regressão_logistica.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CEtG6c4koygW"
   },
   "source": [
    "# Projeto #1 - Regressão\n",
    "\n",
    "<p style='text-align: justify;'><font size=3.>Análise de acidentes nas rodovias federais brasileira entre 2007 e 2021.</font></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IGJiaCUD1ncA"
   },
   "source": [
    "### Bibliotecas básicas e outros imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:40:59.466251Z",
     "start_time": "2022-02-15T09:40:57.705366Z"
    },
    "id": "4bG6r2Bwoyge"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "%run modules/text.py\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.family'] = 'Arial, Helvetica, sans-serif'\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "pd.set_option('display.max_columns', 200)\n",
    "np.set_printoptions(suppress=True, precision=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rjtF8niV1ncF"
   },
   "source": [
    "### Carregamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:03.774738Z",
     "start_time": "2022-02-15T09:40:59.467680Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "id": "H8Xxqiwcoygh",
    "outputId": "948da79f-e337-4029-b318-a0ef40832d18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linhas: 1635078 | Coluna:  24\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('dataset/regressor_acidentes_rodovias_brasileiras_2007_a_2021.csv', low_memory=False, encoding='utf-8')\n",
    "print(f'Linhas: {df.shape[0]} | Coluna:  {df.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove variáveis possíveis outliers\n",
    "> Condutores com mais de 100 e menos de 10 anos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:31.870785Z",
     "start_time": "2022-02-15T09:41:03.776206Z"
    }
   },
   "outputs": [],
   "source": [
    "df['outlier'] = df.apply(lambda x: identifierConductorsOver100OrUnder10YearsOld(x.tipo_envolvido, x.idade), axis=1)\n",
    "df.drop(df[df['outlier'] == 'outlier'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center"
   },
   "source": [
    "### Remove os indivíduos não expostos ao risco de morte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:32.577917Z",
     "start_time": "2022-02-15T09:41:31.872433Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linhas: 495313 | Coluna:  25\n"
     ]
    }
   ],
   "source": [
    "df.drop(df[df['estado_fisico'] == 'ileso'].index, inplace=True)\n",
    "print(f'Linhas: {df.shape[0]} | Coluna:  {df.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-05T13:14:42.729177Z",
     "start_time": "2022-02-05T13:14:42.681896Z"
    }
   },
   "source": [
    "### Definição da variável resposta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:32.673030Z",
     "start_time": "2022-02-15T09:41:32.579115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sobrevivente    93.87\n",
       "morto            6.13\n",
       "Name: estado_fisico, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['estado_fisico'] = df['estado_fisico'].map({'morto': 'morto', 'ferido leve': 'sobrevivente', 'ferido grave': 'sobrevivente'})\n",
    "round(df['estado_fisico'].value_counts(normalize=True)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geração de features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Causa presumível do acidente com base nas características"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:32.960518Z",
     "start_time": "2022-02-15T09:41:32.674448Z"
    }
   },
   "outputs": [],
   "source": [
    "df['causa_presumivel_acidente'] = df['causa_acidente'].apply(mapCausaAcidentes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define o horário do acidente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:33.064096Z",
     "start_time": "2022-02-15T09:41:32.961818Z"
    }
   },
   "outputs": [],
   "source": [
    "df['horario_acidente'] = df['horario'].apply(mapHorarioAcidentes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-05T14:28:09.231486Z",
     "start_time": "2022-02-05T14:28:09.225981Z"
    }
   },
   "source": [
    "Categorização do ano de fabricação do veículo com base na data do acidente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:33.082911Z",
     "start_time": "2022-02-15T09:41:33.065455Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = ['ate dez anos', 'mais de dez anos']\n",
    "classes = [0, 10, 170]\n",
    "\n",
    "df['tempo_fabricacao_no_ano_do_acidente'] = df['ano'] - df['ano_fabricacao_veiculo']\n",
    "df['faixa_ano_fabricacao'] = pd.cut(x=df['tempo_fabricacao_no_ano_do_acidente'], bins=classes, labels=labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupamento tipo de envolvido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:41.890563Z",
     "start_time": "2022-02-15T09:41:33.084224Z"
    }
   },
   "outputs": [],
   "source": [
    "df['tipo_envolvido'] = df.apply(lambda x: mapTipoEnvolvido(x.tipo_veiculo, x.tipo_envolvido), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-06T11:48:11.208743Z",
     "start_time": "2022-02-06T11:48:10.564076Z"
    }
   },
   "source": [
    "Combinação das variáveis tipo de veículo e tipo de envolvido\n",
    "\n",
    "> Identifica a forma como se deu a participação da vítima no acidente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:50.700926Z",
     "start_time": "2022-02-15T09:41:41.892918Z"
    }
   },
   "outputs": [],
   "source": [
    "df['condicao_envolvimento'] = df.apply(lambda x: mapCondicaoEnvolvimento(x.tipo_envolvido, x.tipo_veiculo), axis=1)\n",
    "df.drop(df[df['condicao_envolvimento'] == 'outros'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-06T12:19:56.673663Z",
     "start_time": "2022-02-06T12:19:56.666923Z"
    }
   },
   "source": [
    "Agrupamento dos tipos de acidentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:50.835627Z",
     "start_time": "2022-02-15T09:41:50.702198Z"
    }
   },
   "outputs": [],
   "source": [
    "df['tipo_acidente'] = df['tipo_acidente'].apply(mapTiposDeAcidentes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupamento traçado da via"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:50.908062Z",
     "start_time": "2022-02-15T09:41:50.836736Z"
    }
   },
   "outputs": [],
   "source": [
    "df['tracado_via'] = df['tracado_via'].apply(mapTracadoVia)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupamento idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:50.928270Z",
     "start_time": "2022-02-15T09:41:50.910263Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = ['0-14', '15-30', '30-44', '45-59', '60+']\n",
    "classes = [0, 14, 30, 44, 59, 99]\n",
    "df['faixa_etaria'] = pd.cut(x=df['idade'], bins=classes, labels=labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seleção de features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:50.977029Z",
     "start_time": "2022-02-15T09:41:50.929799Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linhas: 495173 | Coluna:  13\n"
     ]
    },
    {
     "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>sexo</th>\n",
       "      <th>faixa_etaria</th>\n",
       "      <th>condicao_envolvimento</th>\n",
       "      <th>tipo_acidente</th>\n",
       "      <th>tipo_pista</th>\n",
       "      <th>tracado_via</th>\n",
       "      <th>dia_semana</th>\n",
       "      <th>horario_acidente</th>\n",
       "      <th>uso_solo</th>\n",
       "      <th>regiao_pais</th>\n",
       "      <th>faixa_ano_fabricacao</th>\n",
       "      <th>causa_presumivel_acidente</th>\n",
       "      <th>estado_fisico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>masculino</td>\n",
       "      <td>30-44</td>\n",
       "      <td>ocupante de motocicleta</td>\n",
       "      <td>colisao traseira</td>\n",
       "      <td>simples</td>\n",
       "      <td>reta</td>\n",
       "      <td>quarta</td>\n",
       "      <td>madrugada</td>\n",
       "      <td>urbano</td>\n",
       "      <td>centro-oeste</td>\n",
       "      <td>ate dez anos</td>\n",
       "      <td>outros aspectos não identificados</td>\n",
       "      <td>sobrevivente</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sexo faixa_etaria    condicao_envolvimento     tipo_acidente  \\\n",
       "8  masculino        30-44  ocupante de motocicleta  colisao traseira   \n",
       "\n",
       "  tipo_pista tracado_via dia_semana horario_acidente uso_solo   regiao_pais  \\\n",
       "8    simples        reta     quarta        madrugada   urbano  centro-oeste   \n",
       "\n",
       "  faixa_ano_fabricacao          causa_presumivel_acidente estado_fisico  \n",
       "8         ate dez anos  outros aspectos não identificados  sobrevivente  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas = [\n",
    "    'sexo',\n",
    "    'faixa_etaria',\n",
    "    'condicao_envolvimento',\n",
    "    'tipo_acidente',\n",
    "    'tipo_pista',\n",
    "    'tracado_via',    \n",
    "    'dia_semana',\n",
    "    'horario_acidente',\n",
    "    'uso_solo',\n",
    "    'regiao_pais',\n",
    "    'faixa_ano_fabricacao',\n",
    "    'causa_presumivel_acidente',\n",
    "    'estado_fisico'\n",
    "]\n",
    "dados = df[colunas]\n",
    "print(f'Linhas: {dados.shape[0]} | Coluna:  {dados.shape[1]}')\n",
    "dados.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribuição das variáveis explicativas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:41:51.400240Z",
     "start_time": "2022-02-15T09:41:50.978294Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "masculino    84.58\n",
      "feminino     15.42\n",
      "Name: sexo, dtype: float64\n",
      "---------------------------------\n",
      "15-30    40.11\n",
      "30-44    34.52\n",
      "45-59    18.65\n",
      "60+       6.27\n",
      "0-14      0.45\n",
      "Name: faixa_etaria, dtype: float64\n",
      "---------------------------------\n",
      "ocupante de motocicleta              37.14\n",
      "ocupante de automóveis               35.28\n",
      "ocupante de caminhão                 13.38\n",
      "ocupante de caminhonete/camioneta     7.94\n",
      "ocupante de motoneta                  4.05\n",
      "pedestre                              1.33\n",
      "ocupante de micro-ônibus/ônibus       0.80\n",
      "ciclista                              0.08\n",
      "Name: condicao_envolvimento, dtype: float64\n",
      "---------------------------------\n",
      "saída da pista              20.10\n",
      "capotamento/tombamento      16.53\n",
      "colisao traseira            12.79\n",
      "colisao transversal         10.77\n",
      "queda de veículo             9.89\n",
      "colisao lateral              9.71\n",
      "outros                       7.52\n",
      "colisao frontal              7.48\n",
      "atropelamento de pessoas     2.54\n",
      "atropelamento de animal      2.17\n",
      "colisao com bicicleta        0.49\n",
      "Name: tipo_acidente, dtype: float64\n",
      "---------------------------------\n",
      "simples     57.55\n",
      "dupla       36.62\n",
      "multipla     5.84\n",
      "Name: tipo_pista, dtype: float64\n",
      "---------------------------------\n",
      "reta          66.85\n",
      "curva         25.48\n",
      "outros         4.07\n",
      "cruzamento     3.59\n",
      "Name: tracado_via, dtype: float64\n",
      "---------------------------------\n",
      "domingo    17.68\n",
      "sabado     17.25\n",
      "sexta      15.03\n",
      "segunda    13.26\n",
      "quinta     12.70\n",
      "quarta     12.17\n",
      "terca      11.92\n",
      "Name: dia_semana, dtype: float64\n",
      "---------------------------------\n",
      "tarde        31.62\n",
      "manhã        27.07\n",
      "noite        26.87\n",
      "madrugada    14.45\n",
      "Name: horario_acidente, dtype: float64\n",
      "---------------------------------\n",
      "rural     56.62\n",
      "urbano    43.38\n",
      "Name: uso_solo, dtype: float64\n",
      "---------------------------------\n",
      "sudeste         32.50\n",
      "sul             29.24\n",
      "nordeste        20.79\n",
      "centro-oeste    11.78\n",
      "norte            5.69\n",
      "Name: regiao_pais, dtype: float64\n",
      "---------------------------------\n",
      "ate dez anos        74.68\n",
      "mais de dez anos    25.32\n",
      "Name: faixa_ano_fabricacao, dtype: float64\n",
      "---------------------------------\n",
      "aspectos relacionados ao comportamento do condutor    52.95\n",
      "outros aspectos não identificados                     34.73\n",
      "aspectos relacionados ao veículo                       7.24\n",
      "aspectos relacionados a via ou ambiente                5.09\n",
      "Name: causa_presumivel_acidente, dtype: float64\n",
      "---------------------------------\n",
      "sobrevivente    93.87\n",
      "morto            6.13\n",
      "Name: estado_fisico, dtype: float64\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in dados.columns:\n",
    "    print(round(dados[col].value_counts(normalize=True)*100, 2))\n",
    "    print('---------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regressão logística binomial\n",
    "\n",
    "> Para todos os testes será empregado o nível de confiança de 95%, ou seja, p-valor < 0.05 indica que os coeficientes estimados foram estatisticamente significativos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testando interação entre sexo e idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:42:28.030045Z",
