module-1/central_tendency.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1ef7202",
   "metadata": {},
   "source": [
    "# Measures of Central Tendency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e249f1",
   "metadata": {},
   "source": [
    "**Ukuran Pemusatan** adalah statistik deskriptif yang digunakan untuk menggambarkan pusat distribusi data. Ukuran pemusatan memberikan titik tengah atau \"pusat\" dari suatu distribusi data. Ada beberapa ukuran pemusatan yang umum digunakan, yaitu:\n",
    "\n",
    "1. Mean (Rata-rata)\n",
    "2. Median\n",
    "3. Mode"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd310cc",
   "metadata": {},
   "source": [
    "## Case Study\n",
    "\n",
    "Didalam sebuah ruangan, terdapat 12 orang yang sedang berkumpul dengan umur sebagai berikut:\n",
    "\n",
    "25 | 12 | 20 | 20 | 15 | 21 | 22 | 19 | 19 | 24 | 17 | 65\n",
    "\n",
    "Coba analisis pusat distribusi data, untuk melihat apakah ada outlier pada data tersebut."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8fd3c27",
   "metadata": {},
   "source": [
    "## a. Mean (Rata-Rata)\n",
    "\n",
    "**Mean atau rata-rata** adalah ukuran pemusatan yang paling umum digunakan, yaitu jumlah dari semua nilai dalam dataset dibagi dengan jumlah total nilai."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ccfcad4",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data: [25, 12, 20, 20, 15, 21, 22, 19, 19, 24, 17, 65]\n",
      "Jumlah semua nilai: 279\n",
      "Jumlah total data: 12\n",
      "Mean: 23.25\n"
     ]
    }
   ],
   "source": [
    "# Inisialisasi dataset\n",
    "data = [25, 12, 20, 20, 15, 21, 22, 19, 19, 24, 17, 65]\n",
    "print(f\"Data: {data}\")\n",
    "\n",
    "# Jumlah nilai dalam dataset \n",
    "jumlah_nilai = sum(data)\n",
    "print(f\"Jumlah semua nilai: {jumlah_nilai}\")\n",
    "\n",
    "# Jumlah total data dalam dataset \n",
    "jumlah_total_data = len(data)\n",
    "print(f\"Jumlah total data: {jumlah_total_data}\")\n",
    "\n",
    "# Jumlah semua nilai dalam dataset \n",
    "mean_value = jumlah_nilai / jumlah_total_data\n",
    "print(f\"Mean: {mean_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a5acbcd",
   "metadata": {},
   "source": [
    "Jika divisualisasikan dalam bentuk grafik, maka Mean akan terlihat sebagai berikut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad39d680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a histogram\n",
    "plt.hist(data, bins=30, edgecolor='black', alpha=0.5, color='blue')\n",
    "\n",
    "# Add a vertical line at the mean\n",
    "plt.axvline(x=mean_value, color='red', linestyle='dashed', linewidth=1, label=f'Mean = {mean_value:.2f}')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Mean')\n",
    "\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c139a235",
   "metadata": {},
   "source": [
    "Namun, terkadang Mean kurang mewakili pusat data yang sebenarnya. Terlebih jika ada data **\"Outlier\"**, yaitu nilai yang secara signifikan berbeda dari sebagian besar data dalam kumpulan sampel. Outlier dapat menjadi nilai yang sangat tinggi atau sangat rendah dibandingkan dengan nilai lain dalam kumpulan data.\n",
    "\n",
    "Umur 60 tahun adalah outlier dalam sampel studi kasus. Coba perhatikan pada histogram di atas, terlihat jelas bahwa sebagian besar umur orang-orang di dalam ruangan terkonsentrasi sekitar 10 hingga 30 tahun. Namun, ada satu orang sepuh yang memiliki umur 60 tahun. \n",
    "\n",
    "Kehadiran outlier dapat mempengaruhi analisis statistik dan mengubah interpretasi dari distribusi data. Dalam beberapa kasus, outlier mungkin mengindikasikan kesalahan pengukuran atau kejadian yang tidak biasa. Oleh karena itu, penting untuk mengidentifikasi dan memahami outlier saat menganalisis data.\n",
    "\n",
    "Salah satu masalah adanya outlier adalah dapat **mengacaukan Mean**. Garis putus-putus merah pada histogram mewakili umur rata-rata di ruangan tersebut, yaitu 23,25, lebih tinggi dari umur 9 dari 12 orang dalam ruangan. Outlier mendorong Mean terlalu ke kanan.\n",
    "\n",
