe3r3G8AwcOhLOzM/z8/JCXlwcHBwccPnwYP/zwg7y/y9X297Y606ZNk/dXXZ+mRkSvKEXdMU2vr7S0NDFu3Djx9ttvCzU1NaGtrS06d+4s5s6dK3JycuT1CgoKxKxZs4SZmZnQ1NQUvXr1EmlpaVU+3ej5p3+UPxHl9u3bkvJx48YJbW1tSVlWVpYYOXKkMDAwEHp6euKzzz4Tx44dq9HTjZKTk4Wjo6PQ0tISLVq0EN7e3uL48eMVti0oKBDe3t6iRYsWQiaTCQDi8uXLQoiKTzcql5ubKzQ1NQUAsXbt2kr78/bt22Lq1KnCwsJCqKqqCgMDA+Hg4CC+/vprkZ+fX+k25cqfbvQiAMTkyZMrXXfw4EHx4YcfCm1tbaGpqSnef/99sWPHDkmd8p/R77//Lj7//HOhr68vNDU1haurq7hw4YKk7pkzZ4SLi4vQ0dERzZs3F6NGjRLXrl2r8ISW8p9FamqqGDp0qGjWrJnQ0dERn376qfj7778rHGddnm5U3c+sXExMjAAg4uPjq+/EZ7Rp00YMHjxY/Pzzz6J9+/ZCTU1NmJubV3gS17M8PDyEioqKuHHjRo33U93PbebMmQKAOHDggBBCiPz8fPHNN98IW1tboaamJvT09IS9vb2YPn26yM7OFkIIsXPnTjFo0CDRqlUroaamJlq2bClcXV3FwYMH5e2W9+P69evF1KlTRYsWLYS6urro0aOH5IlK5X755RfRvXt3oaGhIbS1tUXfvn1FUlKSpE5Vv8vl51X5z6Qm8dX0WF/kP//5jxg8eLBo0aKFUFFREc2bNxd9+vQRkZGRoqCgQF4vKipK2NraCnV1dWFpaSmCg4PFunXrKpxLV65cEf379xc6OjoCgGjTpk2t4713757w9PQU+vr6QktLS7i4uIgjR44IAGLFihWS+Gvze/v839bnmZubCzs7uxr1GxG9PmRCPPe2ICIikit/Es+VK1de6r0M1SksLIS5uTk++OADbN68uUH2UV/279+PPn364KeffsLIkSMVHc4br/xdFElJSXBycqr39k+ePIl3330Xq1evlo8IEtGbgdONiIieU1BQgOPHjyMlJQXbtm1DaGhogyQIt2/fxrlz5xAdHY2///4bs2fPrvd90OsjLi4ON2/ehL29PZSUlHDkyBEsWbIEPXv2rPcEITMzE1evXsVXX30FExMT+RvjiejNwSSBiOg5WVlZcHJygq6uLiZOnIgpU6Y0yH5+/fVXeHp6wsTEBOHh4TV+7Cm9mXR0dBAfH4/58+fj0aNH8ov3+fPn1/u+vvvuO6xfvx52dnb46aefXuomcyJ6NXG6ERERERERSfARqEREREREJMEkgYiIiIiIJJgkEBERERGRBG9cRtmbKG/dugUdHZ1avWSGiIiIqLaEEHj48CFMTU0lL8IjakqYJAC4desWzMzMFB0GERERvUGuX7+O1q1bKzoMokoxSUDZY+WAsl9WXV1dBUdDREREr7O8vDyYmZnJrz+ImiImCYB8ipGuri6TBCIiImoUnOJMTRknwhERERERkQSTBCIiIiIikmCSQEREREREEgq9J6G4uBhBQUHYuHEjsrOzYWJiAg8PD3zzzTfyR4IJITBv3jysWbMG9+/fR/fu3bF69Wq0b99e3k5BQQH8/f0RFxeHJ0+eoG/fvggPD+cTA4iIiIieIYRAcXExSkpKFB0KKYCysjJUVFRqdD+MQpOERYsWITIyErGxsWjfvj2OHTsGT09P6OnpYdq0aQCAxYsXIzQ0FDExMbCxscH8+fPh4uKCc+fOyZ8K4Ovrix07diA+Ph6Ghobw8/PDkCFDkJqaCmVlZUUeIhEREVGTUFhYiKysLDx+/FjRoZACaWlpwcTEBGpqatXWkwkhRCPFVMGQIUNgZGSEdevWycs++eQTaGlpYf369RBCwNTUFL6+vvjXv/4FoGzUwMjICIsWLcLEiRORm5uLFi1aYP369XB3dwfwv/ce7Nq1CwMGDHhhHHl5edDT00Nubi6fbkREREQNShHXHaWlpbhw4QKUlZXRokULqKmp8elKbxghBAoLC3H79m2UlJSgbdu21b7MT6EjCR988AEiIyNx/vx52NjY4K+//sKhQ4ewfPlyAMDly5eRnZ2N/v37y7dRV1dHr169kJycjIkTJyI1NRVFRUWSOqampujQoQOSk5MrTRIKCgpQUFAgX87Ly2u4gyQiIiJSsMLCQpSWlsLMzAxaWlqKDocURFNTE6qqqrh69SoKCwuhoaFRZV2FJgn/+te/kJubi3bt2kFZWRklJSVYsGABPv30UwBAdnY2AMDIyEiynZGREa5evSqvo6amhubNm1eoU77984KDgzFv3rz6PhwiIiKiJq26b47pzVDTc0ChZ8qPP/6IDRs2YNOmTTh+/DhiY2MREhKC2NhYSb3nh8OEEC8cIquuTkBAAHJzc+Wf69evv9yBEBER1VLKjktI2XFJ0WEQEVVKoSMJM2fOxOzZs/GPf/wDAGBvb4+rV68iODgY48aNg7GxMQDIn3xULicnRz66YGxsjMLCQty/f18ympCTkwMnJ6dK96uurg51dfWGOiwiIiIioleaQkcSHj9+XGHIQ1lZGaWlpQAACwsLGBsbIyEhQb6+sLAQBw4ckCcADg4OUFVVldTJyspCenp6lUkCERERERFVTaEjCUOHDsWCBQvw9ttvo3379jhx4gRCQ0Ph5eUFoGyaka+vLxYuXIi2bduibdu2WLhwIbS0tDB69GgAgJ6eHsaPHw8/Pz8YGhrCwMAA/v7+sLe3R79+/RR5eERERERN3rKE8422r+kuNrXeJjs7GwsWLMCvv/6KmzdvomXLlujUqRN8fX3Rt2/fGrURExMDX19fPHjwoNb7b8oa8l1hCk0SwsLCMGfOHPj4+CAnJwempqaYOHEi5s6dK68za9YsPHnyBD4+PvKXqf3+++/ydyQAwLJly6CiogI3Nzd5B8XExPAdCURERESvsCtXrsDZ2Rn6+vpYvHgxOnbsiKKiIvz222+YPHkyzp49q+gQ66SoqAiqqqov3U5DvitModONdHR0sHz5cly9ehVPnjxBZmYm5s+fL3m5g0wmQ1BQELKysvD06VMcOHAAHTp0kLSjoaGBsLAw3L17F48fP8aOHTtgZmbW2IdDREREr4jknzYqOgSqAR8fH8hkMqSkpGDkyJGwsbFB+/btMWPGDBw5ckReLzQ0FPb29tDW1oaZmRl8fHyQn58PANi/fz88PT2Rm5sLmUwmv7YEyqaxz5o1C61atYK2tja6d++O/fv3S2JYu3at/NGxI0aMQGhoKPT19SV1IiIiYGVlBTU1Ndja2mL9+vWS9TKZDJGRkRg2bBi0tbUxf/58WFtbIyQkRFIvPT0dSkpKyMzMfGHf5ObmYt26dVi6dCn69euHzp07Y8OGDTh16hT++OOPGvZw1fgcLCIiIiJqcu7du4c9e/Zg8uTJ0NbWrrD+2Qt1JSUlrFy5Eunp6YiNjUViYiJmzZoFAHBycsLy5cuhq6uLrKwsZGVlwd/fHwDg6emJpKQkxMfH4+TJkxg1ahQGDhyICxcuAACSkpIwadIkTJs2DWlpaXBxccGCBQskcWzbtg3Tpk2Dn58f0tPTMXHiRHh6emLfvn2SeoGBgRg2bBhOnToFLy8veHl5ITo6WlInKioKPXr0gJWVFTw8PNC7d+8q++dF7wp7WQqdbkREREREVJmLFy9CCIF27dq9sK6vr6/83xYWFvjuu+/wxRdfIDw8HGpqatDT04NMJpM/ORMAMjMzERcXhxs3bsDU1BQA4O/vjz179iA6OhoLFy5EWFgYBg0aJE8qbGxskJycjJ07d8rbCQkJgYeHB3x8fABAPsoREhKCPn36yOuNHj1aft8tUJagzJ07FykpKejWrRuKioqwYcMGLFmyBABgYmIif5hPZeryrrDa4EgCERERETU5QggAFd+XVZl9+/bBxcUFrVq1go6ODsaOHYu7d+/i0aNHVW5z/PhxCCFgY2ODZs2ayT8HDhyQT/c5d+4cunXrJtnu+eWMjAw4OztLypydnZGRkSEp69q1q2TZxMQEgwcPRlRUFABg586dePr0KUaNGgWg7OW/P/zwwwuP/Xk1eZ9YTTBJICIiIqImp23btpDJZBUutp939epVuLq6okOHDtiyZQtSU1OxevVqAGU3CFeltLQUysrKSE1NRVpamvyTkZGBFStWAKj8grs8eXlWTV78W9mUKW9vb8THx+PJkyeIjo6Gu7s7tLS0qj3ecs++K+xZz75P7GUwSWgCliWcb9THjxERERE1dQYGBhgwYABWr15d6YhA+eNMjx07huLiYixduhTvv/8+bGxscOvWLUldNTU1lJSUSMo6d+6MkpIS5OTkwNraWvIpn5bUrl07pKSkSLY7duyYZNnOzg6HDh2SlCUnJ8POzu6Fx+jq6gptbW1ERERg9+7dkulIL9LQ7wpjkkCKsS+47ENERERUhfDwcJSUlKBbt27YsmULLly4gIyMDKxcuRKOjo4AACsrKxQXFyMsLAyXLl3C+vXrERkZKWnH3Nwc+fn52Lt3L+7cuYPHjx/DxsYGY8aMwdixY7F161ZcvnwZR48exaJFi7Br1y4AwJQpU7Br1y6EhobiwoUL+P7777F7927JKMHMmTMRExODyMhIXLhwAaGhodi6dav8PobqKCsrw8PDAwEBAbC2tpYfEwAEBARg7NixVW777LvC9u7dixMnTuCzzz6rv3eFCRK5ubkCgMjNzVXI/kN/PydCfz+nkH0rTOLCsg8R0Rvqz+2Z4s/tmYoO442VtHmDwvatiOuOJ0+eiDNnzognT5402j7ry61bt8TkyZNFmzZthJqammjVqpX46KOPxL59++R1QkNDhYmJidDU1BQDBgwQP/zwgwAg7t+/L68zadIkYWhoKACIwMBAIYQQhYWFYu7cucLc3FyoqqoKY2NjMWLECHHy5En5dmvWrBGtWrUSmpqaYvjw4WL+/PnC2NhYEmN4eLiwtLQUqqqqwsbGRvzwww+S9QDEtm3bKj2+zMxMAUAsXrxYUj5u3DjRq1evavvmyZMn4ssvvxQGBgZCU1NTDBkyRFy7du2F29TkXJD9/8DfaHl5edDT00Nubi50dXUbff/lU43q8hbCV1b5KEKfAMXGQUSkICk7LgEAug21VHAkb6bknzbCadQYhexbEdcdT58+xeXLl2FhYQENDY1G2efrasKECTh79iwOHjxYL+0lJSWhd+/euHHjRr3cS/AiNT0X+AhUIiIiIqIqhISEwMXFBdra2ti9ezdiY2MRHh7+0u0WFBTg+vXrmDNnDtzc3BolQagN3pNARPSGuR22CrfDVik6DCKiV0JKSgpcXFxgb2+PyMhIrFy5Et7e3i/dblxcHGxtbZGbm4vFixfXQ6T1iyMJRERERERV2Lx5c4O06+HhAQ8PjwZpuz5wJIGIiIiIiCSYJBARERERkQSTBCKiNxTvSyAioqowSSAiIiIiIgkmCUREREREJMEkgRpf+YvUiIiIiKhJ4iNQiYiIiN5kjfnlXZ+AxtsXvRSOJBARERFRk5WdnY0pU6bA0tIS6urqMDMzw9ChQ7F3794atxETEwN9ff2GC1JB1qxZg969e0NXVxcymQwPHjyot7aZJBARERFRk3TlyhU4ODggMTERixcvxqlTp7Bnzx706dMHkydPVnR4dVZUVFQv7Tx+/BgDBw7EV199VS/tPYtJAhERERE1ST4+PpDJZEhJScHIkSNhY2OD9u3bY8aMGThy5Ii8XmhoKOzt7aGtrQ0zMzP4+PggPz8fALB//354enoiNzcXMpkMMpkMQUFBAIDCwkLMmjULrVq1gra2Nrp37479+/dLYli7di3MzMygpaWFESNGIDQ0tMKoREREBKysrKCmpgZbW1usX79esl4mkyEyMhLDhg2DtrY25s+fD2tra4SEhEjqpaenQ0lJCZmZmTXqH19fX8yePRvvv/9+jerXBpMEoiYoPC1c0SEQEREp1L1797Bnzx5MnjwZ2traFdY/e6GupKSElStXIj09HbGxsUhMTMSsWbMAAE5OTli+fDl0dXWRlZWFrKws+Pv7AwA8PT2RlJSE+Ph4nDx5EqNGjcLAgQNx4cIFAEBSUhImTZqEadOmIS0tDS4uLliwYIEkjm3btmHatGnw8/NDeno6Jk6cCE9PT+zbt09SLzAwEMOGDcOpU6fg5eUFLy8vREdHS+pERUWhR48esLKygoeHB3r37v2y3VhnvHGZiIiI3ijJP21UdAhUAxcvXoQQAu3atXthXV9fX/m/LSws8N133+GLL75AeHg41NTUoKenB5lMBmNjY3m9zMxMxMXF4caNGzA1NQUA+Pv7Y8+ePYiOjsbChQsRFhaGQYMGyZMKGxsbJCcnY+fOnfJ2QkJC4OHhAR8fHwCQj3KEhISgT58+8nqjR4+Gl5eXfNnT0xNz585FSkoKunXrhqKiImzYsAFLliwBAJiYmKC0tLQOPVc/OJJARETUyFJ2XFJ0CERNnhACQNlUnRfZt28fXFxc0KpVK+jo6GDs2LG4e/cuHj16VOU2x48fhxACNjY2aNasmfxz4MAB+XSfc+fOoVu3bpLtnl/OyMiAs7OzpMzZ2RkZGRmSsq5du0qWTUxMMHjwYERFRQEAdu7ciadPn2LUqFEAgODgYPzwww8vPPaGwiSBiIiIiJqctm3bQiaTVbjYft7Vq1fh6uqKDh06YMuWLUhNTcXq1asBVH+DcGlpKZSVlZGamoq0tDT5JyMjAytWrABQlqg8n6SUJy/PqqzO82WVTZny9vZGfHw8njx5gujoaLi7u0NLS6va420sTBKIiIiIqMkxMDDAgAEDsHr16kpHBMof93ns2DEUFxdj6dKleP/992FjY4Nbt25J6qqpqaGkpERS1rlzZ5SUlCAnJwfW1taST/m0pHbt2iElJUWy3bFjxyTLdnZ2OHTokKQsOTkZdnZ2LzxGV1dXaGtrIyIiArt375ZMR1I03pNA1ITwhmUiIqL/CQ8Ph5OTE7p164Zvv/0WHTt2RHFxMRISEhAREYGMjAxYWVmhuLgYYWFhGDp0KJKSkhAZGSlpx9zcHPn5+di7dy/effddaGlpwcbGBmPGjMHYsWOxdOlSdO7cGXfu3EFiYiLs7e3h6uqKKVOmoGfPnggNDcXQoUORmJiI3bt3S0YJZs6cCTc3N3Tp0gV9+/bFjh07sHXrVvzxxx8vPD5lZWV4eHggICAA1tbWcHR0lK8LCAjAzZs3q51ylJ2djezsbFy8eBEAcOrUKejo6ODtt9+GgYFBbbtbgkkCERER0ZusCb8F2cLCAsePH8eCBQvg5+eHrKwstGjRAg4ODoiIiAAAdOrUCaGhoVi0aBECAgLQs2dPBAcHY+zYsfJ2nJycMGnSJLi7u+Pu3bsIDAxEUFAQoqOjMX/+fPj5+eHmzZswNDSEo6MjXF1dAZTdWxAZGYl58+bhm2++wYABAzB9+nSsWrVK3vbw4cOxYsUKLFmyBFOnToWFhQWio6Nr/GSi8ePHY+HChRVGEbKysnDt2rVqty2PrVzPnj0BANHR0fDw8KjR/qsiE5VNrHrD5OXlQU9PD7m5udDV1W30/S9LOA8AmO5i0+j7VohnX//ehP8wKcKzIwk+nXwUGAm9zm6H/e9/bi2mfKnASN5cz9643G2opQIjeTOVP93IadQYhexfEdcdT58+xeXLl2FhYQENDY1G2efrasKECTh79iwOHjxYL+0lJSWhd+/euHHjBoyMjOqlzerU9FzgSAIRERERURVCQkLg4uICbW1t7N69G7GxsQgPf/npwQUFBbh+/TrmzJkDNze3RkkQaoM3LhMRERERVSElJQUuLi6wt7dHZGQkVq5cCW9v75duNy4uDra2tsjNzcXixYvrIdL6xZEEIiIiIqIqbN68uUHa9fDweOn7BhoSRxKIiIiIiEiCSQIREREREUkwSSAiIiIiIgkmCUREREREJMEkgYiIiIiIJJgkEBERERGRBB+BSkRERPQGC097+ReD1ZRPJ59G2xe9HI4kEBEREVGTlZ2djSlTpsDS0hLq6uowMzPD0KFDsXfv3hq3ERMTA319/YYLUgHu3buHKVOmwNbWFlpaWnj77bcxdepU5Obm1kv7HEkgIiKiN1LyTxvhNGqMosOgaly5cgXOzs7Q19fH4sWL0bFjRxQVFeG3337D5MmTcfbsWUWHWCdFRUVQVVV9qTZu3bqFW7duISQkBO+88w6uXr2KSZMm4datW/j5559fOkaOJBARERFRk+Tj4wOZTIaUlBSMHDkSNjY2aN++PWbMmIEjR47I64WGhsLe3h7a2towMzODj48P8vPzAQD79++Hp6cncnNzIZPJIJPJEBQUBAAoLCzErFmz0KpVK2hra6N79+7Yv3+/JIa1a9fCzMwMWlpaGDFiBEJDQyuMSkRERMDKygpqamqwtbXF+vXrJetlMhkiIyMxbNgwaGtrY/78+bC2tkZISIikXnp6OpSUlJCZmfnCvunQoQO2bNmCoUOHwsrKCh9++CEWLFiAHTt2oLi4uIY9XDUmCURERETU5Ny7dw979uzB5MmToa2tXWH9sxfqSkpKWLlyJdLT0xEbG4vExETMmjULAODk5ITly5dDV1cXWVlZyMrKgr+/PwDA09MTSUlJiI+Px8mTJzFq1CgMHDgQFy5cAAAkJSVh0qRJmDZtGtLS0uDi4oIFCxZI4ti2bRumTZsGPz8/pKenY+LEifD09MS+ffsk9QIDAzFs2DCcOnUKXl5e8PLyQnR0tKROVFQUevToASsrK3h4eKB379616rPc3Fzo6upCReXlJwtxuhERERERNTkXL16EEALt2rV7YV1fX1/5vy0sLPDdd9/hiy++QHh4ONTU1KCnpweZTAZjY2N5vczMTMTFxeHGjRswNTUFAPj7+2PPnj2Ijo7GwoULERYWhkGDBsmTChsbGyQnJ2Pnzp3ydkJCQuDh4QEfn7KbsstHOUJCQtCnTx95vdGjR8PLy0u+7Onpiblz5yIlJQXdunVDUVERNmzYgCVLlgAATExMUFpaWuP+unv3Lr777jtMnDixxttUR+EjCTdv3sRnn30GQ0NDaGlpoVOnTkhNTZWvF0IgKCgIpqam0NTURO/evXH69GlJGwUFBZgyZQreeustaGtr46OPPsKNGzca+1CIiIiIqJ4IIQCUTdV5kX379sHFxQWtWrWCjo4Oxo4di7t37+LRo0dVbnP8+HEIIWBjY4NmzZrJPwcOHJBP9zl37hy6desm2e755YyMDDg7O0vKnJ2dkZGRISnr2rWrZNnExASDBw9GVFQUAGDnzp14+vQpRo0aBQAIDg7GDz/88MJjB4C8vDwMHjwY77zzDgIDA2u0zYsoNEm4f/8+nJ2doaqqit27d+PMmTNYunSpZPho8eLFCA0NxapVq3D06FEYGxvDxcUFDx8+lNfx9fXFtm3bEB8fj0OHDiE/Px9DhgxBSUmJAo6KiIiIiF5W27ZtIZPJKlxsP+/q1atwdXWVz9FPTU3F6tWrAZTdIFyV0tJSKCsrIzU1FWlpafJPRkYGVqxYAaAsUXk+SSlPXp5VWZ3nyyqbMuXt7Y34+Hg8efIE0dHRcHd3h5aWVrXH+7yHDx9i4MCBaNasGbZt2/bSN0SXU2iSsGjRIpiZmSE6OhrdunWDubk5+vbtCysrKwBlHbx8+XJ8/fXX+Pjjj9GhQwfExsbi8ePH2LRpE4CyuVfr1q3D0qVL0a9fP3Tu3BkbNmzAqVOn8Mcffyjy8Kgm9gUrOgIiIiJqggwMDDBgwACsXr260hGBBw8eAACOHTuG4uJiLF26FO+//z5sbGxw69YtSV01NbUKXx537twZJSUlyMnJgbW1teRTPi2pXbt2SElJkWx37NgxybKdnR0OHTokKUtOToadnd0Lj9HV1RXa2tqIiIjA7t27JdORaiIvLw/9+/eHmpoatm/fDg0NjVptXx2FJgnbt29H165dMWrUKLRs2RKdO3fG2rVr5esvX76M7Oxs9O/fX16mrq6OXr16ITk5GQCQmpqKoqIiSR1TU1N06NBBXud5BQUFyMvLk3yImprwtPBGfcENERFRUxMeHo6SkhJ069YNW7ZswYULF5CRkYGVK1fC0dERAGBlZYXi4mKEhYXh0qVLWL9+PSIjIyXtmJubIz8/H3v37sWdO3fw+PFj2NjYYMyYMRg7diy2bt2Ky5cv4+jRo1i0aBF27doFAJgyZQp27dqF0NBQXLhwAd9//z12794tGSWYOXMmYmJiEBkZiQsXLiA0NBRbt26V38dQHWVlZXh4eCAgIADW1tbyYwKAgIAAjB07tsptHz58iP79++PRo0dYt24d8vLykJ2djezs7HqZTaPQG5cvXbqEiIgIzJgxA1999RVSUlIwdepUqKurY+zYscjOzgYAGBkZSbYzMjLC1atXAZS9YENNTQ3NmzevUKd8++cFBwdj3rx5DXBERERERK+WpvwWZAsLCxw/fhwLFiyAn58fsrKy0KJFCzg4OCAiIgIA0KlTJ4SGhmLRokUICAhAz549ERwcLLnAdnJywqRJk+Du7o67d+8iMDAQQUFBiI6Oxvz58+Hn54ebN2/C0NAQjo6OcHV1BVB2b0FkZCTmzZuHb775BgMGDMD06dOxatUqedvDhw/HihUrsGTJEkydOhUWFhaIjo6u8ZOJxo8fj4ULF1YYRcjKysK1a9eq3C41NRV//vknAMDa2lqy7vLlyzA3N6/R/qsiE5VNrGokampq6Nq1q+Qb/6lTp+Lo0aM4fPgwkpOT4ezsjFu3bsHExEReZ8KECbh+/Tr27NmDTZs2wdPTEwUFBZK2XVxcYGVlVSGTBMpGEp6tn5eXBzMzM/ljoxrbsoTz8n9Pd7Fp9P03uuenGPUJUEwcTVBlIwdN+Y83vZpuh/3vf24tpnypwEjeXCk7LkmWuw21VFAkb6bknzbK/62Il6nl5eVBT0+vUa87nj59isuXL8PCwqJep6S8iSZMmICzZ8/i4MGD9dJeUlISevfujRs3blT4Yrwh1PRcUOh0IxMTE7zzzjuSMjs7O3nWVD4f7PkRgZycHHknGhsbo7CwEPfv36+yzvPU1dWhq6sr+RARERERPS8kJAR//fUXLl68iLCwMMTGxmLcuHEv3W5BQQEuXryIOXPmwM3NrVEShNpQaJLg7OyMc+fOScrOnz+PNm3aACgbYjI2NkZCQoJ8fWFhIQ4cOAAnJycAgIODA1RVVSV1srKykJ6eLq9DRERERFQXKSkpcHFxgb29PSIjI7Fy5Up4e3u/dLtxcXGwtbVFbm4uFi9eXA+R1i+F3pMwffp0ODk5YeHChXBzc0NKSgrWrFmDNWvWACh7nJSvry8WLlyItm3bom3btli4cCG0tLQwevRoAICenh7Gjx8PPz8/GBoawsDAAP7+/rC3t0e/fv0UeXhERERE9IrbvHlzg7Tr4eEBDw+PBmm7Pig0SXjvvfewbds2BAQE4Ntvv4WFhQWWL1+OMWP+Nz9w1qxZePLkCXx8fHD//n10794dv//+O3R0dOR1li1bBhUVFbi5ueHJkyfo27cvYmJioKysrIjDIiIiIiJ6pSk0SQCAIUOGYMiQIVWul8lkCAoKQlBQUJV1NDQ0EBYWhrCwsAaIkIiIiIjozaLQexKIiIiIiKjpYZJAjYtvWK4SX5xGRERETQWTBCIiIiIikmCSQET0Bnn2RWpERERVUfiNy0RERESkOI355QHf8v7q4EgCERERETVZ2dnZmDJlCiwtLaGurg4zMzMMHToUe/furXEbMTEx0NfXb7ggFWTixImwsrKCpqYmWrRogWHDhuHs2bP10jaTBCIiIiJqkq5cuQIHBwckJiZi8eLFOHXqFPbs2YM+ffpg8uTJig6vzoqKiuqlHQcHB0RHRyMjIwO//fYbhBDo378/SkpKXrptJglERERE1CT5+PhAJpMhJSUFI0eOhI2NDdq3b48ZM2bgyJEj8nqhoaGwt7eHtrY2zMzM4OPjg/z8fADA/v374enpidzcXMhkMvk7uACgsLAQs2bNQqtWraCtrY3u3btj//79khjWrl0LMzMzaGlpYcSIEQgNDa0wKhEREQErKyuoqanB1tYW69evl6yXyWSIjIzEsGHDoK2tjfnz58Pa2hohISGSeunp6VBSUkJmZmaN+uef//wnevbsCXNzc3Tp0gXz58/H9evXceXKlRptXx0mCQq2LOG8okMgIiIianLu3buHPXv2YPLkydDW1q6w/tkLdSUlJaxcuRLp6emIjY1FYmIiZs2aBQBwcnLC8uXLoauri6ysLGRlZcHf3x8A4OnpiaSkJMTHx+PkyZMYNWoUBg4ciAsXLgAAkpKSMGnSJEybNg1paWlwcXHBggULJHFs27YN06ZNg5+fH9LT0zFx4kR4enpi3759knqBgYEYNmwYTp06BS8vL3h5eSE6OlpSJyoqCj169ICVlRU8PDzQu3fvGvfXo0ePEB0dDQsLC5iZmdV4u6owSSAieoPdDlvFJx4RUZN08eJFCCHQrl27F9b19fVFnz59YGFhgQ8//BDfffcdNm/eDABQU1ODnp4eZDIZjI2NYWxsjGbNmiEzMxNxcXH46aef5Bfm/v7++OCDD+QX72FhYRg0aBD8/f1hY2MDHx8fDBo0SLLvkJAQeHh4wMfHBzY2NpgxYwY+/vjjCqMEo0ePhpeXFywtLdGmTRt4enri3LlzSElJAVA2BWnDhg3w8vICAJiYmODtt99+4bGHh4ejWbNmaNasGfbs2YOEhASoqam9uINfgEkCERERETU5QggAZVN1XmTfvn1wcXFBq1atoKOjg7Fjx+Lu3bt49OhRldscP34cQgjY2NjIL7KbNWuGAwcOyKf7nDt3Dt26dZNs9/xyRkYGnJ2dJWXOzs7IyMiQlHXt2lWybGJigsGDByMqKgoAsHPnTjx9+hSjRo0CAAQHB+OHH3544bGPGTMGJ06cwIEDB9C2bVu4ubnh6dOnL9zuRZgkEBEREVGT07ZtW8hksgoX28+7evUqXF1d0aFDB2zZsgWpqalYvXo1gOpvEC4tLYWysjJSU1ORlpYm/2RkZGDFihUAyhKV55OU8uTlWZXVeb6ssilT3t7eiI+Px5MnTxAdHQ13d3doaWlVe7zP09PTQ9u2bdGzZ0/8/PPPOHv2LLZt21arNirDJIGIiIiImhwDAwMMGDAAq1evrnRE4MGDBwCAY8eOobi4GEuXLsX7778PGxsb3Lp1S1JXTU2twhN/OnfujJKSEuTk5MDa2lryMTY2BgC0a9dOPh2o3LFjxyTLdnZ2OHTokKQsOTkZdnZ2LzxGV1dXaGtrIyIiArt375ZPNXoZQggUFBS8dDtMEoiIiIioSQoPD0dJSQm6deuGLVu24MKFC8jIyMDKlSvh6OgIALCyskJxcTHCwsJw6dIlrF+/HpGRkZJ2zM3NkZ+fj7179+LOnTt4/PgxbGxsMGbMGIwdOxZbt27F5cuXcfToUSxatAi7du0CAEyZMgW7du1CaGgoLly4gO+//x67d++WjBLMnDkTMTExiIyMxIULFxAaGoqtW7fKb46ujrKyMjw8PBAQEABra2v5MQFAQEAAxo4dW+W2ly5dQnBwMFJTU3Ht2jUcPnwYbm5u0NTUhKura636uTJ84zIRERHRG6wpvwXZwsICx48fx4IFC+Dn54esrCy0aNECDg4OiIiIAAB06tQJoaGhWLRoEQICAtCzZ08EBwdLLrCdnJwwadIkuLu74+7duwgMDERQUBCio6Mxf/58+Pn54ebNmzA0NISjo6P8ItvZ2RmRkZGYN28evvnmGwwYMADTp0/HqlX/e+DD8OHDsWLFCixZsgRTp06FhYUFoqOja/xkovHjx2PhwoUVRhGysrJw7dq1KrfT0NDAwYMHsXz5cty/fx9GRkbo2bMnkpOT0bJly5p2cZVkorKJVW+YvLw86OnpITc3F7q6uo267+cfgTrdxaZR99/o9gVXLOsT0PhxNEHhaeGVlvt08mnkSOh1VtWTjJryRcLrJGXHpUrLuw21bORI3mzJP22U/9tp1JhG378irjuePn2Ky5cvw8LCAhoaGo2yz9fVhAkTcPbsWRw8eLBe2ktKSkLv3r1x48YNGBkZ1Uub1anpucCRBCIiIiKiKoSEhMDFxQXa2trYvXs3YmNjER5e+Rd7tVFQUIDr169jzpw5cHNza5QEoTZ4TwIRERERURVSUlLg4uICe3t7REZGYuXKlfD29n7pduPi4mBra4vc3FwsXry4HiKtXxxJICIiojfGs1ONiGqi/KVs9c3DwwMeHh4N0nZ94EgCERERERFJMEkgIiIiIiIJJglERERERCTBJIGIiIiIiCSYJBARERERkQSTBCIiIiIikqjTI1AtLS1x9OhRGBoaSsofPHiALl264NKlyt8oSURERERNS1VvAm8IfLv4q6NOIwlXrlxBSUlJhfKCggLcvHnzpYMiIiIiIgKA7OxsTJkyBZaWllBXV4eZmRmGDh2KvXv31riNmJgY6OvrN1yQCiaEwKBBgyCTyfDLL7/US5u1GknYvn27/N+//fYb9PT05MslJSXYu3cvzM3N6yUwIiIiInqzXblyBc7OztDX18fixYvRsWNHFBUV4bfffsPkyZNx9uxZRYdYJ0VFRVBVVa239pYvXw6ZTFZv7QG1HEkYPnw4hg8fDplMhnHjxsmXhw8fjn/84x9ISEjA0qVL6zVAIiIiInoz+fj4QCaTISUlBSNHjoSNjQ3at2+PGTNm4MiRI/J6oaGhsLe3h7a2NszMzODj44P8/HwAwP79++Hp6Ync3FzIZDLIZDIEBQUBAAoLCzFr1iy0atUK2tra6N69O/bv3y+JYe3atTAzM4OWlhZGjBiB0NDQCqMSERERsLKygpqaGmxtbbF+/XrJeplMhsjISAwbNgza2tqYP38+rK2tERISIqmXnp4OJSUlZGZm1riP/vrrL4SGhiIqKqrG29RErZKE0tJSlJaW4u2330ZOTo58ubS0FAUFBTh37hyGDBlSrwESERER0Zvn3r172LNnDyZPngxtbe0K65+9UFdSUsLKlSuRnp6O2NhYJCYmYtasWQAAJycnLF++HLq6usjKykJWVhb8/f0BAJ6enkhKSkJ8fDxOnjyJUaNGYeDAgbhw4QIAICkpCZMmTcK0adOQlpYGFxcXLFiwQBLHtm3bMG3aNPj5+SE9PR0TJ06Ep6cn9u3bJ6kXGBiIYcOG4dSpU/Dy8oKXlxeio6MldaKiotCjRw9YWVnBw8MDvXv3rraPHj9+jE8//RSrVq2CsbFxjfq1pup04/Lly5frNQgiIiIiomddvHgRQgi0a9fuhXV9fX3l/7awsMB3332HL774AuHh4VBTU4Oenh5kMpnkQjozMxNxcXG4ceMGTE1NAQD+/v7Ys2cPoqOjsXDhQoSFhWHQoEHypMLGxgbJycnYuXOnvJ2QkBB4eHjAx8cHAOSjHCEhIejTp4+83ujRo+Hl5SVf9vT0xNy5c5GSkoJu3bqhqKgIGzZswJIlSwAAJiYmKC0trfa4p0+fDicnJwwbNuyFfVRbdUoSAGDv3r3Yu3evfEThWfU93EFEREREbxYhBADUaK79vn37sHDhQpw5cwZ5eXkoLi7G06dP8ejRo0pHIQDg+PHjEELAxsZGUl5QUCB/gue5c+cwYsQIyfpu3bpJkoSMjAz885//lNRxdnbGihUrJGVdu3aVLJuYmGDw4MGIioqSt/n06VOMGjUKABAcHFztMW/fvh2JiYk4ceJEtfXqqk5PN5o3bx769++PvXv34s6dO7h//77kQ0RERET0Mtq2bQuZTIaMjIxq6129ehWurq7o0KEDtmzZgtTUVKxevRpA2Q3CVSktLYWysjJSU1ORlpYm/2RkZMgv8IUQFZKU8uTlWZXVeb6ssmTF29sb8fHxePLkCaKjo+Hu7g4tLa1qj7dcYmIiMjMzoa+vDxUVFaiolH33/8knn7xwmlJN1GkkITIyEjExMfj8889fOgAiIiIioucZGBhgwIABWL16NaZOnVrhIvvBgwfQ19fHsWPHUFxcjKVLl0JJqez7782bN0vqqqmpVXh8f+fOnVFSUoKcnBz06NGj0hjatWuHlJQUSdmxY8cky3Z2djh06BDGjh0rL0tOToadnd0Lj9HV1RXa2tqIiIjA7t278d///veF25SbPXs2vL29JWX29vZYtmwZhg4dWuN2qlKnJKGwsBBOTk4vvXMiIiIioqqEh4fDyckJ3bp1w7fffouOHTuiuLgYCQkJiIiIQEZGBqysrFBcXIywsDAMHToUSUlJiIyMlLRjbm6O/Px87N27F++++y60tLRgY2ODMWPGYOzYsVi6dCk6d+6MO3fuIDExEfb29nB1dcWUKVPQs2dPhIaGYujQoUhMTMTu3bslowQzZ86Em5sbunTpgr59+2LHjh3YunUr/vjjjxcen7KyMjw8PBAQEABra2s4OjrK1wUEBODmzZv44YcfKt3W2Ni40puV3377bVhYWNS0i6tUpyTB29sbmzZtwpw5c146ACIiIiJSnKb8FmQLCwscP34cCxYsgJ+fH7KystCiRQs4ODggIiICANCpUyeEhoZi0aJFCAgIQM+ePREcHCz5Zt/JyQmTJk2Cu7s77t69i8DAQAQFBSE6Ohrz58+Hn58fbt68CUNDQzg6OsLV1RVA2b0FkZGRmDdvHr755hsMGDAA06dPx6pVq+RtDx8+HCtWrMCSJUswdepUWFhYIDo6usZTfsaPH4+FCxdKbmoGgKysLFy7du0le7DuZKKyiVUvMG3aNPzwww/o2LEjOnbsWOFlEKGhofUWYGPIy8uDnp4ecnNzoaur26j7XpZwXrI83cWmipqviX2V3ITTJ6Dx42iCwtPCKy336eTTyJHQ6+x22KpKy1tM+bKRI3kzpey4VGl5U75Ie90k/7RRsuw0akyjx6CI646nT5/i8uXLsLCwgIaGRqPs83U1YcIEnD17FgcPHqyX9pKSktC7d2/cuHEDRkZG9dJmdWp6LtRpJOHkyZPo1KkTgLKXPjyrvt/2RkRERESkKCEhIXBxcYG2tjZ2796N2NhYhIdX/sVebRQUFOD69euYM2cO3NzcGiVBqI06JQnPvxyCiIiIiOh1lJKSgsWLF+Phw4ewtLTEypUrK9wwXBdxcXEYP348OnXqVOENzU1Bnd+TQERERET0unv+SUn1xcPDAx4eHg3Sdn2oU5LQp0+faqcVJSYm1jkgIiIiIiJSrDolCeX3I5QrKipCWloa0tPTMW7cuPqIi+iNUdUNy0RERESKUqckYdmyZZWWBwUFIT8//6UCIiIietOk7LjEJxwRUZOiVJ+NffbZZ4iKiqrPJomIiIiIqJHVa5Jw+PDhOj97Nzg4GDKZDL6+vvIyIQSCgoJgamoKTU1N9O7dG6dPn5ZsV1BQgClTpuCtt96CtrY2PvroI9y4ceNlDoOIiIiI6I1Wp+lGH3/8sWRZCIGsrCwcO3asTm9hPnr0KNasWYOOHTtKyhcvXozQ0FDExMTAxsYG8+fPh4uLC86dOwcdHR0AgK+vL3bs2IH4+HgYGhrCz88PQ4YMQWpqKpSVletyeEREREREb7Q6JQl6enqSZSUlJdja2uLbb79F//79a9VWfn4+xowZg7Vr12L+/PnyciEEli9fjq+//lqelMTGxsLIyAibNm3CxIkTkZubi3Xr1mH9+vXo168fAGDDhg0wMzPDH3/8gQEDBtTl8IiIiIjeGM+/hbohKeIN11Q3dZpuFB0dLfmsW7cO//73v2udIADA5MmTMXjwYPlFfrnLly8jOztb0qa6ujp69eqF5ORkAEBqaiqKiookdUxNTdGhQwd5ncoUFBQgLy9P8iEiIiKipic7OxtTpkyBpaUl1NXVYWZmhqFDh2Lv3r01biMmJgb6+voNF6SC9O7dGzKZTPL5xz/+US9tv9TL1FJTU5GRkQGZTIZ33nkHnTt3rtX28fHxOH78OI4ePVphXXZ2NgBUeEW1kZERrl69Kq+jpqaG5s2bV6hTvn1lgoODMW/evFrFSg1oXzDQJ0DRURAREVETc+XKFTg7O0NfXx+LFy9Gx44dUVRUhN9++w2TJ0/G2bNnFR1inRQVFUFVVbVe2powYQK+/fZb+bKmpma9tFunkYScnBx8+OGHeO+99zB16lR8+eWXcHBwQN++fXH79u0atXH9+nVMmzYNGzZsqPZm5+df2iaEqPZFbjWpExAQgNzcXPnn+vXrNYqZiOhVdTtsFW6HrVJ0GEREteLj4wOZTIaUlBSMHDkSNjY2aN++PWbMmIEjR47I64WGhsLe3h7a2towMzODj4+P/LH8+/fvh6enJ3Jzc+XftgcFBQEACgsLMWvWLLRq1Qra2tro3r079u/fL4lh7dq1MDMzg5aWFkaMGIHQ0NAKoxIRERGwsrKCmpoabG1tsX79esl6mUyGyMhIDBs2DNra2pg/fz6sra0REhIiqZeeng4lJSVkZmbWuI+0tLRgbGws/zx/W0Bd1SlJmDJlCvLy8nD69Gncu3cP9+/fR3p6OvLy8jB16tQatZGamoqcnBw4ODhARUUFKioqOHDgAFauXAkVFRX5CMLzIwI5OTnydcbGxigsLMT9+/errFMZdXV16OrqSj5ERET05kn+aWOjzsmnmrt37x727NmDyZMnQ1tbu8L6Zy/UlZSUsHLlSqSnpyM2NhaJiYmYNWsWAMDJyQnLly+Hrq4usrKykJWVBX9/fwCAp6cnkpKSEB8fj5MnT2LUqFEYOHAgLly4AABISkrCpEmTMG3aNKSlpcHFxQULFiyQxLFt2zZMmzYNfn5+SE9Px8SJE+Hp6Yl9+/ZJ6gUGBmLYsGE4deoUvLy84OXlhejoaEmdqKgo9OjRA1ZWVvDw8EDv3r1f2E8bN27EW2+9hfbt28Pf3x8PHz584TY1UackYc+ePYiIiICdnZ287J133sHq1auxe/fuGrXRt29fnDp1CmlpafJP165dMWbMGKSlpcHS0hLGxsZISEiQb1NYWIgDBw7AyckJAODg4ABVVVVJnaysLKSnp8vrEBEREdGr5+LFixBCoF27di+s6+vriz59+sDCwgIffvghvvvuO2zevBkAoKamBj09PchkMvm37c2aNUNmZibi4uLw008/yS/M/f398cEHH8gv3sPCwjBo0CD4+/vDxsYGPj4+GDRokGTfISEh8PDwgI+PD2xsbDBjxgx8/PHHFUYJRo8eDS8vL1haWqJNmzbw9PTEuXPnkJKSAqBsCtKGDRvg5eUFADAxMcHbb79d7XGPGTMGcXFx2L9/P+bMmYMtW7ZUeAppXdXpnoTS0tJK51GpqqqitLS0Rm3o6OigQ4cOkjJtbW0YGhrKy319fbFw4UK0bdsWbdu2xcKFC6GlpYXRo0cDKHvK0vjx4+Hn5wdDQ0MYGBjA398f9vb2FW6EflUsSziP6S42ig6DiIiISKGEEAAqTj2vzL59+7Bw4UKcOXMGeXl5KC4uxtOnT/Ho0aNKRyEA4Pjx4xBCwMZGet1VUFAAQ0NDAMC5c+cwYsQIyfpu3bph586d8uWMjAz885//lNRxdnbGihUrJGVdu3aVLJuYmGDw4MGIioqSt/n06VOMGjUKQNk9tC8yYcIE+b87dOiAtm3bomvXrjh+/Di6dOnywu2rU6eRhA8//BDTpk3DrVu35GU3b97E9OnT0bdv35cK6FmzZs2Cr68vfHx80LVrV9y8eRO///67/B0JALBs2TIMHz4cbm5ucHZ2hpaWFnbs2MF3JBARERG9wtq2bQuZTIaMjIxq6129ehWurq7o0KEDtmzZgtTUVKxevRpA2bfzVSktLYWysjJSU1MlM1syMjLkF/iV3edanrw8qyb30FaWrHh7eyM+Ph5PnjxBdHQ03N3doaWlVe3xVqdLly5QVVWVT5d6GXUaSVi1ahWGDRsGc3NzmJmZQSaT4dq1a7C3t8eGDRvqHMzzN4qU31hSfnNJZTQ0NBAWFoawsLA675eIiIiImhYDAwMMGDAAq1evxtSpUytcZD948AD6+vo4duwYiouLsXTpUigplX3/XT7VqJyamhpKSkokZZ07d0ZJSQlycnLQo0ePSmNo166dfDpQuWPHjkmW7ezscOjQIYwdO1ZelpycLJmWXxVXV1doa2sjIiICu3fvxn//+98XblOd06dPo6ioCCYmJi/VDlDHJMHMzAzHjx9HQkICzp49CyEE3nnnnVd2ig8R0ZvudtgqtJjypaLDICKSCA8Ph5OTE7p164Zvv/0WHTt2RHFxMRISEhAREYGMjAxYWVmhuLgYYWFhGDp0KJKSkhAZGSlpx9zcHPn5+di7dy/effddaGlpwcbGBmPGjMHYsWOxdOlSdO7cGXfu3EFiYiLs7e3h6uqKKVOmoGfPnggNDcXQoUORmJiI3bt3S0YJZs6cCTc3N3Tp0gV9+/bFjh07sHXrVvzxxx8vPD5lZWV4eHggICAA1tbWcHR0lK8LCAjAzZs38cMPP1S6bWZmJjZu3AhXV1e89dZbOHPmDPz8/NC5c2c4OzvXscf/p1ZJQmJiIr788kscOXIEurq6cHFxgYuLCwAgNzcX7du3R2RkZJXZGBHVXnhaOHw6+Sg6DCIiek015bcgW1hY4Pjx41iwYAH8/PyQlZWFFi1awMHBAREREQCATp06ITQ0FIsWLUJAQAB69uyJ4OBgyTf7Tk5OmDRpEtzd3XH37l0EBgYiKCgI0dHRmD9/Pvz8/HDz5k0YGhrC0dERrq6uAMruLYiMjMS8efPwzTffYMCAAZg+fTpWrfrfI6WHDx+OFStWYMmSJZg6dSosLCwQHR1doycTAcD48eOxcOFC+Q3L5bKysnDt2rUqt1NTU8PevXuxYsUK5Ofnw8zMDIMHD0ZgYGC9TLuXicomVlXho48+Qp8+fTB9+vRK169cuRL79u3Dtm3bXjqwxpSXlwc9PT3k5uY2+uNQlyWcr1D2Wt+4vK+Km3De4JephaeFv7AOkwR6WTV5RwJHEhpeyo5LVa7rNtSyESN5c1X1uNPGvFBWxHXH06dPcfnyZVhYWFT7fip6sQkTJuDs2bM4ePBgvbSXlJSE3r1748aNG9U+wr++1PRcqNWNy3/99RcGDhxY5fr+/fsjNTW1Nk2+0SpLEIiIiIio6QgJCcFff/2FixcvIiwsDLGxsRg3btxLt1tQUICLFy9izpw5cHNza5QEoTZqlST8/fff1b5CWkVFpcZvXCYiIiIiaupSUlLg4uICe3t7REZGYuXKlfD29n7pduPi4mBra4vc3FwsXry4HiKtX7W6J6FVq1Y4deoUrK2tK11/8uTJermbmoiIiIioKXj+SUn1xcPDAx4eHg3Sdn2o1UiCq6sr5s6di6dPn1ZY9+TJEwQGBmLIkCH1FhwRERERETW+Wo0kfPPNN9i6dStsbGzw5ZdfwtbWVv6Si9WrV6OkpARff/11Q8VKRERERC+hFs+roddUTc+BWiUJRkZGSE5OxhdffIGAgADJ67IHDBiA8PDwJnfTBREREdGbrvye0sePH0NTU1PB0ZAiPX78GACqvc8YqMPL1Nq0aYNdu3bh/v37uHjxIoQQaNu2LZo3b163SOnNUNWjT4mIiKjBKSsrQ19fHzk5OQAALS0tyQvB6PUnhMDjx4+Rk5MDfX39F75LoU5vXAaA5s2b47333qvr5kRERETUiIyNjQFAnijQm0lfX19+LlSnzkkCEREREb06ZDIZTExM0LJlSxQVFSk6HFIAVVXVGr+NmUkCERER0RtEWVm5xheK9Oaq1SNQiYiIiIjo9cckgYiIiIiIJJgkEBERERGRBJMEIiIiIiKSYJJAREREb4TknzYqOgSiVwaTBCIiIiIikmCSQEREREREEkwSiIiIiIhIgkkCERERERFJMEkgIiIiIiIJJglERERERCTBJIGIiIiIiCSYJBARERERkQSTBCIioiYgZcclpOy4pOgwiIgAMEkgIiJqFEwAiOhVwiSBiIiIiIgkmCQQEREREZEEkwQiIiIiIpJgkkBERERvvOSfNio6BKImhUkCERERERFJMEkgIiIiIiIJJglERERERCTBJIGIiIiIiCSYJBARERERkQSTBCIiIiIikmCSQEREREREEkwSiIiIiIhIgkkCERERERFJMEkgIiIiIiIJJglERERERCTBJIGIiIiIiCSYJBApUHhauKJDICIiIqpAoUlCcHAw3nvvPejo6KBly5YYPnw4zp07J6kjhEBQUBBMTU2hqamJ3r174/Tp05I6BQUFmDJlCt566y1oa2vjo48+wo0bNxrzUIiIiIiIXhsKTRIOHDiAyZMn48iRI0hISEBxcTH69++PR48eyessXrwYoaGhWLVqFY4ePQpjY2O4uLjg4cOH8jq+vr7Ytm0b4uPjcejQIeTn52PIkCEoKSlRxGFRXewLLvsQERERkcKpKHLne/bskSxHR0ejZcuWSE1NRc+ePSGEwPLly/H111/j448/BgDExsbCyMgImzZtwsSJE5Gbm4t169Zh/fr16NevHwBgw4YNMDMzwx9//IEBAwY0+nG9rGUJ5zHdxUbRYRARERHRG6pJ3ZOQm5sLADAwMAAAXL58GdnZ2ejfv7+8jrq6Onr16oXk5GQAQGpqKoqKiiR1TE1N0aFDB3md5xUUFCAvL0/yISJ6Xd0OW6XoEIiI6BXTZJIEIQRmzJiBDz74AB06dAAAZGdnAwCMjIwkdY2MjOTrsrOzoaamhubNm1dZ53nBwcHQ09OTf8zMzOr7cIiIiIiIXllNJkn48ssvcfLkScTFxVVYJ5PJJMtCiAplz6uuTkBAAHJzc+Wf69ev1z1wIqLXxO2wVRx1ICIiAE0kSZgyZQq2b9+Offv2oXXr1vJyY2NjAKgwIpCTkyMfXTA2NkZhYSHu379fZZ3nqaurQ1dXV/IhIiIiIqIyCk0ShBD48ssvsXXrViQmJsLCwkKy3sLCAsbGxkhISJCXFRYW4sCBA3BycgIAODg4QFVVVVInKysL6enp8jpERERERFRzCn260eTJk7Fp0yb85z//gY6OjnzEQE9PD5qampDJZPD19cXChQvRtm1btG3bFgsXLoSWlhZGjx4trzt+/Hj4+fnB0NAQBgYG8Pf3h729vfxpR0REREREVHMKTRIiIiIAAL1795aUR0dHw8PDAwAwa9YsPHnyBD4+Prh//z66d++O33//HTo6OvL6y5Ytg4qKCtzc3PDkyRP07dsXMTExUFZWbqxDIWpQ4Wnh8Onko+gwiIiI6A2h0CRBCPHCOjKZDEFBQQgKCqqyjoaGBsLCwhAWFlaP0RERERERvZmaxI3LRERERA0p+aeNig6B6JXCJIEa3r5gRUdARERERLXAJEFBliWcV3QIRERERESVYpJARETUwFJ2XFJ0CEREtcIkgYiIiIiIJJgkEBERERGRBJMEIiIiIiKSYJJAREREREQSTBKIiIiIiEiCSQIREREREUkwSSAiIiIiIgkmCUREREREJMEkgYiIiIiIJJgkEBERERGRBJMEIiIiIiKSYJJAREREREQSTBKIiIiIiEiCSQIREREREUkwSSAiIiIiIgkmCUREREREJKGi6ACI3kThaeGKDoGIiIioShxJICIiidthqxQdwhstZcclRYfwxkr+aaOiQyBqMpgkEBERERGRBJOEJmpZwnlFh0BEREREbyjek0BNy75goE+AoqMgei1w2hAREdUVRxKIiIiIiEiCSQIREREREUkwSSB6RYSnhfPRqUREdcCnFhHVHpMEIiIiIiKSYJJAREREREQSfLoRNax9wYqOgIhIYfhiNCJ6VXEkgYiIiIiIJJgkEBERERGRBJMEBeDblImIiIioKWOSQEREREREEkwSiIiImpiUHZd40zMRKRSTBCIiIiIikmCSQET0GrodtkrRIRAR0SuMSQIRERHR/5f800ZFh0DUJDBJaMLe2Kcg8QVsRERERArFJIGokYWnhSs6BCIiIqJqMUkgesUwyaDGcDtsFe9reEl8OlHTwOlDRHWjougA3jRvzBQiThkiIiIiemVxJIGIiIiIiCRemyQhPDwcFhYW0NDQgIODAw4ePKjokOrFGzPy8DyORBAREREpzGuRJPz444/w9fXF119/jRMnTqBHjx4YNGgQrl27pujQiBpEeFo4700gaqLq823JvK+BiBTltUgSQkNDMX78eHh7e8POzg7Lly+HmZkZIiIiFB3am4mjAEQKU983HPPmZXoT8WZnotfgxuXCwkKkpqZi9uzZkvL+/fsjOTm50m0KCgpQUFAgX87NzQUA5OXlNVygAFYnXqzTdsG/HMfkD63rOZoG9Ohp/bSzM7Dsvz396qc9BVp7cm2DtNvQ5yy9eh4+eVL/bS5eAgB4a9LEem/7dZT/+GG9tpf4418AgK6DLOq13dfdn9t+fKntG/Lva3nbQogG2wfRy3rlk4Q7d+6gpKQERkZGknIjIyNkZ2dXuk1wcDDmzZtXodzMzKxBYqwPXyk6AIX6VtEBNFn+8Fd0CPQm+dcsRUdA1Hg8/tngu3j48CH09PQafD9EdfHKJwnlZDKZZFkIUaGsXEBAAGbMmCFfLi0txb1792BoaFjlNs/Ly8uDmZkZrl+/Dl1d3boH/opjP5RhP5RhP5RhP5RhP5RhP5RhP5Qp74czZ87A1NRU0eEQVemVTxLeeustKCsrVxg1yMnJqTC6UE5dXR3q6uqSMn19/TrtX1dX943+Y1eO/VCG/VCG/VCG/VCG/VCG/VCG/VCmVatWUFJ6LW4NpdfUK392qqmpwcHBAQkJCZLyhIQEODk5KSgqIiIiIqJX1ys/kgAAM2bMwOeff46uXbvC0dERa9aswbVr1zBp0iRFh0ZERERE9Mp5LZIEd3d33L17F99++y2ysrLQoUMH7Nq1C23atGmwfaqrqyMwMLDCtKU3DfuhDPuhDPuhDPuhDPuhDPuhDPuhDPuBXhUywedvERERERHRM175exKIiIiIiKh+MUkgIiIiIiIJJglERERERCTBJIGIiIiIiCSYJFQjPDwcFhYW0NDQgIODAw4ePFhl3aysLIwePRq2trZQUlKCr69v4wXawGrTD1u3boWLiwtatGgBXV1dODo64rfffmvEaBtObfrh0KFDcHZ2hqGhITQ1NdGuXTssW7asEaNtOLXph2clJSVBRUUFnTp1atgAG0lt+mH//v2QyWQVPmfPnm3EiBtGbc+HgoICfP3112jTpg3U1dVhZWWFqKioRoq24dSmHzw8PCo9H9q3b9+IEde/2p4LGzduxLvvvgstLS2YmJjA09MTd+/ebaRoG05t+2H16tWws7ODpqYmbG1t8cMPPzRSpEQvIKhS8fHxQlVVVaxdu1acOXNGTJs2TWhra4urV69WWv/y5cti6tSpIjY2VnTq1ElMmzatcQNuILXth2nTpolFixaJlJQUcf78eREQECBUVVXF8ePHGzny+lXbfjh+/LjYtGmTSE9PF5cvXxbr168XWlpa4vvvv2/kyOtXbfuh3IMHD4SlpaXo37+/ePfddxsn2AZU237Yt2+fACDOnTsnsrKy5J/i4uJGjrx+1eV8+Oijj0T37t1FQkKCuHz5svjzzz9FUlJSI0Zd/2rbDw8ePJCcB9evXxcGBgYiMDCwcQOvR7Xtg4MHDwolJSWxYsUKcenSJXHw4EHRvn17MXz48EaOvH7Vth/Cw8OFjo6OiI+PF5mZmSIuLk40a9ZMbN++vZEjJ6qISUIVunXrJiZNmiQpa9eunZg9e/YLt+3Vq9drkyS8TD+Ue+edd8S8efPqO7RGVR/9MGLECPHZZ5/Vd2iNqq794O7uLr755hsRGBj4WiQJte2H8iTh/v37jRBd46ltP+zevVvo6emJu3fvNkZ4jeZl/z5s27ZNyGQyceXKlYYIr1HUtg+WLFkiLC0tJWUrV64UrVu3brAYG0Nt+8HR0VH4+/tLyqZNmyacnZ0bLEaimuJ0o0oUFhYiNTUV/fv3l5T3798fycnJCoqq8dVHP5SWluLhw4cwMDBoiBAbRX30w4kTJ5CcnIxevXo1RIiNoq79EB0djczMTAQGBjZ0iI3iZc6Hzp07w8TEBH379sW+ffsaMswGV5d+2L59O7p27YrFixejVatWsLGxgb+/P548edIYITeI+vj7sG7dOvTr169BXwDakOrSB05OTrhx4wZ27doFIQT+/vtv/Pzzzxg8eHBjhNwg6tIPBQUF0NDQkJRpamoiJSUFRUVFDRYrUU0wSajEnTt3UFJSAiMjI0m5kZERsrOzFRRV46uPfli6dCkePXoENze3hgixUbxMP7Ru3Rrq6uro2rUrJk+eDG9v74YMtUHVpR8uXLiA2bNnY+PGjVBReS1e8F6nfjAxMcGaNWuwZcsWbN26Fba2tujbty/++9//NkbIDaIu/XDp0iUcOnQI6enp2LZtG5YvX46ff/4ZkydPboyQG8TL/p3MysrC7t2737i/DU5OTti4cSPc3d2hpqYGY2Nj6OvrIywsrDFCbhB16YcBAwbg//7v/5CamgohBI4dO4aoqCgUFRXhzp07jRE2UZVej/9rNxCZTCZZFkJUKHsT1LUf4uLiEBQUhP/85z9o2bJlQ4XXaOrSDwcPHkR+fj6OHDmC2bNnw9raGp9++mlDhtngatoPJSUlGD16NObNmwcbG5vGCq/R1OZ8sLW1ha2trXzZ0dER169fR0hICHr27NmgcTa02vRDaWkpZDIZNm7cCD09PQBAaGgoRo4cidWrV0NTU7PB420odf07GRMTA319fQwfPryBIms8temDM2fOYOrUqZg7dy4GDBiArKwszJw5E5MmTcK6desaI9wGU5t+mDNnDrKzs/H+++9DCAEjIyN4eHhg8eLFUFZWboxwiarEkYRKvPXWW1BWVq6Q+efk5FT4huB19jL98OOPP2L8+PHYvHkz+vXr15BhNriX6QcLCwvY29tjwoQJmD59OoKCghow0oZV2354+PAhjh07hi+//BIqKipQUVHBt99+i7/++gsqKipITExsrNDrVX39fXj//fdx4cKF+g6v0dSlH0xMTNCqVSt5ggAAdnZ2EELgxo0bDRpvQ3mZ80EIgaioKHz++edQU1NryDAbVF36IDg4GM7Ozpg5cyY6duyIAQMGIDw8HFFRUcjKymqMsOtdXfpBU1MTUVFRePz4Ma5cuYJr167B3NwcOjo6eOuttxojbKIqMUmohJqaGhwcHJCQkCApT0hIgJOTk4Kianx17Ye4uDh4eHhg06ZNr/T80nL1dT4IIVBQUFDf4TWa2vaDrq4uTp06hbS0NPln0qRJsLW1RVpaGrp3795Yoder+jofTpw4ARMTk/oOr9HUpR+cnZ1x69Yt5Ofny8vOnz8PJSUltG7dukHjbSgvcz4cOHAAFy9exPjx4xsyxAZXlz54/PgxlJSklyDl35wLIRom0Ab2MueCqqoqWrduDWVlZcTHx2PIkCEV+oeo0TX+vdKvhvLHmK1bt06cOXNG+Pr6Cm1tbfnTJ2bPni0+//xzyTYnTpwQJ06cEA4ODmL06NHixIkT4vTp04oIv97Uth82bdokVFRUxOrVqyWP+Hvw4IGiDqFe1LYfVq1aJbZv3y7Onz8vzp8/L6KiooSurq74+uuvFXUI9aIuvxfPel2eblTbfli2bJnYtm2bOH/+vEhPTxezZ88WAMSWLVsUdQj1orb98PDhQ9G6dWsxcuRIcfr0aXHgwAHRtm1b4e3trahDqBd1/b347LPPRPfu3Rs73AZR2z6Ijo4WKioqIjw8XGRmZopDhw6Jrl27im7duinqEOpFbfvh3LlzYv369eL8+fPizz//FO7u7sLAwEBcvnxZQUdA9D9MEqqxevVq0aZNG6Gmpia6dOkiDhw4IF83btw40atXL0l9ABU+bdq0adygG0Bt+qFXr16V9sO4ceMaP/B6Vpt+WLlypWjfvr3Q0tISurq6onPnziI8PFyUlJQoIPL6Vdvfi2e9LkmCELXrh0WLFgkrKyuhoaEhmjdvLj744APx66+/KiDq+lfb8yEjI0P069dPaGpqitatW4sZM2aIx48fN3LU9a+2/fDgwQOhqakp1qxZ08iRNpza9sHKlSvFO++8IzQ1NYWJiYkYM2aMuHHjRiNHXf9q0w9nzpwRnTp1EpqamkJXV1cMGzZMnD17VgFRE1UkE+IVHdcjIiIiIqIGwQlvREREREQkwSSBiIiIiIgkmCQQEREREZEEkwQiIiIiIpJgkkBERERERBJMEoiIiIiISIJJAhERERERSTBJICIiIiIiCSYJREQAgoKC0KlTp5duRyaT4Zdffqly/ZUrVyCTyZCWlgYA2L9/P2QyGR48eAAAiImJgb6+/kvHQURE9DKYJBDRK8fDwwMymQwymQyqqqqwtLSEv78/Hj16pOjQXsjMzAxZWVno0KFDpevd3d1x/vx5+XJ9JS9ERES1oaLoAIiI6mLgwIGIjo5GUVERDh48CG9vbzx69AgRERGSekVFRVBVVVVQlBUpKyvD2Ni4yvWamprQ1NRsxIiIiIgq4kgCEb2S1NXVYWxsDDMzM4wePRpjxozBL7/8Iv/mPSoqCpaWllBXV4cQAteuXcOwYcPQrFkz6Orqws3NDX///XeFdr///nuYmZlBS0sLo0aNkk8DAoCjR4/CxcUFb731FvT09NCrVy8cP368QhtZWVkYNGgQNDU1YWFhgZ9++km+7vnpRs97drpRTEwM5s2bh7/++ks+chITEwMvLy8MGTJEsl1xcTGMjY0RFRVV+84kIiJ6DpMEInotaGpqoqioCABw8eJFbN68GVu2bJFfjA8fPhz37t3DgQMHkJCQgMzMTLi7u0vaKN9ux44d2LNnD9LS0jB58mT5+ocPH2LcuHE4ePAgjhw5grZt28LV1RUPHz6UtDNnzhx88skn+Ouvv/DZZ5/h008/RUZGRq2Pyd3dHX5+fmjfvj2ysrKQlZUFd3d3eHt7Y8+ePcjKypLX3bVrF/Lz8+Hm5lbr/RARET2P042I6JWXkpKCTZs2oW/fvgCAwsJCrF+/Hi1atAAAJCQk4OTJk7h8+TLMzMwAAOvXr0f79u1x9OhRvPfeewCAp0+fIjY2Fq1btwYAhIWFYfDgwVi6dCmMjY3x4YcfSvb7/fffo3nz5jhw4IDkm/1Ro0bB29sbAPDdd98hISEBYWFhCA8Pr9VxaWpqolmzZlBRUZFMUXJycoKtrS3Wr1+PWbNmAQCio6MxatQoNGvWrFb7ICIiqgxHEojolbRz5040a9YMGhoacHR0RM+ePREWFgYAaNOmjTxBAICMjAyYmZnJEwQAeOedd6Cvry/5hv/tt9+WJwgA4OjoiNLSUpw7dw4AkJOTg0mTJsHGxgZ6enrQ09NDfn4+rl27JonN0dGxwnJdRhKq4+3tjejoaHlcv/76K7y8vOp1H0RE9ObiSAIRvZL69OmDiIgIqKqqwtTUVHJzsra2tqSuEAIymaxCG1WVlytfV/5fDw8P3L59G8uXL0ebNm2grq4OR0dHFBYWvjDe6vZTF2PHjsXs2bNx+PBhHD58GObm5ujRo0e97oOIiN5cHEkgoleStrY2rK2t0aZNmxc+veidd97BtWvXcP36dXnZmTNnkJubCzs7O3nZtWvXcOvWLfny4cOHoaSkBBsbGwDAwYMHMXXqVLi6uqJ9+/ZQV1fHnTt3KuzvyJEjFZbbtWtXp+NUU1NDSUlJhXJDQ0MMHz4c0dHRiI6OhqenZ53aJyIiqgxHEojotdevXz907NgRY8aMwfLly1FcXAwfHx/06tULXbt2ldfT0NDAuHHjEBISgry8PEydOhVubm7y+wGsra2xfv16dO3aFXl5eZg5c2aljyv96aef0LVrV3zwwQfYuHEjUlJSsG7dujrFbm5ujsuXLyMtLQ2tW7eGjo4O1NXVAZRNORoyZAhKSkowbty4OrVPRERUGY4kENFrr/wtyM2bN0fPnj3Rr18/WFpa4scff5TUs7a2xscffwxXV1f0798fHTp0kNxsHBUVhfv376Nz5874/PPPMXXqVLRs2bLC/ubNm4f4+Hh07NgRsbGx2LhxI9555506xf7JJ59g4MCB6NOnD1q0aIG4uDj5un79+sHExAQDBgyAqalpndonIiKqjEwIIRQdBBER1d7jx49hamqKqKgofPzxx4oOh4iIXiOcbkRE9IopLS1FdnY2li5dCj09PXz00UeKDomIiF4zTBKIiF4x165dg4WFBVq3bo2YmBioqPBPORER1S9ONyIiIiIiIgneuExERERERBJMEoiIiIiISIJJAhERERERSTBJICIiIiIiCSYJREREREQkwSSBiIiIiIgkmCQQEREREZEEkwQiIiIiIpL4f4J3WNeS1MCkAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -476,7 +533,7 @@
     "    ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n",
     "ax.set_xlabel(\"Probability\")\n",
     "ax.set_ylabel(\"Count\")\n",
-    "ax.set_title(\"Cumulative Probability by Category\")\n",
+    "ax.set_title(\"Cumulative Probability by Response Category\")\n",
     "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");"
    ]
   },
@@ -508,21 +565,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/Documents/repos/bambi/bambi/formula.py:111: UserWarning: The intercept is omitted in ordinal families\n",
-      "  warnings.warn(\"The intercept is omitted in ordinal families\")\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/formulae/terms/variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/distributions/transforms.py:56: FutureWarning: univariate_ordered has been deprecated, use ordered instead.\n",
       "  warnings.warn(f\"{name} has been deprecated, use ordered instead.\", FutureWarning)\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [response_threshold, action, intention, contact, action:intention, contact:intention]\n"
+      "NUTS: [response_threshold, action, intention, contact, action:intention, contact:intention]\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n"
      ]
     },
     {
@@ -558,7 +619,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 01:11&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 45:32&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -573,17 +634,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 71 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2732 seconds.\n"
      ]
     }
    ],
    "source": [
     "model = bmb.Model(\n",
-    "    \"response ~ action + intention + contact + action:intention + contact:intention\", \n",
+    "    \"response ~ 0 + action + intention + contact + action:intention + contact:intention\", \n",
     "    data=trolly, \n",
     "    family=\"cumulative\"\n",
     ")\n",
@@ -599,9 +656,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/xarray/core/concat.py:546: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
+      "  common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -637,62 +702,62 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>action[1]</th>\n",
-       "      <td>-0.463</td>\n",
+       "      <td>-0.465</td>\n",
        "      <td>0.055</td>\n",
-       "      <td>-0.565</td>\n",
-       "      <td>-0.359</td>\n",
+       "      <td>-0.563</td>\n",
+       "      <td>-0.363</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2662.0</td>\n",
-       "      <td>3107.0</td>\n",
+       "      <td>2647.0</td>\n",
+       "      <td>2834.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>intention[1]</th>\n",
-       "      <td>-0.273</td>\n",
+       "      <td>-0.276</td>\n",
        "      <td>0.058</td>\n",
-       "      <td>-0.375</td>\n",
-       "      <td>-0.161</td>\n",
+       "      <td>-0.387</td>\n",
+       "      <td>-0.171</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2326.0</td>\n",
-       "      <td>2681.0</td>\n",
+       "      <td>2174.0</td>\n",
+       "      <td>2857.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact[1]</th>\n",
-       "      <td>-0.322</td>\n",
-       "      <td>0.070</td>\n",
-       "      <td>-0.453</td>\n",
-       "      <td>-0.195</td>\n",
+       "      <td>-0.325</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>-0.456</td>\n",
+       "      <td>-0.197</td>\n",
        "      <td>0.001</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2894.0</td>\n",
-       "      <td>2920.0</td>\n",
+       "      <td>2660.0</td>\n",
+       "      <td>2896.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>action:intention[1, 1]</th>\n",
-       "      <td>-0.457</td>\n",
+       "      <td>-0.452</td>\n",
        "      <td>0.081</td>\n",
-       "      <td>-0.597</td>\n",
-       "      <td>-0.302</td>\n",
+       "      <td>-0.605</td>\n",
+       "      <td>-0.301</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2432.0</td>\n",
-       "      <td>2892.0</td>\n",
+       "      <td>2515.0</td>\n",
+       "      <td>2580.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact:intention[1, 1]</th>\n",
-       "      <td>-1.284</td>\n",
-       "      <td>0.100</td>\n",
-       "      <td>-1.464</td>\n",
-       "      <td>-1.095</td>\n",
+       "      <td>-1.279</td>\n",
+       "      <td>0.099</td>\n",
+       "      <td>-1.457</td>\n",
+       "      <td>-1.086</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2760.0</td>\n",
-       "      <td>2875.0</td>\n",
+       "      <td>2575.0</td>\n",
+       "      <td>3023.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -700,22 +765,22 @@
        "</div>"
       ],
       "text/plain": [
-       "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd   \n",
-       "action[1]               -0.463  0.055  -0.565   -0.359      0.001    0.001  \\\n",
-       "intention[1]            -0.273  0.058  -0.375   -0.161      0.001    0.001   \n",
-       "contact[1]              -0.322  0.070  -0.453   -0.195      0.001    0.001   \n",
-       "action:intention[1, 1]  -0.457  0.081  -0.597   -0.302      0.002    0.001   \n",
-       "contact:intention[1, 1] -1.284  0.100  -1.464   -1.095      0.002    0.001   \n",
+       "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "action[1]               -0.465  0.055  -0.563   -0.363      0.001    0.001   \n",
+       "intention[1]            -0.276  0.058  -0.387   -0.171      0.001    0.001   \n",
+       "contact[1]              -0.325  0.069  -0.456   -0.197      0.001    0.001   \n",
+       "action:intention[1, 1]  -0.452  0.081  -0.605   -0.301      0.002    0.001   \n",
+       "contact:intention[1, 1] -1.279  0.099  -1.457   -1.086      0.002    0.001   \n",
        "\n",
        "                         ess_bulk  ess_tail  r_hat  \n",
-       "action[1]                  2662.0    3107.0    1.0  \n",
-       "intention[1]               2326.0    2681.0    1.0  \n",
-       "contact[1]                 2894.0    2920.0    1.0  \n",
-       "action:intention[1, 1]     2432.0    2892.0    1.0  \n",
-       "contact:intention[1, 1]    2760.0    2875.0    1.0  "
+       "action[1]                  2647.0    2834.0    1.0  \n",
+       "intention[1]               2174.0    2857.0    1.0  \n",
+       "contact[1]                 2660.0    2896.0    1.0  \n",
+       "action:intention[1, 1]     2515.0    2580.0    1.0  \n",
+       "contact:intention[1, 1]    2575.0    3023.0    1.0  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -737,12 +802,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -771,12 +836,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -836,23 +901,6 @@
     "Looking at the observed and posterior predictive mean, the model captures the observed frequencies of the categories well. "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Changing the default prior of cutpoints\n",
-    "\n",
-    "\n",
-    "**TO DO**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -889,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -956,7 +1004,7 @@
        "6              1   59"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -977,13 +1025,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/formulae/terms/variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
@@ -1023,7 +1073,7 @@
        "\n",
        "    <div>\n",
        "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 03:24&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      100.00% [8000/8000 03:30&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1038,7 +1088,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 205 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 211 seconds.\n"
      ]
     }
    ],
@@ -1049,9 +1099,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/xarray/core/concat.py:546: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
+      "  common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1087,422 +1145,422 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[0]</th>\n",
-       "      <td>-2.571</td>\n",
-       "      <td>0.197</td>\n",
-       "      <td>-2.930</td>\n",
-       "      <td>-2.195</td>\n",
+       "      <td>-2.572</td>\n",
+       "      <td>0.198</td>\n",
+       "      <td>-2.937</td>\n",
+       "      <td>-2.201</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>5509.0</td>\n",
-       "      <td>2243.0</td>\n",
+       "      <td>5129.0</td>\n",
+       "      <td>2350.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[1]</th>\n",
        "      <td>-1.054</td>\n",
-       "      <td>0.109</td>\n",
-       "      <td>-1.248</td>\n",
-       "      <td>-0.838</td>\n",
+       "      <td>0.110</td>\n",
+       "      <td>-1.264</td>\n",
+       "      <td>-0.855</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>3092.0</td>\n",
-       "      <td>2725.0</td>\n",
+       "      <td>3021.0</td>\n",
+       "      <td>2994.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[2]</th>\n",
        "      <td>-1.018</td>\n",
-       "      <td>0.116</td>\n",
-       "      <td>-1.252</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>-1.243</td>\n",
        "      <td>-0.812</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>3545.0</td>\n",
-       "      <td>2514.0</td>\n",
+       "      <td>3451.0</td>\n",
+       "      <td>2661.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[3]</th>\n",
        "      <td>-0.757</td>\n",
-       "      <td>0.117</td>\n",
-       "      <td>-0.970</td>\n",
-       "      <td>-0.538</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>-0.974</td>\n",
+       "      <td>-0.542</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>3458.0</td>\n",
-       "      <td>2916.0</td>\n",
+       "      <td>3384.0</td>\n",
+       "      <td>2770.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[4]</th>\n",
-       "      <td>-0.754</td>\n",
+       "      <td>-0.755</td>\n",
        "      <td>0.122</td>\n",
-       "      <td>-0.969</td>\n",
-       "      <td>-0.520</td>\n",
+       "      <td>-0.992</td>\n",
+       "      <td>-0.538</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2900.0</td>\n",
-       "      <td>2993.0</td>\n",
+       "      <td>3211.0</td>\n",
+       "      <td>2712.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[5]</th>\n",
        "      <td>0.263</td>\n",
-       "      <td>0.105</td>\n",
-       "      <td>0.050</td>\n",
-       "      <td>0.442</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.057</td>\n",
+       "      <td>0.447</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2262.0</td>\n",
-       "      <td>2599.0</td>\n",
+       "      <td>2327.0</td>\n",
+       "      <td>2532.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[6]</th>\n",
-       "      <td>-0.501</td>\n",
+       "      <td>-0.503</td>\n",
        "      <td>0.143</td>\n",
-       "      <td>-0.772</td>\n",
-       "      <td>-0.238</td>\n",
-       "      <td>0.002</td>\n",
+       "      <td>-0.782</td>\n",
+       "      <td>-0.247</td>\n",
+       "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>3734.0</td>\n",
-       "      <td>2865.0</td>\n",
+       "      <td>3170.0</td>\n",
+       "      <td>3111.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[7]</th>\n",
-       "      <td>-0.079</td>\n",
+       "      <td>-0.077</td>\n",
        "      <td>0.140</td>\n",
-       "      <td>-0.336</td>\n",
-       "      <td>0.189</td>\n",
+       "      <td>-0.328</td>\n",
+       "      <td>0.195</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>3139.0</td>\n",
-       "      <td>2944.0</td>\n",
+       "      <td>2829.0</td>\n",
+       "      <td>3449.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[8]</th>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.150</td>\n",
-       "      <td>-0.226</td>\n",
-       "      <td>0.333</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>0.149</td>\n",
+       "      <td>-0.232</td>\n",
+       "      <td>0.326</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2926.0</td>\n",
-       "      <td>2874.0</td>\n",
+       "      <td>3147.0</td>\n",
+       "      <td>3008.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[9]</th>\n",
-       "      <td>0.401</td>\n",
-       "      <td>0.150</td>\n",
-       "      <td>0.132</td>\n",
-       "      <td>0.692</td>\n",
+       "      <td>0.400</td>\n",
+       "      <td>0.149</td>\n",
+       "      <td>0.128</td>\n",
+       "      <td>0.685</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2979.0</td>\n",
-       "      <td>2550.0</td>\n",
+       "      <td>2833.0</td>\n",
+       "      <td>2733.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[10]</th>\n",
        "      <td>1.290</td>\n",
-       "      <td>0.150</td>\n",
-       "      <td>0.997</td>\n",
-       "      <td>1.556</td>\n",
+       "      <td>0.151</td>\n",
+       "      <td>1.013</td>\n",
+       "      <td>1.573</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2092.0</td>\n",
-       "      <td>2430.0</td>\n",
+       "      <td>2323.0</td>\n",
+       "      <td>2543.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[11]</th>\n",
-       "      <td>0.451</td>\n",
+       "      <td>0.449</td>\n",
        "      <td>0.214</td>\n",
-       "      <td>0.055</td>\n",
-       "      <td>0.857</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.832</td>\n",
        "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>2714.0</td>\n",
-       "      <td>2847.0</td>\n",
+       "      <td>3133.0</td>\n",
+       "      <td>3068.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[12]</th>\n",
-       "      <td>-0.117</td>\n",
-       "      <td>0.294</td>\n",
-       "      <td>-0.666</td>\n",
-       "      <td>0.436</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>-0.118</td>\n",
+       "      <td>0.296</td>\n",
+       "      <td>-0.694</td>\n",
+       "      <td>0.414</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>4272.0</td>\n",
-       "      <td>2223.0</td>\n",
+       "      <td>4813.0</td>\n",
+       "      <td>2698.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[13]</th>\n",
        "      <td>0.533</td>\n",
-       "      <td>0.253</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>0.247</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>0.996</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>2965.0</td>\n",
-       "      <td>2717.0</td>\n",
+       "      <td>3253.0</td>\n",
+       "      <td>2503.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[14]</th>\n",
-       "      <td>0.410</td>\n",
-       "      <td>0.287</td>\n",
-       "      <td>-0.144</td>\n",
-       "      <td>0.936</td>\n",
+       "      <td>0.412</td>\n",
+       "      <td>0.289</td>\n",
+       "      <td>-0.129</td>\n",
+       "      <td>0.943</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>3090.0</td>\n",
-       "      <td>2611.0</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>4006.0</td>\n",
+       "      <td>3026.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[15]</th>\n",
        "      <td>0.825</td>\n",
-       "      <td>0.266</td>\n",
-       "      <td>0.314</td>\n",
-       "      <td>1.311</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>0.268</td>\n",
+       "      <td>0.326</td>\n",
+       "      <td>1.328</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>3271.0</td>\n",
-       "      <td>2917.0</td>\n",
+       "      <td>3628.0</td>\n",
+       "      <td>2760.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[16]</th>\n",
-       "      <td>0.468</td>\n",
-       "      <td>0.340</td>\n",
-       "      <td>-0.170</td>\n",
-       "      <td>1.111</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>0.466</td>\n",
+       "      <td>0.342</td>\n",
+       "      <td>-0.188</td>\n",
+       "      <td>1.080</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>3911.0</td>\n",
-       "      <td>2940.0</td>\n",
+       "      <td>3802.0</td>\n",
+       "      <td>2888.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[17]</th>\n",
-       "      <td>0.289</td>\n",
-       "      <td>0.383</td>\n",
-       "      <td>-0.436</td>\n",
-       "      <td>0.964</td>\n",
+       "      <td>0.292</td>\n",
