AAEG_grupoLucasRafaela.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Metódos Quantitativos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "O objetivo desse trabalho é examinar a estrutura e configuração dos dados, além de aprender sobre o relacionamento entre as variáveis da base de dados.\n",
    "\n",
    "A $\\textit{Análise Exploratória de Dados}$ inclui um conjunto de ferramentas descritivas e gráficas para a buscar padrões e tendências que desempenharam o papel de hipóteses para uma análise completa.\n",
    "\n",
    "Empregando técnicas $\\textit{estatísticas descritivas e gráficas}$ para estudar o conjunto de dados, detectando $\\textit{outliers e anomalias}$, e $\\textit{comunicando de forma eficaz os resultados do modelo.}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import statsmodels.api as sm\n",
    "import urllib\n",
    "from urllib import request, response, error, parse \n",
    "from urllib.request import urlopen\n",
    "from bs4 import BeautifulSoup, element"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 1. Extraíndo os Dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requisição bem sucedida!\n"
     ]
    }
   ],
   "source": [
    "req = requests.get('https://projects.fivethirtyeight.com/global-club-soccer-rankings/')\n",
    "if req.status_code == 200:\n",
    "    print ('Requisição bem sucedida!')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global Club Soccer Rankings | FiveThirtyEight\n"
     ]
    }
   ],
   "source": [
    "url = \"https://projects.fivethirtyeight.com/global-club-soccer-rankings/\"\n",
    "html = urlopen(url)\n",
    "soup = BeautifulSoup(html,\"lxml\")\n",
    "title = soup.title\n",
    "titleText = title.get_text()\n",
    "print(titleText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = soup.find('table', {'class':'all-teams'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_str = str(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_level_0</th>\n",
       "      <th>Unnamed: 1_level_0</th>\n",
       "      <th>Unnamed: 2_level_0</th>\n",
       "      <th>Unnamed: 3_level_0</th>\n",
       "      <th>Unnamed: 4_level_0</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Team rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>1-week change</th>\n",
       "      <th>team</th>\n",
       "      <th>League</th>\n",
       "      <th>League country</th>\n",
       "      <th>off.</th>\n",
       "      <th>def.</th>\n",
       "      <th>spi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man. City</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>94.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bayern Munich</td>\n",
       "      <td>Bundesliga</td>\n",
       "      <td>Germany</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>94.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>91.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>90.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>89.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PSG</td>\n",
       "      <td>Ligue 1</td>\n",
       "      <td>France</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>89.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RB Leipzig</td>\n",
       "      <td>Bundesliga</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>88.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man. United</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>86.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dortmund</td>\n",
       "      <td>Bundesliga</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>85.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Serie A</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>84.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>83.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leverkusen</td>\n",
       "      <td>Bundesliga</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>82.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atalanta</td>\n",
       "      <td>Serie A</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>82.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>81.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>80.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inter Milan</td>\n",
       "      <td>Serie A</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>77.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>La Liga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arsenal</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>76.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zenit</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>76.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Premier League</td>\n",
       "      <td>England</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>76.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ajax</td>\n",
       "      <td>Eredivisie</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lazio</td>\n",
       "      <td>Serie A</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>76.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gladbach</td>\n",
       "      <td>Bundesliga</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Roma</td>\n",
       "      <td>Serie A</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>75.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RB Salzburg</td>\n",
       "      <td>Bundesliga</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>Serie A</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tranmere Rovers</td>\n",
       "      <td>League One</td>\n",
       "      <td>England</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NY Red Bulls II</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stellenbosch</td>\n",
       "      <td>Premier Division</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>603</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Port Vale</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bradford City</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FC Tulsa</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hartford Athletic</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Black Leopards</td>\n",
       "      <td>Premier Division</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mansfield Town</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Loudoun United</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oldham Athletic</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southend United</td>\n",
       "      <td>League One</td>\n",
       "      <td>England</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Las Vegas Lights</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Scunthorpe</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Forest Green</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Walsall</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tacoma Defiance</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bolton</td>\n",
       "      <td>League One</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leyton Orient</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grimsby Town</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RGV Toros</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carlisle United</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Newport County</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlanta United 2</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sporting KC II</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Philadelphia II</td>\n",
       "      <td>USL Championship</td>\n",
       "      <td>USA</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Macclesfield</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stevenage</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Morecambe</td>\n",
       "      <td>League Two</td>\n",
       "      <td>England</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>629 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0_level_0 Unnamed: 1_level_0 Unnamed: 2_level_0  \\\n",
       "                  Rank      1-week change               team   \n",
       "0                    1                NaN          Man. City   \n",
       "1                    2                NaN      Bayern Munich   \n",
       "2                    3                NaN          Liverpool   \n",
       "3                    4                NaN          Barcelona   \n",
       "4                    5                NaN        Real Madrid   \n",
       "5                    6                NaN                PSG   \n",
       "6                    7                NaN         RB Leipzig   \n",
       "7                    8                NaN        Man. United   \n",
       "8                    9                NaN            Chelsea   \n",
       "9                   10                NaN           Dortmund   \n",
       "10                  11                NaN           Juventus   \n",
       "11                  12                NaN    Atlético Madrid   \n",
       "12                  13                NaN         Leverkusen   \n",
       "13                  14                NaN           Atalanta   \n",
       "14                  15                NaN          Leicester   \n",
       "15                  16                NaN             Getafe   \n",
       "16                  17                NaN             Wolves   \n",
