Aula09_Atividade_ExplorandoDuasVariaveisQuantitativas_Mundo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Atividade: <p> <center> Análise Exploratória - Associação entre duas Variáveis Quantitativas\n",
    "___\n",
    "\n",
    "## Aula 09\n",
    "\n",
    "**Objetivo da aula:** Ao final desta aula, o aluno deve ser capaz de:\n",
    " * Estudar a relação existente entre duas variáveis quantitativas graficamente; e\n",
    " * Por meio de medidas adequadas, medir o grau de associação entre duas variáveis quantitativas.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"indice\"></div>\n",
    "\n",
    "## Índice\n",
    "\n",
    "- [Mundo](#mundo)\n",
    "\n",
    "\n",
    "- [Análise Exploratória - Gráfico de Dispersão](#grafico-dispersao)\n",
    "    - [Exercício 1](#ex1)\n",
    "    - [Exercício 2](#ex2)\n",
    "    - [Exercício 3](#ex3)\n",
    "    \n",
    "    \n",
    "- [Análise Exploratória - Covariância](#covariancia)\n",
    "    - [Exercício 4](#ex4)\n",
    "    - [Exercício 5](#ex5)\n",
    "    - [Exercício 6](#ex6)\n",
    "   \n",
    "   \n",
    "- [Análise Exploratória - Coeficiente de Correlação Linear](#correlacao)\n",
    "    - [Exercício 7](#ex7)\n",
    "    - [Exercício 8](#ex8)\n",
    "    - [Exercício 9 (opcional)](#ex9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esperamos trabalhar no diretório\n",
      "c:\\Users\\ribei\\OneDrive\\Área de Trabalho\\cdados\\cdados\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# A linha acima é necessária para visualizar os gráficos no jupyter notebook\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "# Módulo com funções matemáticas para arrays multidimensionais (matrizes)\n",
    "import numpy as np\n",
    "# Módulo com funções de geração de gráficos\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print('Esperamos trabalhar no diretório')\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"mundo\"></div>\n",
    "\n",
    "# Mundo\n",
    "\n",
    "<img src=\"mundo-valores.jpg\">\n",
    "\n",
    "Fonte: extraído da internet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A análise bidimensional tem como objetivo encontrar associação ou relação entre as variáveis quantitativas. Essas relações podem ser identificadas através de gráficos ou medidas numéricas. Entende-se por associação a mudança de opinião sobre o comportamento de uma variável na presença de informação sobre a segunda variável.\n",
    "\n",
    "Inicialmente, a base de dados `Mundo.xlsx` será explorada com intuito de entender a associação entre algumas de suas variáveis quantitativas. Esse conjunto de dados contém alguns indicadores socioeconômicos referentes ao ano de 2008 para 85 países, os quais são:  \n",
    "\n",
    "- `X1`: população em milhares de habitantes  \n",
    "- `X2`: densidade populacional  \n",
    "- `X3`: % de população urbana  \n",
    "- `X4`: expectativa de vida feminina  \n",
    "- `X5`: expectativa de vida masculina  \n",
    "- `X6`: crescimento populacional  \n",
    "- `X7`: mortalidade infantil  \n",
    "- `X8`: PIB per capita  \n",
    "- `X9`: % de mulheres alfabetizadas  \n",
    "- `X10`: população em 100.000 habitantes  \n",
    "\n",
    "Neste estudo queremos explorar possíveis associações entre as variáveis da nossa base de dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20500</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>2.80</td>\n",
       "      <td>168.0</td>\n",
       "      <td>205</td>\n",
       "      <td>14</td>\n",
       "      <td>0.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>33900</td>\n",
       "      <td>12.0</td>\n",
       "      <td>86</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>1.30</td>\n",
       "      <td>25.6</td>\n",
       "      <td>3408</td>\n",
       "      <td>95</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3700</td>\n",
       "      <td>126.0</td>\n",
       "      <td>68</td>\n",
       "      <td>75</td>\n",
       "      <td>68</td>\n",
       "      <td>1.40</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17800</td>\n",
       "      <td>2.3</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>74</td>\n",
       "      <td>1.38</td>\n",
       "      <td>7.3</td>\n",
       "      <td>16848</td>\n",
       "      <td>100</td>\n",
       "      <td>0.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7400</td>\n",
       "      <td>86.0</td>\n",
       "      <td>54</td>\n",
       "      <td>75</td>\n",
       "      <td>67</td>\n",