+       "      <td>0.377</td>\n",
+       "      <td>-0.397</td>\n",
+       "      <td>0.994</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>3805.0</td>\n",
-       "      <td>2814.0</td>\n",
+       "      <td>4280.0</td>\n",
+       "      <td>2866.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[18]</th>\n",
-       "      <td>0.868</td>\n",
-       "      <td>0.330</td>\n",
+       "      <td>0.866</td>\n",
+       "      <td>0.324</td>\n",
        "      <td>0.234</td>\n",
-       "      <td>1.472</td>\n",
+       "      <td>1.454</td>\n",
        "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>3684.0</td>\n",
-       "      <td>2862.0</td>\n",
+       "      <td>3796.0</td>\n",
+       "      <td>2870.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[19]</th>\n",
-       "      <td>0.865</td>\n",
-       "      <td>0.346</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>1.498</td>\n",
+       "      <td>0.858</td>\n",
+       "      <td>0.354</td>\n",
+       "      <td>0.177</td>\n",
+       "      <td>1.490</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>3837.0</td>\n",
-       "      <td>2669.0</td>\n",
+       "      <td>4165.0</td>\n",
+       "      <td>2558.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[20]</th>\n",
-       "      <td>2.277</td>\n",
-       "      <td>0.274</td>\n",
-       "      <td>1.741</td>\n",
-       "      <td>2.766</td>\n",
+       "      <td>2.274</td>\n",
+       "      <td>0.272</td>\n",
+       "      <td>1.756</td>\n",
+       "      <td>2.778</td>\n",
        "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2832.0</td>\n",
-       "      <td>2810.0</td>\n",
+       "      <td>2747.0</td>\n",
+       "      <td>2764.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[21]</th>\n",
-       "      <td>1.940</td>\n",
-       "      <td>0.348</td>\n",
-       "      <td>1.242</td>\n",
-       "      <td>2.560</td>\n",
+       "      <td>1.936</td>\n",
+       "      <td>0.357</td>\n",
+       "      <td>1.254</td>\n",
+       "      <td>2.572</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>3193.0</td>\n",
-       "      <td>2981.0</td>\n",
+       "      <td>4105.0</td>\n",
+       "      <td>2921.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[22]</th>\n",
-       "      <td>2.558</td>\n",
-       "      <td>0.353</td>\n",
-       "      <td>1.916</td>\n",
-       "      <td>3.236</td>\n",
+       "      <td>2.554</td>\n",
+       "      <td>0.357</td>\n",
+       "      <td>1.880</td>\n",
+       "      <td>3.215</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>3331.0</td>\n",
-       "      <td>2546.0</td>\n",
+       "      <td>3234.0</td>\n",
+       "      <td>2823.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[23]</th>\n",
-       "      <td>0.423</td>\n",
-       "      <td>0.685</td>\n",
-       "      <td>-0.860</td>\n",
-       "      <td>1.655</td>\n",
+       "      <td>0.427</td>\n",
+       "      <td>0.684</td>\n",
+       "      <td>-0.915</td>\n",
+       "      <td>1.622</td>\n",
        "      <td>0.009</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>5871.0</td>\n",
-       "      <td>2751.0</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>6890.0</td>\n",
+       "      <td>2845.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[24]</th>\n",
-       "      <td>1.843</td>\n",
-       "      <td>0.513</td>\n",
-       "      <td>0.802</td>\n",
-       "      <td>2.709</td>\n",
+       "      <td>1.852</td>\n",
+       "      <td>0.502</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>2.753</td>\n",
        "      <td>0.008</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>4798.0</td>\n",
-       "      <td>2686.0</td>\n",
+       "      <td>4524.0</td>\n",
+       "      <td>2805.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[25]</th>\n",
        "      <td>1.869</td>\n",
-       "      <td>0.553</td>\n",
-       "      <td>0.818</td>\n",
-       "      <td>2.873</td>\n",
-       "      <td>0.008</td>\n",
+       "      <td>0.559</td>\n",
+       "      <td>0.823</td>\n",
+       "      <td>2.876</td>\n",
+       "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>5084.0</td>\n",
-       "      <td>2838.0</td>\n",
+       "      <td>4210.0</td>\n",
+       "      <td>2428.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[26]</th>\n",
-       "      <td>2.139</td>\n",
-       "      <td>0.558</td>\n",
-       "      <td>1.132</td>\n",
-       "      <td>3.231</td>\n",
+       "      <td>2.141</td>\n",
+       "      <td>0.559</td>\n",
+       "      <td>1.133</td>\n",
+       "      <td>3.228</td>\n",
        "      <td>0.008</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>4867.0</td>\n",
-       "      <td>2851.0</td>\n",
+       "      <td>4854.0</td>\n",
+       "      <td>2761.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[27]</th>\n",
-       "      <td>1.688</td>\n",
-       "      <td>0.688</td>\n",
-       "      <td>0.377</td>\n",
-       "      <td>2.902</td>\n",
+       "      <td>1.690</td>\n",
+       "      <td>0.694</td>\n",
+       "      <td>0.298</td>\n",
+       "      <td>2.853</td>\n",
        "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>6452.0</td>\n",
-       "      <td>2998.0</td>\n",
+       "      <td>6195.0</td>\n",
+       "      <td>3292.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[28]</th>\n",
-       "      <td>1.852</td>\n",
-       "      <td>0.687</td>\n",
-       "      <td>0.621</td>\n",
-       "      <td>3.164</td>\n",
+       "      <td>1.855</td>\n",
+       "      <td>0.686</td>\n",
+       "      <td>0.595</td>\n",
+       "      <td>3.168</td>\n",
        "      <td>0.009</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>5523.0</td>\n",
-       "      <td>3164.0</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>6134.0</td>\n",
+       "      <td>2971.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[29]</th>\n",
-       "      <td>1.490</td>\n",
-       "      <td>0.751</td>\n",
-       "      <td>0.184</td>\n",
-       "      <td>2.979</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>5018.0</td>\n",
-       "      <td>2984.0</td>\n",
+       "      <td>1.488</td>\n",
+       "      <td>0.758</td>\n",
+       "      <td>0.091</td>\n",
+       "      <td>2.942</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>5607.0</td>\n",
+       "      <td>2832.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[30]</th>\n",
        "      <td>2.132</td>\n",
-       "      <td>0.694</td>\n",
-       "      <td>0.899</td>\n",
-       "      <td>3.500</td>\n",
+       "      <td>0.687</td>\n",
+       "      <td>0.827</td>\n",
+       "      <td>3.433</td>\n",
        "      <td>0.009</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>5767.0</td>\n",
-       "      <td>2584.0</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>5868.0</td>\n",
+       "      <td>3002.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[31]</th>\n",
-       "      <td>2.351</td>\n",
-       "      <td>0.699</td>\n",
-       "      <td>1.085</td>\n",
-       "      <td>3.722</td>\n",
+       "      <td>2.344</td>\n",
+       "      <td>0.709</td>\n",
+       "      <td>0.974</td>\n",
+       "      <td>3.661</td>\n",
        "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>6991.0</td>\n",
-       "      <td>2987.0</td>\n",
+       "      <td>6329.0</td>\n",
+       "      <td>2897.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[32]</th>\n",
-       "      <td>3.486</td>\n",
-       "      <td>0.648</td>\n",
-       "      <td>2.305</td>\n",
-       "      <td>4.714</td>\n",
+       "      <td>3.490</td>\n",
+       "      <td>0.629</td>\n",
+       "      <td>2.251</td>\n",
+       "      <td>4.607</td>\n",
        "      <td>0.009</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>4981.0</td>\n",
-       "      <td>3083.0</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>5403.0</td>\n",
+       "      <td>3306.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[33]</th>\n",
-       "      <td>2.421</td>\n",
-       "      <td>0.841</td>\n",
-       "      <td>0.949</td>\n",
-       "      <td>4.105</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>5710.0</td>\n",
-       "      <td>2938.0</td>\n",
+       "      <td>2.412</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>0.829</td>\n",
+       "      <td>4.044</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>6733.0</td>\n",
+       "      <td>2959.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[34]</th>\n",
-       "      <td>3.333</td>\n",
-       "      <td>0.860</td>\n",
-       "      <td>1.647</td>\n",
-       "      <td>4.913</td>\n",
+       "      <td>3.335</td>\n",
+       "      <td>0.846</td>\n",
+       "      <td>1.682</td>\n",
+       "      <td>4.886</td>\n",
        "      <td>0.012</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>5747.0</td>\n",
-       "      <td>2698.0</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>4869.0</td>\n",
+       "      <td>3133.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1510,11 +1568,11 @@
        "      <td>0.130</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.119</td>\n",
-       "      <td>0.140</td>\n",
+       "      <td>0.139</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>968.0</td>\n",
-       "      <td>1511.0</td>\n",
+       "      <td>977.0</td>\n",
+       "      <td>1385.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1522,84 +1580,84 @@
        "</div>"
       ],
       "text/plain": [
-       "                               mean     sd  hdi_3%  hdi_97%  mcse_mean   \n",
-       "YearsAtCompany_threshold[0]  -2.571  0.197  -2.930   -2.195      0.003  \\\n",
-       "YearsAtCompany_threshold[1]  -1.054  0.109  -1.248   -0.838      0.002   \n",
-       "YearsAtCompany_threshold[2]  -1.018  0.116  -1.252   -0.812      0.002   \n",
-       "YearsAtCompany_threshold[3]  -0.757  0.117  -0.970   -0.538      0.002   \n",
-       "YearsAtCompany_threshold[4]  -0.754  0.122  -0.969   -0.520      0.002   \n",
-       "YearsAtCompany_threshold[5]   0.263  0.105   0.050    0.442      0.002   \n",
-       "YearsAtCompany_threshold[6]  -0.501  0.143  -0.772   -0.238      0.002   \n",
-       "YearsAtCompany_threshold[7]  -0.079  0.140  -0.336    0.189      0.003   \n",
-       "YearsAtCompany_threshold[8]   0.040  0.150  -0.226    0.333      0.003   \n",
-       "YearsAtCompany_threshold[9]   0.401  0.150   0.132    0.692      0.003   \n",
-       "YearsAtCompany_threshold[10]  1.290  0.150   0.997    1.556      0.003   \n",
-       "YearsAtCompany_threshold[11]  0.451  0.214   0.055    0.857      0.004   \n",
-       "YearsAtCompany_threshold[12] -0.117  0.294  -0.666    0.436      0.005   \n",
-       "YearsAtCompany_threshold[13]  0.533  0.253   0.037    0.984      0.005   \n",
-       "YearsAtCompany_threshold[14]  0.410  0.287  -0.144    0.936      0.005   \n",
-       "YearsAtCompany_threshold[15]  0.825  0.266   0.314    1.311      0.005   \n",
-       "YearsAtCompany_threshold[16]  0.468  0.340  -0.170    1.111      0.005   \n",
-       "YearsAtCompany_threshold[17]  0.289  0.383  -0.436    0.964      0.006   \n",
-       "YearsAtCompany_threshold[18]  0.868  0.330   0.234    1.472      0.005   \n",
-       "YearsAtCompany_threshold[19]  0.865  0.346   0.214    1.498      0.006   \n",
-       "YearsAtCompany_threshold[20]  2.277  0.274   1.741    2.766      0.005   \n",
-       "YearsAtCompany_threshold[21]  1.940  0.348   1.242    2.560      0.006   \n",
-       "YearsAtCompany_threshold[22]  2.558  0.353   1.916    3.236      0.006   \n",
-       "YearsAtCompany_threshold[23]  0.423  0.685  -0.860    1.655      0.009   \n",
-       "YearsAtCompany_threshold[24]  1.843  0.513   0.802    2.709      0.008   \n",
-       "YearsAtCompany_threshold[25]  1.869  0.553   0.818    2.873      0.008   \n",
-       "YearsAtCompany_threshold[26]  2.139  0.558   1.132    3.231      0.008   \n",
-       "YearsAtCompany_threshold[27]  1.688  0.688   0.377    2.902      0.009   \n",
-       "YearsAtCompany_threshold[28]  1.852  0.687   0.621    3.164      0.009   \n",
-       "YearsAtCompany_threshold[29]  1.490  0.751   0.184    2.979      0.011   \n",
-       "YearsAtCompany_threshold[30]  2.132  0.694   0.899    3.500      0.009   \n",
-       "YearsAtCompany_threshold[31]  2.351  0.699   1.085    3.722      0.009   \n",
-       "YearsAtCompany_threshold[32]  3.486  0.648   2.305    4.714      0.009   \n",
-       "YearsAtCompany_threshold[33]  2.421  0.841   0.949    4.105      0.011   \n",
-       "YearsAtCompany_threshold[34]  3.333  0.860   1.647    4.913      0.012   \n",
-       "TotalWorkingYears             0.130  0.006   0.119    0.140      0.000   \n",
+       "                               mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
+       "YearsAtCompany_threshold[0]  -2.572  0.198  -2.937   -2.201      0.003   \n",
+       "YearsAtCompany_threshold[1]  -1.054  0.110  -1.264   -0.855      0.002   \n",
+       "YearsAtCompany_threshold[2]  -1.018  0.117  -1.243   -0.812      0.002   \n",
+       "YearsAtCompany_threshold[3]  -0.757  0.118  -0.974   -0.542      0.002   \n",
+       "YearsAtCompany_threshold[4]  -0.755  0.122  -0.992   -0.538      0.002   \n",
+       "YearsAtCompany_threshold[5]   0.263  0.104   0.057    0.447      0.002   \n",
+       "YearsAtCompany_threshold[6]  -0.503  0.143  -0.782   -0.247      0.003   \n",
+       "YearsAtCompany_threshold[7]  -0.077  0.140  -0.328    0.195      0.003   \n",
+       "YearsAtCompany_threshold[8]   0.039  0.149  -0.232    0.326      0.003   \n",
+       "YearsAtCompany_threshold[9]   0.400  0.149   0.128    0.685      0.003   \n",
+       "YearsAtCompany_threshold[10]  1.290  0.151   1.013    1.573      0.003   \n",
+       "YearsAtCompany_threshold[11]  0.449  0.214   0.037    0.832      0.004   \n",
+       "YearsAtCompany_threshold[12] -0.118  0.296  -0.694    0.414      0.004   \n",
+       "YearsAtCompany_threshold[13]  0.533  0.247   0.069    0.996      0.004   \n",
+       "YearsAtCompany_threshold[14]  0.412  0.289  -0.129    0.943      0.005   \n",
+       "YearsAtCompany_threshold[15]  0.825  0.268   0.326    1.328      0.004   \n",
+       "YearsAtCompany_threshold[16]  0.466  0.342  -0.188    1.080      0.006   \n",
+       "YearsAtCompany_threshold[17]  0.292  0.377  -0.397    0.994      0.006   \n",
+       "YearsAtCompany_threshold[18]  0.866  0.324   0.234    1.454      0.005   \n",
+       "YearsAtCompany_threshold[19]  0.858  0.354   0.177    1.490      0.006   \n",
+       "YearsAtCompany_threshold[20]  2.274  0.272   1.756    2.778      0.005   \n",
+       "YearsAtCompany_threshold[21]  1.936  0.357   1.254    2.572      0.006   \n",
+       "YearsAtCompany_threshold[22]  2.554  0.357   1.880    3.215      0.006   \n",
+       "YearsAtCompany_threshold[23]  0.427  0.684  -0.915    1.622      0.009   \n",
+       "YearsAtCompany_threshold[24]  1.852  0.502   0.896    2.753      0.008   \n",
+       "YearsAtCompany_threshold[25]  1.869  0.559   0.823    2.876      0.009   \n",
+       "YearsAtCompany_threshold[26]  2.141  0.559   1.133    3.228      0.008   \n",
+       "YearsAtCompany_threshold[27]  1.690  0.694   0.298    2.853      0.009   \n",
+       "YearsAtCompany_threshold[28]  1.855  0.686   0.595    3.168      0.009   \n",
+       "YearsAtCompany_threshold[29]  1.488  0.758   0.091    2.942      0.010   \n",
+       "YearsAtCompany_threshold[30]  2.132  0.687   0.827    3.433      0.009   \n",
+       "YearsAtCompany_threshold[31]  2.344  0.709   0.974    3.661      0.009   \n",
+       "YearsAtCompany_threshold[32]  3.490  0.629   2.251    4.607      0.009   \n",
+       "YearsAtCompany_threshold[33]  2.412  0.850   0.829    4.044      0.010   \n",
+       "YearsAtCompany_threshold[34]  3.335  0.846   1.682    4.886      0.012   \n",
+       "TotalWorkingYears             0.130  0.006   0.119    0.139      0.000   \n",
        "\n",
        "                              mcse_sd  ess_bulk  ess_tail  r_hat  \n",
-       "YearsAtCompany_threshold[0]     0.002    5509.0    2243.0    1.0  \n",
-       "YearsAtCompany_threshold[1]     0.001    3092.0    2725.0    1.0  \n",
-       "YearsAtCompany_threshold[2]     0.001    3545.0    2514.0    1.0  \n",
-       "YearsAtCompany_threshold[3]     0.001    3458.0    2916.0    1.0  \n",
-       "YearsAtCompany_threshold[4]     0.002    2900.0    2993.0    1.0  \n",
-       "YearsAtCompany_threshold[5]     0.002    2262.0    2599.0    1.0  \n",
-       "YearsAtCompany_threshold[6]     0.002    3734.0    2865.0    1.0  \n",
-       "YearsAtCompany_threshold[7]     0.002    3139.0    2944.0    1.0  \n",
-       "YearsAtCompany_threshold[8]     0.002    2926.0    2874.0    1.0  \n",
-       "YearsAtCompany_threshold[9]     0.002    2979.0    2550.0    1.0  \n",
-       "YearsAtCompany_threshold[10]    0.002    2092.0    2430.0    1.0  \n",
-       "YearsAtCompany_threshold[11]    0.003    2714.0    2847.0    1.0  \n",
-       "YearsAtCompany_threshold[12]    0.005    4272.0    2223.0    1.0  \n",
-       "YearsAtCompany_threshold[13]    0.003    2965.0    2717.0    1.0  \n",
-       "YearsAtCompany_threshold[14]    0.004    3090.0    2611.0    1.0  \n",
-       "YearsAtCompany_threshold[15]    0.003    3271.0    2917.0    1.0  \n",
-       "YearsAtCompany_threshold[16]    0.004    3911.0    2940.0    1.0  \n",
-       "YearsAtCompany_threshold[17]    0.005    3805.0    2814.0    1.0  \n",
-       "YearsAtCompany_threshold[18]    0.004    3684.0    2862.0    1.0  \n",
-       "YearsAtCompany_threshold[19]    0.004    3837.0    2669.0    1.0  \n",
-       "YearsAtCompany_threshold[20]    0.004    2832.0    2810.0    1.0  \n",
-       "YearsAtCompany_threshold[21]    0.004    3193.0    2981.0    1.0  \n",
-       "YearsAtCompany_threshold[22]    0.004    3331.0    2546.0    1.0  \n",
-       "YearsAtCompany_threshold[23]    0.010    5871.0    2751.0    1.0  \n",
-       "YearsAtCompany_threshold[24]    0.005    4798.0    2686.0    1.0  \n",
-       "YearsAtCompany_threshold[25]    0.006    5084.0    2838.0    1.0  \n",
-       "YearsAtCompany_threshold[26]    0.006    4867.0    2851.0    1.0  \n",
-       "YearsAtCompany_threshold[27]    0.006    6452.0    2998.0    1.0  \n",
-       "YearsAtCompany_threshold[28]    0.007    5523.0    3164.0    1.0  \n",
-       "YearsAtCompany_threshold[29]    0.008    5018.0    2984.0    1.0  \n",
-       "YearsAtCompany_threshold[30]    0.007    5767.0    2584.0    1.0  \n",
-       "YearsAtCompany_threshold[31]    0.006    6991.0    2987.0    1.0  \n",
-       "YearsAtCompany_threshold[32]    0.007    4981.0    3083.0    1.0  \n",
-       "YearsAtCompany_threshold[33]    0.008    5710.0    2938.0    1.0  \n",
-       "YearsAtCompany_threshold[34]    0.008    5747.0    2698.0    1.0  \n",
-       "TotalWorkingYears               0.000     968.0    1511.0    1.0  "
+       "YearsAtCompany_threshold[0]     0.002    5129.0    2350.0    1.0  \n",
+       "YearsAtCompany_threshold[1]     0.001    3021.0    2994.0    1.0  \n",
+       "YearsAtCompany_threshold[2]     0.001    3451.0    2661.0    1.0  \n",
+       "YearsAtCompany_threshold[3]     0.001    3384.0    2770.0    1.0  \n",
+       "YearsAtCompany_threshold[4]     0.002    3211.0    2712.0    1.0  \n",
+       "YearsAtCompany_threshold[5]     0.002    2327.0    2532.0    1.0  \n",
+       "YearsAtCompany_threshold[6]     0.002    3170.0    3111.0    1.0  \n",
+       "YearsAtCompany_threshold[7]     0.002    2829.0    3449.0    1.0  \n",
+       "YearsAtCompany_threshold[8]     0.002    3147.0    3008.0    1.0  \n",
+       "YearsAtCompany_threshold[9]     0.002    2833.0    2733.0    1.0  \n",
+       "YearsAtCompany_threshold[10]    0.002    2323.0    2543.0    1.0  \n",
+       "YearsAtCompany_threshold[11]    0.003    3133.0    3068.0    1.0  \n",
+       "YearsAtCompany_threshold[12]    0.005    4813.0    2698.0    1.0  \n",
+       "YearsAtCompany_threshold[13]    0.003    3253.0    2503.0    1.0  \n",
+       "YearsAtCompany_threshold[14]    0.003    4006.0    3026.0    1.0  \n",
+       "YearsAtCompany_threshold[15]    0.003    3628.0    2760.0    1.0  \n",
+       "YearsAtCompany_threshold[16]    0.004    3802.0    2888.0    1.0  \n",
+       "YearsAtCompany_threshold[17]    0.005    4280.0    2866.0    1.0  \n",
+       "YearsAtCompany_threshold[18]    0.004    3796.0    2870.0    1.0  \n",
+       "YearsAtCompany_threshold[19]    0.004    4165.0    2558.0    1.0  \n",
+       "YearsAtCompany_threshold[20]    0.004    2747.0    2764.0    1.0  \n",
+       "YearsAtCompany_threshold[21]    0.004    4105.0    2921.0    1.0  \n",
+       "YearsAtCompany_threshold[22]    0.004    3234.0    2823.0    1.0  \n",
+       "YearsAtCompany_threshold[23]    0.011    6890.0    2845.0    1.0  \n",
+       "YearsAtCompany_threshold[24]    0.005    4524.0    2805.0    1.0  \n",
+       "YearsAtCompany_threshold[25]    0.006    4210.0    2428.0    1.0  \n",
+       "YearsAtCompany_threshold[26]    0.006    4854.0    2761.0    1.0  \n",
+       "YearsAtCompany_threshold[27]    0.006    6195.0    3292.0    1.0  \n",
+       "YearsAtCompany_threshold[28]    0.006    6134.0    2971.0    1.0  \n",
+       "YearsAtCompany_threshold[29]    0.007    5607.0    2832.0    1.0  \n",
+       "YearsAtCompany_threshold[30]    0.006    5868.0    3002.0    1.0  \n",
+       "YearsAtCompany_threshold[31]    0.006    6329.0    2897.0    1.0  \n",
+       "YearsAtCompany_threshold[32]    0.006    5403.0    3306.0    1.0  \n",
+       "YearsAtCompany_threshold[33]    0.007    6733.0    2959.0    1.0  \n",
+       "YearsAtCompany_threshold[34]    0.009    4869.0    3133.0    1.0  \n",
+       "TotalWorkingYears               0.000     977.0    1385.0    1.0  "
       ]
      },
-     "execution_count": 43,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1612,19 +1670,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**THIS IS WRONG. CUMULATIVE PROBS. CANNOT DECREASE**\n",
+    "**THIS IS WRONG. IF THIS REALLY IS CUMULATIVE PROBS., IT CANNOT DECREASE**\n",
     "\n",
-    "The coefficients are still on the logits scale, so we need to apply the inverse of the logit function to transform back to cumulative probabilities. Below, we plot the cumulative probabilities for each category."
+    "The coefficients are still on the logits scale, so we need to apply the inverse of the logit function to transform back to probabilities. Below, we plot the probabilities for each category."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -1634,12 +1692,12 @@
     }
    ],
    "source": [
-    "cumprobs = expit_func(sequence_idata.posterior.YearsAtCompany_threshold).mean((\"chain\", \"draw\"))\n",
-    "cumprobs = np.append(cumprobs, 1)\n",
+    "probs = expit_func(sequence_idata.posterior.YearsAtCompany_threshold).mean((\"chain\", \"draw\"))\n",
+    "probs = np.append(probs, 1)\n",
     "\n",
     "plt.figure(figsize=(7, 3))\n",
-    "plt.plot(sorted(attrition.YearsAtCompany.unique()), cumprobs, marker='o')\n",
-    "plt.ylabel(\"Cumulative probability\")\n",
+    "plt.plot(sorted(attrition.YearsAtCompany.unique()), probs, marker='o')\n",
+    "plt.ylabel(\"Probability\")\n",
     "plt.xlabel(\"Response category\");"
    ]
   },