       "17                  18                NaN            Sevilla   \n",
       "18                  19                NaN        Inter Milan   \n",
       "19                  20                NaN            Everton   \n",
       "20                  21                NaN      Real Sociedad   \n",
       "21                  22                NaN            Arsenal   \n",
       "22                  23                NaN              Zenit   \n",
       "23                  24                NaN          Tottenham   \n",
       "24                  25                NaN               Ajax   \n",
       "25                  26                NaN              Lazio   \n",
       "26                  27                NaN           Gladbach   \n",
       "27                  28                NaN               Roma   \n",
       "28                  29                NaN        RB Salzburg   \n",
       "29                  30                NaN             Napoli   \n",
       "..                 ...                ...                ...   \n",
       "599                600                NaN    Tranmere Rovers   \n",
       "600                601                NaN    NY Red Bulls II   \n",
       "601                602                NaN       Stellenbosch   \n",
       "602                603                NaN          Port Vale   \n",
       "603                604                NaN      Bradford City   \n",
       "604                605                NaN           FC Tulsa   \n",
       "605                606                NaN  Hartford Athletic   \n",
       "606                607                NaN     Black Leopards   \n",
       "607                608                NaN     Mansfield Town   \n",
       "608                609                NaN     Loudoun United   \n",
       "609                610                NaN    Oldham Athletic   \n",
       "610                611                NaN    Southend United   \n",
       "611                612                NaN   Las Vegas Lights   \n",
       "612                613                NaN         Scunthorpe   \n",
       "613                614                NaN       Forest Green   \n",
       "614                615                NaN            Walsall   \n",
       "615                616                NaN    Tacoma Defiance   \n",
       "616                617                NaN             Bolton   \n",
       "617                618                NaN      Leyton Orient   \n",
       "618                619                NaN       Grimsby Town   \n",
       "619                620                NaN          RGV Toros   \n",
       "620                621                NaN    Carlisle United   \n",
       "621                622                NaN     Newport County   \n",
       "622                623                NaN   Atlanta United 2   \n",
       "623                624                NaN     Sporting KC II   \n",
       "624                625                NaN    Philadelphia II   \n",
       "625                626                NaN       Macclesfield   \n",
       "626                627                NaN          Cambridge   \n",
       "627                628                NaN          Stevenage   \n",
       "628                629                NaN          Morecambe   \n",
       "\n",
       "    Unnamed: 3_level_0 Unnamed: 4_level_0 Team rating             \n",
       "                League     League country        off. def.   spi  \n",
       "0       Premier League            England         3.2  0.2  94.8  \n",
       "1           Bundesliga            Germany         3.5  0.4  94.4  \n",
       "2       Premier League            England         2.9  0.3  91.9  \n",
       "3              La Liga              Spain         2.9  0.4  90.3  \n",
       "4              La Liga              Spain         2.7  0.4  89.5  \n",
       "5              Ligue 1             France         3.2  0.7  89.2  \n",
       "6           Bundesliga            Germany         2.6  0.4  88.1  \n",
       "7       Premier League            England         2.4  0.4  86.2  \n",
       "8       Premier League            England         2.4  0.5  85.5  \n",
       "9           Bundesliga            Germany         2.6  0.6  85.4  \n",
       "10             Serie A              Italy         2.4  0.5  84.3  \n",
       "11             La Liga              Spain         2.1  0.4  83.8  \n",
       "12          Bundesliga            Germany         2.4  0.6  82.9  \n",
       "13             Serie A              Italy         2.7  0.7  82.8  \n",
       "14      Premier League            England         2.2  0.5  81.6  \n",
       "15             La Liga              Spain         1.8  0.3  81.3  \n",
       "16      Premier League            England         2.0  0.5  80.5  \n",
       "17             La Liga              Spain         2.2  0.6  80.3  \n",
       "18             Serie A              Italy         2.3  0.7  79.5  \n",
       "19      Premier League            England         2.1  0.6  77.7  \n",
       "20             La Liga              Spain         2.1  0.7  77.0  \n",
       "21      Premier League            England         2.1  0.7  76.8  \n",
       "22      Premier League             Russia         2.0  0.6  76.7  \n",
       "23      Premier League            England         2.2  0.8  76.6  \n",
       "24          Eredivisie        Netherlands         2.5  1.0  76.5  \n",
       "25             Serie A              Italy         2.2  0.7  76.4  \n",
       "26          Bundesliga            Germany         2.2  0.8  75.9  \n",
       "27             Serie A              Italy         2.2  0.8  75.2  \n",
       "28          Bundesliga            Austria         2.7  1.2  75.1  \n",
       "29             Serie A              Italy         2.1  0.8  75.1  \n",
       "..                 ...                ...         ...  ...   ...  \n",
       "599         League One            England         0.5  2.2  12.4  \n",
       "600   USL Championship                USA         0.6  2.3  12.4  \n",
       "601   Premier Division       South Africa         0.4  1.9  12.3  \n",
       "602         League Two            England         0.4  2.1  12.0  \n",
       "603         League Two            England         0.4  2.1  12.0  \n",
       "604   USL Championship                USA         0.5  2.3  11.9  \n",
       "605   USL Championship                USA         0.6  2.5  11.8  \n",
       "606   Premier Division       South Africa         0.4  2.0  11.8  \n",
       "607         League Two            England         0.5  2.4  11.7  \n",
       "608   USL Championship                USA         0.6  2.4  11.6  \n",
       "609         League Two            England         0.5  2.4  11.1  \n",
       "610         League One            England         0.6  2.6  11.0  \n",
       "611   USL Championship                USA         0.5  2.3  10.9  \n",
       "612         League Two            England         0.4  2.3  10.3  \n",
       "613         League Two            England         0.4  2.2  10.2  \n",
       "614         League Two            England         0.4  2.3  10.0  \n",
       "615   USL Championship                USA         0.5  2.5   9.9  \n",
       "616         League One            England         0.4  2.3   9.8  \n",
       "617         League Two            England         0.4  2.4   9.7  \n",
       "618         League Two            England         0.4  2.3   9.5  \n",
       "619   USL Championship                USA         0.4  2.4   8.0  \n",
       "620         League Two            England         0.3  2.3   7.6  \n",
       "621         League Two            England         0.2  2.1   7.5  \n",
       "622   USL Championship                USA         0.4  2.6   7.4  \n",
       "623   USL Championship                USA         0.4  2.6   7.2  \n",
       "624   USL Championship                USA         0.4  2.6   6.9  \n",
       "625         League Two            England         0.2  2.3   6.6  \n",
       "626         League Two            England         0.2  2.3   6.4  \n",
       "627         League Two            England         0.2  2.3   6.4  \n",
       "628         League Two            England         0.2  2.4   5.8  \n",
       "\n",
       "[629 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_html(table_str)[0]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizamos a biblioteca $\\textit{BeautifulSoup}$ para análise dos dados HTML e XML extraídos do website. Porém, para fazermos isso, é necessário a utilização da ferramenta $\\textit{urllib}$ que faz a conexão com a pagína web.\n",
    "Pela $\\textit{urllib}$  também é feita a verificação da conexão com a pagína web.\n",
    "\n",
    "Criamos o objeto $\\textit{BeautifulSoup}$, onde, 'lxml' é o analisador de html.\n",
    "Usando a ferramenta $\\textit{Inspecionar}$ do navegador procuramos pela tag 'table' e sua classe 'all-teams', e usando o $\\textit{BeautifulSoup}$ extraimos a tabela."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Outro formato para ler os dados.\n",
    "Quando o banco de dados é disponibilizado, podemos fazer o download dos dados e ler o arquivo csv diretamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as scs\n",
    "import statsmodels.api as sm \n",
    "import statistics as stats\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>rank</th>\n",