    "Dalam kasus seperti ini, median mungkin memberi kita analisis pusat distribusi data yang lebih baik."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6bd0e6",
   "metadata": {},
   "source": [
    "## b. Median\n",
    "**Median** adalah nilai tengah dalam dataset ketika data diurutkan dari terkecil hingga terbesar. Jika ada jumlah data genap, median adalah rata-rata dari dua nilai tengah."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9cc754b6",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data: [25, 12, 20, 20, 15, 21, 22, 19, 19, 24, 17, 65]\n",
      "Data berurut: [12, 15, 17, 19, 19, 20, 20, 21, 22, 24, 25, 65]\n",
      "Jumlah total data: 12\n",
      "Jumlah total data Genap\n",
      "Nilai tengah: 20 20\n",
      "Median: 20.0\n"
     ]
    }
   ],
   "source": [
    "# Inisialisasi dataset\n",
    "data = [25, 12, 20, 20, 15, 21, 22, 19, 19, 24, 17, 65]\n",
    "print(f\"Data: {data}\")\n",
    "\n",
    "# Mengurutkan data dari terkecil hingga terbesar\n",
    "sorted_data = sorted(data)\n",
    "print(f\"Data berurut: {sorted_data}\")\n",
    "\n",
    "# Jumlah total data dalam dataset \n",
    "n = len(sorted_data)\n",
    "print(f\"Jumlah total data: {n}\")\n",
    "\n",
    "index_tengah = n//2 # double slash // is used for floor division\n",
    "\n",
    "# Jika jumlah data genap, median adalah rata-rata dari dua nilai tengah \n",
    "if n % 2 == 0:\n",
    "    print(f\"Jumlah total data Genap\")\n",
    "    median1 = sorted_data[index_tengah]\n",
    "    median2 = sorted_data[index_tengah - 1]\n",
    "    print(f\"Nilai tengah: {median1} {median2}\")\n",
    "    median_value = (median1 + median2) / 2\n",
    "    \n",
    "# Jika jumlah data ganjil, median adalah nilai tengah\n",
    "else:\n",
    "    print(f\"Jumlah total data Ganjil\")\n",
    "    median_value = sorted_data[index_tengah]\n",
    "    print(f\"Nilai tengah: {median_value}\")\n",
    "\n",
    "print(f\"Median: {median_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5ee71e",
   "metadata": {},
   "source": [
    "Jika divisualisasikan dalam bentuk grafik, maka Median akan terlihat sebagai berikut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6897cf57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a histogram\n",
    "plt.hist(data, bins=30, edgecolor='black', alpha=0.5, color='blue')\n",
    "\n",
    "# Add a vertical line at the mean\n",
    "plt.axvline(x=median_value, color='red', linestyle='dashed', linewidth=1, label=f'Median = {median_value:.2f}')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Median')\n",
    "\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f880b4e",
   "metadata": {},
   "source": [
    "Perhatikan bahwa, dalam studi kasus ini, median menggambarkan umur rata-rata lebih akurat dibandingkan Mean."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5724f27b",
   "metadata": {},
   "source": [
    "## c. Mode (Modus)\n",
    "**Modus** adalah nilai yang paling sering muncul dalam dataset. Perlu diingat bahwa sebuah dataset dapat memiliki banyak modus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18b2305e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data: [25, 12, 20, 20, 15, 21, 22, 19, 19, 24, 17, 65]\n",
      "Max Counter: 2\n",
      "25 muncul 1 kali\n",
      "12 muncul 1 kali\n",
      "20 muncul 2 kali\n",
      "15 muncul 1 kali\n",
      "21 muncul 1 kali\n",
      "22 muncul 1 kali\n",
      "19 muncul 2 kali\n",
      "24 muncul 1 kali\n",
      "17 muncul 1 kali\n",
      "65 muncul 1 kali\n",
      "Mode: [20, 19]\n"
     ]
    }
   ],
   "source": [
    "# Import library Counter untuk menghitung modus\n",
    "from collections import Counter\n",
    "\n",
    "# Inisialisasi dataset\n",
    "data = [25, 12, 20, 20, 15, 21, 22, 19, 19, 24, 17, 65]\n",
    "print(f\"Data: {data}\")\n",
    "\n",
    "counter = Counter(data)\n",
    "mode_data = dict(counter) # ubah ke tipe data dictionary\n",
    "max_counter = max(list(counter.values())) # mencari data yang memiliki counter paling banyak\n",
    "print(f\"Max Counter: {max_counter}\")\n",