From e27194fbc2558ba229e4c31ffd07dd7ba6b7ea1c Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Fri, 22 Sep 2023 14:37:53 +0200
Subject: [PATCH 07/13] zero mu vector prior for sratio family

---
 bambi/priors/scaler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/bambi/priors/scaler.py b/bambi/priors/scaler.py
index 9e88a64d5..d6ffc716c 100644
--- a/bambi/priors/scaler.py
+++ b/bambi/priors/scaler.py
@@ -114,7 +114,7 @@ def scale_threshold(self):
             threshold = self.model.components["threshold"]
             if isinstance(threshold, ConstantComponent) and threshold.prior.auto_scale:
                 response_level_n = len(np.unique(self.response_component.response_term.data))
-                mu = np.round(np.linspace(-2, 2, num=response_level_n - 1), 2)
+                mu = np.zeros(response_level_n - 1)
                 threshold.prior = Prior("Normal", mu=mu, sigma=1)
 
     def scale(self):

From 6f357f27281929d48fd4b48f2372b5c0b56fbcde Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Fri, 22 Sep 2023 14:39:04 +0200
Subject: [PATCH 08/13] code review and add section on default priors

---
 docs/notebooks/ordinal_regression.ipynb | 922 +++++++++++-------------
 1 file changed, 406 insertions(+), 516 deletions(-)

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index faf2e09a8..d3841ad87 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -18,11 +18,11 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import warnings\n",
     "\n",
     "import bambi as bmb\n",
     "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
    ]
   },
   {
@@ -39,15 +39,15 @@
     "- 4 = Agree\n",
     "- 5 = Strongly agree\n",
     "\n",
-    "The result is a set of **ordered categories** where each category has an associated numeric value (1-5). However, you can't compute a meaningful difference between the categories. Moreover, the response variable can also be a discrete count, and ordered. For example, a restaurant can be rated on a scale of 1-5 stars where 1 is the worst and 5 is the best. Yes, you can compute the difference between 1 and 2 stars, but it is often treated as ordinal in an applied setting.\n",
+    "The result is a set of **ordered categories** where each category has an associated numeric value (1-5). However, you can't compute a meaningful difference between the categories. Moreover, the response variable can also be a count where meaningful differences can be computed. For example, a restaurant can be rated on a scale of 1-5 stars where 1 is the worst and 5 is the best. Yes, you can compute the difference between 1 and 2 stars, but it is often treated as ordinal in an applied setting.\n",
     "\n",
     "Ordinal data presents three challenges when modelling:\n",
     "\n",
     "1. Unlike a count, the differences in the values are not necessarily equidistant or meaningful. For example, computing the difference between \"Strongly disagree\" and \"Disagree\". Or, in the case of the restaurant rating, it may be much harder for a restuarant to go from 4 to 5 stars than from 2 to 3 stars. \n",
-    "2. The distribution of ordinal responses may be nonnormal, particularly if very low or high values are infrequently chosen.\n",
+    "2. The distribution of ordinal responses may be nonnormal as the response is not continuous; particularly if larger response levels are infrequently chosen compared to lower ones.\n",
     "3. The variances of the unobserved variables that underlie the observed ordered category may differ between the category, time points, etc. \n",
     "\n",
-    "Thus, treating ordered categories as continuous is not appropriate. To this extent, Bambi supports two classes of ordinal regression models: (1) cumulative link, and (2) sequential link. Below, it is demonstrated how to fit these two models using Bambi to overcome the challenges of ordered category response data."
+    "Thus, treating ordered categories as continuous is not appropriate. To this extent, Bambi supports two classes of ordinal regression models: (1) cumulative, and (2) sequential. Below, it is demonstrated how to fit these two models using Bambi to overcome the challenges of ordered category response data."
    ]
   },
   {
@@ -58,15 +58,24 @@
     "\n",
     "A cumulative model assumes that the observed ordinal variable $Y$ originates from the \"categorization\" of a latent continuous variable $Z$. To model the categorization process, the model assumes that there are $K$ thresholds (cutpoints) $\\tau_k$ that partition $Z$ into $K+1$ observable, ordered categories. Additionally, if we assume $Z$ to have a certain distribution (e.g., Normal) with a cumulative distribution function $F$, the probability of $Y$ being equal to category $k$ is\n",
     "\n",
-    "$$Pr(Y = k) = F(\\tau_k) - F(\\tau_{k-1})$$\n",
+    "$$P(Y = k) = F(\\tau_k) - F(-\\infty) = F(\\tau_k) - 0 \\ \\text{if} \\ k = 1$$\n",
     "\n",
-    "where each $F(\\tau)$ is a cumulative probability. For example, suppose we have 3 categories and we are interested in the probability of $Y=3$, and have three thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$ for three categories. Additionally, if we assume $Z$ to be normally distributed with $\\sigma = 1$ then\n",
+    "$$P(Y = k) = F(\\tau_k) - F(\\tau_{k-1}) \\ \\text{if} \\ k \\ \\text{in} \\ {2, 3, 4, ..., K - 1}$$\n",
     "\n",
-    "$$Pr(Y = 3) = \\Phi(\\tau_2) - \\Phi(\\tau_1) - \\Phi(\\tau_0)$$\n",
+    "$$P(Y = k) = F(+\\infty) - F(\\tau_{k-1}) \\ \\text{if} \\ k = K$$\n",
     "\n",
-    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of categories as the prior for the thresholds. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
+    "where each $F(\\tau)$ is a cumulative probability. For example, suppose we have three response levels and we are interested in the probability of $Y=k$ and have two thresholds $\\tau_1 = -1, \\tau_2 = 1$ for three categories. Additionally, if we assume $Z$ to be normally distributed with $\\sigma = 1$ and a cumulative distribution function $\\Phi$ then\n",
     "\n",
-    "Lastly, as each $F(\\tau)$ implies a cumulative probability for each $k$, the largest response value always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 3$ response values, we only need $K − 1 = 2$ intercepts."
+    "$$P(Y = 1) = \\Phi(\\tau_1)$$\n",
+    "\n",
+    "$$P(Y = 2) = \\Phi(\\tau_2) - \\Phi(\\tau_1)$$\n",
+    "\n",
+    "$$P(Y = 3) = 1 - \\Phi(\\tau_2)$$\n",
+    "\n",
+    "\n",
+    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of categories as the prior for the thresholds. Additionally, since the thresholds need to be orderd, Bambi applies a transformation to the values such that the order is preserved. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
+    "\n",
+    "Lastly, as each $F(\\tau)$ implies a cumulative probability for each $k$, the largest response value always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 3$ response values, we only need $K − 1 = 2$ thresholds."
    ]
   },
   {
@@ -75,7 +84,7 @@
    "source": [
     "### The moral intuition dataset\n",
     "\n",
-    "To illustrate an ordinal model with a cumulative link function, we will model data from a series of experiments conducted by philsophers (this example comes from Richard McElreath's [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)). The experiments aim to collect empirical evidence relevant to debates about moral intuition, the forms of reasoning through which people develop judgments about the moral goodness and badness of actions. \n",
+    "To illustrate an cumulative ordinal model, we will model data from a series of experiments conducted by philsophers (this example comes from Richard McElreath's [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)). The experiments aim to collect empirical evidence relevant to debates about moral intuition, the forms of reasoning through which people develop judgments about the moral goodness and badness of actions. \n",
     "\n",
     "In the dataset there are 12 columns and 9930 rows, comprising data for 331 unique individuals. The response we are interested in `response`, is an integer from 1 to 7 indicating how morally permissible the participant found the action to be taken (or not) in the story. The predictors are as follows:\n",
     "\n",
@@ -86,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -110,7 +119,7 @@
        "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -126,19 +135,19 @@
    "source": [
     "### Intercept only model\n",
     "\n",
-    "Before we fit a model with predictors, let's attempt to recover the parameters of an ordered distribution using a model with only the thresholds to get a feel for the cumulative link function. Traditionally, in Bambi if we wanted to recover the parameters of the likelihood, we would use an intercept only model and write the formula as `response ~ 1` where `1` indicates to include the intercept. However, in the case of ordinal regression, the thresholds \"take the place\" of the intercept. Thus, we can write the formula as `response ~ 0` to indicate that we do not want to include an intercept. To fit a cumulative ordinal model, we pass `family=\"cumulative\"`. To compare the thresholds only model, we compute the empirical log-cumulative-odds of the categories directly from the data below. "
+    "Before we fit a model with predictors, let's attempt to recover the parameters of an ordinal model using only the thresholds to get a feel for the cumulative family. Traditionally, in Bambi if we wanted to recover the parameters of the likelihood, we would use an intercept only model and write the formula as `response ~ 1` where `1` indicates to include the intercept. However, in the case of ordinal regression, the thresholds \"take the place\" of the intercept. Thus, we can write the formula as `response ~ 0` to indicate that we do not want to include an intercept. To fit a cumulative ordinal model, we pass `family=\"cumulative\"`. To compare the thresholds only model, we compute the empirical log-cumulative-odds of the categories directly from the data below. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_30380/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_9835/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
       "  logit_func = lambda x: np.log(x / (1 - x))\n"
      ]
     },
@@ -149,7 +158,7 @@
        "        1.76938091,         nan])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,17 +173,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/formulae/terms/variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/distributions/transforms.py:56: FutureWarning: univariate_ordered has been deprecated, use ordered instead.\n",
-      "  warnings.warn(f\"{name} has been deprecated, use ordered instead.\", FutureWarning)\n",
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (4 chains in 4 jobs)\n",
@@ -259,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +285,7 @@
        "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,17 +296,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/xarray/core/concat.py:546: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
-      "  common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -429,7 +426,7 @@
        "response_threshold[5]    4785.0    3368.0    1.0  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -449,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -512,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -565,79 +562,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/formulae/terms/variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pymc/distributions/transforms.py:56: FutureWarning: univariate_ordered has been deprecated, use ordered instead.\n",
-      "  warnings.warn(f\"{name} has been deprecated, use ordered instead.\", FutureWarning)\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [response_threshold, action, intention, contact, action:intention, contact:intention]\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
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-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 45:32&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
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-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2732 seconds.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = bmb.Model(\n",
     "    \"response ~ 0 + action + intention + contact + action:intention + contact:intention\", \n",
@@ -656,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -802,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -836,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -865,14 +792,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Posterior predictive distribution\n",
+    "### Posterior predictive distribution\n",
     "\n",
     "To get a sense of how well the ordinal model fits the data, we can plot samples from the posterior predictive distribution. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -913,11 +840,11 @@
     "\n",
     "As an example, lets suppose we are interested in modeling the probability a boxer makes it to round 3. This implies that the particular boxer in question survived round 1 $Z_1 > \\tau_1$ , 2 $Z_2 > \\tau_2$, and 3 $Z_3 > \\tau_3$. This can be written as \n",
     "\n",
-    "$$Pr(Y = 3) = (1 - Pr(Z_1))(1 - Pr(Z_2))(1 - Pr(Z_3))$$\n",
+    "$$Pr(Y = 3) = (1 - P(Z_1 <= \\tau_1)) * (1 - P(Z_2 <= \\tau_2)) * P(Z_3 <= \\tau_3)$$\n",
     "\n",
     "As in the cumulative model above, if we assume $Y$ to be normally distributed with the thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$ and cumulative distribution function $\\Phi$ then\n",
     "\n",
-    "$$Pr(Y = 3) = (1 - \\Phi(\\tau_0))(1 - \\Phi(\\tau_1))(1 - \\Phi(\\tau_2))$$\n",
+    "$$Pr(Y = 3) = (1 - \\Phi(\\tau_1)) * (1 - \\Phi(\\tau_2)) * \\Phi(\\tau_3)$$\n",
     "\n",
     "To add predictors to this sequential model, we follow the same specification in the _Adding Predictors_ section above. Thus, the sequential model with predictor terms becomes\n",
     "\n",
@@ -937,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -1004,7 +931,7 @@
        "6              1   59"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1020,96 +947,63 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Fitting a sequential model is similar to fitting a cumulative model. The only difference is that we pass `family=\"sratio\"` to the `bambi.Model` constructor. "
+    "### Default prior of thresholds\n",
+    "\n",
+    "Before we fit the sequential model, it's worth mentioning that the default priors for the thresholds in a sequential model are different than the cumulative model. In the cumulative model, the default prior for the thresholds is a Normal distribution with a grid of evenly spaced $\\mu$ where an ordered transformation is applied to ensure the ordering of the values. However, in the sequential model, the ordering of the thresholds does not matter. Thus, the default prior for the thresholds is a Normal distribution with a zero $\\mu$ vector of length $K-1$. Refer to the [getting started](https://bambinos.github.io/bambi/notebooks/getting_started.html#specifying-priors) docs if you need a refresher on priors in Bambi. \n",
+    "\n",
+    "Subsequently, fitting a sequential model is similar to fitting a cumulative model. The only difference is that we pass `family=\"sratio\"` to the `bambi.Model` constructor. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequence_model = bmb.Model(\n",
+    "    \"YearsAtCompany ~ 0 + TotalWorkingYears\", \n",
+    "    data=attrition, \n",
+    "    family=\"sratio\"\n",
+    ")\n",
+    "sequence_idata = sequence_model.fit(random_seed=1234)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/formulae/terms/variable.py:87: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  elif is_string_dtype(x) or is_categorical_dtype(x):\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [YearsAtCompany_threshold, TotalWorkingYears]\n"
-     ]
-    },
     {
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-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 03:30&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "       Formula: YearsAtCompany ~ 0 + TotalWorkingYears\n",
+       "        Family: sratio\n",
+       "          Link: p = logit\n",
+       "  Observations: 1233\n",
+       "        Priors: \n",
+       "    target = p\n",
+       "        Common-level effects\n",
+       "            TotalWorkingYears ~ Normal(mu: 0.0, sigma: 0.3223)\n",
+       "        \n",
+       "        Auxiliary parameters\n",
+       "            threshold ~ Normal(mu: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       "             0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.], sigma: 1.0)"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 211 seconds.\n"
-     ]
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "sequence_model = bmb.Model(\"YearsAtCompany ~ 0 + TotalWorkingYears\", data=attrition, family=\"sratio\")\n",
-    "sequence_idata = sequence_model.fit(random_seed=1234)"
+    "sequence_model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/xarray/core/concat.py:546: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
-      "  common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1145,434 +1039,434 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[0]</th>\n",
-       "      <td>-2.572</td>\n",
-       "      <td>0.198</td>\n",
-       "      <td>-2.937</td>\n",
-       "      <td>-2.201</td>\n",
+       "      <td>-2.521</td>\n",
+       "      <td>0.185</td>\n",
+       "      <td>-2.865</td>\n",
+       "      <td>-2.176</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>5129.0</td>\n",
-       "      <td>2350.0</td>\n",
+       "      <td>4693.0</td>\n",
+       "      <td>3047.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[1]</th>\n",
        "      <td>-1.054</td>\n",
-       "      <td>0.110</td>\n",
-       "      <td>-1.264</td>\n",
-       "      <td>-0.855</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>-1.263</td>\n",
+       "      <td>-0.847</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>3021.0</td>\n",
-       "      <td>2994.0</td>\n",
+       "      <td>2939.0</td>\n",
+       "      <td>2937.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[2]</th>\n",
-       "      <td>-1.018</td>\n",
+       "      <td>-1.016</td>\n",
        "      <td>0.117</td>\n",
-       "      <td>-1.243</td>\n",
-       "      <td>-0.812</td>\n",
+       "      <td>-1.235</td>\n",
+       "      <td>-0.798</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>3451.0</td>\n",
-       "      <td>2661.0</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2746.0</td>\n",
+       "      <td>2884.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[3]</th>\n",
-       "      <td>-0.757</td>\n",
-       "      <td>0.118</td>\n",
-       "      <td>-0.974</td>\n",
-       "      <td>-0.542</td>\n",
+       "      <td>-0.761</td>\n",
+       "      <td>0.114</td>\n",
+       "      <td>-0.980</td>\n",
+       "      <td>-0.555</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>3384.0</td>\n",
-       "      <td>2770.0</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>2578.0</td>\n",
+       "      <td>2651.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[4]</th>\n",
-       "      <td>-0.755</td>\n",
-       "      <td>0.122</td>\n",
-       "      <td>-0.992</td>\n",
-       "      <td>-0.538</td>\n",
+       "      <td>-0.757</td>\n",
+       "      <td>0.126</td>\n",
+       "      <td>-0.986</td>\n",
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        "      <td>0.002</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>3211.0</td>\n",
-       "      <td>2712.0</td>\n",
+       "      <td>2657.0</td>\n",
+       "      <td>2766.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[5]</th>\n",
-       "      <td>0.263</td>\n",
-       "      <td>0.104</td>\n",
-       "      <td>0.057</td>\n",
-       "      <td>0.447</td>\n",
-       "      <td>0.002</td>\n",
+       "      <td>0.249</td>\n",
+       "      <td>0.107</td>\n",
+       "      <td>0.043</td>\n",
+       "      <td>0.448</td>\n",
+       "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2327.0</td>\n",
-       "      <td>2532.0</td>\n",
+       "      <td>1819.0</td>\n",
+       "      <td>2318.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[6]</th>\n",
-       "      <td>-0.503</td>\n",
+       "      <td>-0.508</td>\n",
        "      <td>0.143</td>\n",
-       "      <td>-0.782</td>\n",
-       "      <td>-0.247</td>\n",
+       "      <td>-0.771</td>\n",
+       "      <td>-0.237</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>3170.0</td>\n",
-       "      <td>3111.0</td>\n",
+       "      <td>2619.0</td>\n",
+       "      <td>2668.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[7]</th>\n",
-       "      <td>-0.077</td>\n",
-       "      <td>0.140</td>\n",
-       "      <td>-0.328</td>\n",
-       "      <td>0.195</td>\n",
+       "      <td>-0.088</td>\n",
+       "      <td>0.141</td>\n",
+       "      <td>-0.360</td>\n",
+       "      <td>0.168</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2829.0</td>\n",
-       "      <td>3449.0</td>\n",
+       "      <td>2886.0</td>\n",
+       "      <td>2952.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[8]</th>\n",
-       "      <td>0.039</td>\n",
-       "      <td>0.149</td>\n",
-       "      <td>-0.232</td>\n",
-       "      <td>0.326</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>0.144</td>\n",
+       "      <td>-0.236</td>\n",
+       "      <td>0.303</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>3147.0</td>\n",
-       "      <td>3008.0</td>\n",
+       "      <td>2678.0</td>\n",
+       "      <td>2740.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[9]</th>\n",
-       "      <td>0.400</td>\n",
+       "      <td>0.386</td>\n",
        "      <td>0.149</td>\n",
-       "      <td>0.128</td>\n",
-       "      <td>0.685</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>0.665</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2833.0</td>\n",
-       "      <td>2733.0</td>\n",
+       "      <td>2525.0</td>\n",
+       "      <td>2372.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[10]</th>\n",
-       "      <td>1.290</td>\n",
-       "      <td>0.151</td>\n",
-       "      <td>1.013</td>\n",
-       "      <td>1.573</td>\n",
-       "      <td>0.003</td>\n",
+       "      <td>1.268</td>\n",
+       "      <td>0.150</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>1.540</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2323.0</td>\n",
-       "      <td>2543.0</td>\n",
+       "      <td>1794.0</td>\n",
+       "      <td>2562.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[11]</th>\n",
-       "      <td>0.449</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>0.037</td>\n",
-       "      <td>0.832</td>\n",
+       "      <td>0.437</td>\n",
+       "      <td>0.219</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>0.852</td>\n",
        "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>3133.0</td>\n",
-       "      <td>3068.0</td>\n",
+       "      <td>2626.0</td>\n",
+       "      <td>3164.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[12]</th>\n",
-       "      <td>-0.118</td>\n",
+       "      <td>-0.108</td>\n",
        "      <td>0.296</td>\n",
-       "      <td>-0.694</td>\n",
-       "      <td>0.414</td>\n",
-       "      <td>0.004</td>\n",
+       "      <td>-0.635</td>\n",
+       "      <td>0.470</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>4813.0</td>\n",
-       "      <td>2698.0</td>\n",
+       "      <td>3531.0</td>\n",
+       "      <td>2796.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[13]</th>\n",
-       "      <td>0.533</td>\n",
-       "      <td>0.247</td>\n",
-       "      <td>0.069</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.004</td>\n",
+       "      <td>0.513</td>\n",
+       "      <td>0.254</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>0.983</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>3253.0</td>\n",
-       "      <td>2503.0</td>\n",
+       "      <td>2934.0</td>\n",
+       "      <td>2621.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[14]</th>\n",
-       "      <td>0.412</td>\n",
-       "      <td>0.289</td>\n",
-       "      <td>-0.129</td>\n",
-       "      <td>0.943</td>\n",
+       "      <td>0.394</td>\n",
+       "      <td>0.285</td>\n",
+       "      <td>-0.171</td>\n",
+       "      <td>0.910</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>4006.0</td>\n",
-       "      <td>3026.0</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>3363.0</td>\n",
+       "      <td>2791.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[15]</th>\n",
-       "      <td>0.825</td>\n",
+       "      <td>0.793</td>\n",
+       "      <td>0.264</td>\n",
        "      <td>0.268</td>\n",
-       "      <td>0.326</td>\n",
-       "      <td>1.328</td>\n",
-       "      <td>0.004</td>\n",
+       "      <td>1.253</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>3628.0</td>\n",
-       "      <td>2760.0</td>\n",
+       "      <td>2885.0</td>\n",
+       "      <td>3035.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[16]</th>\n",
-       "      <td>0.466</td>\n",
-       "      <td>0.342</td>\n",
-       "      <td>-0.188</td>\n",
-       "      <td>1.080</td>\n",
-       "      <td>0.006</td>\n",
+       "      <td>0.431</td>\n",
+       "      <td>0.335</td>\n",
+       "      <td>-0.229</td>\n",
+       "      <td>1.033</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>3802.0</td>\n",
-       "      <td>2888.0</td>\n",
+       "      <td>4075.0</td>\n",
+       "      <td>2936.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[17]</th>\n",
-       "      <td>0.292</td>\n",
-       "      <td>0.377</td>\n",
-       "      <td>-0.397</td>\n",
-       "      <td>0.994</td>\n",
+       "      <td>0.246</td>\n",
+       "      <td>0.383</td>\n",
+       "      <td>-0.455</td>\n",
+       "      <td>0.958</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>4280.0</td>\n",
-       "      <td>2866.0</td>\n",
+       "      <td>4258.0</td>\n",
+       "      <td>3055.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[18]</th>\n",
-       "      <td>0.866</td>\n",
-       "      <td>0.324</td>\n",
-       "      <td>0.234</td>\n",
-       "      <td>1.454</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>0.328</td>\n",
+       "      <td>0.182</td>\n",
+       "      <td>1.402</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>3796.0</td>\n",
-       "      <td>2870.0</td>\n",
+       "      <td>3185.0</td>\n",
+       "      <td>3306.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[19]</th>\n",
-       "      <td>0.858</td>\n",
-       "      <td>0.354</td>\n",
-       "      <td>0.177</td>\n",
-       "      <td>1.490</td>\n",
+       "      <td>0.798</td>\n",
+       "      <td>0.350</td>\n",
+       "      <td>0.147</td>\n",
+       "      <td>1.452</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>4165.0</td>\n",
-       "      <td>2558.0</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3066.0</td>\n",
+       "      <td>2756.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[20]</th>\n",
-       "      <td>2.274</td>\n",
-       "      <td>0.272</td>\n",
-       "      <td>1.756</td>\n",
-       "      <td>2.778</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>2.199</td>\n",
+       "      <td>0.277</td>\n",
+       "      <td>1.688</td>\n",
+       "      <td>2.728</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2747.0</td>\n",
-       "      <td>2764.0</td>\n",
+       "      <td>2254.0</td>\n",
+       "      <td>2483.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[21]</th>\n",
-       "      <td>1.936</td>\n",
-       "      <td>0.357</td>\n",
-       "      <td>1.254</td>\n",
-       "      <td>2.572</td>\n",
+       "      <td>1.841</td>\n",
+       "      <td>0.345</td>\n",
+       "      <td>1.241</td>\n",
+       "      <td>2.536</td>\n",
        "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>4105.0</td>\n",
-       "      <td>2921.0</td>\n",
+       "      <td>3367.0</td>\n",
+       "      <td>2923.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[22]</th>\n",
-       "      <td>2.554</td>\n",
-       "      <td>0.357</td>\n",
-       "      <td>1.880</td>\n",
-       "      <td>3.215</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>3234.0</td>\n",
-       "      <td>2823.0</td>\n",
+       "      <td>2.432</td>\n",
+       "      <td>0.361</td>\n",
+       "      <td>1.690</td>\n",
+       "      <td>3.054</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>3032.0</td>\n",
+       "      <td>2780.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[23]</th>\n",
-       "      <td>0.427</td>\n",
-       "      <td>0.684</td>\n",
-       "      <td>-0.915</td>\n",
-       "      <td>1.622</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>6890.0</td>\n",
-       "      <td>2845.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.734</td>\n",
+       "      <td>-1.405</td>\n",
+       "      <td>1.354</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>5666.0</td>\n",
+       "      <td>2933.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[24]</th>\n",
-       "      <td>1.852</td>\n",
-       "      <td>0.502</td>\n",
-       "      <td>0.896</td>\n",
-       "      <td>2.753</td>\n",
+       "      <td>1.594</td>\n",
+       "      <td>0.529</td>\n",
+       "      <td>0.602</td>\n",
+       "      <td>2.555</td>\n",
        "      <td>0.008</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>4524.0</td>\n",
-       "      <td>2805.0</td>\n",
+       "      <td>4998.0</td>\n",
+       "      <td>2865.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[25]</th>\n",
-       "      <td>1.869</td>\n",
-       "      <td>0.559</td>\n",
-       "      <td>0.823</td>\n",
-       "      <td>2.876</td>\n",
+       "      <td>1.529</td>\n",
+       "      <td>0.580</td>\n",
+       "      <td>0.379</td>\n",
+       "      <td>2.556</td>\n",
        "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>4210.0</td>\n",
-       "      <td>2428.0</td>\n",
+       "      <td>4642.0</td>\n",
+       "      <td>2944.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[26]</th>\n",
-       "      <td>2.141</td>\n",
-       "      <td>0.559</td>\n",
-       "      <td>1.133</td>\n",
-       "      <td>3.228</td>\n",
-       "      <td>0.008</td>\n",
+       "      <td>1.746</td>\n",
+       "      <td>0.603</td>\n",
+       "      <td>0.601</td>\n",
+       "      <td>2.868</td>\n",
+       "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>4854.0</td>\n",
-       "      <td>2761.0</td>\n",
+       "      <td>5042.0</td>\n",
+       "      <td>3080.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[27]</th>\n",
-       "      <td>1.690</td>\n",
-       "      <td>0.694</td>\n",
-       "      <td>0.298</td>\n",
-       "      <td>2.853</td>\n",
+       "      <td>1.060</td>\n",
+       "      <td>0.761</td>\n",
+       "      <td>-0.476</td>\n",
+       "      <td>2.406</td>\n",
        "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>6195.0</td>\n",
-       "      <td>3292.0</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>6828.0</td>\n",
+       "      <td>2783.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[28]</th>\n",
-       "      <td>1.855</td>\n",
-       "      <td>0.686</td>\n",
-       "      <td>0.595</td>\n",
-       "      <td>3.168</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>6134.0</td>\n",
-       "      <td>2971.0</td>\n",
+       "      <td>1.141</td>\n",
+       "      <td>0.790</td>\n",
+       "      <td>-0.360</td>\n",
+       "      <td>2.566</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>5847.0</td>\n",
+       "      <td>2871.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[29]</th>\n",
-       "      <td>1.488</td>\n",
-       "      <td>0.758</td>\n",
-       "      <td>0.091</td>\n",
-       "      <td>2.942</td>\n",
+       "      <td>0.561</td>\n",
+       "      <td>0.856</td>\n",
+       "      <td>-1.088</td>\n",
+       "      <td>2.090</td>\n",
        "      <td>0.010</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>5607.0</td>\n",
-       "      <td>2832.0</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>7709.0</td>\n",
+       "      <td>3190.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[30]</th>\n",
-       "      <td>2.132</td>\n",
-       "      <td>0.687</td>\n",
-       "      <td>0.827</td>\n",
-       "      <td>3.433</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>5868.0</td>\n",
-       "      <td>3002.0</td>\n",
+       "      <td>1.267</td>\n",
+       "      <td>0.781</td>\n",
+       "      <td>-0.244</td>\n",
+       "      <td>2.684</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>5645.0</td>\n",
+       "      <td>3055.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[31]</th>\n",
-       "      <td>2.344</td>\n",
-       "      <td>0.709</td>\n",
-       "      <td>0.974</td>\n",
-       "      <td>3.661</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>6329.0</td>\n",
-       "      <td>2897.0</td>\n",
+       "      <td>1.372</td>\n",
+       "      <td>0.796</td>\n",
+       "      <td>-0.121</td>\n",
+       "      <td>2.832</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>6632.0</td>\n",
+       "      <td>2663.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[32]</th>\n",
-       "      <td>3.490</td>\n",
-       "      <td>0.629</td>\n",
-       "      <td>2.251</td>\n",
-       "      <td>4.607</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>5403.0</td>\n",
-       "      <td>3306.0</td>\n",
+       "      <td>2.654</td>\n",
+       "      <td>0.726</td>\n",
+       "      <td>1.285</td>\n",
+       "      <td>4.005</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>5443.0</td>\n",
+       "      <td>2777.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[33]</th>\n",
-       "      <td>2.412</td>\n",
-       "      <td>0.850</td>\n",
-       "      <td>0.829</td>\n",
-       "      <td>4.044</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>6733.0</td>\n",
-       "      <td>2959.0</td>\n",
+       "      <td>0.872</td>\n",
+       "      <td>0.927</td>\n",
+       "      <td>-0.888</td>\n",
+       "      <td>2.581</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>6702.0</td>\n",
+       "      <td>2934.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[34]</th>\n",
-       "      <td>3.335</td>\n",
-       "      <td>0.846</td>\n",
-       "      <td>1.682</td>\n",
-       "      <td>4.886</td>\n",
-       "      <td>0.012</td>\n",
+       "      <td>1.770</td>\n",
+       "      <td>0.905</td>\n",
        "      <td>0.009</td>\n",
-       "      <td>4869.0</td>\n",
-       "      <td>3133.0</td>\n",
+       "      <td>3.394</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>6067.0</td>\n",
+       "      <td>2884.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TotalWorkingYears</th>\n",
-       "      <td>0.130</td>\n",
+       "      <td>0.127</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>0.119</td>\n",
-       "      <td>0.139</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>0.137</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>977.0</td>\n",
-       "      <td>1385.0</td>\n",
+       "      <td>878.0</td>\n",
+       "      <td>1520.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1581,83 +1475,83 @@
       ],
       "text/plain": [
        "                               mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
-       "YearsAtCompany_threshold[0]  -2.572  0.198  -2.937   -2.201      0.003   \n",
-       "YearsAtCompany_threshold[1]  -1.054  0.110  -1.264   -0.855      0.002   \n",
-       "YearsAtCompany_threshold[2]  -1.018  0.117  -1.243   -0.812      0.002   \n",
-       "YearsAtCompany_threshold[3]  -0.757  0.118  -0.974   -0.542      0.002   \n",
-       "YearsAtCompany_threshold[4]  -0.755  0.122  -0.992   -0.538      0.002   \n",
-       "YearsAtCompany_threshold[5]   0.263  0.104   0.057    0.447      0.002   \n",
-       "YearsAtCompany_threshold[6]  -0.503  0.143  -0.782   -0.247      0.003   \n",
-       "YearsAtCompany_threshold[7]  -0.077  0.140  -0.328    0.195      0.003   \n",
-       "YearsAtCompany_threshold[8]   0.039  0.149  -0.232    0.326      0.003   \n",
-       "YearsAtCompany_threshold[9]   0.400  0.149   0.128    0.685      0.003   \n",
-       "YearsAtCompany_threshold[10]  1.290  0.151   1.013    1.573      0.003   \n",
-       "YearsAtCompany_threshold[11]  0.449  0.214   0.037    0.832      0.004   \n",
-       "YearsAtCompany_threshold[12] -0.118  0.296  -0.694    0.414      0.004   \n",
-       "YearsAtCompany_threshold[13]  0.533  0.247   0.069    0.996      0.004   \n",
-       "YearsAtCompany_threshold[14]  0.412  0.289  -0.129    0.943      0.005   \n",
-       "YearsAtCompany_threshold[15]  0.825  0.268   0.326    1.328      0.004   \n",
-       "YearsAtCompany_threshold[16]  0.466  0.342  -0.188    1.080      0.006   \n",
-       "YearsAtCompany_threshold[17]  0.292  0.377  -0.397    0.994      0.006   \n",
-       "YearsAtCompany_threshold[18]  0.866  0.324   0.234    1.454      0.005   \n",
-       "YearsAtCompany_threshold[19]  0.858  0.354   0.177    1.490      0.006   \n",
-       "YearsAtCompany_threshold[20]  2.274  0.272   1.756    2.778      0.005   \n",
-       "YearsAtCompany_threshold[21]  1.936  0.357   1.254    2.572      0.006   \n",
-       "YearsAtCompany_threshold[22]  2.554  0.357   1.880    3.215      0.006   \n",
-       "YearsAtCompany_threshold[23]  0.427  0.684  -0.915    1.622      0.009   \n",
-       "YearsAtCompany_threshold[24]  1.852  0.502   0.896    2.753      0.008   \n",
-       "YearsAtCompany_threshold[25]  1.869  0.559   0.823    2.876      0.009   \n",
-       "YearsAtCompany_threshold[26]  2.141  0.559   1.133    3.228      0.008   \n",
-       "YearsAtCompany_threshold[27]  1.690  0.694   0.298    2.853      0.009   \n",
-       "YearsAtCompany_threshold[28]  1.855  0.686   0.595    3.168      0.009   \n",
-       "YearsAtCompany_threshold[29]  1.488  0.758   0.091    2.942      0.010   \n",
-       "YearsAtCompany_threshold[30]  2.132  0.687   0.827    3.433      0.009   \n",
-       "YearsAtCompany_threshold[31]  2.344  0.709   0.974    3.661      0.009   \n",
-       "YearsAtCompany_threshold[32]  3.490  0.629   2.251    4.607      0.009   \n",
-       "YearsAtCompany_threshold[33]  2.412  0.850   0.829    4.044      0.010   \n",
-       "YearsAtCompany_threshold[34]  3.335  0.846   1.682    4.886      0.012   \n",
-       "TotalWorkingYears             0.130  0.006   0.119    0.139      0.000   \n",
+       "YearsAtCompany_threshold[0]  -2.521  0.185  -2.865   -2.176      0.003   \n",
+       "YearsAtCompany_threshold[1]  -1.054  0.111  -1.263   -0.847      0.002   \n",
+       "YearsAtCompany_threshold[2]  -1.016  0.117  -1.235   -0.798      0.002   \n",
+       "YearsAtCompany_threshold[3]  -0.761  0.114  -0.980   -0.555      0.002   \n",
+       "YearsAtCompany_threshold[4]  -0.757  0.126  -0.986   -0.515      0.002   \n",
+       "YearsAtCompany_threshold[5]   0.249  0.107   0.043    0.448      0.003   \n",
+       "YearsAtCompany_threshold[6]  -0.508  0.143  -0.771   -0.237      0.003   \n",
+       "YearsAtCompany_threshold[7]  -0.088  0.141  -0.360    0.168      0.003   \n",
+       "YearsAtCompany_threshold[8]   0.030  0.144  -0.236    0.303      0.003   \n",
+       "YearsAtCompany_threshold[9]   0.386  0.149   0.117    0.665      0.003   \n",
+       "YearsAtCompany_threshold[10]  1.268  0.150   0.992    1.540      0.004   \n",
+       "YearsAtCompany_threshold[11]  0.437  0.219   0.040    0.852      0.004   \n",
+       "YearsAtCompany_threshold[12] -0.108  0.296  -0.635    0.470      0.005   \n",
+       "YearsAtCompany_threshold[13]  0.513  0.254   0.035    0.983      0.005   \n",
+       "YearsAtCompany_threshold[14]  0.394  0.285  -0.171    0.910      0.005   \n",
+       "YearsAtCompany_threshold[15]  0.793  0.264   0.268    1.253      0.005   \n",
+       "YearsAtCompany_threshold[16]  0.431  0.335  -0.229    1.033      0.005   \n",
+       "YearsAtCompany_threshold[17]  0.246  0.383  -0.455    0.958      0.006   \n",
+       "YearsAtCompany_threshold[18]  0.801  0.328   0.182    1.402      0.006   \n",
+       "YearsAtCompany_threshold[19]  0.798  0.350   0.147    1.452      0.006   \n",
+       "YearsAtCompany_threshold[20]  2.199  0.277   1.688    2.728      0.006   \n",
+       "YearsAtCompany_threshold[21]  1.841  0.345   1.241    2.536      0.006   \n",
+       "YearsAtCompany_threshold[22]  2.432  0.361   1.690    3.054      0.007   \n",
+       "YearsAtCompany_threshold[23]  0.025  0.734  -1.405    1.354      0.010   \n",
+       "YearsAtCompany_threshold[24]  1.594  0.529   0.602    2.555      0.008   \n",
+       "YearsAtCompany_threshold[25]  1.529  0.580   0.379    2.556      0.009   \n",
+       "YearsAtCompany_threshold[26]  1.746  0.603   0.601    2.868      0.009   \n",
+       "YearsAtCompany_threshold[27]  1.060  0.761  -0.476    2.406      0.009   \n",
+       "YearsAtCompany_threshold[28]  1.141  0.790  -0.360    2.566      0.011   \n",
+       "YearsAtCompany_threshold[29]  0.561  0.856  -1.088    2.090      0.010   \n",
+       "YearsAtCompany_threshold[30]  1.267  0.781  -0.244    2.684      0.010   \n",
+       "YearsAtCompany_threshold[31]  1.372  0.796  -0.121    2.832      0.010   \n",
+       "YearsAtCompany_threshold[32]  2.654  0.726   1.285    4.005      0.010   \n",
+       "YearsAtCompany_threshold[33]  0.872  0.927  -0.888    2.581      0.011   \n",
+       "YearsAtCompany_threshold[34]  1.770  0.905   0.009    3.394      0.012   \n",
+       "TotalWorkingYears             0.127  0.006   0.117    0.137      0.000   \n",
        "\n",
        "                              mcse_sd  ess_bulk  ess_tail  r_hat  \n",
-       "YearsAtCompany_threshold[0]     0.002    5129.0    2350.0    1.0  \n",
-       "YearsAtCompany_threshold[1]     0.001    3021.0    2994.0    1.0  \n",
-       "YearsAtCompany_threshold[2]     0.001    3451.0    2661.0    1.0  \n",
-       "YearsAtCompany_threshold[3]     0.001    3384.0    2770.0    1.0  \n",
-       "YearsAtCompany_threshold[4]     0.002    3211.0    2712.0    1.0  \n",
-       "YearsAtCompany_threshold[5]     0.002    2327.0    2532.0    1.0  \n",
-       "YearsAtCompany_threshold[6]     0.002    3170.0    3111.0    1.0  \n",
-       "YearsAtCompany_threshold[7]     0.002    2829.0    3449.0    1.0  \n",
-       "YearsAtCompany_threshold[8]     0.002    3147.0    3008.0    1.0  \n",
-       "YearsAtCompany_threshold[9]     0.002    2833.0    2733.0    1.0  \n",
-       "YearsAtCompany_threshold[10]    0.002    2323.0    2543.0    1.0  \n",
-       "YearsAtCompany_threshold[11]    0.003    3133.0    3068.0    1.0  \n",
-       "YearsAtCompany_threshold[12]    0.005    4813.0    2698.0    1.0  \n",
-       "YearsAtCompany_threshold[13]    0.003    3253.0    2503.0    1.0  \n",
-       "YearsAtCompany_threshold[14]    0.003    4006.0    3026.0    1.0  \n",
-       "YearsAtCompany_threshold[15]    0.003    3628.0    2760.0    1.0  \n",
-       "YearsAtCompany_threshold[16]    0.004    3802.0    2888.0    1.0  \n",
-       "YearsAtCompany_threshold[17]    0.005    4280.0    2866.0    1.0  \n",
-       "YearsAtCompany_threshold[18]    0.004    3796.0    2870.0    1.0  \n",
-       "YearsAtCompany_threshold[19]    0.004    4165.0    2558.0    1.0  \n",
-       "YearsAtCompany_threshold[20]    0.004    2747.0    2764.0    1.0  \n",
-       "YearsAtCompany_threshold[21]    0.004    4105.0    2921.0    1.0  \n",
-       "YearsAtCompany_threshold[22]    0.004    3234.0    2823.0    1.0  \n",
-       "YearsAtCompany_threshold[23]    0.011    6890.0    2845.0    1.0  \n",
-       "YearsAtCompany_threshold[24]    0.005    4524.0    2805.0    1.0  \n",
-       "YearsAtCompany_threshold[25]    0.006    4210.0    2428.0    1.0  \n",
-       "YearsAtCompany_threshold[26]    0.006    4854.0    2761.0    1.0  \n",
-       "YearsAtCompany_threshold[27]    0.006    6195.0    3292.0    1.0  \n",
-       "YearsAtCompany_threshold[28]    0.006    6134.0    2971.0    1.0  \n",
-       "YearsAtCompany_threshold[29]    0.007    5607.0    2832.0    1.0  \n",
-       "YearsAtCompany_threshold[30]    0.006    5868.0    3002.0    1.0  \n",
-       "YearsAtCompany_threshold[31]    0.006    6329.0    2897.0    1.0  \n",
-       "YearsAtCompany_threshold[32]    0.006    5403.0    3306.0    1.0  \n",
-       "YearsAtCompany_threshold[33]    0.007    6733.0    2959.0    1.0  \n",
-       "YearsAtCompany_threshold[34]    0.009    4869.0    3133.0    1.0  \n",
-       "TotalWorkingYears               0.000     977.0    1385.0    1.0  "
+       "YearsAtCompany_threshold[0]     0.002    4693.0    3047.0    1.0  \n",
+       "YearsAtCompany_threshold[1]     0.001    2939.0    2937.0    1.0  \n",
+       "YearsAtCompany_threshold[2]     0.002    2746.0    2884.0    1.0  \n",
+       "YearsAtCompany_threshold[3]     0.002    2578.0    2651.0    1.0  \n",
+       "YearsAtCompany_threshold[4]     0.002    2657.0    2766.0    1.0  \n",
+       "YearsAtCompany_threshold[5]     0.002    1819.0    2318.0    1.0  \n",
+       "YearsAtCompany_threshold[6]     0.002    2619.0    2668.0    1.0  \n",
+       "YearsAtCompany_threshold[7]     0.002    2886.0    2952.0    1.0  \n",
+       "YearsAtCompany_threshold[8]     0.002    2678.0    2740.0    1.0  \n",
+       "YearsAtCompany_threshold[9]     0.002    2525.0    2372.0    1.0  \n",
+       "YearsAtCompany_threshold[10]    0.002    1794.0    2562.0    1.0  \n",
+       "YearsAtCompany_threshold[11]    0.003    2626.0    3164.0    1.0  \n",
+       "YearsAtCompany_threshold[12]    0.005    3531.0    2796.0    1.0  \n",
+       "YearsAtCompany_threshold[13]    0.003    2934.0    2621.0    1.0  \n",
+       "YearsAtCompany_threshold[14]    0.004    3363.0    2791.0    1.0  \n",
+       "YearsAtCompany_threshold[15]    0.003    2885.0    3035.0    1.0  \n",
+       "YearsAtCompany_threshold[16]    0.004    4075.0    2936.0    1.0  \n",
+       "YearsAtCompany_threshold[17]    0.005    4258.0    3055.0    1.0  \n",
+       "YearsAtCompany_threshold[18]    0.004    3185.0    3306.0    1.0  \n",
+       "YearsAtCompany_threshold[19]    0.005    3066.0    2756.0    1.0  \n",
+       "YearsAtCompany_threshold[20]    0.004    2254.0    2483.0    1.0  \n",
+       "YearsAtCompany_threshold[21]    0.004    3367.0    2923.0    1.0  \n",
+       "YearsAtCompany_threshold[22]    0.005    3032.0    2780.0    1.0  \n",
+       "YearsAtCompany_threshold[23]    0.012    5666.0    2933.0    1.0  \n",
+       "YearsAtCompany_threshold[24]    0.005    4998.0    2865.0    1.0  \n",
+       "YearsAtCompany_threshold[25]    0.006    4642.0    2944.0    1.0  \n",
+       "YearsAtCompany_threshold[26]    0.006    5042.0    3080.0    1.0  \n",
+       "YearsAtCompany_threshold[27]    0.008    6828.0    2783.0    1.0  \n",
+       "YearsAtCompany_threshold[28]    0.008    5847.0    2871.0    1.0  \n",
+       "YearsAtCompany_threshold[29]    0.011    7709.0    3190.0    1.0  \n",
+       "YearsAtCompany_threshold[30]    0.008    5645.0    3055.0    1.0  \n",
+       "YearsAtCompany_threshold[31]    0.008    6632.0    2663.0    1.0  \n",
+       "YearsAtCompany_threshold[32]    0.007    5443.0    2777.0    1.0  \n",
+       "YearsAtCompany_threshold[33]    0.011    6702.0    2934.0    1.0  \n",
+       "YearsAtCompany_threshold[34]    0.008    6067.0    2884.0    1.0  \n",
+       "TotalWorkingYears               0.000     878.0    1520.0    1.0  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1670,19 +1564,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**THIS IS WRONG. IF THIS REALLY IS CUMULATIVE PROBS., IT CANNOT DECREASE**\n",
-    "\n",
     "The coefficients are still on the logits scale, so we need to apply the inverse of the logit function to transform back to probabilities. Below, we plot the probabilities for each category."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -1701,6 +1593,13 @@
     "plt.xlabel(\"Response category\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This plot can seem confusing at first. Remember, the sequential model is a product of probabilities, i.e., the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$. Thus, the probability of category 5 is the probability that the sequential process did not fall in 0, 1, 2, 3, or 4 multiplied by the probability that the sequential process stopped at 5. This makes sense why the probability of category 37 is 1. There is no category after 37, so once you multiply all of the previous probabilities with the current category, you get 1. This is the reason for the \"cumulative-like\" shape of the plot. But if the coefficients were truly cumulative, you could not have decreases as $k$ increases. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1712,20 +1611,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_9466/1303656969.py:2: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
-      "  ax.set_xticklabels(sequence_model.response_component.response_term.levels);\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -1737,8 +1628,7 @@
    "source": [
     "sequence_idata_pps = sequence_model.predict(idata=sequence_idata, kind=\"pps\", inplace=False)\n",
     "\n",
-    "az.plot_ppc(sequence_idata_pps, figsize=(7, 3), textsize=11)\n",
-    "ax.set_xticklabels(sequence_model.response_component.response_term.levels);"
+    "az.plot_ppc(sequence_idata_pps, figsize=(7, 3), textsize=11)"
    ]
   },
   {