       "      <th>prev_rank</th>\n",
       "      <th>name</th>\n",
       "      <th>league</th>\n",
       "      <th>off</th>\n",
       "      <th>def</th>\n",
       "      <th>spi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.21</td>\n",
       "      <td>94.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Bayern Munich</td>\n",
       "      <td>German Bundesliga</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.38</td>\n",
       "      <td>94.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.31</td>\n",
       "      <td>91.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Spanish Primera Division</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.43</td>\n",
       "      <td>90.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>Spanish Primera Division</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.37</td>\n",
       "      <td>89.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.65</td>\n",
       "      <td>89.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>RB Leipzig</td>\n",
       "      <td>German Bundesliga</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0.40</td>\n",
       "      <td>88.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>86.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>85.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Borussia Dortmund</td>\n",
       "      <td>German Bundesliga</td>\n",
       "      <td>2.58</td>\n",
       "      <td>0.56</td>\n",
       "      <td>85.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.54</td>\n",
       "      <td>84.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>Atletico Madrid</td>\n",
       "      <td>Spanish Primera Division</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.36</td>\n",
       "      <td>83.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>Bayer Leverkusen</td>\n",
       "      <td>German Bundesliga</td>\n",
       "      <td>2.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>82.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>Atalanta</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.75</td>\n",
       "      <td>82.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>Leicester City</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.50</td>\n",
       "      <td>81.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>Spanish Primera Division</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.31</td>\n",
       "      <td>81.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>Wolverhampton</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.47</td>\n",
       "      <td>80.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>Sevilla FC</td>\n",
       "      <td>Spanish Primera Division</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.56</td>\n",
       "      <td>80.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>Internazionale</td>\n",
       "      <td>Italy Serie A</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.67</td>\n",
       "      <td>79.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0.64</td>\n",
       "      <td>77.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rank  prev_rank                 name                    league   off  \\\n",
       "0      1          1      Manchester City   Barclays Premier League  3.16   \n",
       "1      2          2        Bayern Munich         German Bundesliga  3.50   \n",
       "2      3          3            Liverpool   Barclays Premier League  2.87   \n",
       "3      4          4            Barcelona  Spanish Primera Division  2.90   \n",
       "4      5          5          Real Madrid  Spanish Primera Division  2.67   \n",
       "5      6          6  Paris Saint-Germain            French Ligue 1  3.19   \n",
       "6      7          7           RB Leipzig         German Bundesliga  2.57   \n",
       "7      8          8    Manchester United   Barclays Premier League  2.38   \n",
       "8      9          9              Chelsea   Barclays Premier League  2.44   \n",
       "9     10         10    Borussia Dortmund         German Bundesliga  2.58   \n",
       "10    11         11             Juventus             Italy Serie A  2.44   \n",
       "11    12         12      Atletico Madrid  Spanish Primera Division  2.08   \n",
       "12    13         13     Bayer Leverkusen         German Bundesliga  2.45   \n",
       "13    14         14             Atalanta             Italy Serie A  2.67   \n",
       "14    15         15       Leicester City   Barclays Premier League  2.16   \n",
       "15    16         16               Getafe  Spanish Primera Division  1.80   \n",
       "16    17         17        Wolverhampton   Barclays Premier League  2.04   \n",
       "17    18         18           Sevilla FC  Spanish Primera Division  2.15   \n",
       "18    19         19       Internazionale             Italy Serie A  2.27   \n",
       "19    20         20              Everton   Barclays Premier League  2.10   \n",
       "\n",
       "     def    spi  \n",
       "0   0.21  94.80  \n",
       "1   0.38  94.44  \n",
       "2   0.31  91.91  \n",
       "3   0.43  90.33  \n",
       "4   0.37  89.49  \n",
       "5   0.65  89.15  \n",
       "6   0.40  88.09  \n",
       "7   0.40  86.23  \n",
       "8   0.47  85.54  \n",
       "9   0.56  85.38  \n",
       "10  0.54  84.28  \n",
       "11  0.36  83.78  \n",
       "12  0.61  82.94  \n",
       "13  0.75  82.78  \n",
       "14  0.50  81.58  \n",
       "15  0.31  81.31  \n",
       "16  0.47  80.53  \n",
       "17  0.56  80.28  \n",
       "18  0.67  79.48  \n",
       "19  0.64  77.72  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dados = pd.read_csv('spiglobalrankings.csv')\n",
    "Dados.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank         0\n",
       "prev_rank    0\n",
       "name         0\n",
       "league       0\n",
       "off          0\n",
       "def          0\n",
       "spi          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dados.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos observar que obtemos uma tabela sem a coluna dos países, porém nessa segunda tabela os dados já estão tratados pois não observamos dados faltantes NaN, confirmado pelo método isnull(). Logo iremos utliza-lá."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Tratamento de dados:\n",
    "\n",
    "Para que seja melhor a interação com os dados, é necessário o tratamento e a limpeza adequada. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dados = Dados.rename(columns={\"rank\": \"Rank\", \"prev_rank\": \"Variação Semanal\", \"name\": \"Time\", \"league\": \"Liga\", \"off\": \"Rating Ofensivo\", \"def\": \"Rating Defensivo\", \"spi\": \"Rating de Força Futebolística\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Rank</th>\n",
       "      <th>Variação Semanal</th>\n",
       "      <th>Time</th>\n",
       "      <th>Liga</th>\n",
       "      <th>Rating Ofensivo</th>\n",
       "      <th>Rating Defensivo</th>\n",
       "      <th>Rating de Força Futebolística</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.21</td>\n",
       "      <td>94.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Bayern Munich</td>\n",
       "      <td>German Bundesliga</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.38</td>\n",
       "      <td>94.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.87</td>\n",
       "      <td>0.31</td>\n",
       "      <td>91.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Spanish Primera Division</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.43</td>\n",
       "      <td>90.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>Spanish Primera Division</td>\n",
       "      <td>2.67</td>\n",
       "      <td>0.37</td>\n",
       "      <td>89.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.65</td>\n",
       "      <td>89.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>RB Leipzig</td>\n",
       "      <td>German Bundesliga</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0.40</td>\n",
       "      <td>88.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.38</td>\n",
       "      <td>0.40</td>\n",
       "      <td>86.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>Barclays Premier League</td>\n",
       "      <td>2.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>85.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Borussia Dortmund</td>\n",
       "      <td>German Bundesliga</td>\n",
       "      <td>2.58</td>\n",
       "      <td>0.56</td>\n",
       "      <td>85.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank  Variação Semanal                 Time                      Liga  \\\n",
       "0     1                 1      Manchester City   Barclays Premier League   \n",
       "1     2                 2        Bayern Munich         German Bundesliga   \n",
       "2     3                 3            Liverpool   Barclays Premier League   \n",
       "3     4                 4            Barcelona  Spanish Primera Division   \n",
       "4     5                 5          Real Madrid  Spanish Primera Division   \n",
       "5     6                 6  Paris Saint-Germain            French Ligue 1   \n",
       "6     7                 7           RB Leipzig         German Bundesliga   \n",
       "7     8                 8    Manchester United   Barclays Premier League   \n",