       "      <td>1.40</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>100</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   i     X1     X2  X3  X4  X5    X6     X7     X8   X9    X10\n",
       "0  1  20500   25.0  18  44  45  2.80  168.0    205   14  0.205\n",
       "1  2  33900   12.0  86  75  68  1.30   25.6   3408   95  0.339\n",
       "2  3   3700  126.0  68  75  68  1.40   27.0   5000  100  0.037\n",
       "3  4  17800    2.3  85  80  74  1.38    7.3  16848  100  0.178\n",
       "4  5   7400   86.0  54  75  67  1.40   35.0   3000  100  0.074"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando os dados\n",
    "mundo = pd.read_excel('Mundo.xlsx')\n",
    "mundo.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"grafico-dispersao\"></div>\n",
    "\n",
    "# Análise Exploratória - Gráfico de Dispersão\n",
    "\n",
    "Um gráfico de dispersão pode ser utilizado para compreender a relação entre duas variáveis quantitativas. Não necessariamente essa relação implica em causalidade (https://economiadependrive.wordpress.com/2014/09/25/correlacao-nao-implica-em-causalidade/)  e, em alguns casos, pode se tratar de uma relação espúria (http://www.tylervigen.com/spurious-correlations).\n",
    "\n",
    "Vamos construir um gráfico de dispersão entre as variáveis `X3` (eixo das abscissas) e `X4` (eixo das ordenadas). Cada ponto ⧫ desse gráfico representa um país da base de dados com o par de coordenadas nessas duas variáveis quantitativas.\n",
    "\n",
    "Utilizaremos a biblioteca `matplotlib` para construir nossos gráficos. Se tiver curiosidade, um artigo introdutório sobre a biblioteca pode ser encontrado aqui: https://realpython.com/python-matplotlib-guide/\n",
    "\n",
    "<div id=\"plt-scatter\"></div>\n",
    "<div id=\"plt-show\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lembre-se que mundo.X3 é equivalente a mundo['X3']\n",
    "plt.scatter(mundo.X3, mundo.X4)\n",
    "# Mostrando o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O gráfico acima ainda está bastante incompleto. Suponha que você precisa apresentar essas visualizações para outra pessoa. O que está faltando?\n",
    "\n",
    "...\n",
    "\n",
    "É importante que uma visualização seja compreensível sem a necessidade de uma pessoa apresentando. Para chegar nesse ponto com o nosso gráfico faltam algumas coisas básicas: sobre o que esse gráfico trata (título)? O que significam os valores em cada eixo (x e y)?\n",
    "\n",
    "Além disso, é difícil saber se uma região tem mais pontos do que outra se os pontos estão sobrepostos. Vamos mudar a cor dos pontos e adicionar transparência. Assim, se muitos pontos estiverem sobrepostos a região será mais escura. Procure pela documentação da função `scatter` do `matplotlib` para saber outras possíveis modificações.\n",
    "\n",
    "Vamos adicionar essas informações:\n",
    "\n",
    "<div id=\"plt-title\"></div>\n",
    "<div id=\"plt-xlabel\"></div>\n",
    "<div id=\"plt-ylabel\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mundo.X3, mundo.X4, c='red', alpha=0.4)\n",
    "plt.title('X3 versus X4')\n",
    "plt.xlabel('% de população urbana')\n",
    "plt.ylabel('Expectativa de vida Feminina')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bem mais claro! Mas o que podemos aprender com esse gráfico de dispersão? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observação\n",
    "\n",
    "Note que o `DataFrame` do *pandas* também possui um método que gera um gráfico de dispersão (*scatterplot*). Ele é compatível com o `matplotlib`, então pode ser utilizado para substituir o comando `plt.scatter` mantendo o resto do código intacto.\n",
    "\n",
    "<div id=\"df-scatter\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mundo.plot.scatter(x=\"X3\", y=\"X4\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mundo.plot.scatter(x=\"X3\", y=\"X4\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex1\"></div>\n",
    "\n",
    "### EXERCÍCIO 1:\n",
    "\n",
    "Considerando apenas o gráfico, você espera que exista algum tipo de associação entre as variáveis `X3` e `X4`? Se sim, você espera que essa associação seja positiva ou negativa? Forte ou fraca?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ESCREVA AQUI SUA RESPOSTA\n",
    "Positiva e média"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nessa análise exploratória nós vamos explorar diversas relações entre duas variáveis. Ao criarmos um novo gráfico para cada relação, vamos precisar de bastante espaço. Uma maneira de utilizar o espaço mais eficientemente é desenhar os gráficos lado a lado.\n",