    "\n",
    "mode_value = []\n",
    "for k, v in mode_data.items():\n",
    "    print(f\"{k} muncul {v} kali\")\n",
    "    if v == max(list(counter.values())):\n",
    "        mode_value.append(k)\n",
    "    \n",
    "print(f\"Mode: {mode_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3181ade6",
   "metadata": {},
   "source": [
    "Jika divisualisasikan dalam bentuk grafik, maka Modus akan terlihat sebagai berikut:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd2a492b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a histogram\n",
    "plt.hist(data, bins=30, edgecolor='black', alpha=0.5, color='green')\n",
    "\n",
    "# Add a vertical line at the mean\n",
    "plt.axvline(x=mode_value[0], color='red', linestyle='dashed', linewidth=1, label=f'Modus 1 = {mode_value[0]:.2f}')\n",
    "plt.axvline(x=mode_value[1], color='blue', linestyle='dashed', linewidth=1, label=f'Modus 2 = {mode_value[1]:.2f}')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Modes')\n",
    "\n",
    "# Add legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "171f23ef",
   "metadata": {},
   "source": [
    "Melihat dari histogram di atas, sekilas kita tidak bisa melihat perbedaan antara Modus dengan Median karena menghasilkan pusat data yang mirip. \n",
    "\n",
    "Jadi kapan kita harus menggunakan modus untuk menggambarkan pusat distribusi data?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff0f0a5",
   "metadata": {},
   "source": [
    "**Perhatikan contoh studi kasus dibawah:**\n",
    "\n",
    "Didalam sebuah ruangan, terdapat 12 orang yang sedang berkumpul dengan umur sebagai berikut:\n",
    "\n",
    "25 | 12 | 20 | 20 | 15 | 21 | 70 | 61 | 62 | 65 | 63 | 65\n",
    "\n",
    "**Jika kita menggunakan Mean dan Median:**\n",
    "\n",
    "![img1](img/img1.png)\n",
    "\n",
    "Perhatikan bahwa kita mempunyai sekelompok nilai di sekitar 10 dan 30 dan kelompok lain di sekitar 60 dan 70. Namun, mean dan mediannya adalah 41 dan 43, jauh dari nilai tersebut. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59f20918",
   "metadata": {},
   "source": [
    "**Jika kita menggunakan Modus:**\n",
    "\n",
    "![img2](img/img2.png)\n",
    "\n",
    "Fitur utama Modus adalah **bisa terdiri lebih dari satu nilai**. Satu dataset dapat memiliki lebih dari satu modus, sehingga bagus untuk menggambarkan pusat distribusi data yang berkelompok seperti studi kasus di atas.\n",
    "\n",
    "Pada histogram di atas, kita memiliki dua modus, yaitu 20 dan 65, karena ini adalah nilai yang paling sering muncul dalam kumpulan data. Kita dapat menyebut bahwa data kita adalah **bimodal** (istilah yang digunakan dalam statistik dan analisis data untuk menggambarkan distribusi data yang memiliki dua puncak atau dua nilai modus)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b176d18b",
   "metadata": {},
   "source": [
    "## Practice 1.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f827c833",
   "metadata": {},
   "source": [
    "1. Diketahui tinggi badan siswa kelas A adalah 100,5 ; 99,7 ; 101 ; 101,3 ; 99,9 ; 98,9 ; 165 ;  102,2 ; 98,7 ; 102,9 ; dan 100. Menggunakan Mean, Median, dan Modus, coba gambarkan pusat distribusi data berikut. Manakah yang paling bagus?\n",
    "\n",
    "2. Berikut adalah nilai UAS pada mata pelajaran matematika di kelas A: 95, 35, 50, 65, 70, 85, 25, 100, 80, 90, 45, 40, 35, 20, 75, 50, 40, 60, 80, 75. Menggunakan Mean, Median, dan Modus, coba gambarkan pusat distribusi data berikut. Manakah yang paling bagus?\n",
    "\n",
    "Catatan: \n",
    "* Kalian dapat memodifikasi/mengeksplore code dengan **membuat fungsi** (misal fungsi mean, modus, dan median)\n",
    "* Ada library dalam Python yang berisi fungsi-fungsi statistik seperti median, modus, dan mean. Nama librarynya **\"statistics\"**. Boleh dicoba dan dibuktikan apakah hasilnya sama dengan menghitung manual, tapi tidak boleh digunakan standalone (tanpa mencoba yang manual)\n",
    "* Cara pengumpulan practice bisa dilihat [disini](../collecting.md)"
   ]
  }
 ],
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