Material de Estudo/5-estatistica-avancada.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(\"ggplot2\")\n",
    "library(\"dplyr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult <- read.csv(\"dados/adult.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testes de Hipótese"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muitas vezes, quando estamos fazendo alguma análise, queremos provar que **duas médias são diferentes**. <br>\n",
    "Por exemplo, temos dois grupos de pacientes, um que tomou analgésico e outro que tomou um placebo, e queremos saber se existe diferença na nota que eles atribuem para sua dor antes e depois de tomar o remédio/placebo. <br>\n",
    "<br>\n",
    "Esse é o típico cenário em que devemos aplicar um **teste de hipótese**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Amostra vs População"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![censovssample](img/census-vs-sample.jpg)\n",
    "Os testes de hipótese precisam ser feitos porque, muitas vezes, nós não temos a totalidade dos dados e sim uma **amostra**.\n",
    "<br> <br>\n",
    "Existem teoremas matemáticos que garantem que as médias tiradas de amostras aleatorias de uma população convergem para a própria média da população.\n",
    "Mas pode ser que, por acaso, vc tenha pegado uma amostra extrema que não representa essa população - **os testes de hipótese são uma forma de verificar qual é essa probabilidade e se ela é considerável**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hipóteses e p-valor\n",
    "\n",
    "Existem teoremas matemáticos que garantem que as médias tiradas de amostras aleatorias de uma população convergem para a própria média da população.\n",
    "Mas pode ser que, por acaso, vc tenha pegado uma amostra extrema que não representa essa população - **os testes de hipótese são uma forma de verificar qual é essa probabilidade e se ela é considerável**.\n",
    "Os **testes de hipóteses** têm uma estrutura comum. Eles assumem uma *hipótese nula* e uma *hipótese alternativa*.<br>\n",
    "Em geral: <br>\n",
    "$H_0 =$ (hipótese nula) as médias são iguais <br>\n",
    "$H_A =$ (hipótese alternativa) as médias são diferentes (você pode mudar isso para \"maior\" ou \"menor\" também\n",
    "<br>\n",
    "O que o teste de hipótese nos retorna é o **p-valor**, ele nos dá **a probabilidade de encontrar uma amostra igual ou mais extrema do que a que obtemos dada a hipótese nula** <br>\n",
    "Em geral, quando o $p-valor \\leq 0.05$ aceitamos $H_A$\n",
    "\n",
    "\\begin{equation}\n",
    "p-valor = p(estatistica-teste \\geq estatística-teste_{encontrada} \\mid H_0)\n",
    "\\end{equation}\n",
    "<br>\n",
    "\n",
    "![erros](img/pvalue.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testes paramétricos VS não-paramétricos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![distribuicaonormal](img/normal.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os testes de hipótese podem ser divididos entre paramétricos e não paramétricos.\n",
    "<br>\n",
    "Os testes **paramétricos** assumem a **distribuição normal dos dados**, enquanto os **não paramétricos** não possuem tal premissa.<br>\n",
    "A distribuição normal é a famosa curva de sino, em que a maioria dos valores estão próximos de uma média."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kolgomorov-Smirnov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para saber se devemos ou não aplicar um teste paramétrico ou não paramétrico, precisamos saber se nossos dados seguem ou não uma distribuição normal. Para isso usamos o teste de *Kolgomorov-Smirnov* comparando a nossa amostra com uma distribuição normal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesse teste, as hipóteses são as seguintes:<br>\n",
    "$H_0 = $ a distribuição é normal <br>\n",
    "$H_A = $ a distribuição não é normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos fazer esse teste largura da sépala das flores da espécie iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in ks.test(iris$Sepal.Width, \"pnorm\", mean(iris$Sepal.Width), sd(iris$Sepal.Width)):\n",
      "“ties should not be present for the Kolmogorov-Smirnov test”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-sample Kolmogorov-Smirnov test\n",
       "\n",
       "data:  iris$Sepal.Width\n",
       "D = 0.10566, p-value = 0.07023\n",
       "alternative hypothesis: two-sided\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks.test(iris$Sepal.Width,\"pnorm\", mean(iris$Sepal.Width), sd(iris$Sepal.Width))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nosso $p_{valor}$ deu 7%, então podemos considerar a distribuição normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos ver agora se isso se mantém para as sub-espécies das flores iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'setosa'</li><li>'versicolor'</li><li>'virginica'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'setosa'\n",