     "start_time": "2022-02-15T09:41:51.401577Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['estado_fisico[morto]', 'estado_fisico[sobrevivente]']</td> <th>  No. Observations:  </th>   <td>495173</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                     <td>GLM</td>                           <th>  Df Residuals:      </th>   <td>495123</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>                           <td>Binomial</td>                         <th>  Df Model:          </th>   <td>    49</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>                            <td>Logit</td>                          <th>  Scale:             </th>  <td>  1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                                   <td>IRLS</td>                           <th>  Log-Likelihood:    </th>  <td> -99211.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                               <td>Tue, 15 Feb 2022</td>                     <th>  Deviance:          </th> <td>1.9842e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                                   <td>06:42:27</td>                         <th>  Pearson chi2:      </th>  <td>4.82e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>                             <td>7</td>                            <th>  Pseudo R-squ. (CS):</th>   <td>0.05854</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                        <td>nonrobust</td>                        <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                         <td></td>                                            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                                       <td>   -2.5725</td> <td>    0.274</td> <td>   -9.399</td> <td> 0.000</td> <td>   -3.109</td> <td>   -2.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]</th>                                                               <td>    0.0624</td> <td>    0.258</td> <td>    0.242</td> <td> 0.808</td> <td>   -0.442</td> <td>    0.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.15-30]</th>                                                           <td>    0.1326</td> <td>    0.200</td> <td>    0.664</td> <td> 0.506</td> <td>   -0.259</td> <td>    0.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.30-44]</th>                                                           <td>    0.3026</td> <td>    0.200</td> <td>    1.515</td> <td> 0.130</td> <td>   -0.089</td> <td>    0.694</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.45-59]</th>                                                           <td>    0.4982</td> <td>    0.202</td> <td>    2.467</td> <td> 0.014</td> <td>    0.102</td> <td>    0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.60+]</th>                                                             <td>    0.8914</td> <td>    0.208</td> <td>    4.290</td> <td> 0.000</td> <td>    0.484</td> <td>    1.299</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de automóveis]</th>                                 <td>   -1.4537</td> <td>    0.179</td> <td>   -8.138</td> <td> 0.000</td> <td>   -1.804</td> <td>   -1.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de caminhonete/camioneta]</th>                      <td>   -1.4665</td> <td>    0.180</td> <td>   -8.163</td> <td> 0.000</td> <td>   -1.819</td> <td>   -1.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de caminhão]</th>                                   <td>   -1.0797</td> <td>    0.179</td> <td>   -6.031</td> <td> 0.000</td> <td>   -1.431</td> <td>   -0.729</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]</th>                        <td>   -1.6495</td> <td>    0.192</td> <td>   -8.590</td> <td> 0.000</td> <td>   -2.026</td> <td>   -1.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de motocicleta]</th>                                <td>   -0.8320</td> <td>    0.178</td> <td>   -4.663</td> <td> 0.000</td> <td>   -1.182</td> <td>   -0.482</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de motoneta]</th>                                   <td>   -1.0682</td> <td>    0.183</td> <td>   -5.832</td> <td> 0.000</td> <td>   -1.427</td> <td>   -0.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.pedestre]</th>                                               <td>    1.0603</td> <td>    0.188</td> <td>    5.629</td> <td> 0.000</td> <td>    0.691</td> <td>    1.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.atropelamento de pessoas]</th>                                       <td>   -0.4162</td> <td>    0.080</td> <td>   -5.201</td> <td> 0.000</td> <td>   -0.573</td> <td>   -0.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.capotamento/tombamento]</th>                                         <td>   -0.5225</td> <td>    0.059</td> <td>   -8.860</td> <td> 0.000</td> <td>   -0.638</td> <td>   -0.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao com bicicleta]</th>                                          <td>   -1.2984</td> <td>    0.157</td> <td>   -8.277</td> <td> 0.000</td> <td>   -1.606</td> <td>   -0.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao frontal]</th>                                                <td>    1.4685</td> <td>    0.058</td> <td>   25.474</td> <td> 0.000</td> <td>    1.355</td> <td>    1.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao lateral]</th>                                                <td>   -0.1998</td> <td>    0.061</td> <td>   -3.295</td> <td> 0.001</td> <td>   -0.319</td> <td>   -0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao transversal]</th>                                            <td>    0.0098</td> <td>    0.061</td> <td>    0.162</td> <td> 0.871</td> <td>   -0.109</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao traseira]</th>                                               <td>   -0.3250</td> <td>    0.060</td> <td>   -5.394</td> <td> 0.000</td> <td>   -0.443</td> <td>   -0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.outros]</th>                                                         <td>    0.0918</td> <td>    0.060</td> <td>    1.531</td> <td> 0.126</td> <td>   -0.026</td> <td>    0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.queda de veículo]</th>                                               <td>   -0.8793</td> <td>    0.062</td> <td>  -14.209</td> <td> 0.000</td> <td>   -1.001</td> <td>   -0.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.saída da pista]</th>                                                 <td>   -0.2065</td> <td>    0.058</td> <td>   -3.573</td> <td> 0.000</td> <td>   -0.320</td> <td>   -0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.multipla]</th>                                                          <td>   -0.0544</td> <td>    0.038</td> <td>   -1.436</td> <td> 0.151</td> <td>   -0.129</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.simples]</th>                                                           <td>    0.3011</td> <td>    0.016</td> <td>   19.018</td> <td> 0.000</td> <td>    0.270</td> <td>    0.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.curva]</th>                                                            <td>    0.3728</td> <td>    0.047</td> <td>    7.961</td> <td> 0.000</td> <td>    0.281</td> <td>    0.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.outros]</th>                                                           <td>   -0.0540</td> <td>    0.060</td> <td>   -0.898</td> <td> 0.369</td> <td>   -0.172</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.reta]</th>                                                             <td>    0.3623</td> <td>    0.045</td> <td>    7.966</td> <td> 0.000</td> <td>    0.273</td> <td>    0.451</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quarta]</th>                                                            <td>   -0.2052</td> <td>    0.023</td> <td>   -8.838</td> <td> 0.000</td> <td>   -0.251</td> <td>   -0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quinta]</th>                                                            <td>   -0.2002</td> <td>    0.023</td> <td>   -8.827</td> <td> 0.000</td> <td>   -0.245</td> <td>   -0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sabado]</th>                                                            <td>   -0.0992</td> <td>    0.020</td> <td>   -5.017</td> <td> 0.000</td> <td>   -0.138</td> <td>   -0.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.segunda]</th>                                                           <td>   -0.2192</td> <td>    0.023</td> <td>   -9.738</td> <td> 0.000</td> <td>   -0.263</td> <td>   -0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sexta]</th>                                                             <td>   -0.1958</td> <td>    0.021</td> <td>   -9.124</td> <td> 0.000</td> <td>   -0.238</td> <td>   -0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.terca]</th>                                                             <td>   -0.2322</td> <td>    0.024</td> <td>   -9.863</td> <td> 0.000</td> <td>   -0.278</td> <td>   -0.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.manhã]</th>                                                       <td>   -0.7492</td> <td>    0.020</td> <td>  -38.158</td> <td> 0.000</td> <td>   -0.788</td> <td>   -0.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.noite]</th>                                                       <td>   -0.3944</td> <td>    0.018</td> <td>  -21.961</td> <td> 0.000</td> <td>   -0.430</td> <td>   -0.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.tarde]</th>                                                       <td>   -0.7310</td> <td>    0.019</td> <td>  -39.105</td> <td> 0.000</td> <td>   -0.768</td> <td>   -0.694</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uso_solo[T.urbano]</th>                                                              <td>   -0.6766</td> <td>    0.016</td> <td>  -43.029</td> <td> 0.000</td> <td>   -0.707</td> <td>   -0.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.nordeste]</th>                                                         <td>    0.2343</td> <td>    0.021</td> <td>   11.163</td> <td> 0.000</td> <td>    0.193</td> <td>    0.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.norte]</th>                                                            <td>    0.0136</td> <td>    0.031</td> <td>    0.440</td> <td> 0.660</td> <td>   -0.047</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sudeste]</th>                                                          <td>   -0.3436</td> <td>    0.021</td> <td>  -16.158</td> <td> 0.000</td> <td>   -0.385</td> <td>   -0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sul]</th>                                                              <td>   -0.3390</td> <td>    0.022</td> <td>  -15.502</td> <td> 0.000</td> <td>   -0.382</td> <td>   -0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_ano_fabricacao[T.mais de dez anos]</th>                                        <td>    0.2065</td> <td>    0.014</td> <td>   14.607</td> <td> 0.000</td> <td>    0.179</td> <td>    0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]</th> <td>    0.3082</td> <td>    0.044</td> <td>    6.996</td> <td> 0.000</td> <td>    0.222</td> <td>    0.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao veículo]</th>                   <td>    0.0342</td> <td>    0.053</td> <td>    0.643</td> <td> 0.520</td> <td>   -0.070</td> <td>    0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.outros aspectos não identificados]</th>                  <td>    0.9956</td> <td>    0.044</td> <td>   22.724</td> <td> 0.000</td> <td>    0.910</td> <td>    1.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:faixa_etaria[T.15-30]</th>                                         <td>    0.5458</td> <td>    0.261</td> <td>    2.094</td> <td> 0.036</td> <td>    0.035</td> <td>    1.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:faixa_etaria[T.30-44]</th>                                         <td>    0.5210</td> <td>    0.261</td> <td>    1.998</td> <td> 0.046</td> <td>    0.010</td> <td>    1.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:faixa_etaria[T.45-59]</th>                                         <td>    0.5177</td> <td>    0.263</td> <td>    1.971</td> <td> 0.049</td> <td>    0.003</td> <td>    1.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:faixa_etaria[T.60+]</th>                                           <td>    0.3952</td> <td>    0.268</td> <td>    1.476</td> <td> 0.140</td> <td>   -0.129</td> <td>    0.920</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                    Generalized Linear Model Regression Results                                    \n",
       "===================================================================================================================\n",
       "Dep. Variable:     ['estado_fisico[morto]', 'estado_fisico[sobrevivente]']   No. Observations:               495173\n",
       "Model:                                                                 GLM   Df Residuals:                   495123\n",
       "Model Family:                                                     Binomial   Df Model:                           49\n",
       "Link Function:                                                       Logit   Scale:                          1.0000\n",
       "Method:                                                               IRLS   Log-Likelihood:                -99211.\n",
       "Date:                                                     Tue, 15 Feb 2022   Deviance:                   1.9842e+05\n",
       "Time:                                                             06:42:27   Pearson chi2:                 4.82e+05\n",
       "No. Iterations:                                                          7   Pseudo R-squ. (CS):            0.05854\n",
       "Covariance Type:                                                 nonrobust                                         \n",
       "===================================================================================================================================================\n",
       "                                                                                      coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                                          -2.5725      0.274     -9.399      0.000      -3.109      -2.036\n",
       "sexo[T.masculino]                                                                   0.0624      0.258      0.242      0.808      -0.442       0.567\n",
       "faixa_etaria[T.15-30]                                                               0.1326      0.200      0.664      0.506      -0.259       0.524\n",