       "8     9                 9              Chelsea   Barclays Premier League   \n",
       "9    10                10    Borussia Dortmund         German Bundesliga   \n",
       "\n",
       "   Rating Ofensivo  Rating Defensivo  Rating de Força Futebolística  \n",
       "0             3.16              0.21                          94.80  \n",
       "1             3.50              0.38                          94.44  \n",
       "2             2.87              0.31                          91.91  \n",
       "3             2.90              0.43                          90.33  \n",
       "4             2.67              0.37                          89.49  \n",
       "5             3.19              0.65                          89.15  \n",
       "6             2.57              0.40                          88.09  \n",
       "7             2.38              0.40                          86.23  \n",
       "8             2.44              0.47                          85.54  \n",
       "9             2.58              0.56                          85.38  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(629, 7)\n"
     ]
    }
   ],
   "source": [
    "print(dados.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>Rank</th>\n",
       "      <th>Variação Semanal</th>\n",
       "      <th>Rating Ofensivo</th>\n",
       "      <th>Rating Defensivo</th>\n",
       "      <th>Rating de Força Futebolística</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>629.00000</td>\n",
       "      <td>629.00000</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>315.00000</td>\n",
       "      <td>315.00000</td>\n",
       "      <td>1.226184</td>\n",
       "      <td>1.432544</td>\n",
       "      <td>41.420397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>181.72094</td>\n",
       "      <td>181.72094</td>\n",
       "      <td>0.499345</td>\n",
       "      <td>0.445965</td>\n",
       "      <td>18.661617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>5.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>158.00000</td>\n",
       "      <td>158.00000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>27.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>315.00000</td>\n",
       "      <td>315.00000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>39.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>472.00000</td>\n",
       "      <td>472.00000</td>\n",
       "      <td>1.530000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>54.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>629.00000</td>\n",
       "      <td>629.00000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>94.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rank  Variação Semanal  Rating Ofensivo  Rating Defensivo  \\\n",
       "count  629.00000         629.00000       629.000000        629.000000   \n",
       "mean   315.00000         315.00000         1.226184          1.432544   \n",
       "std    181.72094         181.72094         0.499345          0.445965   \n",
       "min      1.00000           1.00000         0.200000          0.210000   \n",
       "25%    158.00000         158.00000         0.870000          1.130000   \n",
       "50%    315.00000         315.00000         1.190000          1.430000   \n",
       "75%    472.00000         472.00000         1.530000          1.740000   \n",
       "max    629.00000         629.00000         3.500000          2.640000   \n",
       "\n",
       "       Rating de Força Futebolística  \n",
       "count                     629.000000  \n",
       "mean                       41.420397  \n",
       "std                        18.661617  \n",
       "min                         5.850000  \n",
       "25%                        27.560000  \n",
       "50%                        39.430000  \n",
       "75%                        54.920000  \n",
       "max                        94.800000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                               int64\n",
       "Variação Semanal                   int64\n",
       "Time                              object\n",
       "Liga                              object\n",
       "Rating Ofensivo                  float64\n",
       "Rating Defensivo                 float64\n",
       "Rating de Força Futebolística    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Descrevendo os Dados estatísticamente:\n",
    "\n",
    "Após ler a base de dados, vamos descrever algumas técnicas estatísticas. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "qualitativa = ['Time', 'Liga']\n",
    "discreta = ['Rank', 'Variação Semanal']\n",
    "continua = dados.columns[4:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Tabela de Frequência para Variáveis Discretas:}$  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "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>Freq.</th>\n",
       "      <th>Acum.</th>\n",
       "      <th>Rel. (%)</th>\n",
       "      <th>Rel. Acum. (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United Soccer League</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>5.564388</td>\n",
       "      <td>5.564388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major League Soccer</th>\n",
       "      <td>26</td>\n",
       "      <td>61</td>\n",
       "      <td>4.133545</td>\n",
       "      <td>9.697933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina Primera Division</th>\n",
       "      <td>24</td>\n",
       "      <td>85</td>\n",
       "      <td>3.815580</td>\n",
       "      <td>13.513514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English League Two</th>\n",
       "      <td>24</td>\n",
       "      <td>109</td>\n",
       "      <td>3.815580</td>\n",
       "      <td>17.329094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English League Championship</th>\n",
       "      <td>24</td>\n",
       "      <td>133</td>\n",
       "      <td>3.815580</td>\n",
       "      <td>21.144674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English League One</th>\n",
       "      <td>23</td>\n",
       "      <td>156</td>\n",
       "      <td>3.656598</td>\n",
       "      <td>24.801272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spanish Segunda Division</th>\n",
       "      <td>22</td>\n",
       "      <td>178</td>\n",
       "      <td>3.497615</td>\n",
       "      <td>28.298887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brasileiro Série A</th>\n",
       "      <td>20</td>\n",
       "      <td>198</td>\n",
       "      <td>3.179650</td>\n",
       "      <td>31.478537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy Serie A</th>\n",
       "      <td>20</td>\n",
       "      <td>218</td>\n",
       "      <td>3.179650</td>\n",
       "      <td>34.658188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Ligue 1</th>\n",
       "      <td>20</td>\n",
       "      <td>238</td>\n",
       "      <td>3.179650</td>\n",
       "      <td>37.837838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Ligue 2</th>\n",
       "      <td>20</td>\n",
       "      <td>258</td>\n",
       "      <td>3.179650</td>\n",
       "      <td>41.017488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barclays Premier League</th>\n",
       "      <td>20</td>\n",
       "      <td>278</td>\n",
       "      <td>3.179650</td>\n",
       "      <td>44.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spanish Primera Division</th>\n",
       "      <td>20</td>\n",
       "      <td>298</td>\n",
       "      <td>3.179650</td>\n",
       "      <td>47.376789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy Serie B</th>\n",
       "      <td>20</td>\n",
       "      <td>318</td>\n",
       "      <td>3.179650</td>\n",
       "      <td>50.556439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>German 2. Bundesliga</th>\n",
       "      <td>18</td>\n",
       "      <td>336</td>\n",
       "      <td>2.861685</td>\n",
       "      <td>53.418124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japanese J League</th>\n",
       "      <td>18</td>\n",
       "      <td>354</td>\n",
       "      <td>2.861685</td>\n",
       "      <td>56.279809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portuguese Liga</th>\n",
       "      <td>18</td>\n",
       "      <td>372</td>\n",
       "      <td>2.861685</td>\n",
       "      <td>59.141494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkish Turkcell Super Lig</th>\n",
       "      <td>18</td>\n",
       "      <td>390</td>\n",
       "      <td>2.861685</td>\n",
       "      <td>62.003180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexican Primera Division Torneo Clausura</th>\n",
       "      <td>18</td>\n",
       "      <td>408</td>\n",
       "      <td>2.861685</td>\n",
       "      <td>64.864865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>German Bundesliga</th>\n",
       "      <td>18</td>\n",
       "      <td>426</td>\n",
       "      <td>2.861685</td>\n",
       "      <td>67.726550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dutch Eredivisie</th>\n",
       "      <td>18</td>\n",
       "      <td>444</td>\n",
       "      <td>2.861685</td>\n",
       "      <td>70.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norwegian Tippeligaen</th>\n",
       "      <td>16</td>\n",
       "      <td>460</td>\n",