       "\\item 'versicolor'\n",
       "\\item 'virginica'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'setosa'\n",
       "2. 'versicolor'\n",
       "3. 'virginica'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"setosa\"     \"versicolor\" \"virginica\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels(iris$Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_setosa <- iris %>% filter(Species == \"setosa\")\n",
    "iris_versicolor <- iris %>% filter(Species == \"versicolor\")\n",
    "iris_virginica <- iris %>% filter(Species == \"virginica\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in ks.test(iris_setosa$Sepal.Width, \"pnorm\", mean(iris_setosa$Sepal.Width), :\n",
      "“ties should not be present for the Kolmogorov-Smirnov test”\n",
      "Warning message in ks.test(iris_versicolor$Sepal.Width, \"pnorm\", mean(iris_versicolor$Sepal.Width), :\n",
      "“ties should not be present for the Kolmogorov-Smirnov test”\n",
      "Warning message in ks.test(iris_virginica$Sepal.Width, \"pnorm\", mean(iris_virginica$Sepal.Width), :\n",
      "“ties should not be present for the Kolmogorov-Smirnov test”\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"P-valor iris setosa =  0.643712515886468  | P-valor iris versicolor =  0.460425126089097 | P-valor iris virginica =  0.386975907628897\"\n"
     ]
    }
   ],
   "source": [
    "ks_setosa <- ks.test(iris_setosa$Sepal.Width,\"pnorm\", mean(iris_setosa$Sepal.Width), sd(iris_setosa$Sepal.Width))\n",
    "ks_versicolor <- ks.test(iris_versicolor$Sepal.Width,\"pnorm\", mean(iris_versicolor$Sepal.Width), sd(iris_versicolor$Sepal.Width))\n",
    "ks_virginica <- ks.test(iris_virginica$Sepal.Width,\"pnorm\", mean(iris_virginica$Sepal.Width), sd(iris_virginica$Sepal.Width))\n",
    "print(paste(\"P-valor iris setosa = \",ks_setosa$p.value,\" | P-valor iris versicolor = \",ks_versicolor$p.value,\"| P-valor iris virginica = \", ks_virginica$p.value ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os valores p foram ainda mais altos, podemos assumir premissas paramétricas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paramétricos - Teste t, teste z e ANOVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existem dois testes mais conhecidos para comparar médias, o teste T e o teste Z. O teste Z é mais usado para comparar uma média amostral com uma média teórica, apesar de poder ser usado para comparar duas médias amostrais também. <br>\n",
    "O teste Z não é nativo do R, mas aceitando-se $H_A$ de teste T = também aceitaria-se ela via teste Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Teste T - Comparar as flores iris setosa e iris virginica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos aqui aplicar um teste que compara as médias, o teste t. A hipótese nula é de que as médias são iguais e a alternativa é de que são diferentes. <br>\n",
    "Esse teste precisa saber se as variâncias são iguais ou diferentes. Pra isso vamos aplicar um teste chamado teste F. <br>\n",
    "Para o teste  F, a hipótese nula é de que as variâncias são diferentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_test <- var.test(iris_setosa$Sepal.Width,iris_virginica$Sepal.Width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.261389316614015"
      ],
      "text/latex": [
       "0.261389316614015"
      ],
      "text/markdown": [
       "0.261389316614015"
      ],
      "text/plain": [
       "[1] 0.2613893"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_test$p.value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui, podemos ver que as variâncias podem ser consideradas iguais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tTwo Sample t-test\n",
       "\n",
       "data:  iris_setosa$Sepal.Width and iris_virginica$Sepal.Width\n",
       "t = 6.4503, df = 98, p-value = 4.246e-09\n",
       "alternative hypothesis: true difference in means is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.3143257 0.5936743\n",
       "sample estimates:\n",
       "mean of x mean of y \n",
       "    3.428     2.974 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.test(iris_setosa$Sepal.Width, iris_virginica$Sepal.Width, var.equal = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O nosso $p_{valor} \\leq 5%$, podemos assumir que as médias sejam diferentes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importante: \n",
    "Quando as amostras são **dependentes**, por exemplo, consumo de doces de pessoas antes e depois de uma dieta, dizemos que os dois número são **pareados**.\n",