       "faixa_etaria[T.30-44]                                                               0.3026      0.200      1.515      0.130      -0.089       0.694\n",
       "faixa_etaria[T.45-59]                                                               0.4982      0.202      2.467      0.014       0.102       0.894\n",
       "faixa_etaria[T.60+]                                                                 0.8914      0.208      4.290      0.000       0.484       1.299\n",
       "condicao_envolvimento[T.ocupante de automóveis]                                    -1.4537      0.179     -8.138      0.000      -1.804      -1.104\n",
       "condicao_envolvimento[T.ocupante de caminhonete/camioneta]                         -1.4665      0.180     -8.163      0.000      -1.819      -1.114\n",
       "condicao_envolvimento[T.ocupante de caminhão]                                      -1.0797      0.179     -6.031      0.000      -1.431      -0.729\n",
       "condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]                           -1.6495      0.192     -8.590      0.000      -2.026      -1.273\n",
       "condicao_envolvimento[T.ocupante de motocicleta]                                   -0.8320      0.178     -4.663      0.000      -1.182      -0.482\n",
       "condicao_envolvimento[T.ocupante de motoneta]                                      -1.0682      0.183     -5.832      0.000      -1.427      -0.709\n",
       "condicao_envolvimento[T.pedestre]                                                   1.0603      0.188      5.629      0.000       0.691       1.430\n",
       "tipo_acidente[T.atropelamento de pessoas]                                          -0.4162      0.080     -5.201      0.000      -0.573      -0.259\n",
       "tipo_acidente[T.capotamento/tombamento]                                            -0.5225      0.059     -8.860      0.000      -0.638      -0.407\n",
       "tipo_acidente[T.colisao com bicicleta]                                             -1.2984      0.157     -8.277      0.000      -1.606      -0.991\n",
       "tipo_acidente[T.colisao frontal]                                                    1.4685      0.058     25.474      0.000       1.355       1.581\n",
       "tipo_acidente[T.colisao lateral]                                                   -0.1998      0.061     -3.295      0.001      -0.319      -0.081\n",
       "tipo_acidente[T.colisao transversal]                                                0.0098      0.061      0.162      0.871      -0.109       0.129\n",
       "tipo_acidente[T.colisao traseira]                                                  -0.3250      0.060     -5.394      0.000      -0.443      -0.207\n",
       "tipo_acidente[T.outros]                                                             0.0918      0.060      1.531      0.126      -0.026       0.209\n",
       "tipo_acidente[T.queda de veículo]                                                  -0.8793      0.062    -14.209      0.000      -1.001      -0.758\n",
       "tipo_acidente[T.saída da pista]                                                    -0.2065      0.058     -3.573      0.000      -0.320      -0.093\n",
       "tipo_pista[T.multipla]                                                             -0.0544      0.038     -1.436      0.151      -0.129       0.020\n",
       "tipo_pista[T.simples]                                                               0.3011      0.016     19.018      0.000       0.270       0.332\n",
       "tracado_via[T.curva]                                                                0.3728      0.047      7.961      0.000       0.281       0.465\n",
       "tracado_via[T.outros]                                                              -0.0540      0.060     -0.898      0.369      -0.172       0.064\n",
       "tracado_via[T.reta]                                                                 0.3623      0.045      7.966      0.000       0.273       0.451\n",
       "dia_semana[T.quarta]                                                               -0.2052      0.023     -8.838      0.000      -0.251      -0.160\n",
       "dia_semana[T.quinta]                                                               -0.2002      0.023     -8.827      0.000      -0.245      -0.156\n",
       "dia_semana[T.sabado]                                                               -0.0992      0.020     -5.017      0.000      -0.138      -0.060\n",
       "dia_semana[T.segunda]                                                              -0.2192      0.023     -9.738      0.000      -0.263      -0.175\n",
       "dia_semana[T.sexta]                                                                -0.1958      0.021     -9.124      0.000      -0.238      -0.154\n",
       "dia_semana[T.terca]                                                                -0.2322      0.024     -9.863      0.000      -0.278      -0.186\n",
       "horario_acidente[T.manhã]                                                          -0.7492      0.020    -38.158      0.000      -0.788      -0.711\n",
       "horario_acidente[T.noite]                                                          -0.3944      0.018    -21.961      0.000      -0.430      -0.359\n",
       "horario_acidente[T.tarde]                                                          -0.7310      0.019    -39.105      0.000      -0.768      -0.694\n",
       "uso_solo[T.urbano]                                                                 -0.6766      0.016    -43.029      0.000      -0.707      -0.646\n",
       "regiao_pais[T.nordeste]                                                             0.2343      0.021     11.163      0.000       0.193       0.275\n",
       "regiao_pais[T.norte]                                                                0.0136      0.031      0.440      0.660      -0.047       0.074\n",
       "regiao_pais[T.sudeste]                                                             -0.3436      0.021    -16.158      0.000      -0.385      -0.302\n",
       "regiao_pais[T.sul]                                                                 -0.3390      0.022    -15.502      0.000      -0.382      -0.296\n",
       "faixa_ano_fabricacao[T.mais de dez anos]                                            0.2065      0.014     14.607      0.000       0.179       0.234\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]     0.3082      0.044      6.996      0.000       0.222       0.395\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao veículo]                       0.0342      0.053      0.643      0.520      -0.070       0.138\n",
       "causa_presumivel_acidente[T.outros aspectos não identificados]                      0.9956      0.044     22.724      0.000       0.910       1.081\n",
       "sexo[T.masculino]:faixa_etaria[T.15-30]                                             0.5458      0.261      2.094      0.036       0.035       1.057\n",
       "sexo[T.masculino]:faixa_etaria[T.30-44]                                             0.5210      0.261      1.998      0.046       0.010       1.032\n",
       "sexo[T.masculino]:faixa_etaria[T.45-59]                                             0.5177      0.263      1.971      0.049       0.003       1.032\n",
       "sexo[T.masculino]:faixa_etaria[T.60+]                                               0.3952      0.268      1.476      0.140      -0.129       0.920\n",
       "===================================================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula='estado_fisico ~ sexo+faixa_etaria+condicao_envolvimento+tipo_acidente+tipo_pista+tracado_via+dia_semana+horario_acidente+uso_solo+regiao_pais+faixa_ano_fabricacao+causa_presumivel_acidente+sexo*faixa_etaria', \n",
    "                 data=dados, family=sm.families.Binomial()).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> <p style='text-align: justify;'><font size=2.85>O p-valor indica que não existe efeito de interação entre as variáveis sexo e idade.<font size=2.85></font></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testando interação entre condição de envolvimento e sexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:43:01.215254Z",
     "start_time": "2022-02-15T09:42:28.031186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['estado_fisico[morto]', 'estado_fisico[sobrevivente]']</td> <th>  No. Observations:  </th>   <td>495173</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                     <td>GLM</td>                           <th>  Df Residuals:      </th>   <td>495124</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>                           <td>Binomial</td>                         <th>  Df Model:          </th>   <td>    48</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>                            <td>Logit</td>                          <th>  Scale:             </th>  <td>  1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                                   <td>IRLS</td>                           <th>  Log-Likelihood:    </th>  <td> -99613.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                               <td>Tue, 15 Feb 2022</td>                     <th>  Deviance:          </th> <td>1.9923e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                                   <td>06:43:01</td>                         <th>  Pearson chi2:      </th>  <td>4.85e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>                             <td>7</td>                            <th>  Pseudo R-squ. (CS):</th>   <td>0.05701</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                        <td>nonrobust</td>                        <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                         <td></td>                                            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                                       <td>   -2.6206</td> <td>    1.028</td> <td>   -2.549</td> <td> 0.011</td> <td>   -4.636</td> <td>   -0.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]</th>                                                               <td>    0.9982</td> <td>    1.041</td> <td>    0.959</td> <td> 0.337</td> <td>   -1.041</td> <td>    3.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de automóveis]</th>                                 <td>   -1.1232</td> <td>    1.026</td> <td>   -1.094</td> <td> 0.274</td> <td>   -3.135</td> <td>    0.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de caminhonete/camioneta]</th>                      <td>   -0.9426</td> <td>    1.029</td> <td>   -0.916</td> <td> 0.360</td> <td>   -2.959</td> <td>    1.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de caminhão]</th>                                   <td>   -0.4606</td> <td>    1.035</td> <td>   -0.445</td> <td> 0.656</td> <td>   -2.488</td> <td>    1.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]</th>                        <td>   -1.0522</td> <td>    1.050</td> <td>   -1.002</td> <td> 0.316</td> <td>   -3.110</td> <td>    1.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de motocicleta]</th>                                <td>   -0.6659</td> <td>    1.027</td> <td>   -0.648</td> <td> 0.517</td> <td>   -2.679</td> <td>    1.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de motoneta]</th>                                   <td>   -0.9144</td> <td>    1.030</td> <td>   -0.888</td> <td> 0.375</td> <td>   -2.933</td> <td>    1.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.pedestre]</th>                                               <td>    1.5868</td> <td>    1.030</td> <td>    1.541</td> <td> 0.123</td> <td>   -0.431</td> <td>    3.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.atropelamento de pessoas]</th>                                       <td>   -0.4188</td> <td>    0.080</td> <td>   -5.263</td> <td> 0.000</td> <td>   -0.575</td> <td>   -0.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.capotamento/tombamento]</th>                                         <td>   -0.5380</td> <td>    0.059</td> <td>   -9.130</td> <td> 0.000</td> <td>   -0.654</td> <td>   -0.423</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao com bicicleta]</th>                                          <td>   -1.3040</td> <td>    0.157</td> <td>   -8.324</td> <td> 0.000</td> <td>   -1.611</td> <td>   -0.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao frontal]</th>                                                <td>    1.4657</td> <td>    0.058</td> <td>   25.457</td> <td> 0.000</td> <td>    1.353</td> <td>    1.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao lateral]</th>                                                <td>   -0.1966</td> <td>    0.061</td> <td>   -3.245</td> <td> 0.001</td> <td>   -0.315</td> <td>   -0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao transversal]</th>                                            <td>    0.0249</td> <td>    0.061</td> <td>    0.411</td> <td> 0.681</td> <td>   -0.094</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao traseira]</th>                                               <td>   -0.3243</td> <td>    0.060</td> <td>   -5.389</td> <td> 0.000</td> <td>   -0.442</td> <td>   -0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.outros]</th>                                                         <td>    0.0890</td> <td>    0.060</td> <td>    1.486</td> <td> 0.137</td> <td>   -0.028</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.queda de veículo]</th>                                               <td>   -0.8698</td> <td>    0.062</td> <td>  -14.071</td> <td> 0.000</td> <td>   -0.991</td> <td>   -0.