    "\n",
    "Para criar mais do que um gráfico na mesma figura, precisamos inicialmente criar uma `plt.figure`.\n",
    "\n",
    "<div id=\"plt-figure\"></div>\n",
    "<div id=\"plt-subplot\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figsize é a largura e altura da figura em polegadas\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Vamos dividir a figura em partes menores (subplots). \n",
    "# Os dois primeiros argumentos são o número de linhas e o número de colunas de subplots nós desejamos.\n",
    "# O terceiro argumento representa o índice do subplot que vamos ativar para ser desenhado. \n",
    "# Ele começa em 1 no canto superior esquerdo e aumenta para a direita. No nosso exemplo temos 3 linhas e 2 colunas.\n",
    "# Assim, a ordem dos índices será:\n",
    "#\n",
    "# 1 | 2 \n",
    "# 3 | 4\n",
    "# 5 | 6\n",
    "#\n",
    "# Em alguns lugares você vai encontrar a forma alternativa plt.subplot(321). \n",
    "# Procure a documentação do matplotlib subplot para entender o que essa entrada de 3 dígitos significa.\n",
    "plt.subplot(3, 2, 1)\n",
    "\n",
    "# Vamos desenhar um plot simples como exemplo. Lembre-se que quando for implementar a sua versão\n",
    "# é importante adicionar informações como o título e o significado de cada eixo.\n",
    "plt.scatter(mundo.X3, mundo.X4)\n",
    "\n",
    "# Ativando outro subplot...\n",
    "plt.subplot(3, 2, 6)\n",
    "# E desenhando outro gráfico\n",
    "plt.scatter(mundo.X3, mundo.X4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex2\"></div>\n",
    "\n",
    "### EXERCÍCIO 2:\n",
    "\n",
    "Na figura acima nós geramos apenas dois gráficos (iguais). Utilize essa abordagem de `subplot`'s para construir gráficos de dispersão entre as variáveis `X3` e `X4`, as variáveis `X3` e `X5`, as variáveis `X3` e `X6` e as variáveis `X3` e `X7`. Utilize 2 linhas e 2 colunas e lembre-se de indicar o que cada um dos gráficos representa (título e significado dos eixos)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coloque seu código aqui\n",
    "# figsize é a largura e altura da figura em polegadas\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.scatter(mundo.X3, mundo.X4, c='red', alpha=0.4)\n",
    "plt.title('X3 versus X4')\n",
    "plt.xlabel('% de população urbana')\n",
    "plt.ylabel('Expectativa de vida Feminina')\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.scatter(mundo.X3, mundo.X5, c='red', alpha=0.4)\n",
    "plt.title('X3 versus X5')\n",
    "plt.xlabel('% de população urbana')\n",
    "plt.ylabel('Expectativa de vida Masculina')\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.scatter(mundo.X3, mundo.X6, c='red', alpha=0.4)\n",
    "plt.title('X3 versus X6')\n",
    "plt.xlabel('% de população urbana')\n",
    "plt.ylabel('Crescimento populacional')\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.scatter(mundo.X3, mundo.X7, c='red', alpha=0.4)\n",
    "plt.title('X3 versus X7')\n",
    "plt.xlabel('% de população urbana')\n",
    "plt.ylabel('Mortalidade infantil')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex3\"></div>\n",
    "\n",
    "### EXERCÍCIO 3:\n",
    "\n",
    "Para cada um dos três novos gráficos, descreva a relação entre as variáveis (associação positiva/negativa, forte/fraca)."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ESCREVA AQUI SUA RESPOSTA\n",
    "1 - Positiva - Forte\n",
    "2 - Positiva - Forte\n",
    "3 - Negativa - Fraca\n",
    "4 - Negativa - Forte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"covariancia\"></div>\n",
    "\n",
    "# Análise Exploratória - Covariância\n",
    "\n",
    "O gráfico de dispersão é uma ferramenta descritiva simples, porém útil para examinar uma possível relação entre variáveis quantitativas. A literatura estatística apresenta uma medida, nomeada de covariância, cujo sinal pode ser um indicativo do tipo de associação linear: positiva, se maior que zero; negativa, se menor que zero; e ausente de associação linear, se igual a zero. \n",
    "\n",
    "$$\\mathrm{Cov}(X, Y) = \\frac{1}{n} \\sum^n_{i=1}(x_i - \\overline{x})(y_i - \\overline{y}) \\quad\\quad\\quad\\quad (1)$$\n",
    "\n",
    "A biblioteca `pandas` já possui a função `.cov` em um `pd.Series` (coluna do `DataFrame`) para o cálculo da covariância:\n",
    "\n",
    "<div id=\"cov\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194.34985994397758"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mundo.X3.cov(mundo.X4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex4\"></div>\n",