       "      <td>2.543720</td>\n",
       "      <td>73.131955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South African ABSA Premier League</th>\n",
       "      <td>16</td>\n",
       "      <td>476</td>\n",
       "      <td>2.543720</td>\n",
       "      <td>75.675676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swedish Allsvenskan</th>\n",
       "      <td>16</td>\n",
       "      <td>492</td>\n",
       "      <td>2.543720</td>\n",
       "      <td>78.219396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinese Super League</th>\n",
       "      <td>16</td>\n",
       "      <td>508</td>\n",
       "      <td>2.543720</td>\n",
       "      <td>80.763116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Premier Liga</th>\n",
       "      <td>16</td>\n",
       "      <td>524</td>\n",
       "      <td>2.543720</td>\n",
       "      <td>83.306836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgian Jupiler League</th>\n",
       "      <td>16</td>\n",
       "      <td>540</td>\n",
       "      <td>2.543720</td>\n",
       "      <td>85.850556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greek Super League</th>\n",
       "      <td>14</td>\n",
       "      <td>554</td>\n",
       "      <td>2.225755</td>\n",
       "      <td>88.076312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danish SAS-Ligaen</th>\n",
       "      <td>14</td>\n",
       "      <td>568</td>\n",
       "      <td>2.225755</td>\n",
       "      <td>90.302067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austrian T-Mobile Bundesliga</th>\n",
       "      <td>12</td>\n",
       "      <td>580</td>\n",
       "      <td>1.907790</td>\n",
       "      <td>92.209857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UEFA Europa League</th>\n",
       "      <td>12</td>\n",
       "      <td>592</td>\n",
       "      <td>1.907790</td>\n",
       "      <td>94.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scottish Premiership</th>\n",
       "      <td>12</td>\n",
       "      <td>604</td>\n",
       "      <td>1.907790</td>\n",
       "      <td>96.025437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australian A-League</th>\n",
       "      <td>11</td>\n",
       "      <td>615</td>\n",
       "      <td>1.748808</td>\n",
       "      <td>97.774245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swiss Raiffeisen Super League</th>\n",
       "      <td>10</td>\n",
       "      <td>625</td>\n",
       "      <td>1.589825</td>\n",
       "      <td>99.364070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UEFA Champions League</th>\n",
       "      <td>3</td>\n",
       "      <td>628</td>\n",
       "      <td>0.476948</td>\n",
       "      <td>99.841017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexican Primera Division Torneo Apertura</th>\n",
       "      <td>1</td>\n",
       "      <td>629</td>\n",
       "      <td>0.158983</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Freq.  Acum.  Rel. (%)  \\\n",
       "United Soccer League                         35     35  5.564388   \n",
       "Major League Soccer                          26     61  4.133545   \n",
       "Argentina Primera Division                   24     85  3.815580   \n",
       "English League Two                           24    109  3.815580   \n",
       "English League Championship                  24    133  3.815580   \n",
       "English League One                           23    156  3.656598   \n",
       "Spanish Segunda Division                     22    178  3.497615   \n",
       "Brasileiro Série A                           20    198  3.179650   \n",
       "Italy Serie A                                20    218  3.179650   \n",
       "French Ligue 1                               20    238  3.179650   \n",
       "French Ligue 2                               20    258  3.179650   \n",
       "Barclays Premier League                      20    278  3.179650   \n",
       "Spanish Primera Division                     20    298  3.179650   \n",
       "Italy Serie B                                20    318  3.179650   \n",
       "German 2. Bundesliga                         18    336  2.861685   \n",
       "Japanese J League                            18    354  2.861685   \n",
       "Portuguese Liga                              18    372  2.861685   \n",
       "Turkish Turkcell Super Lig                   18    390  2.861685   \n",
       "Mexican Primera Division Torneo Clausura     18    408  2.861685   \n",
       "German Bundesliga                            18    426  2.861685   \n",
       "Dutch Eredivisie                             18    444  2.861685   \n",
       "Norwegian Tippeligaen                        16    460  2.543720   \n",
       "South African ABSA Premier League            16    476  2.543720   \n",
       "Swedish Allsvenskan                          16    492  2.543720   \n",
       "Chinese Super League                         16    508  2.543720   \n",
       "Russian Premier Liga                         16    524  2.543720   \n",
       "Belgian Jupiler League                       16    540  2.543720   \n",
       "Greek Super League                           14    554  2.225755   \n",
       "Danish SAS-Ligaen                            14    568  2.225755   \n",
       "Austrian T-Mobile Bundesliga                 12    580  1.907790   \n",
       "UEFA Europa League                           12    592  1.907790   \n",
       "Scottish Premiership                         12    604  1.907790   \n",
       "Australian A-League                          11    615  1.748808   \n",
       "Swiss Raiffeisen Super League                10    625  1.589825   \n",
       "UEFA Champions League                         3    628  0.476948   \n",
       "Mexican Primera Division Torneo Apertura      1    629  0.158983   \n",
       "\n",
       "                                          Rel. Acum. (%)  \n",
       "United Soccer League                            5.564388  \n",
       "Major League Soccer                             9.697933  \n",
       "Argentina Primera Division                     13.513514  \n",
       "English League Two                             17.329094  \n",
       "English League Championship                    21.144674  \n",
       "English League One                             24.801272  \n",
       "Spanish Segunda Division                       28.298887  \n",
       "Brasileiro Série A                             31.478537  \n",
       "Italy Serie A                                  34.658188  \n",
       "French Ligue 1                                 37.837838  \n",
       "French Ligue 2                                 41.017488  \n",
       "Barclays Premier League                        44.197138  \n",
       "Spanish Primera Division                       47.376789  \n",
       "Italy Serie B                                  50.556439  \n",
       "German 2. Bundesliga                           53.418124  \n",
       "Japanese J League                              56.279809  \n",
       "Portuguese Liga                                59.141494  \n",
       "Turkish Turkcell Super Lig                     62.003180  \n",
       "Mexican Primera Division Torneo Clausura       64.864865  \n",
       "German Bundesliga                              67.726550  \n",
       "Dutch Eredivisie                               70.588235  \n",
       "Norwegian Tippeligaen                          73.131955  \n",
       "South African ABSA Premier League              75.675676  \n",
       "Swedish Allsvenskan                            78.219396  \n",
       "Chinese Super League                           80.763116  \n",
       "Russian Premier Liga                           83.306836  \n",
       "Belgian Jupiler League                         85.850556  \n",
       "Greek Super League                             88.076312  \n",
       "Danish SAS-Ligaen                              90.302067  \n",
       "Austrian T-Mobile Bundesliga                   92.209857  \n",
       "UEFA Europa League                             94.117647  \n",
       "Scottish Premiership                           96.025437  \n",
       "Australian A-League                            97.774245  \n",
       "Swiss Raiffeisen Super League                  99.364070  \n",
       "UEFA Champions League                          99.841017  \n",
       "Mexican Primera Division Torneo Apertura      100.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_freq_liga = dados.Liga.value_counts(sort=True)\n",
    "tab_freq_liga = pd.DataFrame({\"Freq.\": tab_freq_liga})\n",
    "\n",
    "tab_freq_liga['Acum.'] = tab_freq_liga['Freq.'].cumsum()\n",
    "tab_freq_liga['Rel. (%)'] = ((tab_freq_liga['Freq.']/629)*100)\n",
    "tab_freq_liga['Rel. Acum. (%)'] = tab_freq_liga['Rel. (%)'].cumsum()\n",
    "\n",
    "tab_freq_liga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Freq.               int64\n",
       "Acum.               int64\n",
       "Rel. (%)          float64\n",
       "Rel. Acum. (%)    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_freq_liga.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Tabela de Frequência para Variáveis Contínuas:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "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>Freq.</th>\n",
       "      <th>Acum.</th>\n",