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.saída da pista]</th>                                                 <td>   -0.2156</td> <td>    0.058</td> <td>   -3.735</td> <td> 0.000</td> <td>   -0.329</td> <td>   -0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.multipla]</th>                                                          <td>   -0.0535</td> <td>    0.038</td> <td>   -1.414</td> <td> 0.157</td> <td>   -0.128</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.simples]</th>                                                           <td>    0.3067</td> <td>    0.016</td> <td>   19.393</td> <td> 0.000</td> <td>    0.276</td> <td>    0.338</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.curva]</th>                                                            <td>    0.3658</td> <td>    0.047</td> <td>    7.815</td> <td> 0.000</td> <td>    0.274</td> <td>    0.457</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.outros]</th>                                                           <td>   -0.0414</td> <td>    0.060</td> <td>   -0.689</td> <td> 0.491</td> <td>   -0.159</td> <td>    0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.reta]</th>                                                             <td>    0.3622</td> <td>    0.045</td> <td>    7.969</td> <td> 0.000</td> <td>    0.273</td> <td>    0.451</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quarta]</th>                                                            <td>   -0.1864</td> <td>    0.023</td> <td>   -8.040</td> <td> 0.000</td> <td>   -0.232</td> <td>   -0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quinta]</th>                                                            <td>   -0.1821</td> <td>    0.023</td> <td>   -8.042</td> <td> 0.000</td> <td>   -0.226</td> <td>   -0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sabado]</th>                                                            <td>   -0.0944</td> <td>    0.020</td> <td>   -4.783</td> <td> 0.000</td> <td>   -0.133</td> <td>   -0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.segunda]</th>                                                           <td>   -0.2028</td> <td>    0.022</td> <td>   -9.027</td> <td> 0.000</td> <td>   -0.247</td> <td>   -0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sexta]</th>                                                             <td>   -0.1814</td> <td>    0.021</td> <td>   -8.469</td> <td> 0.000</td> <td>   -0.223</td> <td>   -0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.terca]</th>                                                             <td>   -0.2146</td> <td>    0.024</td> <td>   -9.129</td> <td> 0.000</td> <td>   -0.261</td> <td>   -0.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.manhã]</th>                                                       <td>   -0.6967</td> <td>    0.020</td> <td>  -35.729</td> <td> 0.000</td> <td>   -0.735</td> <td>   -0.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.noite]</th>                                                       <td>   -0.3580</td> <td>    0.018</td> <td>  -20.021</td> <td> 0.000</td> <td>   -0.393</td> <td>   -0.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.tarde]</th>                                                       <td>   -0.6742</td> <td>    0.019</td> <td>  -36.363</td> <td> 0.000</td> <td>   -0.711</td> <td>   -0.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uso_solo[T.urbano]</th>                                                              <td>   -0.6905</td> <td>    0.016</td> <td>  -43.946</td> <td> 0.000</td> <td>   -0.721</td> <td>   -0.660</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.nordeste]</th>                                                         <td>    0.2319</td> <td>    0.021</td> <td>   11.072</td> <td> 0.000</td> <td>    0.191</td> <td>    0.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.norte]</th>                                                            <td>    0.0094</td> <td>    0.031</td> <td>    0.305</td> <td> 0.760</td> <td>   -0.051</td> <td>    0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sudeste]</th>                                                          <td>   -0.3459</td> <td>    0.021</td> <td>  -16.295</td> <td> 0.000</td> <td>   -0.388</td> <td>   -0.304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sul]</th>                                                              <td>   -0.3523</td> <td>    0.022</td> <td>  -16.148</td> <td> 0.000</td> <td>   -0.395</td> <td>   -0.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_ano_fabricacao[T.mais de dez anos]</th>                                        <td>    0.2298</td> <td>    0.014</td> <td>   16.297</td> <td> 0.000</td> <td>    0.202</td> <td>    0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]</th> <td>    0.3020</td> <td>    0.044</td> <td>    6.865</td> <td> 0.000</td> <td>    0.216</td> <td>    0.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao veículo]</th>                   <td>    0.0238</td> <td>    0.053</td> <td>    0.449</td> <td> 0.653</td> <td>   -0.080</td> <td>    0.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.outros aspectos não identificados]</th>                  <td>    0.9761</td> <td>    0.044</td> <td>   22.313</td> <td> 0.000</td> <td>    0.890</td> <td>    1.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:condicao_envolvimento[T.ocupante de automóveis]</th>               <td>   -0.3864</td> <td>    1.041</td> <td>   -0.371</td> <td> 0.711</td> <td>   -2.427</td> <td>    1.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:condicao_envolvimento[T.ocupante de caminhonete/camioneta]</th>    <td>   -0.5533</td> <td>    1.044</td> <td>   -0.530</td> <td> 0.596</td> <td>   -2.599</td> <td>    1.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:condicao_envolvimento[T.ocupante de caminhão]</th>                 <td>   -0.6606</td> <td>    1.049</td> <td>   -0.630</td> <td> 0.529</td> <td>   -2.717</td> <td>    1.396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]</th>      <td>   -0.6286</td> <td>    1.067</td> <td>   -0.589</td> <td> 0.556</td> <td>   -2.720</td> <td>    1.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:condicao_envolvimento[T.ocupante de motocicleta]</th>              <td>   -0.2935</td> <td>    1.042</td> <td>   -0.282</td> <td> 0.778</td> <td>   -2.336</td> <td>    1.749</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:condicao_envolvimento[T.ocupante de motoneta]</th>                 <td>   -0.2470</td> <td>    1.045</td> <td>   -0.236</td> <td> 0.813</td> <td>   -2.296</td> <td>    1.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]:condicao_envolvimento[T.pedestre]</th>                             <td>   -0.5411</td> <td>    1.044</td> <td>   -0.518</td> <td> 0.604</td> <td>   -2.587</td> <td>    1.505</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                    Generalized Linear Model Regression Results                                    \n",
       "===================================================================================================================\n",
       "Dep. Variable:     ['estado_fisico[morto]', 'estado_fisico[sobrevivente]']   No. Observations:               495173\n",
       "Model:                                                                 GLM   Df Residuals:                   495124\n",
       "Model Family:                                                     Binomial   Df Model:                           48\n",
       "Link Function:                                                       Logit   Scale:                          1.0000\n",
       "Method:                                                               IRLS   Log-Likelihood:                -99613.\n",
       "Date:                                                     Tue, 15 Feb 2022   Deviance:                   1.9923e+05\n",
       "Time:                                                             06:43:01   Pearson chi2:                 4.85e+05\n",
       "No. Iterations:                                                          7   Pseudo R-squ. (CS):            0.05701\n",
       "Covariance Type:                                                 nonrobust                                         \n",
       "===================================================================================================================================================\n",
       "                                                                                      coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                                          -2.6206      1.028     -2.549      0.011      -4.636      -0.605\n",
       "sexo[T.masculino]                                                                   0.9982      1.041      0.959      0.337      -1.041       3.038\n",
       "condicao_envolvimento[T.ocupante de automóveis]                                    -1.1232      1.026     -1.094      0.274      -3.135       0.889\n",
       "condicao_envolvimento[T.ocupante de caminhonete/camioneta]                         -0.9426      1.029     -0.916      0.360      -2.959       1.074\n",
       "condicao_envolvimento[T.ocupante de caminhão]                                      -0.4606      1.035     -0.445      0.656      -2.488       1.567\n",
       "condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]                           -1.0522      1.050     -1.002      0.316      -3.110       1.006\n",
       "condicao_envolvimento[T.ocupante de motocicleta]                                   -0.6659      1.027     -0.648      0.517      -2.679       1.348\n",
       "condicao_envolvimento[T.ocupante de motoneta]                                      -0.9144      1.030     -0.888      0.375      -2.933       1.104\n",
       "condicao_envolvimento[T.pedestre]                                                   1.5868      1.030      1.541      0.123      -0.431       3.605\n",
       "tipo_acidente[T.atropelamento de pessoas]                                          -0.4188      0.080     -5.263      0.000      -0.575      -0.263\n",
       "tipo_acidente[T.capotamento/tombamento]                                            -0.5380      0.059     -9.130      0.000      -0.654      -0.423\n",
       "tipo_acidente[T.colisao com bicicleta]                                             -1.3040      0.157     -8.324      0.000      -1.611      -0.997\n",
       "tipo_acidente[T.colisao frontal]                                                    1.4657      0.058     25.457      0.000       1.353       1.579\n",
       "tipo_acidente[T.colisao lateral]                                                   -0.1966      0.061     -3.245      0.001      -0.315      -0.078\n",
       "tipo_acidente[T.colisao transversal]                                                0.0249      0.061      0.411      0.681      -0.094       0.144\n",
       "tipo_acidente[T.colisao traseira]                                                  -0.3243      0.060     -5.389      0.000      -0.442      -0.206\n",
       "tipo_acidente[T.outros]                                                             0.0890      0.060      1.486      0.137      -0.028       0.206\n",
       "tipo_acidente[T.queda de veículo]                                                  -0.8698      0.062    -14.071      0.000      -0.991      -0.749\n",
       "tipo_acidente[T.saída da pista]                                                    -0.2156      0.058     -3.735      0.000      -0.329      -0.102\n",
       "tipo_pista[T.multipla]                                                             -0.0535      0.038     -1.414      0.157      -0.128       0.021\n",
       "tipo_pista[T.simples]                                                               0.3067      0.016     19.393      0.000       0.276       0.338\n",
       "tracado_via[T.curva]                                                                0.3658      0.047      7.815      0.000       0.274       0.457\n",
       "tracado_via[T.outros]                                                              -0.0414      0.060     -0.689      0.491      -0.159       0.076\n",
       "tracado_via[T.reta]                                                                 0.3622      0.045      7.969      0.000       0.273       0.451\n",
       "dia_semana[T.quarta]                                                               -0.1864      0.023     -8.040      0.000      -0.232      -0.141\n",
       "dia_semana[T.quinta]                                                               -0.1821      0.023     -8.042      0.000      -0.226      -0.138\n",
       "dia_semana[T.sabado]                                                               -0.0944      0.020     -4.783      0.000      -0.133      -0.056\n",
       "dia_semana[T.segunda]                                                              -0.2028      0.022     -9.027      0.000      -0.247      -0.159\n",
       "dia_semana[T.sexta]                                                                -0.1814      0.021     -8.469      0.000      -0.223      -0.139\n",
       "dia_semana[T.terca]                                                                -0.2146      0.024     -9.129      0.000      -0.261      -0.169\n",
       "horario_acidente[T.manhã]                                                          -0.6967      0.020    -35.729      0.000      -0.735      -0.659\n",
       "horario_acidente[T.noite]                                                          -0.3580      0.018    -20.021      0.000      -0.393      -0.323\n",
       "horario_acidente[T.tarde]                                                          -0.6742      0.019    -36.363      0.000      -0.711      -0.638\n",
       "uso_solo[T.urbano]                                                                 -0.6905      0.016    -43.946      0.000      -0.721      -0.660\n",
       "regiao_pais[T.nordeste]                                                             0.2319      0.021     11.072      0.000       0.191       0.273\n",
       "regiao_pais[T.norte]                                                                0.0094      0.031      0.305      0.760      -0.051       0.070\n",
       "regiao_pais[T.sudeste]                                                             -0.3459      0.021    -16.295      0.000      -0.388      -0.304\n",