From 0521b0108a934f2abae3371167e02c2915baf748 Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Mon, 25 Sep 2023 20:52:30 +0200
Subject: [PATCH 09/13] explicit explanation of K and k and added summary
 section

---
 docs/notebooks/ordinal_regression.ipynb | 43 ++++++++++++++-----------
 1 file changed, 25 insertions(+), 18 deletions(-)

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index d3841ad87..811b51bcb 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -56,26 +56,22 @@
    "source": [
     "## Cumulative model\n",
     "\n",
-    "A cumulative model assumes that the observed ordinal variable $Y$ originates from the \"categorization\" of a latent continuous variable $Z$. To model the categorization process, the model assumes that there are $K$ thresholds (cutpoints) $\\tau_k$ that partition $Z$ into $K+1$ observable, ordered categories. Additionally, if we assume $Z$ to have a certain distribution (e.g., Normal) with a cumulative distribution function $F$, the probability of $Y$ being equal to category $k$ is\n",
+    "A cumulative model assumes that the observed ordinal variable $Y$ originates from the \"categorization\" of a latent continuous variable $Z$. To model the categorization process, the model assumes that there are $K$ thresholds (or cutpoints) $\\tau_k$ that partition $Z$ into $K+1$ observable, ordered categories of $Y$. The subscript $k$ in $\\tau_k$ is an index that associates that threshold to a particular category $k$. For example, if the response has three categories such as \"disagree\", \"neither agree nor disagree\", and \"agree\", then there are two thresholds $\\tau_1$ and $\\tau_2$ that partition $Z$ into $K+1 = 3$ categories. Additionally, if we assume $Z$ to have a certain distribution (e.g., Normal) with a cumulative distribution function $F$, the probability of $Y$ being equal to category $k$ is\n",
     "\n",
-    "$$P(Y = k) = F(\\tau_k) - F(-\\infty) = F(\\tau_k) - 0 \\ \\text{if} \\ k = 1$$\n",
+    "$$P(Y = k) = F(\\tau_k) - F(\\tau_{k-1})$$\n",
     "\n",
-    "$$P(Y = k) = F(\\tau_k) - F(\\tau_{k-1}) \\ \\text{if} \\ k \\ \\text{in} \\ {2, 3, 4, ..., K - 1}$$\n",
+    "where $F(\\tau)$ is a cumulative probability. For example, suppose we are interested in the probability of each category stated above, and have two thresholds $\\tau_1 = -1, \\tau_2 = 1$ for the three categories. Additionally, if we assume $Z$ to be normally distributed with $\\sigma = 1$ and a cumulative distribution function $\\Phi$ then\n",
     "\n",
-    "$$P(Y = k) = F(+\\infty) - F(\\tau_{k-1}) \\ \\text{if} \\ k = K$$\n",
+    "$$P(Y = 1) = \\Phi(\\tau_1) = \\Phi(-1)$$\n",
     "\n",
-    "where each $F(\\tau)$ is a cumulative probability. For example, suppose we have three response levels and we are interested in the probability of $Y=k$ and have two thresholds $\\tau_1 = -1, \\tau_2 = 1$ for three categories. Additionally, if we assume $Z$ to be normally distributed with $\\sigma = 1$ and a cumulative distribution function $\\Phi$ then\n",
+    "$$P(Y = 2) = \\Phi(\\tau_2) - \\Phi(\\tau_1) = \\Phi(1) - \\Phi(-1)$$\n",
     "\n",
-    "$$P(Y = 1) = \\Phi(\\tau_1)$$\n",
+    "$$P(Y = 3) = 1 - \\Phi(\\tau_2) = 1 - \\Phi(1)$$\n",
     "\n",
-    "$$P(Y = 2) = \\Phi(\\tau_2) - \\Phi(\\tau_1)$$\n",
     "\n",
-    "$$P(Y = 3) = 1 - \\Phi(\\tau_2)$$\n",
+    "But how to set the values of the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of response levels as the prior for the thresholds. Additionally, since the thresholds need to be orderd, Bambi applies a transformation to the values such that the order is preserved. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
     "\n",
-    "\n",
-    "How to set the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of categories as the prior for the thresholds. Additionally, since the thresholds need to be orderd, Bambi applies a transformation to the values such that the order is preserved. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n",
-    "\n",
-    "Lastly, as each $F(\\tau)$ implies a cumulative probability for each $k$, the largest response value always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for $K = 3$ response values, we only need $K − 1 = 2$ thresholds."
+    "Lastly, as each $F(\\tau)$ implies a cumulative probability for each category, the largest response level always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for three response values, we only need two thresholds as two thresholds partition $Z$ into $K+1$ categories."
    ]
   },
   {
@@ -439,7 +435,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Viewing the summary dataframe, we see a total of six cutpoints (`response_threshold`) coefficients. Why six? Remember, we get the last parameter for free, so we only need $K-1$ cutpoints. The index (using zero based indexing) of the `response_threshold` indicates the category that the cutpoint is associated with. Comparing to the empirical log-cumulative-odds computation above, the mean of the posterior distribution for each category is close to the empirical value.\n",
+    "Viewing the summary dataframe, we see a total of six  `response_threshold` coefficients. Why six? Remember, we get the last parameter for free. Since there are seven categories, we only need six cutpoints. The index (using zero based indexing) of the `response_threshold` indicates the category that the threshold is associated with. Comparing to the empirical log-cumulative-odds computation above, the mean of the posterior distribution for each category is close to the empirical value.\n",
     "\n",
     "As the the log cumalative link is used, we need to apply the inverse of the logit function to transform back to cumulative probabilities. Below, we plot the cumulative probabilities for each category. "
    ]
@@ -836,13 +832,13 @@
     "\n",
     "For some ordinal variables, the assumption of a **single** underlying continuous variable (as in cumulative models) may not be appropriate. If the response can be understood as being the result of a sequential process, such that a higher response category is possible only after all lower categories are achieved, then a sequential model may be more appropriate than a cumulative model.\n",
     "\n",
-    "Sequential models assume that for **every** category $k$ there is a latent continuous variable $Z$ that determines the transition between categories $k$ and $k+1$. Now, a threshold $\\tau$ belongs to each latent process. If there are 3 categories, then there are 3 latent processes. If $Z_k$ is greater than the threshold $\\tau_k$, the sequential process continues, otherwise it stops at category $k$. As with the cumulative model, we still assume a distribution for $Z_k$ with a cumulative distribution function $F$.\n",
+    "Sequential models assume that for **every** category $k$ there is a latent continuous variable $Z$ that determines the transition between categories $k$ and $k+1$. Now, a threshold $\\tau$ belongs to each latent process. If there are 3 categories, then there are 3 latent processes. If $Z_k$ is greater than the threshold $\\tau_k$, the sequential process continues, otherwise it stops at category $k$. As with the cumulative model, we assume a distribution for $Z_k$ with a cumulative distribution function $F$.\n",
     "\n",
     "As an example, lets suppose we are interested in modeling the probability a boxer makes it to round 3. This implies that the particular boxer in question survived round 1 $Z_1 > \\tau_1$ , 2 $Z_2 > \\tau_2$, and 3 $Z_3 > \\tau_3$. This can be written as \n",
     "\n",
-    "$$Pr(Y = 3) = (1 - P(Z_1 <= \\tau_1)) * (1 - P(Z_2 <= \\tau_2)) * P(Z_3 <= \\tau_3)$$\n",
+    "$$Pr(Y = 3) = (1 - P(Z_1 \\leq \\tau_1)) * (1 - P(Z_2 \\leq \\tau_2)) * P(Z_3 \\leq \\tau_3)$$\n",
     "\n",
-    "As in the cumulative model above, if we assume $Y$ to be normally distributed with the thresholds $\\tau_0 = -1, \\tau_1 = 0, \\tau_2 = 1$ and cumulative distribution function $\\Phi$ then\n",
+    "As in the cumulative model above, if we assume $Y$ to be normally distributed with the thresholds $\\tau_1 = -1, \\tau_2 = 0, \\tau_3 = 1$ and cumulative distribution function $\\Phi$ then\n",
     "\n",
     "$$Pr(Y = 3) = (1 - \\Phi(\\tau_1)) * (1 - \\Phi(\\tau_2)) * \\Phi(\\tau_3)$$\n",
     "\n",
@@ -949,7 +945,7 @@
    "source": [
     "### Default prior of thresholds\n",
     "\n",
-    "Before we fit the sequential model, it's worth mentioning that the default priors for the thresholds in a sequential model are different than the cumulative model. In the cumulative model, the default prior for the thresholds is a Normal distribution with a grid of evenly spaced $\\mu$ where an ordered transformation is applied to ensure the ordering of the values. However, in the sequential model, the ordering of the thresholds does not matter. Thus, the default prior for the thresholds is a Normal distribution with a zero $\\mu$ vector of length $K-1$. Refer to the [getting started](https://bambinos.github.io/bambi/notebooks/getting_started.html#specifying-priors) docs if you need a refresher on priors in Bambi. \n",
+    "Before we fit the sequential model, it's worth mentioning that the default priors for the thresholds in a sequential model are different than the cumulative model. In the cumulative model, the default prior for the thresholds is a Normal distribution with a grid of evenly spaced $\\mu$ where an ordered transformation is applied to ensure the ordering of the values. However, in the sequential model, the ordering of the thresholds does not matter. Thus, the default prior for the thresholds is a Normal distribution with a zero $\\mu$ vector of length $k - 1$ where $k$ is the number of response levels. Refer to the [getting started](https://bambinos.github.io/bambi/notebooks/getting_started.html#specifying-priors) docs if you need a refresher on priors in Bambi. \n",
     "\n",
     "Subsequently, fitting a sequential model is similar to fitting a cumulative model. The only difference is that we pass `family=\"sratio\"` to the `bambi.Model` constructor. "
    ]
@@ -1597,7 +1593,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This plot can seem confusing at first. Remember, the sequential model is a product of probabilities, i.e., the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$. Thus, the probability of category 5 is the probability that the sequential process did not fall in 0, 1, 2, 3, or 4 multiplied by the probability that the sequential process stopped at 5. This makes sense why the probability of category 37 is 1. There is no category after 37, so once you multiply all of the previous probabilities with the current category, you get 1. This is the reason for the \"cumulative-like\" shape of the plot. But if the coefficients were truly cumulative, you could not have decreases as $k$ increases. "
+    "This plot can seem confusing at first. Remember, the sequential model is a product of probabilities, i.e., the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$. Thus, the probability of category 5 is the probability that the sequential process did not fall in 0, 1, 2, 3, or 4 multiplied by the probability that the sequential process stopped at 5. This makes sense why the probability of category 37 is 1. There is no category after 37, so once you multiply all of the previous probabilities with the current category, you get 1. This is the reason for the \"cumulative-like\" shape of the plot. But if the coefficients were truly cumulative, the probability could not decreases as $k$ increases. "
    ]
   },
   {
@@ -1631,6 +1627,17 @@
     "az.plot_ppc(sequence_idata_pps, figsize=(7, 3), textsize=11)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrated how to fit cumulative and sequential ordinal regression models using Bambi. Cumulative models focus on modeling the cumulative probabilities of an ordinal outcome variable taking on values up to and including a certain category, whereas a sequential model focuses on modeling the probability that an ordinal outcome variable stops at a particular category, rather than continuing to higher categories. To achieve this, both models assume that the reponse variable originates from a categorization of a latent continuous variable $Z$. However, the cumulative model assumes that there are $K$ thresholds $\\tau_k$ that partition $Z$ into $K+1$ observable, ordered categories of $Y$. The sequential model assumes that for every category $k$ there is a latent continuous variable $Z$ that determines the transition between categories $k$ and $k+1$; thus, a threshold $\\tau$ belongs to each latent process.\n",
+    "\n",
+    "Cumulative models can be used in situations where the outcome variable is on the Likert scale, and you are interested in understanding the impact of predictors on the probability of reaching or exceeding specific categories. Sequential models are particularly useful when you are interested in understanding the predictors that influence the decision to stop at a specific response level. It's well-suited for analyzing data where categories represent stages, and the focus is on the transitions between these stages."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 32e49fd68227d44153fb95fc88c081c15d021ba6 Mon Sep 17 00:00:00 2001
From: Gabriel Stechschulte <63432018+GStechschulte@users.noreply.github.com>
Date: Thu, 28 Sep 2023 13:19:10 +0200
Subject: [PATCH 10/13] Zero inflated docs (#725)

* zero inflated poisson and hurdle poisson models

* grammar fix and sort imports

* interpret coeff. and model comparison section

* code review changes

* change wording in hurdle Poisson section

* change posterior predictive bins to use np.arange
---
 docs/notebooks/gallery.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/notebooks/gallery.yml b/docs/notebooks/gallery.yml
index ae157ebb5..d368e7bfa 100644
--- a/docs/notebooks/gallery.yml
+++ b/docs/notebooks/gallery.yml
@@ -92,6 +92,10 @@
       subtitle: Model ordered category outcomes
       href: ordinal_regression.ipynb
       thumbnail: thumbnails/ordinal_regression.png
+    - title: Zero inflated models
+      subtitle: When the outcome is mostly zeros and or is overdispersed
+      href: zero_inflated_regression.ipynb
+      thumbnail: thumbnails/zero_inflated_pps.png
 - category: More advanced models
   description: ""
   tiles:

From ff9c044ee99451f3297133e842683d95056592ea Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Wed, 13 Sep 2023 18:39:32 +0200
Subject: [PATCH 11/13] ordinal model with cumulative link function

ordinal models (cumulative and sratio)
---
 docs/notebooks/gallery.yml              |  4 +++
 docs/notebooks/ordinal_regression.ipynb | 36 ++++++++++++++++++++++++-
 2 files changed, 39 insertions(+), 1 deletion(-)

diff --git a/docs/notebooks/gallery.yml b/docs/notebooks/gallery.yml
index d368e7bfa..c9864ce68 100644
--- a/docs/notebooks/gallery.yml
+++ b/docs/notebooks/gallery.yml
@@ -96,6 +96,10 @@
       subtitle: When the outcome is mostly zeros and or is overdispersed
       href: zero_inflated_regression.ipynb
       thumbnail: thumbnails/zero_inflated_pps.png
+    - title: Ordinal regression
+      subtitle: Model ordered category outcomes
+      href: ordinal_regression.ipynb
+      thumbnail: thumbnails/ordinal_regression.png
 - category: More advanced models
   description: ""
   tiles:
diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index 811b51bcb..2dbc951b4 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -468,6 +468,33 @@
     "plt.title(\"Cumulative probabilities of response categories\");"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(7, 3))\n",
+    "for i in range(6):\n",
+    "    outcome = expit_func(idata.posterior.response_threshold).sel(response_threshold_dim=i).to_numpy().flatten()\n",
+    "    ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n",
+    "ax.set_xlabel(\"Probability\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Cumulative Probability by Category\")\n",
+    "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -577,6 +604,13 @@
     "In the summary dataframe below, we only select the predictor variables as the thresholds are not of interest at the moment."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the summary dataframe below, we only select the predictor variables as the cutpoints are not of interest at the moment."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -625,7 +659,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>action[1]</th>\n",
-       "      <td>-0.465</td>\n",
+       "      <td>-0.466</td>\n",
        "      <td>0.055</td>\n",
        "      <td>-0.563</td>\n",
        "      <td>-0.363</td>\n",

From d7acdeb56d3fb748de10def159f0405583bdff87 Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Tue, 26 Sep 2023 21:02:31 +0200
Subject: [PATCH 12/13] use plot_ppc_discrete for posterior predictive samples

---
 docs/notebooks/ordinal_regression.ipynb | 1179 +++++++++++++----------
 1 file changed, 690 insertions(+), 489 deletions(-)