       "      <th>Rel. (%)</th>\n",
       "      <th>Rel. Acum. (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[0, 10)</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2.384738</td>\n",
       "      <td>2.384738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[10, 20)</th>\n",
       "      <td>63</td>\n",
       "      <td>78</td>\n",
       "      <td>10.015898</td>\n",
       "      <td>12.400636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[20, 30)</th>\n",
       "      <td>118</td>\n",
       "      <td>196</td>\n",
       "      <td>18.759936</td>\n",
       "      <td>31.160572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[30, 40)</th>\n",
       "      <td>124</td>\n",
       "      <td>320</td>\n",
       "      <td>19.713831</td>\n",
       "      <td>50.874404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[40, 50)</th>\n",
       "      <td>117</td>\n",
       "      <td>437</td>\n",
       "      <td>18.600954</td>\n",
       "      <td>69.475358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[50, 60)</th>\n",
       "      <td>75</td>\n",
       "      <td>512</td>\n",
       "      <td>11.923688</td>\n",
       "      <td>81.399046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[60, 70)</th>\n",
       "      <td>65</td>\n",
       "      <td>577</td>\n",
       "      <td>10.333863</td>\n",
       "      <td>91.732909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[70, 80)</th>\n",
       "      <td>34</td>\n",
       "      <td>611</td>\n",
       "      <td>5.405405</td>\n",
       "      <td>97.138315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[80, 90)</th>\n",
       "      <td>14</td>\n",
       "      <td>625</td>\n",
       "      <td>2.225755</td>\n",
       "      <td>99.364070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[90, 100)</th>\n",
       "      <td>4</td>\n",
       "      <td>629</td>\n",
       "      <td>0.635930</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Freq.  Acum.   Rel. (%)  Rel. Acum. (%)\n",
       "[0, 10)       15     15   2.384738        2.384738\n",
       "[10, 20)      63     78  10.015898       12.400636\n",
       "[20, 30)     118    196  18.759936       31.160572\n",
       "[30, 40)     124    320  19.713831       50.874404\n",
       "[40, 50)     117    437  18.600954       69.475358\n",
       "[50, 60)      75    512  11.923688       81.399046\n",
       "[60, 70)      65    577  10.333863       91.732909\n",
       "[70, 80)      34    611   5.405405       97.138315\n",
       "[80, 90)      14    625   2.225755       99.364070\n",
       "[90, 100)      4    629   0.635930      100.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spi = dados['Rating de Força Futebolística']\n",
    "bins_range = range(0, int(round(spi.max()))+10,10)\n",
    "tab_freq_spi = pd.cut(spi, bins=bins_range, include_lowest=True, right=False)\n",
    "tab_freq_spi = tab_freq_spi.value_counts(sort=False)\n",
    "\n",
    "tab_freq_spi = pd.DataFrame({\"Freq.\": tab_freq_spi})\n",
    "tab_freq_spi['Acum.'] = tab_freq_spi['Freq.'].cumsum()\n",
    "tab_freq_spi['Rel. (%)'] = ((tab_freq_spi['Freq.']/tab_freq_spi['Freq.'].sum())*100)\n",
    "tab_freq_spi['Rel. Acum. (%)'] = tab_freq_spi['Rel. (%)'].cumsum()\n",
    "\n",
    "tab_freq_spi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Freq.               int64\n",
       "Acum.               int64\n",
       "Rel. (%)          float64\n",
       "Rel. Acum. (%)    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_freq_spi.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Rating Ofensivo</th>\n",
       "      <th>Rating Defensivo</th>\n",
       "      <th>Rating de Força Futebolística</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>629.000000</td>\n",
       "      <td>629.000000</td>\n",
       "      <td>629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.226184</td>\n",
       "      <td>1.432544</td>\n",
       "      <td>41.420397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.499345</td>\n",
       "      <td>0.445965</td>\n",
       "      <td>18.661617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>5.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.870000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>27.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.190000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>39.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.530000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>54.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>94.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rating Ofensivo  Rating Defensivo  Rating de Força Futebolística\n",
       "count       629.000000        629.000000                     629.000000\n",
       "mean          1.226184          1.432544                      41.420397\n",
       "std           0.499345          0.445965                      18.661617\n",
       "min           0.200000          0.210000                       5.850000\n",
       "25%           0.870000          1.130000                      27.560000\n",
       "50%           1.190000          1.430000                      39.430000\n",
       "75%           1.530000          1.740000                      54.920000\n",
       "max           3.500000          2.640000                      94.800000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados[continua].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Visualizando as informações estátisticas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Gráfico Massa de Probabilidade para Variáveis Discretas:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tab_freq_liga['Rel. (%)'].plot(kind='bar',zorder=2)\n",
    "plt.grid(axis='y', zorder=1)\n",
    "plt.rcParams['figure.figsize'] = (15,10) \n",
    "plt.xticks(fontsize = 10, rotation=90)\n",
    "plt.ylim(0,10)\n",
    "plt.ylabel(r'Frequência Relativa (%)')\n",
    "plt.title('Frequência ')\n",
    "\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esse gráfico nos dá a frequência relativa (probabilidade) de algum clube da lista participar de alguma dessas ligas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Gráficos Densidade de Kernel para Variáveis Contínuas:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dados['Rating de Força Futebolística'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dados['Rating Ofensivo'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dados['Rating Defensivo'], hist=True, kde=True, \n",
    "             bins=int(180/5), color = 'darkblue', \n",
    "             hist_kws={'edgecolor':'black'},\n",
    "             kde_kws={'linewidth': 4})\n",
    "plt.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfico de densidade pela estimativa da densidade de Kernel de uma versão contínua e suave de um histograma estimado a partir dos dados.\n",
    "Nesse método, uma curva contínua (o kernel) é desenhada em todos os pontos de dados individuais e todas essas curvas são adicionadas juntas para fazer uma única estimativa de densidade suave. O kernel mais usado é um gaussiano (que produz uma curva de sino gaussiano em cada ponto de dados). \n",
    "O eixo x é o valor da variável como em um histograma. O eixo y é a função de densidade de probabilidade para a estimativa da densidade do kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Gráficos de Distribuição Acumulativa para Variáveis Contínuas:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.sort(dados['Rating de Força Futebolística']) \n",
    "func = sm.distributions.empirical_distribution.ECDF(x) \n",
    "y = func(x) \n",
    "plt.title('CDF')\n",
    "plt.xlabel('Rating de Força Futebolística')\n",
    "plt.ylabel('F(x) = P([X <= x])')\n",
    "plt.plot(x, y, marker = ',')\n",
    "plt.rcParams['figure.figsize'] = (15,7) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.sort(dados['Rating Ofensivo']) \n",
    "func = sm.distributions.empirical_distribution.ECDF(x) \n",
    "y = func(x)\n",
    "plt.title('CDF')\n",
    "plt.xlabel('Rating Ofensivo')\n",
    "plt.ylabel('F(x) = P([X <= x])')\n",
    "plt.plot(x, y, marker = ',')\n",
    "plt.rcParams['figure.figsize'] = (15,7) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.sort(dados['Rating Defensivo'])  \n",
    "func = sm.distributions.empirical_distribution.ECDF(x) \n",
    "y = func(x) \n",
    "plt.title('CDF')\n",
    "plt.xlabel('Rating Defensivo')\n",
    "plt.ylabel('F(x) = P([X <= x])')\n",
    "plt.plot(x, y, marker = ',')\n",
    "plt.rcParams['figure.figsize'] = (15,7) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Gráficos de Distribuição de Densidade para Variáveis Contínuas:}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dados['Rating de Força Futebolística'].plot(kind='hist', density=True, histtype='bar', rwidth=1,\n",
    "xlim=(min(dados['Rating de Força Futebolística'])-0.05, max(dados['Rating de Força Futebolística'])+0.05), ylim=(0,0.05), \n",
    " legend=False, bins=np.arange(len(dados['Rating de Força Futebolística']))-0.5)\n",
    "ax.set_xlabel(r'Avaliação de Força Futebolística')\n",
    "ax.set_ylabel(r'$F(X)$')\n",
    "plt.rcParams['figure.figsize'] = (15,7) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dados['Rating Ofensivo'].plot(kind='hist', density=True, histtype='bar', rwidth=1,\n",
    "                     xlim=(min(dados['Rating Ofensivo'])-0.05, max(dados['Rating Ofensivo'])+0.05), ylim=(0,0.05), \n",