    "Nesse caso o arumento `paired` do `t.test` deve ser `TRUE`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparando as três médias das flores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comparar três ou mais médias, utilizaremos um teste de hipóteses chamado ANOVA (Análise de variância) de um fator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui, a $H_A$ é que *alguma das médias é diferentes da outras*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "anova <- aov(Sepal.Width ~ Species, data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             Df Sum Sq Mean Sq F value Pr(>F)    \n",
       "Species       2  11.35   5.672   49.16 <2e-16 ***\n",
       "Residuals   147  16.96   0.115                   \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(anova)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos que `Pr(>F)` é o nosso valor P, ele é bem baixo, então alguma das médias é diferente das outras."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Não paramétricos - Wilcoxon-Mann-Whitney e Kruskal-Wallis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Técnicas não paramétricas fazem poucas suposições sobre as distribuições originais."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wilcoxon-Mann-Whitney"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O teste U de Mann-Whitney (também conhecido por teste da soma dos postos de Wilcoxon, teste de Wilcoxon-Mann-Whitney ou teste de Mann-Whitney) é a versão da rotina de teste não-paramétrico de t de Student."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'age'</li><li>'workclass'</li><li>'fnlwgt'</li><li>'education'</li><li>'educational.num'</li><li>'marital.status'</li><li>'occupation'</li><li>'relationship'</li><li>'race'</li><li>'gender'</li><li>'capital.gain'</li><li>'capital.loss'</li><li>'hours.per.week'</li><li>'native.country'</li><li>'income'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'age'\n",
       "\\item 'workclass'\n",
       "\\item 'fnlwgt'\n",
       "\\item 'education'\n",
       "\\item 'educational.num'\n",
       "\\item 'marital.status'\n",
       "\\item 'occupation'\n",
       "\\item 'relationship'\n",
       "\\item 'race'\n",
       "\\item 'gender'\n",
       "\\item 'capital.gain'\n",
       "\\item 'capital.loss'\n",
       "\\item 'hours.per.week'\n",
       "\\item 'native.country'\n",
       "\\item 'income'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'age'\n",
       "2. 'workclass'\n",
       "3. 'fnlwgt'\n",
       "4. 'education'\n",
       "5. 'educational.num'\n",
       "6. 'marital.status'\n",
       "7. 'occupation'\n",
       "8. 'relationship'\n",
       "9. 'race'\n",
       "10. 'gender'\n",
       "11. 'capital.gain'\n",
       "12. 'capital.loss'\n",
       "13. 'hours.per.week'\n",
       "14. 'native.country'\n",
       "15. 'income'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"age\"             \"workclass\"       \"fnlwgt\"          \"education\"      \n",
       " [5] \"educational.num\" \"marital.status\"  \"occupation\"      \"relationship\"   \n",
       " [9] \"race\"            \"gender\"          \"capital.gain\"    \"capital.loss\"   \n",
       "[13] \"hours.per.week\"  \"native.country\"  \"income\"         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names(adult)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que a variável de ganho de capital não segue uma descrição normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in ks.test(adult$capital.gain, \"pnorm\", mean(adult$capital.gain), :\n",
      "“ties should not be present for the Kolmogorov-Smirnov test”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tOne-sample Kolmogorov-Smirnov test\n",
       "\n",
       "data:  adult$capital.gain\n",
       "D = 0.47495, p-value < 2.2e-16\n",
       "alternative hypothesis: two-sided\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks.test(adult$capital.gain ,\"pnorm\", mean(adult$capital.gain), sd(adult$capital.gain))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos verificar se existe diferença de ganho de capital por gênero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'Female'</li><li>'Male'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Female'\n",
       "\\item 'Male'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Female'\n",
       "2. 'Male'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Female\" \"Male\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels(adult$gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult_male = adult %>% filter(gender == \"Male\")\n",
    "adult_female = adult %>% filter(gender == \"Female\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  adult_male$capital.gain and adult_female$capital.gain\n",