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index 2dbc951b4..b9bbb406e 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -2,26 +2,31 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
      ]
     }
    ],
    "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
+    "from matplotlib.lines import Line2D\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import warnings\n",
     "\n",
     "import bambi as bmb\n",
     "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
    ]
   },
@@ -91,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -115,7 +120,7 @@
        "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,14 +141,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_9835/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_58515/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
       "  logit_func = lambda x: np.log(x / (1 - x))\n"
      ]
     },
@@ -154,7 +159,7 @@
        "        1.76938091,         nan])"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -230,11 +235,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
       "  self.vm()\n",
@@ -242,6 +245,8 @@
       "  self.vm()\n",
       "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
       "  self.vm()\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
+      "  self.vm()\n",
       "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
      ]
     }
@@ -260,19 +265,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "       Formula: response ~ 0\n",
+       "       Formula: response ~ 0 + action + intention + contact + action:intention + contact:intention\n",
        "        Family: cumulative\n",
        "          Link: p = logit\n",
        "  Observations: 9930\n",
        "        Priors: \n",
        "    target = p\n",
-       "        \n",
+       "        Common-level effects\n",
+       "            action ~ Normal(mu: 0.0, sigma: 5.045)\n",
+       "            intention ~ Normal(mu: 0.0, sigma: 5.0111)\n",
+       "            contact ~ Normal(mu: 0.0, sigma: 6.25)\n",
+       "            action:intention ~ Normal(mu: 0.0, sigma: 6.7082)\n",
+       "            contact:intention ~ Normal(mu: 0.0, sigma: 8.3333)\n",
        "        \n",
        "        Auxiliary parameters\n",
        "            threshold ~ Normal(mu: [-2.  -1.2 -0.4  0.4  1.2  2. ], sigma: 1.0, transform: ordered)\n",
@@ -281,7 +291,7 @@
        "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -292,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +432,7 @@
        "response_threshold[5]    4785.0    3368.0    1.0  "
       ]
      },
-     "execution_count": 57,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -442,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -532,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -570,11 +580,15 @@
    "source": [
     "### Adding predictors\n",
     "\n",
-    "In the cumulative model described above, adding predictors was explicitly left out. In this section, it is described how predictors are added to ordinal cumulative models. When adding predictor variables, what we would like is for any predictor, as it increases, predictions are moved progressively (increased) through the categories in sequence. A predictor term $\\eta$ is defined as\n",
+    "In the cumulative model described above, adding predictors was explicitly left out. In this section, it is described how predictors are added to ordinal cumulative models. When adding predictor variables, what we would like is for any predictor, as it increases, predictions are moved progressively (increased) through the categories in sequence. A linear regression is formed for $Z$ by adding a predictor term $\\eta$\n",
+    "\n",
+    "$$\\eta = \\beta_1 x_1 + \\beta_2 x_2 +, . . ., \\beta_n x_n$$\n",
+    "\n",
+    "where $\\epsilon$ is an error term. Notice how similar this looks to an ordinary linear model. However, there is no intercept or error term. This is because the intercept is replaced by the threshold $\\tau$ and the error term $\\epsilon$ is added seperately to obtain\n",
     "\n",
-    "$$\\eta = \\beta_1 x_1 + \\beta_2 x_2 +, . . ., \\beta_n x_n + \\epsilon$$\n",
+    "$$Z = \\eta + \\epsilon$$ \n",
     "\n",
-    "Note how similar this looks to an ordinary linear model. However, there is no intercept term. This is because the intercept is replaced by the threshold $\\tau$. Putting the predictor term together with the thresholds and cumulative distribution function, we obtain the probability of $Y$ being equal to a category $k$ as\n",
+    "Putting the predictor term together with the thresholds and cumulative distribution function, we obtain the probability of $Y$ being equal to a category $k$ as\n",
     "\n",
     "$$Pr(Y = k | \\eta) = F(\\tau_k - \\eta) - F(\\tau_{k-1} - \\eta)$$\n",
     "\n",
@@ -585,9 +599,83 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Only 250 samples in chain.\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [response_threshold, action, intention, contact, action:intention, contact:intention]\n",
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  self.vm()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='2000' class='' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [2000/2000 13:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 250 tune and 250 draw iterations (1_000 + 1_000 draws total) took 784 seconds.\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
    "source": [
     "model = bmb.Model(\n",
     "    \"response ~ 0 + action + intention + contact + action:intention + contact:intention\", \n",
@@ -613,17 +701,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/xarray/core/concat.py:546: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
-      "  common_dims = tuple(pd.unique([d for v in vars for d in v.dims]))\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -663,59 +743,59 @@
        "      <td>0.055</td>\n",
        "      <td>-0.563</td>\n",
        "      <td>-0.363</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>2647.0</td>\n",
-       "      <td>2834.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>412.0</td>\n",
+       "      <td>645.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>intention[1]</th>\n",
-       "      <td>-0.276</td>\n",
-       "      <td>0.058</td>\n",
-       "      <td>-0.387</td>\n",
-       "      <td>-0.171</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>2174.0</td>\n",
-       "      <td>2857.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-0.278</td>\n",
+       "      <td>0.060</td>\n",
+       "      <td>-0.390</td>\n",
+       "      <td>-0.167</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>379.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact[1]</th>\n",
-       "      <td>-0.325</td>\n",
-       "      <td>0.069</td>\n",
-       "      <td>-0.456</td>\n",
-       "      <td>-0.197</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>2660.0</td>\n",
-       "      <td>2896.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-0.327</td>\n",
+       "      <td>0.072</td>\n",
+       "      <td>-0.460</td>\n",
+       "      <td>-0.204</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>525.0</td>\n",
+       "      <td>688.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>action:intention[1, 1]</th>\n",
-       "      <td>-0.452</td>\n",
-       "      <td>0.081</td>\n",
-       "      <td>-0.605</td>\n",
-       "      <td>-0.301</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>2515.0</td>\n",
-       "      <td>2580.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-0.450</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>-0.609</td>\n",
+       "      <td>-0.300</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>396.0</td>\n",
+       "      <td>479.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>contact:intention[1, 1]</th>\n",
-       "      <td>-1.279</td>\n",
-       "      <td>0.099</td>\n",
-       "      <td>-1.457</td>\n",
-       "      <td>-1.086</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>2575.0</td>\n",
-       "      <td>3023.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.278</td>\n",
+       "      <td>0.097</td>\n",
+       "      <td>-1.459</td>\n",
+       "      <td>-1.098</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>557.0</td>\n",
+       "      <td>567.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -723,21 +803,21 @@
       ],
       "text/plain": [
        "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "action[1]               -0.465  0.055  -0.563   -0.363      0.001    0.001   \n",
-       "intention[1]            -0.276  0.058  -0.387   -0.171      0.001    0.001   \n",
-       "contact[1]              -0.325  0.069  -0.456   -0.197      0.001    0.001   \n",
-       "action:intention[1, 1]  -0.452  0.081  -0.605   -0.301      0.002    0.001   \n",
-       "contact:intention[1, 1] -1.279  0.099  -1.457   -1.086      0.002    0.001   \n",
+       "action[1]               -0.464  0.056  -0.567   -0.363      0.003    0.002   \n",
+       "intention[1]            -0.278  0.060  -0.390   -0.167      0.003    0.002   \n",
+       "contact[1]              -0.327  0.072  -0.460   -0.204      0.003    0.002   \n",
+       "action:intention[1, 1]  -0.450  0.080  -0.609   -0.300      0.004    0.003   \n",
+       "contact:intention[1, 1] -1.278  0.097  -1.459   -1.098      0.004    0.003   \n",
        "\n",
        "                         ess_bulk  ess_tail  r_hat  \n",
-       "action[1]                  2647.0    2834.0    1.0  \n",
-       "intention[1]               2174.0    2857.0    1.0  \n",
-       "contact[1]                 2660.0    2896.0    1.0  \n",
-       "action:intention[1, 1]     2515.0    2580.0    1.0  \n",
-       "contact:intention[1, 1]    2575.0    3023.0    1.0  "
+       "action[1]                   412.0     645.0   1.00  \n",
+       "intention[1]                379.0     500.0   1.01  \n",
+       "contact[1]                  525.0     688.0   1.00  \n",
+       "action:intention[1, 1]      396.0     479.0   1.00  \n",
+       "contact:intention[1, 1]     557.0     567.0   1.00  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,17 +904,73 @@
    "source": [
     "### Posterior predictive distribution\n",
     "\n",
-    "To get a sense of how well the ordinal model fits the data, we can plot samples from the posterior predictive distribution. "
+    "To get a sense of how well the ordinal model fits the data, we can plot samples from the posterior predictive distribution. To plot the samples, a utility function is defined below to assist in the plotting of discrete values."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def adjust_lightness(color, amount=0.5):\n",
+    "    import matplotlib.colors as mc\n",
+    "    import colorsys\n",
+    "    try:\n",
+    "        c = mc.cnames[color]\n",
+    "    except:\n",
+    "        c = color\n",
+    "    c = colorsys.rgb_to_hls(*mc.to_rgb(c))\n",
+    "    return colorsys.hls_to_rgb(c[0], c[1] * amount, c[2])\n",
+    "\n",
+    "def plot_ppc_discrete(idata, bins, ax):\n",
+    "    \n",
+    "    def add_discrete_bands(x, lower, upper, ax, **kwargs):\n",
+    "        for i, (l, u) in enumerate(zip(lower, upper)):\n",
+    "            s = slice(i, i + 2)\n",
+    "            ax.fill_between(x[s], [l, l], [u, u], **kwargs)\n",
+    "\n",
+    "    var_name = list(idata.observed_data.data_vars)[0]\n",
+    "    y_obs = idata.observed_data[var_name].to_numpy()\n",
+    "    \n",
+    "    counts_list = []\n",
+    "    for draw_values in az.extract(idata, \"posterior_predictive\")[var_name].to_numpy().T:\n",
+    "        counts, _ = np.histogram(draw_values, bins=bins)\n",
+    "        counts_list.append(counts)\n",
+    "    counts_arr = np.stack(counts_list)\n",
+    "\n",
+    "    qts_90 = np.quantile(counts_arr, (0.05, 0.95), axis=0)\n",
+    "    qts_70 = np.quantile(counts_arr, (0.15, 0.85), axis=0)\n",
+    "    qts_50 = np.quantile(counts_arr, (0.25, 0.75), axis=0)\n",
+    "    qts_30 = np.quantile(counts_arr, (0.35, 0.65), axis=0)\n",
+    "    median = np.quantile(counts_arr, 0.5, axis=0)\n",
+    "\n",
+    "    colors = [adjust_lightness(\"C0\", x) for x in [1.8, 1.6, 1.4, 1.2, 0.9]]\n",
+    "\n",
+    "    add_discrete_bands(bins, qts_90[0], qts_90[1], ax=ax, color=colors[0])\n",
+    "    add_discrete_bands(bins, qts_70[0], qts_70[1], ax=ax, color=colors[1])\n",
+    "    add_discrete_bands(bins, qts_50[0], qts_50[1], ax=ax, color=colors[2])\n",
+    "    add_discrete_bands(bins, qts_30[0], qts_30[1], ax=ax, color=colors[3])\n",
+    "\n",
+    "    \n",
+    "    ax.step(bins[:-1], median, color=colors[4], lw=2, where=\"post\")\n",
+    "    ax.hist(y_obs, bins=bins, histtype=\"step\", lw=2, color=\"black\", align=\"mid\")\n",
+    "    handles = [\n",
+    "        Line2D([], [], label=\"Observed data\", color=\"black\", lw=2),\n",
+    "        Line2D([], [], label=\"Posterior predictive median\", color=colors[4], lw=2)\n",
+    "    ]\n",
+    "    ax.legend(handles=handles)\n",
+    "    return ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -846,16 +982,12 @@
    "source": [
     "idata_pps = model.predict(idata=idata, kind=\"pps\", inplace=False)\n",
     "\n",
-    "ax = az.plot_ppc(idata_pps, figsize=(7, 3), textsize=11)\n",
-    "ax.set_xticks(np.linspace(0.2, 7, 7))\n",
-    "ax.set_xticklabels(model.response_component.response_term.levels);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looking at the observed and posterior predictive mean, the model captures the observed frequencies of the categories well. "
+    "bins = np.arange(7)\n",
+    "fig, ax = plt.subplots(figsize=(7, 3))\n",
+    "ax = plot_ppc_discrete(idata_pps, bins, ax)\n",
+    "ax.set_xlabel(\"Response category\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Cumulative model - Posterior Predictive Distribution\");"
    ]
   },
   {
@@ -894,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -961,7 +1093,7 @@
        "6              1   59"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -986,9 +1118,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Only 250 samples in chain.\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [YearsAtCompany_threshold, TotalWorkingYears]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='2000' class='' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [2000/2000 02:28&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 250 tune and 250 draw iterations (1_000 + 1_000 draws total) took 148 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
    "source": [
     "sequence_model = bmb.Model(\n",
     "    \"YearsAtCompany ~ 0 + TotalWorkingYears\", \n",
@@ -1000,7 +1196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1031,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1069,435 +1265,435 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[0]</th>\n",
-       "      <td>-2.521</td>\n",
-       "      <td>0.185</td>\n",
-       "      <td>-2.865</td>\n",
-       "      <td>-2.176</td>\n",
+       "      <td>-2.525</td>\n",
+       "      <td>0.193</td>\n",
+       "      <td>-2.896</td>\n",
+       "      <td>-2.191</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>4693.0</td>\n",
-       "      <td>3047.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>812.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[1]</th>\n",
-       "      <td>-1.054</td>\n",
-       "      <td>0.111</td>\n",
-       "      <td>-1.263</td>\n",
-       "      <td>-0.847</td>\n",
+       "      <td>-1.057</td>\n",
+       "      <td>0.110</td>\n",
+       "      <td>-1.265</td>\n",
+       "      <td>-0.850</td>\n",
+       "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>2939.0</td>\n",
-       "      <td>2937.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1137.0</td>\n",
+       "      <td>601.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[2]</th>\n",
-       "      <td>-1.016</td>\n",
-       "      <td>0.117</td>\n",
-       "      <td>-1.235</td>\n",
-       "      <td>-0.798</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>2746.0</td>\n",
-       "      <td>2884.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-1.017</td>\n",
+       "      <td>0.119</td>\n",
+       "      <td>-1.238</td>\n",
+       "      <td>-0.792</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>1009.0</td>\n",
+       "      <td>705.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[3]</th>\n",
-       "      <td>-0.761</td>\n",
-       "      <td>0.114</td>\n",
-       "      <td>-0.980</td>\n",
-       "      <td>-0.555</td>\n",
-       "      <td>0.002</td>\n",
+       "      <td>-0.760</td>\n",
+       "      <td>0.117</td>\n",
+       "      <td>-0.984</td>\n",
+       "      <td>-0.553</td>\n",
+       "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2578.0</td>\n",
-       "      <td>2651.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1210.0</td>\n",
+       "      <td>833.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[4]</th>\n",
-       "      <td>-0.757</td>\n",
+       "      <td>-0.754</td>\n",
        "      <td>0.126</td>\n",
-       "      <td>-0.986</td>\n",
-       "      <td>-0.515</td>\n",
-       "      <td>0.002</td>\n",
+       "      <td>-0.969</td>\n",
+       "      <td>-0.493</td>\n",
+       "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>2657.0</td>\n",
-       "      <td>2766.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1327.0</td>\n",
+       "      <td>780.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[5]</th>\n",
-       "      <td>0.249</td>\n",
-       "      <td>0.107</td>\n",
-       "      <td>0.043</td>\n",
-       "      <td>0.448</td>\n",
+       "      <td>0.251</td>\n",
+       "      <td>0.111</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>0.466</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>1819.0</td>\n",
-       "      <td>2318.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1207.0</td>\n",
+       "      <td>808.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[6]</th>\n",
-       "      <td>-0.508</td>\n",
-       "      <td>0.143</td>\n",
-       "      <td>-0.771</td>\n",
-       "      <td>-0.237</td>\n",
+       "      <td>-0.500</td>\n",
+       "      <td>0.145</td>\n",
+       "      <td>-0.769</td>\n",
+       "      <td>-0.230</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>2619.0</td>\n",
-       "      <td>2668.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1394.0</td>\n",
+       "      <td>526.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[7]</th>\n",
-       "      <td>-0.088</td>\n",
-       "      <td>0.141</td>\n",
-       "      <td>-0.360</td>\n",
-       "      <td>0.168</td>\n",
+       "      <td>-0.085</td>\n",
+       "      <td>0.137</td>\n",
+       "      <td>-0.355</td>\n",
+       "      <td>0.155</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>2886.0</td>\n",
-       "      <td>2952.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1115.0</td>\n",
+       "      <td>835.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[8]</th>\n",
-       "      <td>0.030</td>\n",
-       "      <td>0.144</td>\n",
-       "      <td>-0.236</td>\n",
-       "      <td>0.303</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>2678.0</td>\n",
-       "      <td>2740.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.141</td>\n",
+       "      <td>-0.213</td>\n",
+       "      <td>0.310</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>1081.0</td>\n",
+       "      <td>750.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[9]</th>\n",
-       "      <td>0.386</td>\n",
-       "      <td>0.149</td>\n",
-       "      <td>0.117</td>\n",
-       "      <td>0.665</td>\n",
+       "      <td>0.389</td>\n",
+       "      <td>0.146</td>\n",
+       "      <td>0.128</td>\n",
+       "      <td>0.673</td>\n",
+       "      <td>0.004</td>\n",
        "      <td>0.003</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>2525.0</td>\n",
-       "      <td>2372.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1134.0</td>\n",
+       "      <td>865.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[10]</th>\n",
-       "      <td>1.268</td>\n",
-       "      <td>0.150</td>\n",
-       "      <td>0.992</td>\n",
+       "      <td>1.267</td>\n",
+       "      <td>0.154</td>\n",
+       "      <td>0.970</td>\n",
        "      <td>1.540</td>\n",
+       "      <td>0.005</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>0.002</td>\n",
-       "      <td>1794.0</td>\n",
-       "      <td>2562.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>923.0</td>\n",
+       "      <td>737.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[11]</th>\n",
-       "      <td>0.437</td>\n",
-       "      <td>0.219</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>0.852</td>\n",
+       "      <td>0.436</td>\n",
+       "      <td>0.213</td>\n",
+       "      <td>0.055</td>\n",
+       "      <td>0.826</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>2626.0</td>\n",
-       "      <td>3164.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1376.0</td>\n",
+       "      <td>662.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[12]</th>\n",
-       "      <td>-0.108</td>\n",
-       "      <td>0.296</td>\n",
-       "      <td>-0.635</td>\n",
-       "      <td>0.470</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3531.0</td>\n",
-       "      <td>2796.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-0.116</td>\n",
+       "      <td>0.307</td>\n",
+       "      <td>-0.662</td>\n",
+       "      <td>0.457</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1568.0</td>\n",
+       "      <td>693.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[13]</th>\n",
        "      <td>0.513</td>\n",
-       "      <td>0.254</td>\n",
-       "      <td>0.035</td>\n",
-       "      <td>0.983</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.024</td>\n",
+       "      <td>0.943</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>2934.0</td>\n",
-       "      <td>2621.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1537.0</td>\n",
+       "      <td>738.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[14]</th>\n",
-       "      <td>0.394</td>\n",
-       "      <td>0.285</td>\n",
-       "      <td>-0.171</td>\n",
-       "      <td>0.910</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>3363.0</td>\n",
-       "      <td>2791.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.392</td>\n",
+       "      <td>0.293</td>\n",
+       "      <td>-0.126</td>\n",
+       "      <td>0.958</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>1328.0</td>\n",
+       "      <td>696.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[15]</th>\n",
-       "      <td>0.793</td>\n",
-       "      <td>0.264</td>\n",
-       "      <td>0.268</td>\n",
-       "      <td>1.253</td>\n",
+       "      <td>0.791</td>\n",
+       "      <td>0.277</td>\n",
+       "      <td>0.310</td>\n",
+       "      <td>1.348</td>\n",
+       "      <td>0.008</td>\n",
        "      <td>0.005</td>\n",
-       "      <td>0.003</td>\n",
-       "      <td>2885.0</td>\n",
-       "      <td>3035.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1405.0</td>\n",
+       "      <td>619.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[16]</th>\n",
-       "      <td>0.431</td>\n",
-       "      <td>0.335</td>\n",
-       "      <td>-0.229</td>\n",
-       "      <td>1.033</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>4075.0</td>\n",
-       "      <td>2936.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.433</td>\n",
+       "      <td>0.329</td>\n",
+       "      <td>-0.196</td>\n",
+       "      <td>1.017</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>1516.0</td>\n",
+       "      <td>691.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[17]</th>\n",
-       "      <td>0.246</td>\n",
-       "      <td>0.383</td>\n",
-       "      <td>-0.455</td>\n",
-       "      <td>0.958</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>4258.0</td>\n",
-       "      <td>3055.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.252</td>\n",
+       "      <td>0.365</td>\n",
+       "      <td>-0.397</td>\n",
+       "      <td>0.916</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>1468.0</td>\n",
+       "      <td>841.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[18]</th>\n",
-       "      <td>0.801</td>\n",
-       "      <td>0.328</td>\n",
-       "      <td>0.182</td>\n",
-       "      <td>1.402</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>3185.0</td>\n",
-       "      <td>3306.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.791</td>\n",
+       "      <td>0.330</td>\n",
+       "      <td>0.160</td>\n",
+       "      <td>1.394</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>856.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[19]</th>\n",
-       "      <td>0.798</td>\n",
-       "      <td>0.350</td>\n",
-       "      <td>0.147</td>\n",
-       "      <td>1.452</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3066.0</td>\n",
-       "      <td>2756.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.788</td>\n",
+       "      <td>0.347</td>\n",
+       "      <td>0.148</td>\n",
+       "      <td>1.445</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>1306.0</td>\n",
+       "      <td>612.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[20]</th>\n",
-       "      <td>2.199</td>\n",
-       "      <td>0.277</td>\n",
-       "      <td>1.688</td>\n",
-       "      <td>2.728</td>\n",
+       "      <td>2.191</td>\n",
+       "      <td>0.268</td>\n",
+       "      <td>1.684</td>\n",
+       "      <td>2.680</td>\n",
+       "      <td>0.008</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>2254.0</td>\n",
-       "      <td>2483.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1076.0</td>\n",
+       "      <td>720.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[21]</th>\n",
-       "      <td>1.841</td>\n",
-       "      <td>0.345</td>\n",
-       "      <td>1.241</td>\n",
-       "      <td>2.536</td>\n",
+       "      <td>1.842</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>1.323</td>\n",
+       "      <td>2.516</td>\n",
+       "      <td>0.009</td>\n",
        "      <td>0.006</td>\n",
-       "      <td>0.004</td>\n",
-       "      <td>3367.0</td>\n",
-       "      <td>2923.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1447.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[22]</th>\n",
-       "      <td>2.432</td>\n",
-       "      <td>0.361</td>\n",
-       "      <td>1.690</td>\n",
-       "      <td>3.054</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>3032.0</td>\n",
-       "      <td>2780.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.431</td>\n",
+       "      <td>0.369</td>\n",
+       "      <td>1.746</td>\n",
+       "      <td>3.078</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.008</td>\n",
+       "      <td>1230.0</td>\n",
+       "      <td>616.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[23]</th>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.734</td>\n",
-       "      <td>-1.405</td>\n",
-       "      <td>1.354</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>5666.0</td>\n",
-       "      <td>2933.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.017</td>\n",
+       "      <td>0.743</td>\n",
+       "      <td>-1.389</td>\n",
+       "      <td>1.340</td>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>2194.0</td>\n",
+       "      <td>712.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[24]</th>\n",
-       "      <td>1.594</td>\n",
-       "      <td>0.529</td>\n",
-       "      <td>0.602</td>\n",
-       "      <td>2.555</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>4998.0</td>\n",
-       "      <td>2865.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.581</td>\n",
+       "      <td>0.571</td>\n",
+       "      <td>0.489</td>\n",
+       "      <td>2.582</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1671.0</td>\n",
+       "      <td>636.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[25]</th>\n",
-       "      <td>1.529</td>\n",
-       "      <td>0.580</td>\n",
-       "      <td>0.379</td>\n",
-       "      <td>2.556</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>4642.0</td>\n",
-       "      <td>2944.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.530</td>\n",
+       "      <td>0.579</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>2.646</td>\n",
+       "      <td>0.013</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>2056.0</td>\n",
+       "      <td>616.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[26]</th>\n",
-       "      <td>1.746</td>\n",
-       "      <td>0.603</td>\n",
-       "      <td>0.601</td>\n",
-       "      <td>2.868</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>5042.0</td>\n",
-       "      <td>3080.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.727</td>\n",
+       "      <td>0.607</td>\n",
+       "      <td>0.605</td>\n",
+       "      <td>2.844</td>\n",
+       "      <td>0.014</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1982.0</td>\n",
+       "      <td>709.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[27]</th>\n",
-       "      <td>1.060</td>\n",
-       "      <td>0.761</td>\n",
-       "      <td>-0.476</td>\n",
-       "      <td>2.406</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>6828.0</td>\n",
-       "      <td>2783.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.096</td>\n",
+       "      <td>0.732</td>\n",
+       "      <td>-0.351</td>\n",
+       "      <td>2.412</td>\n",
+       "      <td>0.017</td>\n",
+       "      <td>0.016</td>\n",
+       "      <td>1855.0</td>\n",
+       "      <td>791.0</td>\n",
+       "      <td>1.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[28]</th>\n",
-       "      <td>1.141</td>\n",
-       "      <td>0.790</td>\n",
-       "      <td>-0.360</td>\n",
-       "      <td>2.566</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>5847.0</td>\n",
-       "      <td>2871.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.156</td>\n",
+       "      <td>0.722</td>\n",
+       "      <td>-0.288</td>\n",
+       "      <td>2.399</td>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0.013</td>\n",
+       "      <td>2073.0</td>\n",
+       "      <td>852.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[29]</th>\n",
-       "      <td>0.561</td>\n",
-       "      <td>0.856</td>\n",
-       "      <td>-1.088</td>\n",
-       "      <td>2.090</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>7709.0</td>\n",
-       "      <td>3190.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.541</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>-1.149</td>\n",
+       "      <td>2.239</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>2055.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[30]</th>\n",
-       "      <td>1.267</td>\n",
-       "      <td>0.781</td>\n",
-       "      <td>-0.244</td>\n",
-       "      <td>2.684</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>5645.0</td>\n",
-       "      <td>3055.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.247</td>\n",
+       "      <td>0.808</td>\n",
+       "      <td>-0.275</td>\n",
+       "      <td>2.744</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>653.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[31]</th>\n",
-       "      <td>1.372</td>\n",
-       "      <td>0.796</td>\n",
-       "      <td>-0.121</td>\n",
-       "      <td>2.832</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>6632.0</td>\n",
-       "      <td>2663.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.385</td>\n",
+       "      <td>0.813</td>\n",
+       "      <td>0.013</td>\n",
+       "      <td>3.021</td>\n",
+       "      <td>0.019</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>1875.0</td>\n",
+       "      <td>599.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[32]</th>\n",
-       "      <td>2.654</td>\n",
-       "      <td>0.726</td>\n",
-       "      <td>1.285</td>\n",
-       "      <td>4.005</td>\n",
-       "      <td>0.010</td>\n",
-       "      <td>0.007</td>\n",
-       "      <td>5443.0</td>\n",
-       "      <td>2777.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.683</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>1.318</td>\n",
+       "      <td>3.936</td>\n",
+       "      <td>0.017</td>\n",
+       "      <td>0.012</td>\n",
+       "      <td>1698.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[33]</th>\n",
-       "      <td>0.872</td>\n",
-       "      <td>0.927</td>\n",
-       "      <td>-0.888</td>\n",
-       "      <td>2.581</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>0.011</td>\n",
-       "      <td>6702.0</td>\n",
-       "      <td>2934.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>1.018</td>\n",
+       "      <td>-0.974</td>\n",
+       "      <td>2.944</td>\n",
+       "      <td>0.026</td>\n",
+       "      <td>0.024</td>\n",
+       "      <td>1573.0</td>\n",
+       "      <td>781.0</td>\n",
+       "      <td>1.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YearsAtCompany_threshold[34]</th>\n",
-       "      <td>1.770</td>\n",
-       "      <td>0.905</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>3.394</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>6067.0</td>\n",
-       "      <td>2884.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1.756</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>0.348</td>\n",
+       "      <td>3.500</td>\n",
+       "      <td>0.021</td>\n",
+       "      <td>0.017</td>\n",
+       "      <td>1640.0</td>\n",
+       "      <td>876.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TotalWorkingYears</th>\n",
        "      <td>0.127</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>0.117</td>\n",
-       "      <td>0.137</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>0.118</td>\n",
+       "      <td>0.138</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.000</td>\n",
-       "      <td>878.0</td>\n",
-       "      <td>1520.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>482.0</td>\n",
+       "      <td>514.0</td>\n",
+       "      <td>1.01</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1505,83 +1701,83 @@
       ],
       "text/plain": [
        "                               mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
-       "YearsAtCompany_threshold[0]  -2.521  0.185  -2.865   -2.176      0.003   \n",
-       "YearsAtCompany_threshold[1]  -1.054  0.111  -1.263   -0.847      0.002   \n",
-       "YearsAtCompany_threshold[2]  -1.016  0.117  -1.235   -0.798      0.002   \n",
-       "YearsAtCompany_threshold[3]  -0.761  0.114  -0.980   -0.555      0.002   \n",
-       "YearsAtCompany_threshold[4]  -0.757  0.126  -0.986   -0.515      0.002   \n",
-       "YearsAtCompany_threshold[5]   0.249  0.107   0.043    0.448      0.003   \n",
-       "YearsAtCompany_threshold[6]  -0.508  0.143  -0.771   -0.237      0.003   \n",
-       "YearsAtCompany_threshold[7]  -0.088  0.141  -0.360    0.168      0.003   \n",
-       "YearsAtCompany_threshold[8]   0.030  0.144  -0.236    0.303      0.003   \n",
-       "YearsAtCompany_threshold[9]   0.386  0.149   0.117    0.665      0.003   \n",
-       "YearsAtCompany_threshold[10]  1.268  0.150   0.992    1.540      0.004   \n",
-       "YearsAtCompany_threshold[11]  0.437  0.219   0.040    0.852      0.004   \n",
-       "YearsAtCompany_threshold[12] -0.108  0.296  -0.635    0.470      0.005   \n",
-       "YearsAtCompany_threshold[13]  0.513  0.254   0.035    0.983      0.005   \n",
-       "YearsAtCompany_threshold[14]  0.394  0.285  -0.171    0.910      0.005   \n",
-       "YearsAtCompany_threshold[15]  0.793  0.264   0.268    1.253      0.005   \n",
-       "YearsAtCompany_threshold[16]  0.431  0.335  -0.229    1.033      0.005   \n",
-       "YearsAtCompany_threshold[17]  0.246  0.383  -0.455    0.958      0.006   \n",
-       "YearsAtCompany_threshold[18]  0.801  0.328   0.182    1.402      0.006   \n",
-       "YearsAtCompany_threshold[19]  0.798  0.350   0.147    1.452      0.006   \n",
-       "YearsAtCompany_threshold[20]  2.199  0.277   1.688    2.728      0.006   \n",
-       "YearsAtCompany_threshold[21]  1.841  0.345   1.241    2.536      0.006   \n",
-       "YearsAtCompany_threshold[22]  2.432  0.361   1.690    3.054      0.007   \n",
-       "YearsAtCompany_threshold[23]  0.025  0.734  -1.405    1.354      0.010   \n",
-       "YearsAtCompany_threshold[24]  1.594  0.529   0.602    2.555      0.008   \n",
-       "YearsAtCompany_threshold[25]  1.529  0.580   0.379    2.556      0.009   \n",
-       "YearsAtCompany_threshold[26]  1.746  0.603   0.601    2.868      0.009   \n",
-       "YearsAtCompany_threshold[27]  1.060  0.761  -0.476    2.406      0.009   \n",
-       "YearsAtCompany_threshold[28]  1.141  0.790  -0.360    2.566      0.011   \n",
-       "YearsAtCompany_threshold[29]  0.561  0.856  -1.088    2.090      0.010   \n",
-       "YearsAtCompany_threshold[30]  1.267  0.781  -0.244    2.684      0.010   \n",
-       "YearsAtCompany_threshold[31]  1.372  0.796  -0.121    2.832      0.010   \n",
-       "YearsAtCompany_threshold[32]  2.654  0.726   1.285    4.005      0.010   \n",
-       "YearsAtCompany_threshold[33]  0.872  0.927  -0.888    2.581      0.011   \n",
-       "YearsAtCompany_threshold[34]  1.770  0.905   0.009    3.394      0.012   \n",
-       "TotalWorkingYears             0.127  0.006   0.117    0.137      0.000   \n",
+       "YearsAtCompany_threshold[0]  -2.525  0.193  -2.896   -2.191      0.004   \n",
+       "YearsAtCompany_threshold[1]  -1.057  0.110  -1.265   -0.850      0.003   \n",
+       "YearsAtCompany_threshold[2]  -1.017  0.119  -1.238   -0.792      0.004   \n",
+       "YearsAtCompany_threshold[3]  -0.760  0.117  -0.984   -0.553      0.003   \n",
+       "YearsAtCompany_threshold[4]  -0.754  0.126  -0.969   -0.493      0.003   \n",
+       "YearsAtCompany_threshold[5]   0.251  0.111   0.053    0.466      0.003   \n",
+       "YearsAtCompany_threshold[6]  -0.500  0.145  -0.769   -0.230      0.004   \n",
+       "YearsAtCompany_threshold[7]  -0.085  0.137  -0.355    0.155      0.004   \n",
+       "YearsAtCompany_threshold[8]   0.025  0.141  -0.213    0.310      0.004   \n",
+       "YearsAtCompany_threshold[9]   0.389  0.146   0.128    0.673      0.004   \n",
+       "YearsAtCompany_threshold[10]  1.267  0.154   0.970    1.540      0.005   \n",
+       "YearsAtCompany_threshold[11]  0.436  0.213   0.055    0.826      0.006   \n",
+       "YearsAtCompany_threshold[12] -0.116  0.307  -0.662    0.457      0.008   \n",
+       "YearsAtCompany_threshold[13]  0.513  0.250   0.024    0.943      0.006   \n",
+       "YearsAtCompany_threshold[14]  0.392  0.293  -0.126    0.958      0.008   \n",
+       "YearsAtCompany_threshold[15]  0.791  0.277   0.310    1.348      0.008   \n",
+       "YearsAtCompany_threshold[16]  0.433  0.329  -0.196    1.017      0.009   \n",
+       "YearsAtCompany_threshold[17]  0.252  0.365  -0.397    0.916      0.009   \n",
+       "YearsAtCompany_threshold[18]  0.791  0.330   0.160    1.394      0.009   \n",
+       "YearsAtCompany_threshold[19]  0.788  0.347   0.148    1.445      0.010   \n",
+       "YearsAtCompany_threshold[20]  2.191  0.268   1.684    2.680      0.008   \n",
+       "YearsAtCompany_threshold[21]  1.842  0.331   1.323    2.516      0.009   \n",
+       "YearsAtCompany_threshold[22]  2.431  0.369   1.746    3.078      0.011   \n",
+       "YearsAtCompany_threshold[23]  0.017  0.743  -1.389    1.340      0.016   \n",
+       "YearsAtCompany_threshold[24]  1.581  0.571   0.489    2.582      0.015   \n",
+       "YearsAtCompany_threshold[25]  1.530  0.579   0.491    2.646      0.013   \n",
+       "YearsAtCompany_threshold[26]  1.727  0.607   0.605    2.844      0.014   \n",
+       "YearsAtCompany_threshold[27]  1.096  0.732  -0.351    2.412      0.017   \n",
+       "YearsAtCompany_threshold[28]  1.156  0.722  -0.288    2.399      0.016   \n",
+       "YearsAtCompany_threshold[29]  0.541  0.868  -1.149    2.239      0.019   \n",
+       "YearsAtCompany_threshold[30]  1.247  0.808  -0.275    2.744      0.019   \n",
+       "YearsAtCompany_threshold[31]  1.385  0.813   0.013    3.021      0.019   \n",
+       "YearsAtCompany_threshold[32]  2.683  0.710   1.318    3.936      0.017   \n",
+       "YearsAtCompany_threshold[33]  0.868  1.018  -0.974    2.944      0.026   \n",
+       "YearsAtCompany_threshold[34]  1.756  0.853   0.348    3.500      0.021   \n",
+       "TotalWorkingYears             0.127  0.005   0.118    0.138      0.000   \n",
        "\n",
        "                              mcse_sd  ess_bulk  ess_tail  r_hat  \n",
-       "YearsAtCompany_threshold[0]     0.002    4693.0    3047.0    1.0  \n",
-       "YearsAtCompany_threshold[1]     0.001    2939.0    2937.0    1.0  \n",
-       "YearsAtCompany_threshold[2]     0.002    2746.0    2884.0    1.0  \n",
-       "YearsAtCompany_threshold[3]     0.002    2578.0    2651.0    1.0  \n",
-       "YearsAtCompany_threshold[4]     0.002    2657.0    2766.0    1.0  \n",
-       "YearsAtCompany_threshold[5]     0.002    1819.0    2318.0    1.0  \n",
-       "YearsAtCompany_threshold[6]     0.002    2619.0    2668.0    1.0  \n",
-       "YearsAtCompany_threshold[7]     0.002    2886.0    2952.0    1.0  \n",
-       "YearsAtCompany_threshold[8]     0.002    2678.0    2740.0    1.0  \n",
-       "YearsAtCompany_threshold[9]     0.002    2525.0    2372.0    1.0  \n",
-       "YearsAtCompany_threshold[10]    0.002    1794.0    2562.0    1.0  \n",
-       "YearsAtCompany_threshold[11]    0.003    2626.0    3164.0    1.0  \n",
-       "YearsAtCompany_threshold[12]    0.005    3531.0    2796.0    1.0  \n",
-       "YearsAtCompany_threshold[13]    0.003    2934.0    2621.0    1.0  \n",
-       "YearsAtCompany_threshold[14]    0.004    3363.0    2791.0    1.0  \n",
-       "YearsAtCompany_threshold[15]    0.003    2885.0    3035.0    1.0  \n",
-       "YearsAtCompany_threshold[16]    0.004    4075.0    2936.0    1.0  \n",
-       "YearsAtCompany_threshold[17]    0.005    4258.0    3055.0    1.0  \n",
-       "YearsAtCompany_threshold[18]    0.004    3185.0    3306.0    1.0  \n",
-       "YearsAtCompany_threshold[19]    0.005    3066.0    2756.0    1.0  \n",
-       "YearsAtCompany_threshold[20]    0.004    2254.0    2483.0    1.0  \n",
-       "YearsAtCompany_threshold[21]    0.004    3367.0    2923.0    1.0  \n",
-       "YearsAtCompany_threshold[22]    0.005    3032.0    2780.0    1.0  \n",
-       "YearsAtCompany_threshold[23]    0.012    5666.0    2933.0    1.0  \n",
-       "YearsAtCompany_threshold[24]    0.005    4998.0    2865.0    1.0  \n",
-       "YearsAtCompany_threshold[25]    0.006    4642.0    2944.0    1.0  \n",
-       "YearsAtCompany_threshold[26]    0.006    5042.0    3080.0    1.0  \n",
-       "YearsAtCompany_threshold[27]    0.008    6828.0    2783.0    1.0  \n",
-       "YearsAtCompany_threshold[28]    0.008    5847.0    2871.0    1.0  \n",
-       "YearsAtCompany_threshold[29]    0.011    7709.0    3190.0    1.0  \n",
-       "YearsAtCompany_threshold[30]    0.008    5645.0    3055.0    1.0  \n",
-       "YearsAtCompany_threshold[31]    0.008    6632.0    2663.0    1.0  \n",
-       "YearsAtCompany_threshold[32]    0.007    5443.0    2777.0    1.0  \n",
-       "YearsAtCompany_threshold[33]    0.011    6702.0    2934.0    1.0  \n",
-       "YearsAtCompany_threshold[34]    0.008    6067.0    2884.0    1.0  \n",
-       "TotalWorkingYears               0.000     878.0    1520.0    1.0  "
+       "YearsAtCompany_threshold[0]     0.003    2027.0     812.0   1.00  \n",
+       "YearsAtCompany_threshold[1]     0.002    1137.0     601.0   1.01  \n",
+       "YearsAtCompany_threshold[2]     0.003    1009.0     705.0   1.00  \n",
+       "YearsAtCompany_threshold[3]     0.002    1210.0     833.0   1.00  \n",
+       "YearsAtCompany_threshold[4]     0.002    1327.0     780.0   1.00  \n",
+       "YearsAtCompany_threshold[5]     0.002    1207.0     808.0   1.01  \n",
+       "YearsAtCompany_threshold[6]     0.003    1394.0     526.0   1.01  \n",
+       "YearsAtCompany_threshold[7]     0.003    1115.0     835.0   1.00  \n",
+       "YearsAtCompany_threshold[8]     0.004    1081.0     750.0   1.00  \n",
+       "YearsAtCompany_threshold[9]     0.003    1134.0     865.0   1.00  \n",
+       "YearsAtCompany_threshold[10]    0.004     923.0     737.0   1.01  \n",
+       "YearsAtCompany_threshold[11]    0.004    1376.0     662.0   1.00  \n",
+       "YearsAtCompany_threshold[12]    0.010    1568.0     693.0   1.00  \n",
+       "YearsAtCompany_threshold[13]    0.005    1537.0     738.0   1.00  \n",
+       "YearsAtCompany_threshold[14]    0.006    1328.0     696.0   1.01  \n",
+       "YearsAtCompany_threshold[15]    0.005    1405.0     619.0   1.00  \n",
+       "YearsAtCompany_threshold[16]    0.006    1516.0     691.0   1.01  \n",
+       "YearsAtCompany_threshold[17]    0.009    1468.0     841.0   1.00  \n",
+       "YearsAtCompany_threshold[18]    0.007    1274.0     856.0   1.00  \n",
+       "YearsAtCompany_threshold[19]    0.007    1306.0     612.0   1.01  \n",
+       "YearsAtCompany_threshold[20]    0.006    1076.0     720.0   1.00  \n",
+       "YearsAtCompany_threshold[21]    0.006    1447.0     825.0   1.00  \n",
+       "YearsAtCompany_threshold[22]    0.008    1230.0     616.0   1.00  \n",
+       "YearsAtCompany_threshold[23]    0.027    2194.0     712.0   1.00  \n",
+       "YearsAtCompany_threshold[24]    0.010    1671.0     636.0   1.00  \n",
+       "YearsAtCompany_threshold[25]    0.010    2056.0     616.0   1.00  \n",
+       "YearsAtCompany_threshold[26]    0.010    1982.0     709.0   1.00  \n",
+       "YearsAtCompany_threshold[27]    0.016    1855.0     791.0   1.02  \n",
+       "YearsAtCompany_threshold[28]    0.013    2073.0     852.0   1.00  \n",
+       "YearsAtCompany_threshold[29]    0.027    2055.0     669.0   1.01  \n",
+       "YearsAtCompany_threshold[30]    0.015    1821.0     653.0   1.01  \n",
+       "YearsAtCompany_threshold[31]    0.015    1875.0     599.0   1.01  \n",
+       "YearsAtCompany_threshold[32]    0.012    1698.0     815.0   1.01  \n",
+       "YearsAtCompany_threshold[33]    0.024    1573.0     781.0   1.00  \n",
+       "YearsAtCompany_threshold[34]    0.017    1640.0     876.0   1.01  \n",
+       "TotalWorkingYears               0.000     482.0     514.0   1.01  "
       ]
      },
-     "execution_count": 44,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1599,7 +1795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1627,7 +1823,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This plot can seem confusing at first. Remember, the sequential model is a product of probabilities, i.e., the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$. Thus, the probability of category 5 is the probability that the sequential process did not fall in 0, 1, 2, 3, or 4 multiplied by the probability that the sequential process stopped at 5. This makes sense why the probability of category 37 is 1. There is no category after 37, so once you multiply all of the previous probabilities with the current category, you get 1. This is the reason for the \"cumulative-like\" shape of the plot. But if the coefficients were truly cumulative, the probability could not decreases as $k$ increases. "
+    "This plot can seem confusing at first. Remember, the sequential model is a product of probabilities, i.e., the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$. Thus, the probability of category 5 is the probability that the sequential process did not fall in 0, 1, 2, 3, or 4 multiplied by the probability that the sequential process stopped at 5. This makes sense why the probability of category 36 is 1. There is no category after 36, so once you multiply all of the previous probabilities with the current category, you get 1. This is the reason for the \"cumulative-like\" shape of the plot. But if the coefficients were truly cumulative, the probability could not decreases as $k$ increases. "
    ]
   },
   {
@@ -1641,12 +1837,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -1656,9 +1852,14 @@
     }
    ],
    "source": [
-    "sequence_idata_pps = sequence_model.predict(idata=sequence_idata, kind=\"pps\", inplace=False)\n",
+    "idata_pps = model.predict(idata=idata, kind=\"pps\", inplace=False)\n",
     "\n",
-    "az.plot_ppc(sequence_idata_pps, figsize=(7, 3), textsize=11)"
+    "bins = np.arange(35)\n",
+    "fig, ax = plt.subplots(figsize=(7, 3))\n",
+    "ax = plot_ppc_discrete(idata_pps, bins, ax)\n",
+    "ax.set_xlabel(\"Response category\")\n",
+    "ax.set_ylabel(\"Count\")\n",
+    "ax.set_title(\"Sequential model - Posterior Predictive Distribution\");"
    ]
   },
   {