       "regiao_pais[T.sul]                                                                 -0.3523      0.022    -16.148      0.000      -0.395      -0.310\n",
       "faixa_ano_fabricacao[T.mais de dez anos]                                            0.2298      0.014     16.297      0.000       0.202       0.257\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]     0.3020      0.044      6.865      0.000       0.216       0.388\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao veículo]                       0.0238      0.053      0.449      0.653      -0.080       0.128\n",
       "causa_presumivel_acidente[T.outros aspectos não identificados]                      0.9761      0.044     22.313      0.000       0.890       1.062\n",
       "sexo[T.masculino]:condicao_envolvimento[T.ocupante de automóveis]                  -0.3864      1.041     -0.371      0.711      -2.427       1.654\n",
       "sexo[T.masculino]:condicao_envolvimento[T.ocupante de caminhonete/camioneta]       -0.5533      1.044     -0.530      0.596      -2.599       1.492\n",
       "sexo[T.masculino]:condicao_envolvimento[T.ocupante de caminhão]                    -0.6606      1.049     -0.630      0.529      -2.717       1.396\n",
       "sexo[T.masculino]:condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]         -0.6286      1.067     -0.589      0.556      -2.720       1.463\n",
       "sexo[T.masculino]:condicao_envolvimento[T.ocupante de motocicleta]                 -0.2935      1.042     -0.282      0.778      -2.336       1.749\n",
       "sexo[T.masculino]:condicao_envolvimento[T.ocupante de motoneta]                    -0.2470      1.045     -0.236      0.813      -2.296       1.802\n",
       "sexo[T.masculino]:condicao_envolvimento[T.pedestre]                                -0.5411      1.044     -0.518      0.604      -2.587       1.505\n",
       "===================================================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula='estado_fisico ~ sexo+condicao_envolvimento+tipo_acidente+tipo_pista+tracado_via+dia_semana+horario_acidente+uso_solo+regiao_pais+faixa_ano_fabricacao+causa_presumivel_acidente+sexo*condicao_envolvimento', \n",
    "                 data=dados, family=sm.families.Binomial()).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "start_time": "2022-02-07T22:07:54.775Z"
    }
   },
   "source": [
    "> <p style='text-align: justify;'><font size=2.85>O p-valor indica que o efeito de interação entre as variáveis condição de envolvimento e sexo não é estatisticamente significativo.</font></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testando interação entre tipo de pista e tipo de acidente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:43:34.902147Z",
     "start_time": "2022-02-15T09:43:01.216552Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['estado_fisico[morto]', 'estado_fisico[sobrevivente]']</td> <th>  No. Observations:  </th>   <td>495173</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                     <td>GLM</td>                           <th>  Df Residuals:      </th>   <td>495119</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>                           <td>Binomial</td>                         <th>  Df Model:          </th>   <td>    53</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>                            <td>Logit</td>                          <th>  Scale:             </th>  <td>  1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                                   <td>IRLS</td>                           <th>  Log-Likelihood:    </th> <td>-1.0130e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                               <td>Tue, 15 Feb 2022</td>                     <th>  Deviance:          </th> <td>2.0260e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                                   <td>06:43:34</td>                         <th>  Pearson chi2:      </th>  <td>4.82e+05</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>                             <td>7</td>                            <th>  Pseudo R-squ. (CS):</th>   <td>0.05056</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                        <td>nonrobust</td>                        <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                         <td></td>                                            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                                       <td>   -2.8105</td> <td>    0.110</td> <td>  -25.549</td> <td> 0.000</td> <td>   -3.026</td> <td>   -2.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.atropelamento de pessoas]</th>                                       <td>    1.1748</td> <td>    0.111</td> <td>   10.582</td> <td> 0.000</td> <td>    0.957</td> <td>    1.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.capotamento/tombamento]</th>                                         <td>   -0.6880</td> <td>    0.108</td> <td>   -6.381</td> <td> 0.000</td> <td>   -0.899</td> <td>   -0.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao com bicicleta]</th>                                          <td>   -1.5304</td> <td>    0.371</td> <td>   -4.128</td> <td> 0.000</td> <td>   -2.257</td> <td>   -0.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao frontal]</th>                                                <td>    1.1548</td> <td>    0.117</td> <td>    9.906</td> <td> 0.000</td> <td>    0.926</td> <td>    1.383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao lateral]</th>                                                <td>   -0.7108</td> <td>    0.117</td> <td>   -6.095</td> <td> 0.000</td> <td>   -0.939</td> <td>   -0.482</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao transversal]</th>                                            <td>   -0.2552</td> <td>    0.117</td> <td>   -2.186</td> <td> 0.029</td> <td>   -0.484</td> <td>   -0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao traseira]</th>                                               <td>   -0.4409</td> <td>    0.108</td> <td>   -4.070</td> <td> 0.000</td> <td>   -0.653</td> <td>   -0.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.outros]</th>                                                         <td>   -0.1913</td> <td>    0.108</td> <td>   -1.766</td> <td> 0.077</td> <td>   -0.404</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.queda de veículo]</th>                                               <td>   -0.7194</td> <td>    0.111</td> <td>   -6.458</td> <td> 0.000</td> <td>   -0.938</td> <td>   -0.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.saída da pista]</th>                                                 <td>   -0.4433</td> <td>    0.106</td> <td>   -4.165</td> <td> 0.000</td> <td>   -0.652</td> <td>   -0.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.multipla]</th>                                                          <td>   -0.4360</td> <td>    0.464</td> <td>   -0.939</td> <td> 0.348</td> <td>   -1.346</td> <td>    0.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.simples]</th>                                                           <td>    0.2449</td> <td>    0.107</td> <td>    2.284</td> <td> 0.022</td> <td>    0.035</td> <td>    0.455</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.curva]</th>                                                            <td>    0.3430</td> <td>    0.047</td> <td>    7.345</td> <td> 0.000</td> <td>    0.251</td> <td>    0.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.outros]</th>                                                           <td>   -0.0120</td> <td>    0.060</td> <td>   -0.201</td> <td> 0.841</td> <td>   -0.129</td> <td>    0.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.reta]</th>                                                             <td>    0.3359</td> <td>    0.045</td> <td>    7.402</td> <td> 0.000</td> <td>    0.247</td> <td>    0.425</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quarta]</th>                                                            <td>   -0.1548</td> <td>    0.023</td> <td>   -6.759</td> <td> 0.000</td> <td>   -0.200</td> <td>   -0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quinta]</th>                                                            <td>   -0.1519</td> <td>    0.022</td> <td>   -6.786</td> <td> 0.000</td> <td>   -0.196</td> <td>   -0.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sabado]</th>                                                            <td>   -0.0852</td> <td>    0.020</td> <td>   -4.348</td> <td> 0.000</td> <td>   -0.124</td> <td>   -0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.segunda]</th>                                                           <td>   -0.1862</td> <td>    0.022</td> <td>   -8.363</td> <td> 0.000</td> <td>   -0.230</td> <td>   -0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sexta]</th>                                                             <td>   -0.1655</td> <td>    0.021</td> <td>   -7.801</td> <td> 0.000</td> <td>   -0.207</td> <td>   -0.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.terca]</th>                                                             <td>   -0.1827</td> <td>    0.023</td> <td>   -7.864</td> <td> 0.000</td> <td>   -0.228</td> <td>   -0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.manhã]</th>                                                       <td>   -0.7051</td> <td>    0.019</td> <td>  -36.467</td> <td> 0.000</td> <td>   -0.743</td> <td>   -0.667</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.noite]</th>                                                       <td>   -0.3439</td> <td>    0.018</td> <td>  -19.491</td> <td> 0.000</td> <td>   -0.378</td> <td>   -0.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.tarde]</th>                                                       <td>   -0.6896</td> <td>    0.018</td> <td>  -37.601</td> <td> 0.000</td> <td>   -0.726</td> <td>   -0.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uso_solo[T.urbano]</th>                                                              <td>   -0.6276</td> <td>    0.015</td> <td>  -41.174</td> <td> 0.000</td> <td>   -0.657</td> <td>   -0.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.nordeste]</th>                                                         <td>    0.2774</td> <td>    0.021</td> <td>   13.381</td> <td> 0.000</td> <td>    0.237</td> <td>    0.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.norte]</th>                                                            <td>    0.0522</td> <td>    0.031</td> <td>    1.705</td> <td> 0.088</td> <td>   -0.008</td> <td>    0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sudeste]</th>                                                          <td>   -0.3376</td> <td>    0.021</td> <td>  -15.999</td> <td> 0.000</td> <td>   -0.379</td> <td>   -0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sul]</th>                                                              <td>   -0.3765</td> <td>    0.022</td> <td>  -17.389</td> <td> 0.000</td> <td>   -0.419</td> <td>   -0.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_ano_fabricacao[T.mais de dez anos]</th>                                        <td>    0.2032</td> <td>    0.014</td> <td>   14.750</td> <td> 0.000</td> <td>    0.176</td> <td>    0.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]</th> <td>    0.3648</td> <td>    0.044</td> <td>    8.351</td> <td> 0.000</td> <td>    0.279</td> <td>    0.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao veículo]</th>                   <td>    0.0688</td> <td>    0.053</td> <td>    1.306</td> <td> 0.192</td> <td>   -0.034</td> <td>    0.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.outros aspectos não identificados]</th>                  <td>    0.9697</td> <td>    0.043</td> <td>   22.374</td> <td> 0.000</td> <td>    0.885</td> <td>    1.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.atropelamento de pessoas]:tipo_pista[T.multipla]</th>                <td>    0.0827</td> <td>    0.476</td> <td>    0.174</td> <td> 0.862</td> <td>   -0.850</td> <td>    1.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.capotamento/tombamento]:tipo_pista[T.multipla]</th>                  <td>    0.5039</td> <td>    0.480</td> <td>    1.050</td> <td> 0.294</td> <td>   -0.437</td> <td>    1.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao com bicicleta]:tipo_pista[T.multipla]</th>                   <td>    0.7277</td> <td>    0.922</td> <td>    0.789</td> <td> 0.430</td> <td>   -1.080</td> <td>    2.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao frontal]:tipo_pista[T.multipla]</th>                         <td>    0.3267</td> <td>    0.486</td> <td>    0.672</td> <td> 0.502</td> <td>   -0.626</td> <td>    1.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao lateral]:tipo_pista[T.multipla]</th>                         <td>    0.3280</td> <td>    0.483</td> <td>    0.679</td> <td> 0.497</td> <td>   -0.618</td> <td>    1.274</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao transversal]:tipo_pista[T.multipla]</th>                     <td>    0.0572</td> <td>    0.491</td> <td>    0.117</td> <td> 0.907</td> <td>   -0.904</td> <td>    1.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao traseira]:tipo_pista[T.multipla]</th>                        <td>    0.2490</td> <td>    0.474</td> <td>    0.526</td> <td> 0.599</td> <td>   -0.679</td> <td>    1.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.outros]:tipo_pista[T.multipla]</th>                                  <td>    0.6989</td> <td>    0.473</td> <td>    1.479</td> <td> 0.139</td> <td>   -0.227</td> <td>    1.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.queda de veículo]:tipo_pista[T.multipla]</th>                        <td>    0.8512</td> <td>    0.476</td> <td>    1.788</td> <td> 0.074</td> <td>   -0.082</td> <td>    1.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.saída da pista]:tipo_pista[T.multipla]</th>                          <td>    0.6133</td> <td>    0.479</td> <td>    1.279</td> <td> 0.201</td> <td>   -0.326</td> <td>    1.