       "W = 274400000, p-value < 2.2e-16\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(adult_male$capital.gain, adult_female$capital.gain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O teste teve $p_{valor}$ bem pequeno, o que nos pode faz concluir que existe diferença entre ganho de capital entre homens e mulheres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kruskal Wallys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "É um teste não paramétrico utilizado para comparar três ou mais populações. Ele é usado para testar a hipótese nula de que todas as populações possuem funções de distribuição iguais contra a hipótese alternativa de que ao menos duas das populações possuem funções de distribuição diferentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos verificar se o nível educacional interfere no ganho de capital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tKruskal-Wallis rank sum test\n",
       "\n",
       "data:  capital.gain by education\n",
       "Kruskal-Wallis chi-squared = 1129.6, df = 15, p-value < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kruskal.test(capital.gain ~ education, data = adult)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O valor p foi bem pequeno, o que corrobora que existe diferença em ganho de capital por conta da educação"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressão Linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os principais tipos de correlação estatística são: <br>\n",
    "**Spearmann** = correlação não paramétrica <br>\n",
    "**Pearson** = correlação paramétrica <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.124435648500177"
      ],
      "text/latex": [
       "0.124435648500177"
      ],
      "text/markdown": [
       "0.124435648500177"
      ],
      "text/plain": [
       "[1] 0.1244356"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in cor.test.default(adult$capital.gain, adult$age, method = \"spearman\"):\n",
      "“Cannot compute exact p-value with ties”\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tSpearman's rank correlation rho\n",
       "\n",
       "data:  adult$capital.gain and adult$age\n",
       "S = 1.7003e+13, p-value < 2.2e-16\n",
       "alternative hypothesis: true rho is not equal to 0\n",
       "sample estimates:\n",
       "      rho \n",
       "0.1244356 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor(adult$capital.gain, adult$age, method = \"spearman\")\n",
    "cor.test(adult$capital.gain, adult$age, method = \"spearman\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P valor baixo, a relação entre os dois é significativa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "linearMod <- lm(capital.gain ~ age, data=adult)\n",
    "modelSummary <- summary(linearMod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = capital.gain ~ age, data = adult)\n",
       "\n",
       "Residuals:\n",
       "   Min     1Q Median     3Q    Max \n",
       " -3235  -1388   -926   -506  99661 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) -543.035    100.544  -5.401 6.66e-08 ***\n",
       "age           41.976      2.452  17.119  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Residual standard error: 7430 on 48840 degrees of freedom\n",
       "Multiple R-squared:  0.005964,\tAdjusted R-squared:  0.005944 \n",
       "F-statistic:   293 on 1 and 48840 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelSummary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ver que a correlação é considerável, com p <<< 5% <br>\n",
    "Nesse caso a hipótese alternativa é de que há correlação entre os coeficientes e o ganho de capital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"coeficientes -543.034900177983\"    \"coeficientes 41.9759840600675\"    \n",
      "[3] \"coeficientes 100.544017409863\"     \"coeficientes 2.45207363452385\"    \n",
      "[5] \"coeficientes -5.40096680207562\"    \"coeficientes 17.118565881982\"     \n",
      "[7] \"coeficientes 6.65915201816527e-08\" \"coeficientes 1.67674581047673e-65\"\n"
     ]
    }
   ],
   "source": [
    "print(paste(\"coeficientes\",modelSummary$coefficients))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula 'y ~ x'\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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6nE2yj6/vvvvU0puXDhwv79+292dTab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Oh6cbJeyh/fXrV8ZyoVAouXfLli2jlKem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\nYmebbN++nfi6WOMs9MxrYMWJJlbS/dixY0QlDImXvkPPbkhicDHeH4uVU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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(adult,aes(age, capital.gain)) + geom_point() + geom_smooth(method='lm')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}