From 075f5379d05345667299e53b35d73d7d8b895a8e Mon Sep 17 00:00:00 2001
From: GStechschulte <stechschulteg@gmail.com>
Date: Thu, 28 Sep 2023 15:59:20 +0200
Subject: [PATCH 13/13] add plots explaining the ordinal outcome of the dataset

---
 docs/notebooks/ordinal_regression.ipynb | 217 +++++++-----------------
 1 file changed, 63 insertions(+), 154 deletions(-)

diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb
index b9bbb406e..65db2c2fc 100644
--- a/docs/notebooks/ordinal_regression.ipynb
+++ b/docs/notebooks/ordinal_regression.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,19 +136,19 @@
    "source": [
     "### Intercept only model\n",
     "\n",
-    "Before we fit a model with predictors, let's attempt to recover the parameters of an ordinal model using only the thresholds to get a feel for the cumulative family. Traditionally, in Bambi if we wanted to recover the parameters of the likelihood, we would use an intercept only model and write the formula as `response ~ 1` where `1` indicates to include the intercept. However, in the case of ordinal regression, the thresholds \"take the place\" of the intercept. Thus, we can write the formula as `response ~ 0` to indicate that we do not want to include an intercept. To fit a cumulative ordinal model, we pass `family=\"cumulative\"`. To compare the thresholds only model, we compute the empirical log-cumulative-odds of the categories directly from the data below. "
+    "Before we fit a model with predictors, let's attempt to recover the parameters of an ordinal model using only the thresholds to get a feel for the cumulative family. Traditionally, in Bambi if we wanted to recover the parameters of the likelihood, we would use an intercept only model and write the formula as `response ~ 1` where `1` indicates to include the intercept. However, in the case of ordinal regression, the thresholds \"take the place\" of the intercept. Thus, we can write the formula as `response ~ 0` to indicate that we do not want to include an intercept. To fit a cumulative ordinal model, we pass `family=\"cumulative\"`. To compare the thresholds only model, we compute the empirical log-cumulative-odds of the categories directly from the data below and generate a bar plot of the response probabilities."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_58515/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
+      "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_22293/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n",
       "  logit_func = lambda x: np.log(x / (1 - x))\n"
      ]
     },
@@ -159,7 +159,7 @@
        "        1.76938091,         nan])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,83 +174,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [response_threshold]\n"
-     ]
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-    {
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-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
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-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:02&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
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",
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 700x300 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in add\n",
-      "  self.vm()\n",
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n"
-     ]
     }
    ],
+   "source": [
+    "plt.figure(figsize=(7, 3))\n",
+    "plt.bar(np.arange(1, 8), pr_k)\n",
+    "plt.ylabel(\"Probability\")\n",
+    "plt.xlabel(\"Response\")\n",
+    "plt.title(\"Empirical probability of each response category\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "model = bmb.Model(\"response ~ 0\", data=trolly, family=\"cumulative\")\n",
     "idata = model.fit(random_seed=1234)"
@@ -509,7 +459,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can take the derivative of the cumulative probabilities to get the posterior probabilities for each category."
+    "We can take the derivative of the cumulative probabilities to get the posterior probabilities for each category. Notice how the posterior probabilities in the barplot below are close to the empirical probabilities in barplot above."
    ]
   },
   {
@@ -599,83 +549,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Only 250 samples in chain.\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [response_threshold, action, intention, contact, action:intention, contact:intention]\n",
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
-       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/gabestechschulte/miniforge3/envs/bambinos/lib/python3.11/site-packages/pytensor/compile/function/types.py:970: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  self.vm()\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      <progress value='2000' class='' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [2000/2000 13:03&lt;00:00 Sampling 4 chains, 0 divergences]\n",
-       "    </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 250 tune and 250 draw iterations (1_000 + 1_000 draws total) took 784 seconds.\n",
-      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = bmb.Model(\n",
     "    \"response ~ 0 + action + intention + contact + action:intention + contact:intention\", \n",
@@ -909,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -965,12 +841,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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ARo0aAUC5/l7oMiZzBCsrKxgbG+P69esVtg0LCwuNaYVCUeT8p0+flss2Hz16hHbt2uH48eOYO3cuDh8+jOjoaGzfvh3Aswu7y8LAwAB+fn7YsWMHUlNTATw7tWRnZ4du3bpJ5e7cuYPdu3dDLpdrvBo0aAAAuHfv3gu3VZp2AyC13f379wEUfqrY3t4eubm5SElJQUpKCnJzc2Fra1ugXP55d+7cgRACNjY2BfYpKiqqRPvzvA0bNhSopyQWLFiA6OhonDp1CgkJCbh27Zo02vRF+523XKVSISIiAk2aNMH06dPRoEED2NvbY9asWRrXbBaltMe2sHjyS0lJgRCiyNif37/S1Pu8CRMmIDo6GjExMbh69SoSExMxYsSIAuXy15t3yr1ly5YF9nnbtm3S/ubFV5L3U2Hu3r1b7hfqDxkyBE+fPkVoaCiAZ4lpYmIiBg8eLJUp7/d2fhkZGbh//750HAtTu3Zt7N+/H9bW1hgzZgxq166N2rVr47vvvivVtkrznijuOOV/r5W3+/fvl+q9npd458m7trus3+OvG14zR9DX10fnzp3xxx9/4ObNmyX6MlUqlYVebF3eXwB5vUX5t1WSL9eDBw/i9u3bOHz4sNQbB0BKwF7G4MGDsWjRIoSGhqJ///7YtWsXAgICNK7dsLKyQqNGjfD1118XWkdxX+wvK++LLzExscCy27dvQ09PD+bm5hBCQCaTISkpqUC5/POsrKwgk8lw5MiRQgfJFDavOO+++y6io6NLtQ4A1KpVS2PE4POe3+/87+Pbt2/DyspKmnZ3d0doaCiEEDh79iyCg4MxZ84cGBkZ4fPPPy82htIe25KMBDU3N4eenl6Rxyxvu6Wt93kODg5Ftl1x9eZt9+eff4aTk1OR6+W1f0neT4WpVq1agYEqL6t+/fpo1aoVgoKCMHLkSAQFBcHe3h5du3aVypT3ezu/PXv2QK1Wv/B2Iu3atUO7du2gVqtx8uRJ/PDDDwgICICNjQ0+/PDDEm2rNO+J4o5T3rF8me/g4lhaWpbqvU7FY88cAQCmTZsGIQSGDx+OrKysAsuzs7Oxe/duabpmzZo4e/asRpmDBw/i0aNH5RpX3k1G829r165dL1w370st/xfx8yPK8pT2rzw3Nze0bt0aQUFB2LJlCzIzMzX+0geenVo+f/48ateujRYtWhR4VWQy5+rqiurVq2PLli0ap3cyMjLwyy+/SCNc80bCbt++XaNH9OHDhxrHO29/hBC4detWofvj7u5eqhgtLS0L1PGyOnXqBADYvHmzxvzo6GjExcWhc+fOBdaRyWRo3Lgxli5diqpVq2qcXlIqlYW+Jyri2JqYmKB169bYvn27xjZzc3OxefNmODg4oG7duqWutzx069YNBgYGuHr1aqH7m3fsXF1dYWdnh61bt2q87/79919ERka+cDs+Pj44dOhQsaMUy9IjM3jwYBw/fhxHjx7F7t274e/vr/GHV3m/t5+XkJCAyZMnQ6VSYeTIkSVaR19fH61bt5ZG2ee9J8u7N+rChQs4c+aMxrwtW7bA1NRUGulbmu/goj4vhencubP0B/fzNm7cCGNjY97KpJTYM0cAAA8PD6xatQqjR49G8+bNMWrUKDRo0ADZ2dk4ffo01qxZg4YNG0rXUfn5+eHLL7/EzJkz4eXlhYsXL2L58uVQqVTlGpetrS26dOmC+fPnw9zcHE5OTjhw4IB0qrQ4np6eMDc3xyeffIJZs2ZBLpcjJCSkwJcXAOnLesGCBfDx8YG+vj4aNWoknb4szJAhQzBy5Ejcvn0bnp6ecHV11Vg+Z84chIeHw9PTE+PHj4erqyuePn2K+Ph4/P7771i9enWF3ftJT08PCxcuxIABA9CjRw+MHDkSmZmZWLRoEVJTU/HNN99IZb/66it0795dup+gWq3GggULYGJiovHUj7Zt22LEiBEYPHgwTp48ifbt28PExASJiYk4evQo3N3dMWrUqArZn5JydXXFiBEj8MMPP0BPTw8+Pj7SaFZHR0d8+umnAJ5dI7Vy5Ur07t0btWrVghAC27dvR2pqKry9vaX63N3dcfjwYezevRt2dnYwNTWFq6trhR3b+fPnw9vbGx07dsTkyZOhUCiwcuVKnD9/Hlu3btXK0w2AZz/oc+bMwYwZM3Dt2jV0794d5ubmuHPnDk6cOAETExNpdPdXX32FYcOG4b333sPw4cORmpoqjZZ8kTlz5uCPP/5A+/btMX36dLi7uyM1NRVhYWGYOHEi6tWrh9q1a8PIyAghISFwc3NDlSpVYG9vX2wC/dFHH2HixIn46KOPkJmZWeCayvJ6b58/f1663i45ORlHjhxBUFAQ9PX1sWPHjgIjT5+3evVqHDx4EO+88w5q1KiBp0+fYv369QAgjcQ1NTWFk5MTfv31V3Tu3BkWFhawsrIq89NW7O3t0bNnTwQGBsLOzg6bN29GeHg4FixYIF2T27JlS7i6umLy5MnIycmBubk5duzYgaNHjxaoz93dHdu3b8eqVavQvHlz6OnpFflH2qxZs6RrT2fOnAkLCwuEhIRgz549WLhwYbn/lrz2tDLsgiqt2NhY4e/vL2rUqCEUCoUwMTERTZs2FTNnzhTJyclSuczMTDFlyhTh6OgojIyMhJeXl4iNjS1yNGv+UV55o6vu3r2rMd/f31+YmJhozEtMTBTvv/++sLCwECqVSnz88cfSaMQXjWaNjIwUHh4ewtjYWFSrVk0MGzZMnDp1qsC6mZmZYtiwYaJatWpCJpNpjN7Kv0950tLShJGRkQAg1q5dW2h73r17V4wfP144OzsLuVwuLCwsRPPmzcWMGTPEo0ePCl0nj5OTk3jnnXcKzAcgxowZozEvb7TiokWLNObv3LlTtG7dWhgaGgoTExPRuXNn8ddffxWoc9euXaJRo0ZCoVCIGjVqiG+++abI0cHr168XrVu3FiYmJsLIyEjUrl1bDBw4UJw8eVIqU5GjWX/66adiy6nVarFgwQJRt25dIZfLhZWVlfj444/FjRs3pDJ///23+Oijj0Tt2rWFkZGRUKlUolWrViI4OFijrtjYWNG2bVthbGwsAAgvLy9pWUmObVHH5fll+UdlHjlyRHTq1Elq3zZt2ojdu3drlCnJ6MnCtlVYHKWpd+fOnaJjx47CzMxMKJVK4eTkJN5//32xf/9+jXL/+c9/hIuLi1AoFKJu3bpi/fr1hb4nUMjIzBs3boghQ4YIW1tbIZfLhb29vejXr5+4c+eOVGbr1q2iXr16Qi6Xa9RR3Ih2X19fAUC0bdu2yP0vyXu7MHntlvdSKBTC2tpaeHl5iXnz5ml8d+bJH+uxY8fEe++9J5ycnIRSqRSWlpbCy8tLGqGeZ//+/aJp06ZCqVQKANJ3U1HfqUW1S973y88//ywaNGggFAqFqFmzZoE7DAghxD///CO6du0qzMzMRLVq1cS4cePEnj17CoxmffDggXj//fdF1apVpe/RPIUd63Pnzol3331XqFQqoVAoROPGjQt8Hor63Bf1+XlTyYR4wRAbIiIiIqq0eM0cERERkQ5jMkdERESkw5jMEREREekwJnNEREREOozJHBEREZEOYzJHREREpMN40+ASys3Nxe3bt2Fqaqq1G3cSERHRm0EIgYcPH8Le3h56esX3vTGZK6Hbt2/D0dFR22EQERHRG+TGjRsvfKIMk7kSMjU1BfCsUc3MzLQcDREREb3O0tPT4ejoKOUfxWEyV0J5p1bNzMyYzBEREdErUZJLuzgAgoiIiEiHMZkjIiIi0mFaTeZWrVqFRo0aSacuPTw88Mcff0jLhRAIDAyEvb09jIyM0KFDB1y4cEGjjszMTIwbNw5WVlYwMTFBz549cfPmTY0yKSkp8PPzg0qlgkqlgp+fH1JTU1/FLhIRERFVKJkQQmhr47t374a+vj7q1KkDANiwYQMWLVqE06dPo0GDBliwYAG+/vprBAcHo27dupg7dy7+/PNPXLp0SbogcNSoUdi9ezeCg4NhaWmJSZMm4cGDB4iJiYG+vj4AwMfHBzdv3sSaNWsAACNGjEDNmjWxe/fuEseanp4OlUqFtLS0Iq+ZE0IgJycHarX6ZZqFiF4xuVwufV8QEVUGJck78mg1mSuMhYUFFi1ahCFDhsDe3h4BAQGYOnUqgGe9cDY2NliwYAFGjhyJtLQ0VKtWDZs2bUL//v0B/O8WIr///ju6deuGuLg41K9fH1FRUWjdujUAICoqCh4eHvj777/h6upaorhe1KhZWVlITEzE48ePy6kliOhVkclkcHBwQJUqVbQdChERgNIlc5VmNKtarcZPP/2EjIwMeHh44Pr160hKSkLXrl2lMkqlEl5eXoiMjMTIkSMRExOD7OxsjTL29vZo2LAhIiMj0a1bNxw7dgwqlUpK5ACgTZs2UKlUiIyMLDKZy8zMRGZmpjSdnp5eZOy5ubm4fv069PX1YW9vD4VCwRsLE+kIIQTu3r2LmzdvwsXFhT10RKRztJ7MnTt3Dh4eHnj69CmqVKmCHTt2oH79+oiMjAQA2NjYaJS3sbHBv//+CwBISkqCQqGAubl5gTJJSUlSGWtr6wLbtba2lsoUZv78+Zg9e3aJ9iErKwu5ublwdHSEsbFxidYhosqjWrVqiI+PR3Z2NpM5ItI5Wk/mXF1dERsbi9TUVPzyyy/w9/dHRESEtDx/D5cQ4oW9XvnLFFb+RfVMmzYNEydOlKbzbt5XnBc9boOIKif2pNObJOWJGtnqSnWFlc6S68tgbqT9PwC1nswpFAppAESLFi0QHR2N7777TrpOLikpCXZ2dlL55ORkqbfO1tYWWVlZSElJ0eidS05Ohqenp1Tmzp07BbZ79+7dAr1+z1MqlVAqlS+/g0RERJVEyhM1NsamajuM18rAJlW1ntBpPZnLTwiBzMxMODs7w9bWFuHh4WjatCmAZ6czIyIisGDBAgBA8+bNIZfLER4ejn79+gEAEhMTcf78eSxcuBAA4OHhgbS0NJw4cQKtWrUCABw/fhxpaWlSwkdERPQmyFYLZKkFcivX2EedpSeTVYpeTq0mc9OnT4ePjw8cHR3x8OFDhIaG4vDhwwgLC4NMJkNAQADmzZsHFxcXuLi4YN68eTA2Noavry8AQKVSYejQoZg0aRIsLS1hYWGByZMnw93dHV26dAEAuLm5oXv37hg+fDh+/PFHAM9uTdKjR48Sj2QloGbNmggICEBAQIC2QykXZdmfQYMGITU1FTt37qywuIiIKtL9xzlISMvWdhivlfuPc2BdRbt9Y1rd+p07d+Dn54fExESoVCo0atQIYWFh8Pb2BgBMmTIFT548wejRo5GSkoLWrVtj3759Gg+dXbp0KQwMDNCvXz88efIEnTt3RnBwsMZFzCEhIRg/frw06rVnz55Yvnz5q93ZSurGjRsIDAzEH3/8gXv37sHOzg69e/fGzJkzYWlpqe3wdFp8fDycnZ1x+vRpNGnSRNvhEBEhSy2Qpc5FrvY7k14LerJnbaptWk3m1q1bV+xymUyGwMBABAYGFlnG0NAQP/zwA3744Yciy1hYWGDz5s1lDfO1de3aNXh4eKBu3brYunUrnJ2dceHCBXz22Wf4448/EBUVBQsLC63EplarIZPJOKiEiKgc3cvIwZX7meBZ1vIhkz1rU22rdNfM0aszZswYKBQK7Nu3D0ZGRgCAGjVqoGnTpqhduzZmzJiBVatWSeUfPnwIX19f7Nq1C2ZmZpg2bRrGjRsnLQ8MDMT69etx584dWFpa4v3338f3338P4Nn1jl988QVCQkKQmpqKhg0bYsGCBejQoQMAIDg4GAEBAdi8eTOmTJmCf/75BytWrMCECROQlJSEqlWrStsZP348zpw5I416joyMxOeff47o6GhYWVnhvffew/z582FiYgLg2YCYoUOHYv/+/bC1tcXcuXNf2DZqtRqfffYZ1q9fD319fQwdOhT5768dFhaGuXPn4vz589DX14eHhwe+++471K5dGwDg7OwMANI1n15eXjh8+DCio6Mxffp0nD59GtnZ2WjSpAmWLl2KZs2alfjYERGVxbCe7XDzdtG35aLSu2Zvi+sXY7UaA5O5CtKiRYti72NXUWxtbXHy5MkXlnvw4AH27t2Lr7/+Wkrknq9jwIAB2LZtG1auXCndtmHRokWYPn06AgMDsXfvXnz66aeoV68evL298fPPP2Pp0qUIDQ1FgwYNkJSUhDNnzkh1Dh48GPHx8QgNDYW9vT127NiB7t2749y5c3BxcQEAPH78GPPnz8d//vMfWFpawsHBAbNmzcIvv/yCoUOHAniWZP33v//FnDlzADy7T2G3bt3w1VdfYd26dbh79y7Gjh2LsWPHIigoCMCza91u3LiBgwcPQqFQYPz48UhOTi62fRYvXoz169dj3bp1qF+/PhYvXowdO3agU6dOUpmMjAxMnDgR7u7uyMjIwMyZM/Hee+8hNjYWenp60qCb/fv3o0GDBlAoFACeJcX+/v5Sort48WK8/fbbuHz5ssYlBERE5e3WzZvIeXhf22G8VlIV2j+DxGSugiQlJeHWrVvaDqNIly9fhhACbm5uhS53c3NDSkoK7t69K910uW3btvj8888BAHXr1sVff/2FpUuXwtvbGwkJCbC1tUWXLl0gl8tRo0YNafTw1atXsXXrVty8eRP29vYAgMmTJyMsLAxBQUGYN28eACA7OxsrV65E48aNpTj69++PLVu2SMncgQMHkJKSgg8++ADAswTT19dXGsjg4uKC77//Hl5eXli1ahUSEhKkU8Z5TwFZt25dkfudZ9myZZg2bRr69u0LAFi9ejX27t2rUSZvWZ5169bB2toaFy9eRMOGDVGtWjUAgKWlJWxtbaVyzyeEAPDjjz/C3NwcERER6NGjR7FxERG9DOkMg0wGA7OCN9SnkstJvwuIXG2HAYDJXIV5/sdbF7eb94F//maqHh4eGmU8PDywbNkyAMAHH3yAZcuWoVatWujevTvefvttvPvuuzAwMMCpU6cghEDdunU11s/MzNQYZKFQKNCoUSONMgMGDICHhwdu374Ne3t7hISE4O2335buKxgTE4MrV64gJCREI/a8R6z9888/MDAwQIsWLaTl9erV0zhtm19aWhoSExM19jevjudPtV69ehVffvkloqKicO/ePeTmPvtQJyQkoGHDhkXWn5ycjJkzZ+LgwYO4c+cO1Go1Hj9+jISEhCLXISIqT/rG5nAOjHhxQSrS9UAv5KQVvI+tNjCZqyAlOdWpTXXq1IFMJsPFixfRu3fvAsv//vtvmJubw8rKqth68pI9R0dHXLp0CeHh4di/fz9Gjx6NRYsWISIiArm5udDX10dMTEyBRyU9/2BzIyOjAnfib9WqFWrXro3Q0FCMGjUKO3bskE6fAs+eizty5EiMHz++QGw1atTApUuXNOIsT++++y4cHR2xdu1a2NvbIzc3Fw0bNkRWVlax6w0aNAh3797FsmXL4OTkBKVSCQ8PjxeuR0RElU9leIAMk7k3lKWlJby9vbFy5Up8+umnGtfNJSUlISQkBAMHDtRIgqKiojTqiIqKQr169aRpIyMj9OzZEz179sSYMWNQr149nDt3Dk2bNoVarUZycjLatWtX6lh9fX0REhICBwcH6Onp4Z133pGWNWvWDBcuXJCeIpKfm5sbcnJycPLkSem076VLl5Camlrk9lQqFezs7BAVFYX27dsDAHJychATEyMNUrh//z7i4uLw448/Svt09OhRjXryrpFTq9Ua848cOYKVK1fi7bffBvDs9jD37t0raXMQEb08GeBgJtd2FDotQU+GHDy7cbC2MZl7gy1fvhyenp7o1q0b5s6dq3FrkurVq+Prr7/WKP/XX39h4cKF6N27N8LDw/HTTz9hz549AJ6NRlWr1WjdujWMjY2xadMmGBkZwcnJCZaWlhgwYAAGDhyIxYsXo2nTprh37x4OHjwId3d3KakpyoABAzB79mx8/fXXeP/992FoaCgtmzp1Ktq0aYMxY8Zg+PDhMDExQVxcHMLDw/HDDz/A1dVVumn0mjVrYGBggICAgAKDPvKbMGECvvnmG7i4uMDNzQ1LlizRSADNzc1haWmJNWvWwM7ODgkJCdL1hHmsra1hZGSEsLAwODg4wNDQECqVCnXq1MGmTZvQokULpKen47PPPnthPERE5U1poP0L918H2k/lmMy90VxcXHDy5EkEBgaif//+uH//PmxtbdG7d2/MmjWrwD3mJk2ahJiYGMyePRumpqZYvHgxunXrBgCoWrUqvvnmG0ycOBFqtRru7u7YvXu3dE1cUFAQ5s6di0mTJuHWrVuwtLSEh4fHCxO5vDhbtmyJ6Oho6Rq9PI0aNUJERARmzJiBdu3aQQiB2rVro3///lKZoKAgDBs2DF5eXrCxscHcuXPx5ZdfFrvNSZMmITExEYMGDYKenh6GDBmC9957D2lpaQAAPT09hIaGYvz48WjYsCFcXV3x/fffS7daAZ5dZ/f9999jzpw5mDlzJtq1a4fDhw9j/fr1GDFiBJo2bYoaNWpg3rx5mDx58gvbgYjoZT3fieSgYs/cy9DX0/xXm2Qi/82zqFDp6elQqVRIS0uDmZmZxrKnT5/i+vXrcHZ21ug1IiLdwM8wvSkUZpbIfvgABlUsMPf3v7Udjk6b17cx0u8mws6+Om7fulnu9ReXd+THnjkiIqI3kCFPs76UynB6NQ+PJBEREZEOY88cERHRG0YmAz5yV2k7DJ22QK6HNAB6laCLjskcERHRG8i6ClOAl1EZkrg8PM1KREREpMOYzBERERHpMCZzRERERDqMyRwRERGRDmMyR0RERKTDmMyRTggODkbVqlW1HUa5kMlk2LlzJwAgPj4eMpkMsbGxZa6vPOqoTPLvz+HDhyGTyTSejUtERP/DZO4NNmjQIMhkMshkMsjlctSqVQuTJ09GRkbGS9dd3glG//798c8//5RLXZWJo6MjEhMT0bBhwxKVHzRoEHr37v1SdegaT09PJCYmQqXiPbGIiArDm8y84bp3746goCBkZ2fjyJEjGDZsGDIyMrBq1SpthybJzs6GkZERjIyMXroeubx8HixdXnXp6+vD1tZW63VUZgqF4rXePyKil8WeuTecUqmEra0tHB0d4evriwEDBkinADMzMzF+/HhYW1vD0NAQb731FqKjo6V1U1JSMGDAAFSrVg1GRkZwcXFBUFAQAMDZ2RkA0LRpU8hkMnTo0EFaLygoCG5ubjA0NES9evWwcuVKaVlej95///tfdOjQAYaGhti8eXOhp1lXrVqF2rVrQ6FQwNXVFZs2bdJYLpPJsHr1avTq1QsmJiaYO3duoW1Qs2ZNfPXVV/D19UWVKlVgb2+PH374oUR17d69G82bN4ehoSFq1aqF2bNnIycnR1rv8uXLaN++PQwNDVG/fn2Eh4dr1FtYD+aFCxfwzjvvwMzMDKampmjXrh2uXr2KwMBAbNiwAb/++qvUo3r48GGNOnJzc+Hg4IDVq1drbOfUqVOQyWS4du0aACAtLQ0jRoyAtbU1zMzM0KlTJ5w5c6bQ9sl/XNq1awcjIyO0bNkS//zzD6Kjo9GiRQtUqVIF3bt3x927dzXWLe54A8CJEyfQtGlTGBoaokWLFjh9+rTG8vynWe/fv4+PPvoIDg4OMDY2hru7O7Zu3aqxTocOHTB+/HhMmTIFFhYWsLW1RWBgYJH7R0Sky9gzV0HeXrAHdx8+feXbrWZqiN+nvlPm9Y2MjJCdnQ0AmDJlCn755Rds2LABTk5OWLhwIbp164YrV67AwsICX375JS5evIg//vgDVlZWuHLlCp48eQLg2Q90q1atsH//fjRo0AAKhQIAsHbtWsyaNQvLly9H06ZNcfr0aQwfPhwmJibw9/eX4pg6dSoWL16MoKAgKJVK7Nu3TyPOHTt2YMKECVi2bBm6dOmC3377DYMHD4aDgwM6duwolZs1axbmz5+PpUuXQl9fv8j9XrRoEaZPn47AwEDs3bsXn376KerVqwdvb+8i69q7dy8+/vhjfP/991LCNWLECKlsbm4u+vTpAysrK0RFRSE9PR0BAQHFtv+tW7fQvn17dOjQAQcPHoSZmRn++usv5OTkYPLkyYiLi0N6erqUNFtYWOD27dvS+np6evjwww8REhKCTz75RJq/ZcsWeHh4oFatWhBC4J133oGFhQV+//13qFQq/Pjjj+jcuTP++ecfWFhYFBnfrFmzsGzZMtSoUQNDhgzBRx99BDMzM3z33XcwNjZGv379MHPmTKln90XHOyMjAz169ECnTp2wefNmXL9+HRMmTCi2jZ4+fYrmzZtj6tSpMDMzw549e+Dn54datWqhdevWUrkNGzZg4sSJOH78OI4dO4ZBgwahbdu2GseUiOh1wGSugtx9+BRJqY+1HUapnDhxAlu2bEHnzp2lU63BwcHw8fEB8OyHOTw8HOvWrcNnn32GhIQENG3aFC1atADwrIcrT7Vq1QAAlpaWGqfIvvrqKyxevBh9+vQB8KwH7+LFi/jxxx81krmAgACpTGG+/fZbDBo0CKNHjwYATJw4EVFRUfj22281kjlfX18MGTLkhfvetm1bfP755wCAunXr4q+//sLSpUs1fvjz1+Xn54fPP/9cirtWrVr46quvMGXKFMyaNQv79+9HXFwc4uPj4eDgAACYN2+e1J6FWbFiBVQqFUJDQ6XTuHXr1pWWGxkZITMzs9jTjgMGDMCSJUvw77//wsnJCbm5uQgNDcX06dMBAIcOHcK5c+eQnJwMpVIptefOnTvx888/SwlpYSZPnoxu3boBACZMmICPPvoIBw4cQNu2bQEAQ4cORXBwsFT+Rcc7JCQEarUa69evh7GxMRo0aICbN29i1KhRRcZQvXp1TJ48WZoeN24cwsLC8NNPP2kkc40aNcKsWbMAAC4uLli+fDkOHDjAZI6IXjtM5ipINVNDndjub7/9hipVqiAnJwfZ2dno1asXfvjhB1y9ehXZ2dnSjzQAyOVytGrVCnFxcQCAUaNGoW/fvjh16hS6du2K3r17w9PTs8ht3b17Fzdu3MDQoUMxfPhwaX5OTk6Bi9vzEsSixMXFFUg62rZti++++65U9eTx8PAoML1s2bJi64qJiUF0dDS+/vpraZ5arcbTp0/x+PFjxMXFoUaNGlIiV9h28ouNjUW7du1e6nq8pk2bol69eti6dSs+//xzREREIDk5Gf369ZPifvToESwtLTXWe/LkCa5evVps3Y0aNZL+b2NjAwBwd3fXmJecnAygZMc7Li4OjRs3hrGxsbT8RW2kVqvxzTffYNu2bbh16xYyMzORmZkJExOTImMFADs7Oyk2IqLXCZO5CvIypzpfpY4dO2LVqlWQy+Wwt7eXkojExEQAz64Ve54QQprn4+ODf//9F3v27MH+/fvRuXNnjBkzBt9++22h28rNzQXwrIfv+R4UAAVOgeb/YS5McbGVpp6S1p+/rtzcXMyePbvQHkRDQ0MIIV5YZ34vO8gjz4ABA7BlyxZ8/vnn2LJlC7p16wYrKyspbjs7Oxw+fLjAei+6/cvzSWbevuSfl3ecS3K8C2ujF1m8eDGWLl2KZcuWwd3dHSYmJggICEBWVlaRseaPjYjodaLVARDz589Hy5YtYWpqCmtra/Tu3RuXLl3SKPP87TPyXm3atNEok5mZiXHjxsHKygomJibo2bMnbt68qVEmJSUFfn5+UKlUUKlU8PPz432r8CxBqVOnDpycnDR+/OrUqQOFQoGjR49K87Kzs3Hy5Em4ublJ86pVq4ZBgwZh8+bNWLZsGdasWQMA0jVyarVaKmtjY4Pq1avj2rVrqFOnjsYrb8BESbm5uWnEBgCRkZEasZVGVFRUgel69eoVu06zZs1w6dKlAvtSp04d6OnpoX79+khISNC4pu3YsWPF1tmoUSMcOXJEum4xP4VCodGmRfH19cW5c+cQExODn3/+GQMGDNCIOykpCQYGBgXizkv4ykNJjnf9+vVx5swZ6VpLoOCxyO/IkSPo1asXPv74YzRu3Bi1atXC5cuXyy1uIiJdo9VkLiIiAmPGjEFUVBTCw8ORk5ODrl27FrjPWffu3ZGYmCi9fv/9d43lAQEB2LFjB0JDQ3H06FE8evQIPXr00PjR8/X1RWxsLMLCwhAWFobY2Fj4+fm9kv3URSYmJhg1ahQ+++wzhIWF4eLFixg+fDgeP36MoUOHAgBmzpyJX3/9FVeuXMGFCxfw22+/ScmUtbU1jIyMEBYWhjt37iAtLQ0AEBgYiPnz5+O7777DP//8g3PnziEoKAhLliwpVXyfffYZgoODsXr1aly+fBlLlizB9u3bNa6lKo2//voLCxcuxD///IMVK1bgp59+euGF+DNnzsTGjRsRGBiICxcuIC4uDtu2bcMXX3wBAOjSpQtcXV0xcOBAnDlzBkeOHMGMGTOKrXPs2LFIT0/Hhx9+iJMnT+Ly5cvYtGmT9EdOzZo1cfbsWVy6dAn37t0rMulzdnaGp6cnhg4dipycHPTq1Uta1qVLF3h4eKB3797Yu3cv4uPjERkZiS+++AInT54sTbO90IuOt6+vL/T09DB06FBcvHgRv//