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.atropelamento de pessoas]:tipo_pista[T.simples]</th>                 <td>   -0.5435</td> <td>    0.123</td> <td>   -4.425</td> <td> 0.000</td> <td>   -0.784</td> <td>   -0.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.capotamento/tombamento]:tipo_pista[T.simples]</th>                   <td>    0.0531</td> <td>    0.114</td> <td>    0.467</td> <td> 0.641</td> <td>   -0.170</td> <td>    0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao com bicicleta]:tipo_pista[T.simples]</th>                    <td>    0.5198</td> <td>    0.405</td> <td>    1.282</td> <td> 0.200</td> <td>   -0.275</td> <td>    1.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao frontal]:tipo_pista[T.simples]</th>                          <td>    0.1428</td> <td>    0.121</td> <td>    1.181</td> <td> 0.238</td> <td>   -0.094</td> <td>    0.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao lateral]:tipo_pista[T.simples]</th>                          <td>    0.6299</td> <td>    0.123</td> <td>    5.127</td> <td> 0.000</td> <td>    0.389</td> <td>    0.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao transversal]:tipo_pista[T.simples]</th>                      <td>    0.2480</td> <td>    0.122</td> <td>    2.026</td> <td> 0.043</td> <td>    0.008</td> <td>    0.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao traseira]:tipo_pista[T.simples]</th>                         <td>    0.1026</td> <td>    0.116</td> <td>    0.884</td> <td> 0.377</td> <td>   -0.125</td> <td>    0.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.outros]:tipo_pista[T.simples]</th>                                   <td>    0.1736</td> <td>    0.117</td> <td>    1.486</td> <td> 0.137</td> <td>   -0.055</td> <td>    0.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.queda de veículo]:tipo_pista[T.simples]</th>                         <td>   -0.0356</td> <td>    0.121</td> <td>   -0.295</td> <td> 0.768</td> <td>   -0.272</td> <td>    0.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.saída da pista]:tipo_pista[T.simples]</th>                           <td>    0.0334</td> <td>    0.112</td> <td>    0.298</td> <td> 0.766</td> <td>   -0.186</td> <td>    0.253</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                    Generalized Linear Model Regression Results                                    \n",
       "===================================================================================================================\n",
       "Dep. Variable:     ['estado_fisico[morto]', 'estado_fisico[sobrevivente]']   No. Observations:               495173\n",
       "Model:                                                                 GLM   Df Residuals:                   495119\n",
       "Model Family:                                                     Binomial   Df Model:                           53\n",
       "Link Function:                                                       Logit   Scale:                          1.0000\n",
       "Method:                                                               IRLS   Log-Likelihood:            -1.0130e+05\n",
       "Date:                                                     Tue, 15 Feb 2022   Deviance:                   2.0260e+05\n",
       "Time:                                                             06:43:34   Pearson chi2:                 4.82e+05\n",
       "No. Iterations:                                                          7   Pseudo R-squ. (CS):            0.05056\n",
       "Covariance Type:                                                 nonrobust                                         \n",
       "===================================================================================================================================================\n",
       "                                                                                      coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                                          -2.8105      0.110    -25.549      0.000      -3.026      -2.595\n",
       "tipo_acidente[T.atropelamento de pessoas]                                           1.1748      0.111     10.582      0.000       0.957       1.392\n",
       "tipo_acidente[T.capotamento/tombamento]                                            -0.6880      0.108     -6.381      0.000      -0.899      -0.477\n",
       "tipo_acidente[T.colisao com bicicleta]                                             -1.5304      0.371     -4.128      0.000      -2.257      -0.804\n",
       "tipo_acidente[T.colisao frontal]                                                    1.1548      0.117      9.906      0.000       0.926       1.383\n",
       "tipo_acidente[T.colisao lateral]                                                   -0.7108      0.117     -6.095      0.000      -0.939      -0.482\n",
       "tipo_acidente[T.colisao transversal]                                               -0.2552      0.117     -2.186      0.029      -0.484      -0.026\n",
       "tipo_acidente[T.colisao traseira]                                                  -0.4409      0.108     -4.070      0.000      -0.653      -0.229\n",
       "tipo_acidente[T.outros]                                                            -0.1913      0.108     -1.766      0.077      -0.404       0.021\n",
       "tipo_acidente[T.queda de veículo]                                                  -0.7194      0.111     -6.458      0.000      -0.938      -0.501\n",
       "tipo_acidente[T.saída da pista]                                                    -0.4433      0.106     -4.165      0.000      -0.652      -0.235\n",
       "tipo_pista[T.multipla]                                                             -0.4360      0.464     -0.939      0.348      -1.346       0.474\n",
       "tipo_pista[T.simples]                                                               0.2449      0.107      2.284      0.022       0.035       0.455\n",
       "tracado_via[T.curva]                                                                0.3430      0.047      7.345      0.000       0.251       0.434\n",
       "tracado_via[T.outros]                                                              -0.0120      0.060     -0.201      0.841      -0.129       0.105\n",
       "tracado_via[T.reta]                                                                 0.3359      0.045      7.402      0.000       0.247       0.425\n",
       "dia_semana[T.quarta]                                                               -0.1548      0.023     -6.759      0.000      -0.200      -0.110\n",
       "dia_semana[T.quinta]                                                               -0.1519      0.022     -6.786      0.000      -0.196      -0.108\n",
       "dia_semana[T.sabado]                                                               -0.0852      0.020     -4.348      0.000      -0.124      -0.047\n",
       "dia_semana[T.segunda]                                                              -0.1862      0.022     -8.363      0.000      -0.230      -0.143\n",
       "dia_semana[T.sexta]                                                                -0.1655      0.021     -7.801      0.000      -0.207      -0.124\n",
       "dia_semana[T.terca]                                                                -0.1827      0.023     -7.864      0.000      -0.228      -0.137\n",
       "horario_acidente[T.manhã]                                                          -0.7051      0.019    -36.467      0.000      -0.743      -0.667\n",
       "horario_acidente[T.noite]                                                          -0.3439      0.018    -19.491      0.000      -0.378      -0.309\n",
       "horario_acidente[T.tarde]                                                          -0.6896      0.018    -37.601      0.000      -0.726      -0.654\n",
       "uso_solo[T.urbano]                                                                 -0.6276      0.015    -41.174      0.000      -0.657      -0.598\n",
       "regiao_pais[T.nordeste]                                                             0.2774      0.021     13.381      0.000       0.237       0.318\n",
       "regiao_pais[T.norte]                                                                0.0522      0.031      1.705      0.088      -0.008       0.112\n",
       "regiao_pais[T.sudeste]                                                             -0.3376      0.021    -15.999      0.000      -0.379      -0.296\n",
       "regiao_pais[T.sul]                                                                 -0.3765      0.022    -17.389      0.000      -0.419      -0.334\n",
       "faixa_ano_fabricacao[T.mais de dez anos]                                            0.2032      0.014     14.750      0.000       0.176       0.230\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]     0.3648      0.044      8.351      0.000       0.279       0.450\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao veículo]                       0.0688      0.053      1.306      0.192      -0.034       0.172\n",
       "causa_presumivel_acidente[T.outros aspectos não identificados]                      0.9697      0.043     22.374      0.000       0.885       1.055\n",
       "tipo_acidente[T.atropelamento de pessoas]:tipo_pista[T.multipla]                    0.0827      0.476      0.174      0.862      -0.850       1.016\n",
       "tipo_acidente[T.capotamento/tombamento]:tipo_pista[T.multipla]                      0.5039      0.480      1.050      0.294      -0.437       1.445\n",
       "tipo_acidente[T.colisao com bicicleta]:tipo_pista[T.multipla]                       0.7277      0.922      0.789      0.430      -1.080       2.535\n",
       "tipo_acidente[T.colisao frontal]:tipo_pista[T.multipla]                             0.3267      0.486      0.672      0.502      -0.626       1.280\n",
       "tipo_acidente[T.colisao lateral]:tipo_pista[T.multipla]                             0.3280      0.483      0.679      0.497      -0.618       1.274\n",
       "tipo_acidente[T.colisao transversal]:tipo_pista[T.multipla]                         0.0572      0.491      0.117      0.907      -0.904       1.019\n",
       "tipo_acidente[T.colisao traseira]:tipo_pista[T.multipla]                            0.2490      0.474      0.526      0.599      -0.679       1.177\n",
       "tipo_acidente[T.outros]:tipo_pista[T.multipla]                                      0.6989      0.473      1.479      0.139      -0.227       1.625\n",
       "tipo_acidente[T.queda de veículo]:tipo_pista[T.multipla]                            0.8512      0.476      1.788      0.074      -0.082       1.784\n",
       "tipo_acidente[T.saída da pista]:tipo_pista[T.multipla]                              0.6133      0.479      1.279      0.201      -0.326       1.553\n",
       "tipo_acidente[T.atropelamento de pessoas]:tipo_pista[T.simples]                    -0.5435      0.123     -4.425      0.000      -0.784      -0.303\n",
       "tipo_acidente[T.capotamento/tombamento]:tipo_pista[T.simples]                       0.0531      0.114      0.467      0.641      -0.170       0.276\n",
       "tipo_acidente[T.colisao com bicicleta]:tipo_pista[T.simples]                        0.5198      0.405      1.282      0.200      -0.275       1.314\n",
       "tipo_acidente[T.colisao frontal]:tipo_pista[T.simples]                              0.1428      0.121      1.181      0.238      -0.094       0.380\n",
       "tipo_acidente[T.colisao lateral]:tipo_pista[T.simples]                              0.6299      0.123      5.127      0.000       0.389       0.871\n",
       "tipo_acidente[T.colisao transversal]:tipo_pista[T.simples]                          0.2480      0.122      2.026      0.043       0.008       0.488\n",
       "tipo_acidente[T.colisao traseira]:tipo_pista[T.simples]                             0.1026      0.116      0.884      0.377      -0.125       0.330\n",
       "tipo_acidente[T.outros]:tipo_pista[T.simples]                                       0.1736      0.117      1.486      0.137      -0.055       0.402\n",
       "tipo_acidente[T.queda de veículo]:tipo_pista[T.simples]                            -0.0356      0.121     -0.295      0.768      -0.272       0.201\n",
       "tipo_acidente[T.saída da pista]:tipo_pista[T.simples]                               0.0334      0.112      0.298      0.766      -0.186       0.253\n",
       "===================================================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula='estado_fisico ~ tipo_acidente+tipo_pista+tracado_via+dia_semana+horario_acidente+uso_solo+regiao_pais+faixa_ano_fabricacao+causa_presumivel_acidente+tipo_acidente*tipo_pista', \n",
    "                 data=dados, family=sm.families.Binomial()).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> <p style='text-align: justify;'>O p-valor indica que o efeito de interação entre as variáveis tipo de pista e tipo de acidente não é estatisticamente significativo.<font size=2.85></font></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelo final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:44:06.196528Z",