    "                     legend=False, bins=np.arange(len(dados['Rating Ofensivo']))-0.5)\n",
    "ax.set_xlabel(r'Avaliação Ofensiva')\n",
    "ax.set_ylabel(r'$F(X)$')\n",
    "plt.rcParams['figure.figsize'] = (15,7) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dados['Rating Defensivo'].plot(kind='hist', density=True, histtype='bar', rwidth=1,\n",
    "                     xlim=(min(dados['Rating Defensivo'])-0.05, max(dados['Rating Defensivo'])+0.05), ylim=(0,0.05), \n",
    "                     legend=False, bins=np.arange(len(dados['Rating Defensivo']))-0.5)\n",
    "ax.set_xlabel(r'Avaliação Defensiva')\n",
    "ax.set_ylabel(r'$F(X)$')\n",
    "plt.rcParams['figure.figsize'] = (15,7) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Teste Kolmogorov-Smirnov\n",
    "Aplica-se o teste Kolmogorov-Smirnov com o objetivo de comprovação da hipótese nula ($H_0$), que afirma a normalidade da distribuição amostral. Para tal, procuram-se p-values superiores a 0.8, conforme o especificado. Nesse contexto, duas amostras são selecionadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([94.8 , 94.44, 91.91, 90.33, 89.49, 89.15, 88.09, 86.23, 85.54,\n",
       "       85.38, 84.28, 83.78, 82.94, 82.78, 81.58, 81.31, 80.53, 80.28,\n",
       "       79.48, 77.72, 77.04, 76.77, 76.75, 76.59, 76.44, 75.89, 75.15,\n",
       "       75.14, 75.14, 74.53, 73.91, 73.8 , 73.73, 73.46, 73.32, 73.31,\n",
       "       73.27, 72.81, 72.48, 72.46, 71.87, 71.79, 71.79, 71.17, 70.91,\n",
       "       70.78, 70.69, 70.62, 70.53, 70.41, 70.37, 70.27, 69.72, 69.46,\n",
       "       69.26, 68.56, 68.49, 68.44, 68.31, 67.97, 67.91, 67.88, 67.69,\n",
       "       67.62, 67.48, 67.27, 67.12, 67.02, 66.94, 66.83, 66.82, 66.62,\n",
       "       66.32, 65.68, 65.63, 65.48, 65.45, 65.4 , 64.92, 64.81, 64.59,\n",
       "       64.31, 64.05, 63.87, 63.66, 63.52, 63.4 , 63.19, 62.78, 62.75,\n",
       "       62.72, 62.68, 62.55, 62.51, 62.44, 62.42, 62.24, 62.05, 62.05,\n",
       "       61.97, 61.88, 61.79, 61.76, 61.68, 61.63, 61.63, 61.55, 61.51,\n",
       "       61.23, 61.02, 60.98, 60.47, 60.28, 60.28, 60.25, 60.13, 60.08,\n",
       "       59.94, 59.67, 59.63, 58.85, 58.74, 58.71, 58.37, 58.34, 58.25,\n",
       "       58.19, 58.14, 58.05, 58.  , 57.78, 57.47, 57.32, 57.26, 57.21,\n",
       "       57.05, 56.78, 56.48, 56.38, 56.28, 56.21, 56.16, 56.11, 56.09,\n",
       "       56.05, 56.03, 55.9 , 55.76, 55.7 , 55.69, 55.61, 55.51, 55.45,\n",
       "       55.34, 55.21, 55.09, 55.03, 54.92, 54.27, 54.14, 54.01, 53.66,\n",
       "       53.62, 53.34, 53.26, 53.19, 53.14, 53.01, 52.99, 52.71, 52.7 ,\n",
       "       52.63, 52.62, 52.59, 52.54, 52.29, 52.21, 52.14, 51.81, 51.72,\n",
       "       51.6 , 51.49, 51.28, 51.15, 50.91, 50.9 , 50.88, 50.83, 50.79,\n",
       "       50.65, 50.48, 50.06, 49.97, 49.87, 49.81, 49.57, 49.46, 49.37,\n",
       "       49.34, 49.33, 49.29, 48.88, 48.83, 48.78, 48.71, 48.63, 48.58,\n",
       "       48.56, 48.55, 48.53, 48.53, 48.4 , 48.12, 48.05, 48.02, 47.99,\n",
       "       47.98, 47.85, 47.56, 47.52, 47.26, 47.11, 47.1 , 47.08, 47.06,\n",
       "       47.05, 46.89, 46.71, 46.6 , 46.36, 46.35, 46.29, 46.04, 46.  ,\n",
       "       45.69, 45.68, 45.66, 45.57, 45.39, 45.31, 45.26, 45.04, 45.02,\n",
       "       44.96, 44.93, 44.86, 44.82, 44.65, 44.41, 44.27, 44.25, 44.11,\n",
       "       44.09, 44.07, 43.93, 43.64, 43.55, 43.47, 43.43, 43.37, 43.35,\n",
       "       43.31, 43.14, 43.  , 42.94, 42.88, 42.87, 42.82, 42.74, 42.73,\n",
       "       42.72, 42.6 , 42.55, 42.53, 42.37, 42.31, 42.14, 42.13, 42.1 ,\n",
       "       42.09, 42.02, 41.98, 41.83, 41.8 , 41.78, 41.5 , 41.44, 41.4 ,\n",
       "       41.32, 41.23, 41.22, 41.18, 41.09, 41.08, 40.96, 40.92, 40.91,\n",
       "       40.7 , 40.64, 40.54, 40.5 , 40.39, 40.35, 40.3 , 40.27, 40.27,\n",
       "       40.23, 40.11, 40.01, 39.84, 39.67, 39.66, 39.62, 39.58, 39.43,\n",
       "       39.39, 39.37, 39.26, 39.16, 39.15, 39.15, 39.14, 39.  , 38.9 ,\n",
       "       38.84, 38.75, 38.7 , 38.68, 38.64, 38.62, 38.55, 38.5 , 38.4 ,\n",
       "       38.24, 38.22, 38.16, 38.14, 38.12, 38.09, 38.08, 38.03, 37.93,\n",
       "       37.85, 37.71, 37.71, 37.68, 37.62, 37.48, 37.47, 37.45, 37.34,\n",
       "       37.32, 37.16, 37.14, 37.11, 36.69, 36.66, 36.6 , 36.45, 36.44,\n",
       "       36.44, 36.33, 36.09, 36.08, 35.87, 35.85, 35.82, 35.81, 35.74,\n",
       "       35.74, 35.71, 35.56, 35.31, 35.22, 35.2 , 35.14, 35.1 , 34.91,\n",
       "       34.89, 34.78, 34.74, 34.73, 34.57, 34.53, 34.48, 34.11, 34.11,\n",
       "       33.73, 33.73, 33.65, 33.65, 33.63, 33.56, 33.51, 33.39, 33.38,\n",
       "       33.38, 33.38, 33.25, 33.25, 33.21, 33.16, 33.12, 33.11, 32.96,\n",
       "       32.92, 32.76, 32.7 , 32.64, 32.18, 31.91, 31.85, 31.75, 31.56,\n",
       "       31.49, 31.46, 31.41, 31.15, 31.12, 31.05, 31.04, 31.02, 30.89,\n",
       "       30.79, 30.5 , 30.48, 30.47, 30.42, 30.38, 30.35, 30.34, 30.11,\n",
       "       30.06, 29.97, 29.95, 29.95, 29.89, 29.84, 29.76, 29.68, 29.64,\n",
       "       29.57, 29.56, 29.55, 29.51, 29.4 , 29.24, 29.15, 29.13, 29.09,\n",
       "       29.  , 29.  , 28.85, 28.72, 28.53, 28.49, 28.44, 28.42, 28.27,\n",
       "       28.24, 28.17, 28.06, 28.03, 27.95, 27.92, 27.88, 27.85, 27.74,\n",
       "       27.69, 27.64, 27.57, 27.56, 27.53, 27.53, 27.3 , 27.27, 27.24,\n",
       "       27.07, 27.01, 26.87, 26.81, 26.75, 26.7 , 26.69, 26.62, 26.55,\n",
       "       26.54, 26.51, 26.49, 26.23, 26.11, 25.91, 25.85, 25.81, 25.8 ,\n",
       "       25.72, 25.61, 25.45, 25.41, 25.37, 25.06, 25.01, 24.89, 24.89,\n",
       "       24.83, 24.74, 24.62, 24.53, 24.49, 24.28, 24.08, 24.05, 24.05,\n",
       "       23.92, 23.91, 23.81, 23.75, 23.51, 23.5 , 23.38, 23.23, 23.1 ,\n",
       "       23.04, 22.95, 22.94, 22.91, 22.8 , 22.79, 22.77, 22.76, 22.74,\n",
       "       22.37, 22.29, 22.27, 22.03, 21.87, 21.86, 21.8 , 21.77, 21.31,\n",
       "       21.18, 20.91, 20.7 , 20.51, 20.46, 20.35, 20.34, 20.3 , 20.2 ,\n",
       "       20.19, 20.12, 19.81, 19.65, 19.39, 19.27, 19.26, 19.21, 19.15,\n",
       "       19.07, 19.01, 18.95, 18.7 , 18.61, 18.39, 18.38, 18.17, 18.  ,\n",
       "       17.88, 17.78, 17.77, 17.61, 17.58, 17.5 , 17.45, 17.15, 17.14,\n",
       "       17.  , 16.59, 15.92, 15.71, 15.58, 15.52, 15.43, 15.41, 14.96,\n",
       "       14.93, 14.7 , 14.7 , 14.47, 14.25, 14.11, 13.99, 13.92, 13.48,\n",
       "       13.37, 13.23, 12.85, 12.84, 12.62, 12.43, 12.42, 12.29, 12.01,\n",
       "       12.01, 11.88, 11.76, 11.75, 11.73, 11.62, 11.1 , 10.98, 10.91,\n",
       "       10.33, 10.24,  9.99,  9.91,  9.8 ,  9.7 ,  9.49,  8.01,  7.65,\n",
       "        7.51,  7.44,  7.19,  6.9 ,  6.56,  6.42,  6.39,  5.85])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = dados['Rating Ofensivo']\n",
    "y1 = dados['Rating Defensivo']\n",
    "z1 = dados['Rating de Força Futebolística']\n",
    "x1.to_numpy()\n",
    "y1.to_numpy()\n",
    "z1.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rating Ofensivo, \n",
      "p-value= KstestResult(statistic=0.6449307249729884, pvalue=1.6223156750141128e-255)\n",
      "Rating Defensivo, \n",
      "p-value= KstestResult(statistic=0.7164305038835223, pvalue=0.0)\n",
      "Rating de Força Futebolística, \n",
      "p-value= KstestResult(statistic=0.999999997542135, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "ks1 = scs.kstest(x1, 'norm')\n",
    "ks2 = scs.kstest(y1, 'norm')\n",
    "ks3 = scs.kstest(z1, 'norm')\n",
    "\n",
    "print(\"Rating Ofensivo, \\np-value=\", ks1)\n",
    "print(\"Rating Defensivo, \\np-value=\", ks2)\n",
    "print(\"Rating de Força Futebolística, \\np-value=\", ks3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os baixíssimos valores do p-value < 0.001 rejeitam a chance de constatação da Hipótese Nula ($H_0$), que afirma a normalidade da distribuição amostral.\n",
    "\n",
    "É importante frisar que os valores p-value=0 não é uma verdade, mas uma aproximação. Pois sempre há uma chance de obter os resultados da Hipótese Nula ($H_0$), por menor ou improvável que seja a chance.\n",
    "É provavel que a hipótese nula tenha sido rejeitada pelo tamanho da amostra.\n",
    "\n",
    "Vamos tentar provar que há diferença entre os alvos da comparação estatística, confirmando a Hipótese Alternativa ($H_1$)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 Avaliação pelo Teste de Shapiro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rating Ofensivo, \n",
      "p-value= (0.9749813079833984, 6.880295444489093e-09)\n",
      "Rating Defensivo, \n",
      "p-value= (0.9978125095367432, 0.5953747630119324)\n",
      "Rating de Força Futebolística, \n",
      "p-value= KstestResult(statistic=0.999999997542135, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "s1 = scs.shapiro(x1)\n",
    "s2 = scs.shapiro(y1)\n",
    "s3 = scs.shapiro(z1)\n",
    "\n",
    "print(\"Rating Ofensivo, \\np-value=\", s1)\n",
    "print(\"Rating Defensivo, \\np-value=\", s2)\n",
    "print(\"Rating de Força Futebolística, \\np-value=\", ks3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9978125095367432, 0.5953747630119324)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y1, bins=range(0,4,1), fit=scs.norm, kde=False)\n",
    "print(s2)\n",
    "\n",