+e5E9u3nq1KmD8PBwREZGIi4uDiNHjkRSUlK5xk1EpFNEJZKcnCwAiIiICGmev7+/6NWrV5HrpKamCrlcLkJDQ6V5t27dEnp6eiIsLEwIIcTFixcFABEVFSWVOXbsmAAg/v777xLFlpaWJgCItLS0AsuePHkiLl68KJ48eVKiuiqLF7XtkydPxLhx44SVlZVQKpWibdu24sSJE9Lyr776Sri5uQkjIyNhYWEhevXqJa5duyYtX7t2rXB0dBR6enrCy8tLmh8SEiKaNGkiFAqFMDc3F+3btxfbt28XQghx/fp1AUCcPn1aI5agoCChUqk05q1cuVLUqlVLyOVyUbduXbFx40aN5QDEjh07XtgOTk5OYvbs2aJfv37C2NhY2NjYiGXLlpWorrCwMOHp6SmMjIyEmZmZaNWqlVizZo20/NKlS+Ktt94SCoVC1K1bV4SFhWnUVdj+njlzRnTt2lUYGxsLU1NT0a5dO3H16lUhxLPPiLe3t6hSpYoAIA4dOlRkm61YsUIAEAMHDiwQd3p6uhg3bpywt7cXcrlcODo6igEDBoiEhIRC26iwbRw6dEgAECkpKdK8wo5TccdbiGefxcaNGwuFQiGaNGkifvnlF41t5d/O/fv3Ra9evUSVKlWEtbW1+OKLL8TAgQM13steXl5iwoQJGnH06tVL+Pv7F7p/uvoZJiotuamFACDkphbaDkXnVa9eXQAQ1atXr5D6i8s78qtUydzly5cFAHHu3Dlpnr+/v1CpVKJatWrCxcVFDBs2TNy5c0dafuDAAQFAPHjwQKOuRo0aiZkzZwohhFi3bl2BHxghhFCpVGL9+vWFxvL06VORlpYmvW7cuPHaJXP0jJOTk1i6dKm2wyAt4meY3hRM5spPZUrmKs1Ng4UQmDhxIt566y2NxxL5+PggJCQEBw8exOLFixEdHY1OnTohMzMTAJCUlASFQgFzc3ON+mxsbKRTL0lJSbC2ti6wTWtr6yJPz8yfP1+6vk6lUsHR0bG8dpWIiIio3FSa0axjx47F2bNnC1zU3r9/f+n/DRs2RIsWLeDk5IQ9e/YUex8ykW9kY2GjCPOXed60adMwceJEaTo9PZ0JHREREVU6lSKZGzduHHbt2oU///xT455chbGzs4OTk5M0es3W1hZZWVlISUnR6J1LTk6W7nlma2uLO3fuFKjr7t270r2y8lMqldINVen1Fh8fr+0QiIiIykyrp1mFEBg7diy2b9+OgwcPluj2FPfv38eNGzdgZ2cHAGjevDnkcrnGMy8TExNx/vx5KZnz8PBAWloaTpw4IZU5fvw40tLSir3JLREREVFlp9WeuTFjxmDLli349ddfYWpqKl2/plKpYGRkhEePHiEwMBB9+/aFnZ0d4uPjMX36dFhZWeG9996Tyg4dOhSTJk2CpaUlLCwsMHnyZLi7u6NLly4Ant2TrHv37hg+fDh+/PFHAMCIESPQo0cPuLq6ltv+iDLcAJWItI+fXSLSZVpN5vIext2hQweN+UFBQRg0aBD09fVx7tw5bNy4EampqbCzs0PHjh2xbds2mJqaSuWXLl0KAwMD9OvXD0+ePEHnzp0RHBys8VSBkJAQjB8/Hl27dgUA9OzZE8uXLy+X/ci72e7jx4/L7Q7+RPTq5D09Iv+TSMpLyhM1stVMGF+WXF8Gc6OKOUZEukwm+CdpiaSnp0OlUiEtLQ1mZmYFlicmJiI1NRXW1tYwNjZ+4WObiKhyyM3Nxe3btyGXy1GjRo1y/+ymPFFjY2xqudb5JhvYpCoTupegMLNE9sMHkJtaICv9vrbD0WkODg64desWqlevXuCpU+XhRXnH8yrFAIjXga2tLQDwQd5EOkhPT69CEjkA7JErZ2xPooKYzJUTmUwGOzs7WFtbF/mIJSKqnBQKBfT0Km48WJZaIJcnQV6aHs94EBWKyVw509fXr7DrbohI99x/nIMrDzKRy1zupenJnrWndRX+dBE9j58IIqIKdDs9G1fuZ4Idcy9PJnvWnm7WhtoOhahSYTJHRFSBbqZn40YaL70oLzfT2ZZE+TGZIyKqQFM+aIf7yUkAe+ZengyYYm0L/6sXtB0JUaXCZI6IqAKl3E2C+tEDbYfx2kjhGAiiApjMERFVIOlaOZkM+sbmxZaloqkfpwBC8NpDokIwmSMiegX0jc1Ra26ktsPQWde+8IQ6gz2cRIVhMkdE9CrIAAczubaj0FnXeHqVqEhM5oiIKtDz97mtbanUXiA67s///5f3DSYqiMkcEdErYmhQcU+ZIKI3F5M5IqJXQAbAwYxfuWXFDjmiovGbhYjoFWHPHBFVBH6zEBEREekw9swREb0CMhnwkbtK22HorM94npWoSEzmiIheEesq/MolovLH06xEREREOozJHBEREZEOYzJHREREpMOYzBERERHpMCZzRERERDqMyRwRERGRDmMyR0RERKTDmMwRERER6TAmc0REREQ6TKvJ3Pz589GyZUuYmprC2toavXv3xqVLlzTKCCEQGBgIe3t7GBkZoUOHDrhw4YJGmczMTIwbNw5WVlYwMTFBz549cfPmTY0yKSkp8PPzg0qlgkqlgp+fH1JTUyt6F4mIiIgqlFaTuYiICIwZMwZRUVEIDw9HTk4OunbtioyMDKnMwoULsWTJEixfvhzR0dGwtbWFt7c3Hj58KJUJCAjAjh07EBoaiqNHj+LRo0fo0aMH1Gq1VMbX1xexsbEICwtDWFgYYmNj4efn90r3l4iIiKjciUokOTlZABARERFCCCFyc3OFra2t+Oabb6QyT58+FSqVSqxevVoIIURqaqqQy+UiNDRUKnPr1i2hp6cnwsLChBBCXLx4UQAQUVFRUpljx44JAOLvv/8uUWxpaWkCgEhLS3vp/SSiN4fc1EIAEHJTC22HotPYjuWD7Vh+qlevLgCI6tWrV0j9pck7KtU1c2lpaQAACwsLAMD169eRlJSErl27SmWUSiW8vLwQGRkJAIiJiUF2drZGGXt7ezRs2FAqc+zYMahUKrRu3Voq06ZNG6hUKqlMfpmZmUhPT9d4EREREVU2lSaZE0Jg4sSJeOutt9CwYUMAQFJSEgDAxsZGo6yNjY20LCkpCQqFAubm5sWWsba2LrBNa2trqUx+8+fPl66vU6lUcHR0fLkdJCIiIqoAlSaZGzt2LM6ePYutW7cWWCaTyTSmhRAF5uWXv0xh5YurZ9q0aUhLS5NeN27cKMluEBEREb1SlSKZGzduHHbt2oVDhw7BwcFBmm9rawsABXrPkpOTpd46W1tbZGVlISUlpdgyd+7cKbDdu3fvFuj1y6NUKmFmZqbxIiIiIqpstJrMCSEwduxYbN++HQcPHoSzs7PGcmdnZ9ja2iI8PFyal5WVhYiICHh6egIAmjdvDrlcrlEmMTER58+fl8p4eHggLS0NJ06ckMocP34caWlpUhkiIiIiXWSgzY2PGTMGW7Zswa+//gpTU1OpB06lUsHIyAgymQwBAQGYN28eXFxc4OLignnz5sHY2Bi+vr5S2aFDh2LSpEmwtLSEhYUFJk+eDHd3d3Tp0gUA4Obmhu7du2P48OH48ccfAQAjRoxAjx494Orqqp2dJyIiIioHWk3mVq1aBQDo0KGDxvygoCAMGjQIADBlyhQ8efIEo0ePRkpKClq3bo19+/bB1NRUKr906VIYGBigX79+ePLkCTp37ozg4GDo6+tLZUJCQjB+/Hhp1GvPnj2xfPnyit1BIiIiogomE0IIbQehC9LT06FSqZCWlsbr54ioxBRmlsh++AByUwtkpd/Xdjg6i+1YPtiO5cfBwQG3bt1C9erVCzx1qjyUJu+oFAMgiIiIiKhsmMwRERER6TAmc0REREQ6jMkcERERkQ5jMkdERESkw5jMEREREekwJnNEREREOqxMyVytWrVw/37B+9OkpqaiVq1aLx0UEREREZVMmZK5+Ph4qNXqAvMzMzNx69atlw6KiIiIiEqmVI/z2rVrl/T/vXv3QqVSSdNqtRoHDhxAzZo1yy04IiIiIipeqZK53r17AwBkMhn8/f01lsnlctSsWROLFy8ut+CIiIiIqHilSuZyc3MBAM7OzoiOjoaVlVWFBEVEREREJVOqZC7P9evXyzsOIiIiIiqDMiVzAHDgwAEcOHAAycnJUo9dnvXr1790YERERET0YmVK5mbPno05c+agRYsWsLOzg0wmK++4iIiIiKgEypTMrV69GsHBwfDz8yvveIiIiIioFMp0n7msrCx4enqWdyxEREREVEplSuaGDRuGLVu2lHcsRERERFRKZTrN+vTpU6xZswb79+9Ho0aNIJfLNZYvWbKkXIIjIiIiouKVKZk7e/YsmjRpAgA4f/68xjIOhiAiIiJ6dcqUzB06dKi84yAiIiKiMijTNXNEREREVDmUqWeuY8eOxZ5OPXjwYJkDIiIiIqKSK1Myl3e9XJ7s7GzExsbi/Pnz8Pf3L4+4iIiIiKgEypTMLV26tND5gYGBePTo0UsFREREREQlV67XzH388cd8LisRERHRK1SuydyxY8dgaGhY4vJ//vkn3n33Xdjb20Mmk2Hnzp0aywcNGgSZTKbxatOmjUaZzMxMjBs3DlZWVjAxMUHPnj1x8+ZNjTIpKSnw8/ODSqWCSqWCn58fUlNTy7qbRERERJVGmU6z9unTR2NaCIHExEScPHkSX375ZYnrycjIQOPGjTF48GD07du30DLdu3dHUFCQNK1QKDSWBwQEYPfu3QgNDYWlpSUmTZqEHj16ICYmBvr6+gAAX19f3Lx5E2FhYQCAESNGwM/PD7t37y5xrERERESVUZmSOZVKpTGtp6cHV1dXzJkzB127di1xPT4+PvDx8Sm2jFKphK2tbaHL0tLSsG7dOmzatAldunQBAGzevBmOjo7Yv38/unXrhri4OISFhSEqKgqtW7cGAKxduxYeHh64dOkSXF1dSxwvERFpX/KjHG2HQFSplCmZe76nrKIdPnwY1tbWqFq1Kry8vPD111/D2toaABATE4Ps7GyNBNLe3h4NGzZEZGQkunXrhmPHjkGlUkmJHAC0adMGKpUKkZGRRSZzmZmZyMzMlKbT09MraA+JiKikhAC2nkvTdhg6SwhtR0AVoUzJXJ6YmBjExcVBJpOhfv36aNq0aXnFBeBZz90HH3wAJycnXL9+HV9++SU6deqEmJgYKJVKJCUlQaFQwNzcXGM9GxsbJCUlAQCSkpKk5O951tbWUpnCzJ8/H7Nnzy7X/SEiopf3NCdX2yEQVSplSuaSk5Px4Ycf4vDhw6hatSqEEEhLS0PHjh0RGhqKatWqlUtw/fv3l/7fsGFDtGjRAk5OTtizZ0+B6/aeJ4TQuKlxYTc4zl8mv2nTpmHixInSdHp6OhwdHUu7C0REVI4EgCsPsrQdhs5ix9zrqUzJ3Lhx45Ceno4LFy7Azc0NAHDx4kX4+/tj/Pjx2Lp1a7kGmcfOzg5OTk64fPkyAMDW1hZZWVlISUnR6J1LTk6Gp6enVObOnTsF6rp79y5sbGyK3JZSqYRSqSznPSAiorLIOz2ozkjBpsEttBuMDlNnpADg6dbXTZmSubCwMOzfv19K5ACgfv36WLFiRakGQJTW/fv3cePGDdjZ2QEAmjdvDrlcjvDwcPTr1w8AkJiYiPPnz2PhwoUAAA8PD6SlpeHEiRNo1aoVAOD48eNIS0uTEj4iItIRQiAzpehLZIjeRGVK5nJzcyGXywvMl8vlyM0t+bUMjx49wpUrV6Tp69evIzY2FhYWFrCwsEBgYCD69u0LOzs7xMfHY/r06bCyssJ7770H4Nmo2qFDh2LSpEmwtLSEhYUFJk+eDHd3d2l0q5ubG7p3747hw4fjxx9/BPDs1iQ9evTgSFYiIh0hr2IOwe6kciOvYv7iQqQzypTMderUCRMmTMDWrVthb28PALh16xY+/fRTdO7cucT1nDx5Eh07dpSm865R8/f3x6pVq3Du3Dls3LgRqampsLOzQ8eOHbFt2zaYmppK6yxduhQGBgbo168fnjx5gs6dOyM4OFi6xxwAhISEYPz48VKvYc+ePbF8+fKy7DoREWnBiO93YntkHESuWtuh6DyZnj76eLq9uCDpDJkow586N27cQK9evXD+/Hk4OjpCJpMhISEB7u7u+PXXX+Hg4FARsWpVeno6VCoV0tLSYGZmpu1wiEhHKMwskf3wAeSmFshKv6/tcHTWhlMP8PWfd7UdxmtjRvtq8G9moe0wdJqDgwNu3bqF6tWrF3jyVHkoTd5Rpp45R0dHnDp1CuHh4fj7778hhED9+vWlU5tERETlycFMDkeVnBfulwOZ7Fl70uujVMncwYMHMXbsWERFRcHMzAze3t7w9vYG8OxpDA0aNMDq1avRrl27CgmWiIjeTPZmctSxVCKXydxL05M9a096fZQqmVu2bBmGDx9eaHefSqXCyJEjsWTJEiZzZZTyRI1sNb+pyoNcXwZzI/0XFyQinWBpbIA6FkrksmvupenJZLA0fqlnBlAlU6qjeebMGSxYsKDI5V27dsW333770kG9iVKeqLExNlXbYbxWBjapyoSO6DUh15dBoS8DUPTN3qnk5Ppsx9dJqZK5O3fuFHpLEqkyAwPcvcsLVMsiWy2QpRb8q7Oc6Mlk7OUkeo2YG+ljYJOq/FyXA565eP2UKpmrXr06zp07hzp16hS6/OzZs9INfal07j/OQUJatrbDeK3cf5wD6yo8lUD0umACQlS4Uv3Svf3225g5cyZ8fHxgaGiosezJkyeYNWsWevToUa4Bvimy1AJZ6lxe3FtO9GTP2pSIiOh1V6pk7osvvsD27dtRt25djB07Fq6urpDJZIiLi8OKFSugVqsxY8aMior1tXYvIwdX7mdy2H05kcmetSkREdHrrlTJnI2NDSIjIzFq1ChMmzZNerSKTCZDt27dsHLlymIfXk9Fm77pEG6nPtZ2GK8NmZ4+wszqo3OdBtoOhYiIqEKV6QkQAJCSkoIrV65ACAEXFxeYm7/ez3mr6CdAGNnWRvajlHKv902mMLXA48QrLy5IVIH4BAii15POPwECAMzNzdGyZcuyrk75qB+nQZ3BZK48Zcs49J6IiF5/HOpXSaiMlbj3EIBMD8qq1toOR6dlpt4BLz4kIqI3BZO5SkJf79m/xhbW8F13UrvB6LigjxtB/egB2DFHRERvAiZzlYTe/2ceBnoy1LFQaDka3cYcjoiI3iRM5ioZGQBDAz1th0FEREQ6gslcJaH3/91JxnI9fOSu0m4wOu4zds0REdEbhMlcJaMnAx9BRURERCXGrIGIipTyRM0HmxMRVXJM5oioUClP1NgYm6rtMHQe75JDRBWNyRwRFSpbLZClFshlNkJEVKkxmSOiQt1/nIMrDzKRy1zupbD5iKiiMZkjokLdTs/GlfuZPE1YTtiORFRRmMwRUaHCziTg8tmLELlqbYei25jFEVEFYzJHRIUKP3UJIvuptsN4bfDxckRUUZjMEVGhLq37DNmPUv5/iplIWakfpwIAlHJ+3RJRxeC3CxEVKicjFeqMlBcXpBKRG+hrOwQiek1pNZn7888/sWjRIsTExCAxMRE7duxA7969peVCCMyePRtr1qxBSkoKWrdujRUrVqBBgwZSmczMTEyePBlbt27FkydP0LlzZ6xcuRIODg5SmZSUFIwfPx67du0CAPTs2RM//PADqlat+qp2lUh3yWRQVrXRdhQ6r5q1rbZDIKLXlFaTuYyMDDRu3BiDBw9G3759CyxfuHAhlixZguDgYNStWxdz586Ft7c3Ll26BFNTUwBAQEAAdu/ejdDQUFhaWmLSpEno0aMHYmJioK//7C9hX19f3Lx5E2FhYQCAESNGwM/PD7t37351O0ukY/Ku8dI3MYdf0EntBqPj9GRAQBtLbYdBRK8prSZzPj4+8PHxKXSZEALLli3DjBkz0KdPHwDAhg0bYGNjgy1btmDkyJFIS0vDunXrsGnTJnTp0gUAsHnzZjg6OmL//v3o1q0b4uLiEBYWhqioKLRu3RoAsHbtWnh4eODSpUtwdXV9NTtLpKNkAOpYKLQdhk7Tk8lgacyrWoioYlTab5fr168jKSkJXbt2leYplUp4eXkhMjISI0eORExMDLKzszXK2Nvbo2HDhoiMjES3bt1w7NgxqFQqKZEDgDZt2kClUiEyMrLIZC4zMxOZmZnSdHp6egXsJVW05Ec52g7htWBooKftEHSeXJ+DSIioYlTaZC4pKQkAYGOjea2OjY0N/v33X6mMQqGAubl5gTJ56yclJcHa2rpA/dbW1lKZwsyfPx+zZ89+qX0g7RIC2HouTdth6Ky826PJZMBH7irtBqPj5PoymBtxAAQRVYxKm8zlkeW7OZMQosC8/PKXKaz8i+qZNm0aJk6cKE2np6fD0dGxpGFTJfE0J1fbIbwWrKtU+q8KIqI3VqX9hra1fTbyKykpCXZ2dtL85ORkqbfO1tYWWVlZSElJ0eidS05Ohqenp1Tmzp07Beq/e/dugV6/5ymVSiiVynLZF9IOAeDKgyxth6Gz+NwCIiLdUGmTOWdnZ9ja2iI8PBxNmzYFAGRlZSEiIgILFiwAADRv3hxyuRzh4eHo168fACAxMRHnz5/HwoULAQAeHh5IS0vDiRMn0KpVKwDA8ePHkZaWJiV89Hp5/ulJV+9nFl2QSoRPoyIiqty0msw9evQIV65ckaavX7+O2NhYWFhYoEaNGggICMC8efPg4uICFxcXzJs3D8bGxvD19QUAqFQqDB06FJMmTYKlpSUsLCwwefJkuLu7S6Nb3dzc0L17dwwfPhw//vgjgGe3JunRowdHsr7uhMDlMye0HYXuYhZHRKQTtJrMnTx5Eh07dpSm865R8/f3R3BwMKZMmYInT55g9OjR0k2D9+3bJ91jDgCWLl0KAwMD9OvXT7ppcHBwsHSPOQAICQnB+PHjpVGvPXv2xPLly1/RXtKrpnEppDpba3G8LvhMUSKiyk0mBP/8Lon09HSoVCqkpaXBzMys3Ot3cHDArVu3UL16ddy8ebPc63+TmFra4NGDZEAmg76x+YtXoEKpH6cAQqCKhTUe3i943SkR0Zuson+3S5N3VNpr5ojKSpH3DEwhoM54oN1gXgMKPlOUiKhSYzJHrx1bW1s8zREcjVkOZPjfyHIiIqqcmMzRa+fn8EisjE7RdhivjdEteaqaiKgyYzJHrx1bUzlqqOTI5eWgL01PJoOtqVzbYRARUTGYzNFrx9xIH8OamyNbzWTuZfExVERElR+TOXotMQEhIqI3hZ62AyAiIiKismMyR0RERKTDmMwRERER6TAmc0REREQ6jMkcERERkQ5jMkdERESkw5jMEREREekwJnNEREREOozJHBEREZEOYzJHREREpMOYzBERERHpMCZzRERERDqMyRwRERGRDmMyR0RERKTDmMwRERER6TAmc0REREQ6jMkcERERkQ5jMkdERESkw5jMEREREekwJnNEREREOqxSJ3OBgYGQyWQaL1tbW2m5EAKBgYGwt7eHkZEROnTogAsXLmjUkZmZiXHjxsHKygomJibo2bMnbt68+ap3hYiIiKhCVOpkDgAaNGiAxMRE6XXu3Dlp2cKFC7FkyRIsX74c0dHRsLW1hbe3Nx4+fCiVCQgIwI4dOxAaGoqjR4/i0aNH6NGjB9RqtTZ2h4iIiKhcGWg7gBcxMDDQ6I3LI4TAsmXLMGPGDPTp0wcAsGHDBtjY2GDLli0YOXIk0tLSsG7dOmzatAldunQBAGzevBmOjo7Yv38/unXr9kr3hYiIiKi8VfqeucuXL8Pe3h7Ozs748MMPce3aNQDA9evXkZSUhK5du0pllUolvLy8EBkZCQCIiYlBdna2Rhl7e3s0bNhQKlOUzMxMpKena7yIiIiIKptKncy1bt0aGzduxN69e7F27VokJSXB09MT9+/fR1JSEgDAxsZGYx0bGxtpWVJSEhQKBczNzYssU5T58+dDpVJJL0dHx3LcMyIiIqLyUamTOR8fH/Tt2xfu7u7o0qUL9uzZA+DZ6dQ8MplMYx0hRIF5+ZWkzLRp05CWlia9bty4Uca9ICIiIqo4lTqZy8/ExATu7u64fPmydB1d/h625ORkqbfO1tYWWVlZSElJKbJMUZRKJczMzDReRERERJWNTiVzmZmZiIuLg52dHZydnWFra4vw8HBpeVZWFiIiIuDp6QkAaN68OeRyuUaZxMREnD9/XipDREREpMsq9WjWyZMn491330WNGjWQnJyMuXPnIj09Hf7+/pDJZAgICMC8efPg4uICFxcXzJs3D8bGxvD19QUAqFQqDB06FJMmTYKlpSUsLCwwefJk6bQtERERka6r1MnczZs38dFHH+HevXuoVq0a2rRpg6ioKDg5OQEApkyZgidPnmD06NFISUlB69atsW/fPpiamkp1LF26FAYGBujXrx+ePHmCzp07Izg4GPr6+traLSIiIqJyIxNCCG0HoQvS09OhUqmQlpZWIdfPOTg44NatW6hevTqfUEFERFTJVfTvdmnyDp26Zo6IiIiINDGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhb1Qyt3LlSjg7O8PQ0BDNmzfHkSNHtB0SERER0Ut5Y5K5bdu2ISAgADNmzMDp06fRrl07+Pj4ICEhQduhEREREZXZG5PMLVmyBEOHDsWwYcPg5uaGZcuWwdHREatWrdJ2aERERERlZqDtAF6FrKwsxMTE4PPPP9eY37VrV0RGRha6TmZmJjIzM6XptLQ0AEB6enqFxJibmyv9W1HbICIiovJR0b/beXUKIV5Y9o1I5u7duwe1Wg0bGxuN+TY2NkhKSip0nfnz52P27NkF5js6OlZIjHkSExOhUqkqdBtERERUPir6d/vhw4cvrP+NSObyyGQyjWkhRIF5eaZNm4aJEydK07m5uXjw4AEsLS2LXOdlpKenw9HRETdu3ICZmVm51/8mYVuWD7Zj+WA7lg+2Y/lgO5afim5LIQQePnwIe3v7F5Z9I5I5Kysr6OvrF+iFS05OLtBbl0epVEKpVGrMq1q1akWFKDEzM+MHrJywLcsH27F8sB3LB9uxfLAdy09FtmVJe/zeiAEQCoUCzZs3R3h4uMb88PBweHp6aikqIiIiopf3RvTMAcDEiRPh5+eHFi1awMPDA2vWrEFCQgI++eQTbYdGREREVGZvTDLXv39/3L9/H3PmzEFiYiIaNmyI33//HU5OTtoODcCz07qzZs0qcGqXSo9tWT7YjuWD7Vg+2I7lg+1YfipTW8pESca8EhEREVGl9EZcM0dERET0umIyR0RERKTDmMwRERER6TAmc0REREQ6jMlcJbFy5Uo4OzvD0NAQzZs3x5EjR7Qdks75888/8e6778Le3h4ymQw7d+7Udkg6Z/78+WjZsiVMTU1hbW2N3r1749KlS9oOSyetWrUKjRo1km4o6uHhgT/++EPbYem0+fPnQyaTISAgQNuh6JzAwEDIZDKNl62trbbD0km3bt3Cxx9/DEtLSxgbG6NJkyaIiYnRakxM5iqBbdu2ISAgADNmzMDp06fRrl07+Pj4ICEhQduh6ZSMjAw0btwYy5cv13YoOisiIgJjxoxBVFQUwsPDkZOTg65duyIjI0PboekcBwcHfPPNNzh58iROnjyJTp06oVevXrhw4YK2Q9NJ0dHRWLNmDRo1aqTtUHRWgwYNkJiYKL3OnTun7ZB0TkpKCtq2bQu5XI4//vgDFy9exOLFi1/JE6KKw1uTVAKtW7dGs2bNsGrVKmmem5sbevfujfnz52sxMt0lk8mwY8cO9O7dW9uh6LS7d+/C2toaERERaN++vbbD0XkWFhZYtGgRhg4dqu1QdMqjR4/QrFkzrFy5EnPnzkWTJk2wbNkybYelUwIDA7Fz507ExsZqOxSd9vnnn+Ovv/6qdGfP2DOnZVlZWYiJiUHXrl015nft2hWRkZFaiorombS0NADPkhAqO7VajdDQUGRkZMDDw0Pb4eicMWPG4J133kGXLl20HYpOu3z5Muzt7eHs7IwPP/wQ165d03ZIOmfXrl1o0aIFPvjgA1hbW6Np06ZYu3attsNiMqdt9+7dg1qtho2NjcZ8GxsbJCUlaSkqIkAIgYkTJ+Ktt95Cw4YNtR2OTjp37hyqVKkCpVKJTz75BDt27ED9+vW1HZZOCQ0NxalTp3iW4iW1bt0aGzduxN69e7F27VokJSXB09MT9+/f13ZoOuXatWtYtWoVXFxcsHfvXnzyyScYP348Nm7cqNW43pjHeVV2MplMY1oIUWAe0as0duxYnD17FkePHtV2KDrL1dUVsbGxSE1NxS+//AJ/f39EREQwoSuhGzduYMKECdi3bx8MDQ21HY5O8/Hxkf7v7u4ODw8P1K5dGxs2bMDEiRO1GJluyc3NRYsWLTBv3jwAQNOmTXHhwgWsWrUKAwcO1Fpc7JnTMisrK+jr6xfohUtOTi7QW0f0qowbNw67du3CoUOH4ODgoO1wdJZCoUCdOnXQokULzJ8/H40bN8Z3332n7bB0RkxMDJKTk9G8eXMYGBjAwMAAERER+P7772FgYAC1Wq3tEHWWiYkJ3N3dcfnyZW2HolPs7OwK/DHm5uam9QGLTOa0TKFQoHnz5ggPD9eYHx4eDk9PTy1FRW8qIQTGjh2L7du34+DBg3B2dtZ2SK8VIQQyMzO1HYbO6Ny5M86dO4fY2Fjp1aJFCwwYMACxsbHQ19fXdog6KzMzE3FxcbCzs9N2KDqlbdu2BW7X9M8//8DJyUlLET3D06yVwMSJE+Hn54cWLVrAw8MDa9asQUJCAj755BNth6ZTHj16hCtXrkjT169fR2xsLCwsLFCjRg0tRqY7xowZgy1btuDXX3+Fqamp1GOsUqlgZGSk5eh0y/Tp0+Hj4wNHR0c8fPgQoaGhOHz4MMLCwrQdms4wNTUtcL2miYkJLC0teR1nKU2ePBnvvvsuatSogeTkZMydOxfp6enw9/fXdmg65dNPP4WnpyfmzZuHfv364cSJE1izZg3WrFmj3cAEVQorVqwQTk5OQqFQiGbNmomIiAhth6RzDh06JAAUePn7+2s7NJ1RWPsBEEFBQdoOTecMGTJE+kxXq1ZNdO7cWezbt0/bYek8Ly8vMWHCBG2HoXP69+8v7OzshFwuF/b29qJPnz7iwoUL2g5LJ+3evVs0bNhQKJVKUa9ePbFmzRpthyR4nzkiIiIiHcZr5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiqlCDBg2CTCaDTCaDgYEBatSogVGjRiElJUXboem8QYMGoXfv3toOg4i0jMkcEVW47t27IzExEfHx8fjPf/6D3bt3Y/To0doOi8qREAI5OTnaDoPojcRkjogqnFKphK2tLRwcHNC1a1f0798f+/bt0ygTFBQENzc3GBoaol69eli5cqW0LCsrC2PHjoWdnR0MDQ1Rs2ZNzJ8/X1ouk8mwatUq+Pj4wMjICM7Ozvjpp5806j937hw6deoEIyMjWFpaYsSIEXj06JG0PK+X69tvv4WdnR0sLS0xZswYZGdnS2VWrlwJFxcXGBoawsbGBu+//760TAiBhQsXolatWjAyMkLjxo3x888/F9sumZmZmDJlChwdHaFUKuHi4oJ169YBANRqNYYOHQpnZ2cYGRnB1dUV3333nbRuYGAgNmzYgF9//VXq+Tx8+DAA4NatW+jfvz/Mzc1haWmJXr16IT4+Xlo3JycH48ePR9WqVWFpaYmpU6fC399fo5cvMzMT48ePh7W1NQwNDfHWW28hOjpaWn748GHIZDLs3bsXLVq0gFKpxKZNm6Cnp4eTJ09q7OcPP/wAJycn8FHgRBVEEBFVIH9/f9GrVy9p+urVq6J+/frCxsZGmrdmzRphZ2cnfvnlF3Ht2jXxyy+/CAsLCxEcHCyEEGLRokXC0dFR/PnnnyI+Pl4cOXJEbNmyRVofgLC0tBRr164Vly5dEl988YXQ19cXFy9eFEIIkZGRIezt7UWfPn3EuXPnxIEDB4Szs7Pw9/fXiNPMzEx88sknIi4uTuzevVsYGxuLNWvWCCGEiI6OFvr6+mLLli0iPj5enDp1Snz33XfS+tOnTxf16tUTYWFh4urVqyIoKEgolUpx+PDhItumX79+wtHRUWzfvl1cvXpV7N+/X4SGhgohhMjKyhIzZ84UJ06cENeuXRObN28WxsbGYtu2bUIIIR4+fCj69esnunfvLhITE0ViYqLIzMwUGRkZwsXFRQwZMkScPXtWXLx4Ufj6+gpXV1eRmZkphBBi7ty5wsLCQmzfvl3ExcWJTz75RJiZmWkcp/Hjxwt7e3vx+++/iwsXLgh/f39hbm4u7t+/L4QQ4tChQwKAaNSokdi3b5+4cuWKuHfvnvD29hajR4/W2M+mTZuKmTNnFv9GIaIyYzJHRBXK399f6OvrCxMTE2FoaCgACABiyZIlUhlHR0eN5EwIIb766ivh4eEhhBBi3LhxolOnTiI3N7fQbQAQn3zyica81q1bi1GjRgkhniWL5ubm4tGjR9LyPXv2CD09PZGUlCTF6eTkJHJycqQyH3zwgejfv78QQohffvlFmJmZifT09ALbf/TokTA0NBSRkZEa84cOHSo++uijQmO+dOmSACDCw8MLXV6Y0aNHi759+0rT+RNlIYRYt26dcHV11WirzMxMYWRkJPbu3SuEEMLGxkYsWrRIWp6TkyNq1Kgh1fXo0SMhl8tFSEiIVCYrK0vY29uLhQsXCiH+l8zt3LlTY/vbtm0T5ubm4unTp0IIIWJjY4VMJhPXr18v8X4SUenwNCsRVbiOHTsiNjYWx48fx7hx49CtWzeMGzcOAHD37l3cuHEDQ4cORZUqVaTX3LlzcfXqVQDPToHGxsbC1dUV48ePL3CKFgA8PDwKTMfFxQEA4uLi0LhxY5iYmEjL27Zti9zcXFy6dEma16BBA+jr60vTdnZ2SE5OBgB4e3vDyckJtWrVgp+fH0JCQvD48WMAwMWLF/H06VN4e3tr7MPGjRulfcgvNjYW+vr68PLyKrLdVq9ejRYtWqBatWqoUqUK1q5di4SEhKIbGkBMTAyuXLkCU1NTKQ4LCws8ffoUV69eRVpaGu7cuYNWrVpJ6+jr66N58+bS9NWrV5GdnY22bdtK8+RyOVq1aiW1aZ4WLVpoTPfu3RsGBgbYsWMHAGD9+vXo2LEjatasWWzcRFR2BtoOgIhefyYmJqhTpw4A4Pvvv0fHjh0xe/ZsfPXVV8jNzQUArF27Fq1bt9ZYLy+xatasGa5fv44//vgD+/fvR79+/dClS5cXXpMmk8kAPLueLe//RZUBniUs+ZflxWdqaopTp07h8OHD2LdvH2bOnInAwEBER0dLZfbs2YPq1atr1KFUKgvdrpGRUbGx//e//8Wnn36KxYsXw8PDA6ampli0aBGOHz9e7Hq5ublo3rw5QkJCCiyrVq2axr49Tzx3PVve/wsrk3/e8wkyACgUCvj5+SEoKAh9+vTBli1bsGzZsmJjJqKXw545InrlZs2ahW+//Ra3b9+GjY0NqlevjmvXrqFOnToaL2dnZ2kdMzMz9O/fH2vXrsW2bdvwyy+/4MGDB9LyqKgojW1ERUWhXr16AID69esjNjYWGRkZ0vK//voLenp6qFu3bonjNjAwQJcuXbBw4UKcPXsW8fHxOHjwIOrXrw+lUomEhIQC++Do6FhoXe7u7sjNzUVEREShy48cOQJPT0+MHj0aTZs2RZ06dQr08ikUCqjVao15zZo1w+XLl2FtbV0gFpVKBZVKBRsbG5w4cUJaR61W4/Tp09J0nTp1oFAocPToUWlednY2Tp48CTc3txe207Bhw7B//36sXLkS2dnZ6NOnzwvXIaKyY88cEb1yHTp0QIMGDTBv3jwsX74cgYGBGD9+PMzMzODj44PMzEycPHkSKSkpmDhxIpYuXQo7Ozs0adIEenp6+Omnn2Bra4uqVatKdf70009o0aIF3nrrLYSEhODEiRPSyNABAwZg1qxZ8Pf3R2BgIO7evYtx48bBz88PNjY2JYr5t99+w7Vr19C+fXuYm5vj999/R25uLlxdXWFqaorJkyfj008/RW5uLt566y2kp6cjMjISVapUgb+/f4H6atasCX9/fwwZMgTff/89GjdujH///RfJycno168f6tSpg40bN2Lv3r1wdnbGpk2bEB0drZHg1qxZE3v37sWlS5dgaWkJlUqFAQMGYNGiRejVqxfmzJkDBwcHJCQkYPv27fjss8/g4OCAcePGYf78+ahTpw7q1auHH374ASkpKVKvm4mJCUaNGoXPPvsMFhYWqFGjBhYuXIjHjx9j6NChL2wrNzc3tGnTBlOnTsWQIUNe2AtJRC9Ju5fsEdHrrrCL9IUQIiQkRCgUCpGQkCBNN2nSRCgUCmFubi7at28vtm/fLoR4NoChSZMmwsTERJiZmYnOnTuLU6dOSXUBECtWrBDe3t5CqVQKJycnsXXrVo3tnT17VnTs2FEYGhoKCwsLMXz4cPHw4cNi45wwYYLw8vISQghx5MgR4eXlJczNzYWRkZFo1KiRNLJUCCFyc3PFd999J1xdXYVcLhfVqlUT3bp1ExEREUW2zZMnT8Snn34q7OzshEKhEHXq1BHr168XQgjx9OlTMWjQIKFSqUTVqlXFqFGjxOeffy4aN24srZ+cnCy8vb1FlSpVBABx6NAhIYQQiYmJYuDAgcLKykoolUpRq1YtMXz4cJGWliaEECI7O1uMHTtWmJmZCXNzczF16lTxwQcfiA8//FAjtnHjxkl1tG3bVpw4cUJanjcAIiUlpdB9W7dunQCgsQ4RVQyZELzxDxHpNplMhh07dvBpCGWUm5sLNzc39OvXD1999VW51Pn1118jNDQU586dK5f6iKhoPM1KRPSG+ffff7Fv3z54eXkhMzMTy5cvx/Xr1+Hr6/vSdT969AhxcXH44Ycfyi0xJKLicQAEEdEbRk9PD8HBwWjZsiXatm2Lc+fOYf/+/SUa3PAiY8eOxVtvvQUvLy8MGTKkHKIlohfhaVYiIiIiHcaeOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh02P8BcbzpfKDJiyEAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 700x300 with 1 Axes>"
       ]
@@ -1026,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1093,7 +969,7 @@
        "6              1   59"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1105,6 +981,39 @@
     "attrition[[\"YearsAtCompany\", \"Age\"]].head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, the empirical probabilities of the response categories are computed. Employees are most likely to stay at the company between 1 and 10 years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pr_k = attrition.YearsAtCompany.value_counts().sort_index().values / attrition.shape[0]\n",
+    "\n",
+    "plt.figure(figsize=(7, 3))\n",
+    "plt.bar(np.arange(0, 36), pr_k)\n",
+    "plt.xlabel(\"Response category\")\n",
+    "plt.ylabel(\"Probability\")\n",
+    "plt.title(\"Empirical probability of each response category\");"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},