     "start_time": "2022-02-15T09:43:34.903330Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['estado_fisico[morto]', 'estado_fisico[sobrevivente]']</td> <th>  No. Observations:  </th>   <td>495173</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                                     <td>GLM</td>                           <th>  Df Residuals:      </th>   <td>495127</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>                           <td>Binomial</td>                         <th>  Df Model:          </th>   <td>    45</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>                            <td>Logit</td>                          <th>  Scale:             </th>  <td>  1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                                   <td>IRLS</td>                           <th>  Log-Likelihood:    </th>  <td> -99214.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                               <td>Tue, 15 Feb 2022</td>                     <th>  Deviance:          </th> <td>1.9843e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                                   <td>06:44:06</td>                         <th>  Pearson chi2:      </th>  <td>4.82e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>                             <td>7</td>                            <th>  Pseudo R-squ. (CS):</th>   <td>0.05853</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                        <td>nonrobust</td>                        <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                         <td></td>                                            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                                       <td>   -2.8982</td> <td>    0.230</td> <td>  -12.596</td> <td> 0.000</td> <td>   -3.349</td> <td>   -2.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sexo[T.masculino]</th>                                                               <td>    0.5749</td> <td>    0.024</td> <td>   24.075</td> <td> 0.000</td> <td>    0.528</td> <td>    0.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.15-30]</th>                                                           <td>    0.4863</td> <td>    0.128</td> <td>    3.787</td> <td> 0.000</td> <td>    0.235</td> <td>    0.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.30-44]</th>                                                           <td>    0.6336</td> <td>    0.128</td> <td>    4.936</td> <td> 0.000</td> <td>    0.382</td> <td>    0.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.45-59]</th>                                                           <td>    0.8263</td> <td>    0.129</td> <td>    6.428</td> <td> 0.000</td> <td>    0.574</td> <td>    1.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_etaria[T.60+]</th>                                                             <td>    1.1074</td> <td>    0.129</td> <td>    8.552</td> <td> 0.000</td> <td>    0.854</td> <td>    1.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de automóveis]</th>                                 <td>   -1.4510</td> <td>    0.179</td> <td>   -8.124</td> <td> 0.000</td> <td>   -1.801</td> <td>   -1.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de caminhonete/camioneta]</th>                      <td>   -1.4639</td> <td>    0.180</td> <td>   -8.149</td> <td> 0.000</td> <td>   -1.816</td> <td>   -1.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de caminhão]</th>                                   <td>   -1.0769</td> <td>    0.179</td> <td>   -6.016</td> <td> 0.000</td> <td>   -1.428</td> <td>   -0.726</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]</th>                        <td>   -1.6458</td> <td>    0.192</td> <td>   -8.571</td> <td> 0.000</td> <td>   -2.022</td> <td>   -1.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de motocicleta]</th>                                <td>   -0.8289</td> <td>    0.178</td> <td>   -4.646</td> <td> 0.000</td> <td>   -1.179</td> <td>   -0.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.ocupante de motoneta]</th>                                   <td>   -1.0692</td> <td>    0.183</td> <td>   -5.838</td> <td> 0.000</td> <td>   -1.428</td> <td>   -0.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>condicao_envolvimento[T.pedestre]</th>                                               <td>    1.0629</td> <td>    0.188</td> <td>    5.643</td> <td> 0.000</td> <td>    0.694</td> <td>    1.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.atropelamento de pessoas]</th>                                       <td>   -0.4147</td> <td>    0.080</td> <td>   -5.185</td> <td> 0.000</td> <td>   -0.571</td> <td>   -0.258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.capotamento/tombamento]</th>                                         <td>   -0.5231</td> <td>    0.059</td> <td>   -8.869</td> <td> 0.000</td> <td>   -0.639</td> <td>   -0.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao com bicicleta]</th>                                          <td>   -1.2981</td> <td>    0.157</td> <td>   -8.275</td> <td> 0.000</td> <td>   -1.606</td> <td>   -0.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao frontal]</th>                                                <td>    1.4683</td> <td>    0.058</td> <td>   25.470</td> <td> 0.000</td> <td>    1.355</td> <td>    1.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao lateral]</th>                                                <td>   -0.2002</td> <td>    0.061</td> <td>   -3.301</td> <td> 0.001</td> <td>   -0.319</td> <td>   -0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao transversal]</th>                                            <td>    0.0090</td> <td>    0.061</td> <td>    0.148</td> <td> 0.882</td> <td>   -0.110</td> <td>    0.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.colisao traseira]</th>                                               <td>   -0.3253</td> <td>    0.060</td> <td>   -5.399</td> <td> 0.000</td> <td>   -0.443</td> <td>   -0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.outros]</th>                                                         <td>    0.0915</td> <td>    0.060</td> <td>    1.525</td> <td> 0.127</td> <td>   -0.026</td> <td>    0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.queda de veículo]</th>                                               <td>   -0.8795</td> <td>    0.062</td> <td>  -14.213</td> <td> 0.000</td> <td>   -1.001</td> <td>   -0.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_acidente[T.saída da pista]</th>                                                 <td>   -0.2069</td> <td>    0.058</td> <td>   -3.580</td> <td> 0.000</td> <td>   -0.320</td> <td>   -0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.multipla]</th>                                                          <td>   -0.0543</td> <td>    0.038</td> <td>   -1.434</td> <td> 0.152</td> <td>   -0.129</td> <td>    0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tipo_pista[T.simples]</th>                                                           <td>    0.3011</td> <td>    0.016</td> <td>   19.019</td> <td> 0.000</td> <td>    0.270</td> <td>    0.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.curva]</th>                                                            <td>    0.3731</td> <td>    0.047</td> <td>    7.968</td> <td> 0.000</td> <td>    0.281</td> <td>    0.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.outros]</th>                                                           <td>   -0.0538</td> <td>    0.060</td> <td>   -0.895</td> <td> 0.371</td> <td>   -0.172</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tracado_via[T.reta]</th>                                                             <td>    0.3625</td> <td>    0.045</td> <td>    7.972</td> <td> 0.000</td> <td>    0.273</td> <td>    0.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quarta]</th>                                                            <td>   -0.2056</td> <td>    0.023</td> <td>   -8.852</td> <td> 0.000</td> <td>   -0.251</td> <td>   -0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.quinta]</th>                                                            <td>   -0.2006</td> <td>    0.023</td> <td>   -8.842</td> <td> 0.000</td> <td>   -0.245</td> <td>   -0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sabado]</th>                                                            <td>   -0.0994</td> <td>    0.020</td> <td>   -5.026</td> <td> 0.000</td> <td>   -0.138</td> <td>   -0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.segunda]</th>                                                           <td>   -0.2195</td> <td>    0.023</td> <td>   -9.754</td> <td> 0.000</td> <td>   -0.264</td> <td>   -0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.sexta]</th>                                                             <td>   -0.1961</td> <td>    0.021</td> <td>   -9.137</td> <td> 0.000</td> <td>   -0.238</td> <td>   -0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dia_semana[T.terca]</th>                                                             <td>   -0.2326</td> <td>    0.024</td> <td>   -9.878</td> <td> 0.000</td> <td>   -0.279</td> <td>   -0.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.manhã]</th>                                                       <td>   -0.7497</td> <td>    0.020</td> <td>  -38.182</td> <td> 0.000</td> <td>   -0.788</td> <td>   -0.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.noite]</th>                                                       <td>   -0.3949</td> <td>    0.018</td> <td>  -21.991</td> <td> 0.000</td> <td>   -0.430</td> <td>   -0.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>horario_acidente[T.tarde]</th>                                                       <td>   -0.7315</td> <td>    0.019</td> <td>  -39.132</td> <td> 0.000</td> <td>   -0.768</td> <td>   -0.695</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uso_solo[T.urbano]</th>                                                              <td>   -0.6767</td> <td>    0.016</td> <td>  -43.032</td> <td> 0.000</td> <td>   -0.707</td> <td>   -0.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.nordeste]</th>                                                         <td>    0.2345</td> <td>    0.021</td> <td>   11.173</td> <td> 0.000</td> <td>    0.193</td> <td>    0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.norte]</th>                                                            <td>    0.0136</td> <td>    0.031</td> <td>    0.441</td> <td> 0.659</td> <td>   -0.047</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sudeste]</th>                                                          <td>   -0.3432</td> <td>    0.021</td> <td>  -16.141</td> <td> 0.000</td> <td>   -0.385</td> <td>   -0.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>regiao_pais[T.sul]</th>                                                              <td>   -0.3386</td> <td>    0.022</td> <td>  -15.486</td> <td> 0.000</td> <td>   -0.381</td> <td>   -0.296</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>faixa_ano_fabricacao[T.mais de dez anos]</th>                                        <td>    0.2061</td> <td>    0.014</td> <td>   14.583</td> <td> 0.000</td> <td>    0.178</td> <td>    0.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]</th> <td>    0.3086</td> <td>    0.044</td> <td>    7.004</td> <td> 0.000</td> <td>    0.222</td> <td>    0.395</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.aspectos relacionados ao veículo]</th>                   <td>    0.0342</td> <td>    0.053</td> <td>    0.643</td> <td> 0.520</td> <td>   -0.070</td> <td>    0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>causa_presumivel_acidente[T.outros aspectos não identificados]</th>                  <td>    0.9959</td> <td>    0.044</td> <td>   22.729</td> <td> 0.000</td> <td>    0.910</td> <td>    1.082</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                    Generalized Linear Model Regression Results                                    \n",
       "===================================================================================================================\n",
       "Dep. Variable:     ['estado_fisico[morto]', 'estado_fisico[sobrevivente]']   No. Observations:               495173\n",
       "Model:                                                                 GLM   Df Residuals:                   495127\n",
       "Model Family:                                                     Binomial   Df Model:                           45\n",
       "Link Function:                                                       Logit   Scale:                          1.0000\n",
       "Method:                                                               IRLS   Log-Likelihood:                -99214.\n",
       "Date:                                                     Tue, 15 Feb 2022   Deviance:                   1.9843e+05\n",
       "Time:                                                             06:44:06   Pearson chi2:                 4.82e+05\n",
       "No. Iterations:                                                          7   Pseudo R-squ. (CS):            0.05853\n",
       "Covariance Type:                                                 nonrobust                                         \n",
       "===================================================================================================================================================\n",
       "                                                                                      coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                                          -2.8982      0.230    -12.596      0.000      -3.349      -2.447\n",
       "sexo[T.masculino]                                                                   0.5749      0.024     24.075      0.000       0.528       0.622\n",
       "faixa_etaria[T.15-30]                                                               0.4863      0.128      3.787      0.000       0.235       0.738\n",
       "faixa_etaria[T.30-44]                                                               0.6336      0.128      4.936      0.000       0.382       0.885\n",
       "faixa_etaria[T.45-59]                                                               0.8263      0.129      6.428      0.000       0.574       1.078\n",
       "faixa_etaria[T.60+]                                                                 1.1074      0.129      8.552      0.000       0.854       1.361\n",
       "condicao_envolvimento[T.ocupante de automóveis]                                    -1.4510      0.179     -8.124      0.000      -1.801      -1.101\n",
       "condicao_envolvimento[T.ocupante de caminhonete/camioneta]                         -1.4639      0.180     -8.149      0.000      -1.816      -1.112\n",
       "condicao_envolvimento[T.ocupante de caminhão]                                      -1.0769      0.179     -6.016      0.000      -1.428      -0.726\n",