    "plt.text(0, 0.8, 'Shapiro: '+str(round(s2[1], 5) ), bbox=dict(facecolor='red', alpha=0.4), zorder=4 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O teste de Shapiro é independente do tamanho da amostra.\n",
    "Por este metódo a única mudança significativa analisando os resultados foram os dados de Rating Defensivo. Porém, o p-value continuou abaixo de 5%. Logo, rejeitamos a hipótese nula novamente.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Regressão e Predição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = dados[continua]\n",
    "g=sns.pairplot(Y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gráfico matriz de dispersão, onde as diagonais nos mostram o a distribuição de uma única variável em formato de histograma. Enquanto as matrizes triangular inferior e superior mostram a relação entre duas variáveis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = dados.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.gcf()\n",
    "sns.heatmap(corr, linewidths=.5, annot=True)\n",
    "lim_y = plt.ylim()\n",
    "plt.ylim(lim_y[0]+0.5, lim_y[1]-0.5)\n",
    "plt.yticks(rotation=0,va=\"center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cor = Y.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como observado, a correlação para Rating Ofensivo e Defensivo com o Rating de Força Futebolística é muito forte. Pois os melhores times fazem muito gols e levam poucos gols. Logo, nos melhores times, o Rating Ofensivo  é alto e por isso correlação positiva e o Rating Defensivo é baixo e por isso sua correlação negativa.\n",
    "\n",
    "Podemos perceber que a correlação entre Rating Ofensivo(Gols feitos) x Rating Defensivo(Gols levados) é forte. Pois  também é possível existirem times que fazem muitos gols e levam muitos gols."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 Qualificando a Regressão por Mínimos Quadrados\n",
    "\n",
    "Calculamos a regressão de mínimos quadrados para dois conjuntos de medidas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A função scipy.stats.linregress calcula uma regressão de mínimos quadrados para dois conjuntos de medidas:\n",
    "E retorna: a inclinação, interceptação, rvalue, pvalue, erro padrão da estimativa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "z2 = z1.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Conjunto Rating Ofensivo x Rating Defensivo}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b0, b1, r, pvalue, stder  = scs.stats.linregress(x1,y1)\n",
    "print('Parâmetros: b0 =', b0, 'b1 =', b1)\n",
    "print('Valor para o teste de hipótese que a inclinação é nula', pvalue)\n",
    "print(\"Coeficiente de Determininação:\", r**2)\n",
    "print(\"Desvio padrão da estimativa:\", stder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coeficiente de Determinação não indica uma boa qualidade de regressão, porém o desvio padrão dos erros indicam uma baixa variabilidade. Pelo teste de hipótese não podemos considerar a inclinação nula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x1, y1)\n",
    "b0, b1, r, pvalue, stder  = scs.stats.linregress(x1,y1)\n",
    "x_1 = np.linspace(x1.min(), y1.max(), 100)\n",
    "plt.plot(x_1, b0*x_1+b1, 'red')\n",
    "plt.text(3,2.8, r'$Y = \\alpha X + \\beta$' +'\\nY = '+str(round(b0, 3))+'X+'+str(round(b1, 3)), bbox=dict(facecolor='red', alpha=0.4))\n",
    "plt.title('Regressão Linear para Rating Ofensivo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Conjunto Rating Defensivo x Rating Ofensivo}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b0, b1, r, pvalue, stder = scs.stats.linregress(y1,x1)\n",
    "print('Parâmetros: b0 =', b0, 'b1 =', b1)\n",
    "print('Valor para o teste de hipótese que a inclinação é nula', pvalue)\n",
    "print(\"Coeficiente de Determininação:\", r**2)\n",
    "print(\"Desvio padrão da estimativa:\", stder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coeficiente de Determinação não indica uma boa qualidade de regressão, porém o desvio padrão dos erros indicam uma baixa variabilidade. Pelo teste de hipótese não podemos considerar a inclinação nula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(y1, x1)\n",
    "b0, b1, r, pvalue, stder  = scs.stats.linregress(y1,x1)\n",
    "x_1 = np.linspace(y1.min(), x1.max(), 100)\n",
    "plt.plot(x_1, b0*x_1+b1, 'red')\n",
    "plt.text(3,2.8, r'$Y = \\alpha X + \\beta$' +'\\nY = '+str(round(b0, 3))+'X+'+str(round(b1, 3)), bbox=dict(facecolor='red', alpha=0.4))\n",
    "plt.title('Regressão Linear para Rating Defensivo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Conjunto Rating Ofensivo x Rating de Força Futebolística}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b0, b1, r, pvalue, stder = scs.stats.linregress(x1,z2)\n",
    "print('Parâmetros: b0 =', b0, 'b1 =', b1)\n",
    "print('Valor para o teste de hipótese que a inclinação é nula', pvalue)\n",
    "print(\"Coeficiente de Determininação:\", r**2)\n",
    "print(\"Desvio padrão da estimativa:\", stder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coeficiente de Determinação indica uma boa qualidade de regressão, e o desvio padrão dos erros indicam uma baixa variabilidade. Pelo teste de hipótese não podemos considerar a inclinação nula."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\textit{Conjunto Rating Defensivo x Rating de Força Futebolística}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b0, b1, r, pvalue, stder = scs.stats.linregress(y1,z2)\n",
    "print('Parâmetros: b0 =', b0, 'b1 =', b1)\n",
    "print('Valor para o teste de hipótese que a inclinação é nula', pvalue)\n",
    "print(\"Coeficiente de Determininação:\", r**2)\n",
    "print(\"Desvio padrão da estimativa:\", stder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coeficiente de Determinação  indica uma boa qualidade de regressão, e o desvio padrão dos erros indicam uma baixa variabilidade. Pelo teste de hipótese não podemos considerar a inclinação nula."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir dessa análise prêvia podemos fazer um estudo mais detalhado do conjunto Rating Ofensivo x Rating de Força Futebolística pois foi o que apresentou melhor coeficiente de determinação.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xm = stats.mean(x1)\n",
    "ym = stats.mean(z2)\n",
    "xy = np.multiply(x1,z2)\n",
    "sxy = sum(xy)\n",
    "xx = np.multiply(x1,x1)\n",
    "sxx = sum(xx)\n",
    "n = len(x1)\n",
    "\n",
    "b1 = (sxy-n*xm*ym)/(sxx-n*xm*xm)\n",
    "b0 = ym-b1*xm\n",
    "\n",
    "est=[]\n",
    "for i in range(len(x1)):\n",
    "    est.append(b0+(b1*x[i]))\n",
    "print(est)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sse=0\n",
    "for i in range(len(x1)):\n",
    "    sse+=(z2[i]-b0-b1*x1[i])*(z2[i]-b0-b1*x1[i])\n",
    "sst=0\n",
    "for i in range(len(y)):\n",
    "    sst+=(z2[i]-ym)*(z2[i]-ym)\n",
    "r=(sst-sse)/sst\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirmando o R da Função scipy.stats.linregress. Podemos concluir que a qualidade da regressão é alta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ey=[]\n",
    "for i in range(len(z2)):\n",
    "    ey.append(z2[i]-est[i])\n",
    "print(ey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qme=sse/(n-2)\n",
    "serro=np.sqrt(qme)\n",
    "print('Desvio Padrão  dos erros:', serro)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sb0 = serro * math.sqrt(1/n+(xm*xm/(sxx-(n*(xm*xm)))))\n",
    "print('Desvio Padrão do parâmetro b0', sb0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sb1 = serro / math.sqrt(sxx-(n*(xm*xm)))\n",
    "print('Desvio Padrão do parâmetro b1', sb1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 Testes Visual de Pressuposto\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(x1,z2)\n",
    "plt.xlabel(\"Rating Ofensivo\")\n",
    "plt.ylabel(\"Rating de Força Futebólistica\")\n",
    "plt.title(\"Teste de Linearidade\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O teste de linearidade é bem válidado se pensarmos em uma reta ajustada aos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(ey,est)\n",
    "plt.xlabel(\"Estimadores\")\n",
    "plt.ylabel(\"Erros\")\n",
    "plt.title(\"Teste de Independência dos Erros\")\n",
    "plt.rcParams['figure.figsize'] = (15,7) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O erros aparentam seguir um padrão decrescente mostrando uma tendência visível, evidênciando uma dependência dos resíduos. Indicado que um modelo de regressão não linear sobre a amostra pode apresentar melhores resultados. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sm.qqplot(np.array(ey), scs.t, fit=True, line='45')\n",
    "plt.title(\"Erros Normais\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os erros estão seguindo uma distribuição normal. Então podemos predizer e estimar em nosso modelo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mediaerros= np.mean(est)\n",
    "print(mediaerros)\n",
    "plt.scatter(ey,est)\n",
    "plt.axhline(y=mediaerros, color='r', linestyle='-')\n",
    "plt.xlabel(\"Estimadores\")\n",
    "plt.ylabel(\"Erros\")\n",
    "plt.title(\"Homocedastiticidade\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A distribuição dos dados em torno da média dos resíduos está com uma tendência visivel, temos indícios que a variância dos resíduos não são homogêneas existindo heterocedasticidade. Essa tendência é um bom índicio para uso de regressão não-linear."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}