    "\n",
    "### EXERCÍCIO 4:\n",
    "\n",
    "Considere o código a seguir. O resultado deveria ser o mesmo? Justifique. \n",
    "*Dica: olhe para a fórmula da covariância.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194.3499 = 194.3499?\n"
     ]
    }
   ],
   "source": [
    "cov_X3_X4 = mundo.X3.cov(mundo.X4)\n",
    "cov_X4_X3 = mundo.X4.cov(mundo.X3)\n",
    "print(f'{cov_X3_X4:.4f} = {cov_X4_X3:.4f}?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outra maneira para fazer print com edição\n",
    "# https://pyformat.info/#number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ESCREVA AQUI SUA RESPOSTA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex5\"></div>\n",
    "\n",
    "### EXERCÍCIO 5:\n",
    "\n",
    "Calcule a covariância para cada par de variáveis utilizadas nos gráficos acima."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coloque seu código aqui\n",
    "cov_X3_X4 = mundo.X3.cov(mundo.X4)\n",
    "cov_X3_X5 = mundo.X3.cov(mundo.X5)\n",
    "cov_X3_X6 = mundo.X3.cov(mundo.X6)\n",
    "cov_X3_X7 = mundo.X3.cov(mundo.X7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194.34985994397758 166.23011204481793 -6.846322128851538 -671.6780084033612\n"
     ]
    }
   ],
   "source": [
    "print(cov_X3_X4, cov_X3_X5, cov_X3_X6, cov_X3_X7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex6\"></div>\n",
    "\n",
    "### EXERCÍCIO 6:\n",
    "\n",
    "Contraste as suas interpretações gráficas (exercícios [1](#ex1) e [3](#ex3)) e os resultados das covariâncias (exercício [5](#ex5)). Como essa medida traduz o sinal da associação entre as variáveis quantitativas?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ESCREVA AQUI SUA RESPOSTA\n",
    "1 - Positiva - Forte\n",
    "2 - Positiva - Forte\n",
    "3 - Negativa - Fraca\n",
    "4 - Negativa - Forte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"correlacao\"></div>\n",
    "\n",
    "# Análise Exploratória - Coeficiente de Correlação Linear\n",
    "\n",
    "Ignorando-se o sinal, a covariância de `X3` e `X7` é mais do que o dobro da covariância de `X3` e `X4`. Entretanto, considerando os respectivos gráficos, as relações não parecem muito mais fortes no primeiro caso do que no segundo. Existe aqui um perigo de se comparar valores que, na verdade, não são comparáveis. O coeficiente de correlação linear garante que os valores estarão sempre entre -1 e 1:\n",
    "\n",
    "$$\\mathrm{Corr}(X, Y) = \\frac{\\mathrm{Cov}(X, Y)}{\\mathrm{DP}(X)\\mathrm{DP}(Y)}$$\n",
    "\n",
    "De maneira análoga à covariância, a biblioteca `pandas` também possui a função `.corr` em um `pd.Series` (coluna do `DataFrame`) para o cálculo da correlação.\n",
    "\n",
    "<div id=\"corr\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex7\"></div>\n",
    "\n",
    "### EXERCÍCIO 7:\n",
    "\n",
    "Calcule o coeficiente de correlação linear para cada par de variáveis utilizadas nos gráficos acima."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coloque seu código aqui\n",
    "corr_X3_X4 = mundo.X3.corr(mundo.X4)\n",
    "corr_X3_X5 = mundo.X3.corr(mundo.X5)\n",
    "corr_X3_X6 = mundo.X3.corr(mundo.X6)\n",
    "corr_X3_X7 = mundo.X3.corr(mundo.X7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7477732846051044 0.7297231162755045 -0.25355939333997657 -0.7237702331470605\n"
     ]
    }
   ],
   "source": [
    "print(corr_X3_X4, corr_X3_X5, corr_X3_X6, corr_X3_X7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex8\"></div>\n",
    "\n",
    "### EXERCÍCIO 8:\n",
    "\n",
    "Contraste as suas interpretações gráficas (exercícios [1](#ex1) e [3](#ex3)) e os resultados das correlações (exercício [7](#ex7)). <br>\n",
    "Como essa medida traduz a intensidade e o sinal da associação entre as variáveis quantitativas? <br>\n",
    "O que aconteceu com a suspeita (como já comentado, baseada em uma interpretação incorreta da covariância) de que a relação entre `X3` e `X7` é duas vezes mais forte do que a relação entre `X3` e `X4`?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ESCREVA AQUI SUA RESPOSTA\n",
    "1 - Positiva - Forte\n",
    "2 - Positiva - Forte\n",
    "3 - Negativa - Fraca\n",
    "4 - Negativa - Forte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex9\"></div>\n",
    "\n",
    "### EXERCÍCIO 9 (opcional):\n",
    "\n",
    "Jogue o jogo [guess the correlation](http://guessthecorrelation.com/). Até onde você consegue chegar?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  }
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