       "condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]                           -1.6458      0.192     -8.571      0.000      -2.022      -1.269\n",
       "condicao_envolvimento[T.ocupante de motocicleta]                                   -0.8289      0.178     -4.646      0.000      -1.179      -0.479\n",
       "condicao_envolvimento[T.ocupante de motoneta]                                      -1.0692      0.183     -5.838      0.000      -1.428      -0.710\n",
       "condicao_envolvimento[T.pedestre]                                                   1.0629      0.188      5.643      0.000       0.694       1.432\n",
       "tipo_acidente[T.atropelamento de pessoas]                                          -0.4147      0.080     -5.185      0.000      -0.571      -0.258\n",
       "tipo_acidente[T.capotamento/tombamento]                                            -0.5231      0.059     -8.869      0.000      -0.639      -0.407\n",
       "tipo_acidente[T.colisao com bicicleta]                                             -1.2981      0.157     -8.275      0.000      -1.606      -0.991\n",
       "tipo_acidente[T.colisao frontal]                                                    1.4683      0.058     25.470      0.000       1.355       1.581\n",
       "tipo_acidente[T.colisao lateral]                                                   -0.2002      0.061     -3.301      0.001      -0.319      -0.081\n",
       "tipo_acidente[T.colisao transversal]                                                0.0090      0.061      0.148      0.882      -0.110       0.128\n",
       "tipo_acidente[T.colisao traseira]                                                  -0.3253      0.060     -5.399      0.000      -0.443      -0.207\n",
       "tipo_acidente[T.outros]                                                             0.0915      0.060      1.525      0.127      -0.026       0.209\n",
       "tipo_acidente[T.queda de veículo]                                                  -0.8795      0.062    -14.213      0.000      -1.001      -0.758\n",
       "tipo_acidente[T.saída da pista]                                                    -0.2069      0.058     -3.580      0.000      -0.320      -0.094\n",
       "tipo_pista[T.multipla]                                                             -0.0543      0.038     -1.434      0.152      -0.129       0.020\n",
       "tipo_pista[T.simples]                                                               0.3011      0.016     19.019      0.000       0.270       0.332\n",
       "tracado_via[T.curva]                                                                0.3731      0.047      7.968      0.000       0.281       0.465\n",
       "tracado_via[T.outros]                                                              -0.0538      0.060     -0.895      0.371      -0.172       0.064\n",
       "tracado_via[T.reta]                                                                 0.3625      0.045      7.972      0.000       0.273       0.452\n",
       "dia_semana[T.quarta]                                                               -0.2056      0.023     -8.852      0.000      -0.251      -0.160\n",
       "dia_semana[T.quinta]                                                               -0.2006      0.023     -8.842      0.000      -0.245      -0.156\n",
       "dia_semana[T.sabado]                                                               -0.0994      0.020     -5.026      0.000      -0.138      -0.061\n",
       "dia_semana[T.segunda]                                                              -0.2195      0.023     -9.754      0.000      -0.264      -0.175\n",
       "dia_semana[T.sexta]                                                                -0.1961      0.021     -9.137      0.000      -0.238      -0.154\n",
       "dia_semana[T.terca]                                                                -0.2326      0.024     -9.878      0.000      -0.279      -0.186\n",
       "horario_acidente[T.manhã]                                                          -0.7497      0.020    -38.182      0.000      -0.788      -0.711\n",
       "horario_acidente[T.noite]                                                          -0.3949      0.018    -21.991      0.000      -0.430      -0.360\n",
       "horario_acidente[T.tarde]                                                          -0.7315      0.019    -39.132      0.000      -0.768      -0.695\n",
       "uso_solo[T.urbano]                                                                 -0.6767      0.016    -43.032      0.000      -0.707      -0.646\n",
       "regiao_pais[T.nordeste]                                                             0.2345      0.021     11.173      0.000       0.193       0.276\n",
       "regiao_pais[T.norte]                                                                0.0136      0.031      0.441      0.659      -0.047       0.074\n",
       "regiao_pais[T.sudeste]                                                             -0.3432      0.021    -16.141      0.000      -0.385      -0.302\n",
       "regiao_pais[T.sul]                                                                 -0.3386      0.022    -15.486      0.000      -0.381      -0.296\n",
       "faixa_ano_fabricacao[T.mais de dez anos]                                            0.2061      0.014     14.583      0.000       0.178       0.234\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]     0.3086      0.044      7.004      0.000       0.222       0.395\n",
       "causa_presumivel_acidente[T.aspectos relacionados ao veículo]                       0.0342      0.053      0.643      0.520      -0.070       0.138\n",
       "causa_presumivel_acidente[T.outros aspectos não identificados]                      0.9959      0.044     22.729      0.000       0.910       1.082\n",
       "===================================================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.glm(formula='estado_fisico ~ sexo+faixa_etaria+condicao_envolvimento+tipo_acidente+tipo_pista+tracado_via+dia_semana+horario_acidente+uso_solo+regiao_pais+faixa_ano_fabricacao+causa_presumivel_acidente', \n",
    "                 data=dados, family=sm.families.Binomial()).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:44:06.205639Z",
     "start_time": "2022-02-15T09:44:06.197921Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sexo[T.masculino]                                                                   77.694424\n",
      "faixa_etaria[T.15-30]                                                               62.629913\n",
      "faixa_etaria[T.30-44]                                                               88.447142\n",
      "faixa_etaria[T.45-59]                                                              128.486215\n",
      "faixa_etaria[T.60+]                                                                202.657705\n",
      "condicao_envolvimento[T.ocupante de automóveis]                                    -76.566282\n",
      "condicao_envolvimento[T.ocupante de caminhonete/camioneta]                         -76.866149\n",
      "condicao_envolvimento[T.ocupante de caminhão]                                      -65.935036\n",
      "condicao_envolvimento[T.ocupante de micro-ônibus/ônibus]                           -80.714211\n",
      "condicao_envolvimento[T.ocupante de motocicleta]                                   -56.348438\n",
      "condicao_envolvimento[T.ocupante de motoneta]                                      -65.671329\n",
      "condicao_envolvimento[T.pedestre]                                                  189.485866\n",
      "tipo_acidente[T.atropelamento de pessoas]                                          -33.947148\n",
      "tipo_acidente[T.capotamento/tombamento]                                            -40.730801\n",
      "tipo_acidente[T.colisao com bicicleta]                                             -72.693871\n",
      "tipo_acidente[T.colisao frontal]                                                   334.171954\n",
      "tipo_acidente[T.colisao lateral]                                                   -18.144450\n",
      "tipo_acidente[T.colisao transversal]                                                 0.904340\n",
      "tipo_acidente[T.colisao traseira]                                                  -27.767500\n",
      "tipo_acidente[T.outros]                                                              9.578234\n",
      "tipo_acidente[T.queda de veículo]                                                  -58.501449\n",
      "tipo_acidente[T.saída da pista]                                                    -18.692141\n",
      "tipo_pista[T.multipla]                                                              -5.288639\n",
      "tipo_pista[T.simples]                                                               35.139393\n",
      "tracado_via[T.curva]                                                                45.227642\n",
      "tracado_via[T.outros]                                                               -5.240034\n",
      "tracado_via[T.reta]                                                                 43.692055\n",
      "dia_semana[T.quarta]                                                               -18.580894\n",
      "dia_semana[T.quinta]                                                               -18.172937\n",
      "dia_semana[T.sabado]                                                                -9.459361\n",
      "dia_semana[T.segunda]                                                              -19.709857\n",
      "dia_semana[T.sexta]                                                                -17.804014\n",
      "dia_semana[T.terca]                                                                -20.751973\n",
      "horario_acidente[T.manhã]                                                          -52.747848\n",
      "horario_acidente[T.noite]                                                          -32.624607\n",
      "horario_acidente[T.tarde]                                                          -51.879449\n",
      "uso_solo[T.urbano]                                                                 -49.169147\n",
      "regiao_pais[T.nordeste]                                                             26.422891\n",
      "regiao_pais[T.norte]                                                                 1.372617\n",
      "regiao_pais[T.sudeste]                                                             -29.049981\n",
      "regiao_pais[T.sul]                                                                 -28.725291\n",
      "faixa_ano_fabricacao[T.mais de dez anos]                                            22.893116\n",
      "causa_presumivel_acidente[T.aspectos relacionados ao comportamento do condutor]     36.146995\n",
      "causa_presumivel_acidente[T.aspectos relacionados ao veículo]                        3.474545\n",
      "causa_presumivel_acidente[T.outros aspectos não identificados]                     170.707777\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((np.exp(model.params[1:]) - 1) * 100 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Análise dos resultados\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85>A razão de chances da variável sexo mostra que as chances de um acidente em uma rodovia federal ser fatal são, em média, 78% maiores para homens em relação às mulheres. Quanto à faixa etária, em média, essas chances são 63% maiores para vítimas entre 15 e 30 anos, 88% de 30 a 44 anos, 128,5% de 45 a 59 anos e 202,65% para 60 anos ou mais em comparação à faixa de 0 a 14 anos.</font></p>\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85>Quanto à condição de envolvimento das vítimas nos acidentes, em média, as chances de fatalidade são 76,57% menores para ocupantes de automóveis, 77% para os ocupantes de caminhonete, 66% para os ocupantes de caminhão, 81% para ocupantes de ônibus, 56,34% para motociclistas e 66% para ocupantes de motoneta em relação aos ciclistas. Já para pedestres, estas chances são 2,89 vezes maiores.</font></p>\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85>Verifica-se também que as chances de um acidente ser letal são em média 34% menores, em relação ao atropelamento de animal, para atropelamento de pessoas, 40,73% para capotamento ou tombamento, 72,7% para colisões com bicicletas, 18,14% para colisões laterais e 27,76% para as traseiras, 58,5% para queda de veículo e 19% para saída da pista. Para o tipo colisão frontal, as chances de morte aumentam em 334%.</font></p>\n",
    "\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85>Para os tipos de pistas e traçados da via temos que as chances de letalidade são 36% maiores para pistas simples em comparação às pistas duplas e temos um aumento de 45,22% para curvas e 44% para retas em relação aos cruzamentos.</font></p>\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85>Quanto ao horário, as ocorrências apresentam, em média, chances 52,74% menores de serem letais durante a manhã, 32,62% durante a noite e 51,87% durante a tarde, em relação à madrugada. Quanto ao uso do solo, verificou-se que, em média, as chances de um acidente de trânsito ser letal são 49,16% menores em áreas urbanas em comparação com as rurais. Para a faixa do ano de fabricação, veículos com mais de 10 anos têm 22,89% mais chances de morte em relação aos com até 10 anos de fabricação.</font></p>\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85>Para os dias da semana, as chances de um acidente de trânsito ser letal são, em média, 19% menores às quartas, 18,17% às quintas, 9,45% aos sábados, 19,70% às segundas, 18% às sextas e 21% às terças em comparação ao domingo. Percebe-se também que as chances de uma vítima falecer em uma ocorrência são, em média, 26,4% maiores para vítimas na região Nordeste e 29% menores no Sudeste e no Sul em comparação ao Centro-Oeste.</font></p>\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85>Por fim, comparados aos aspectos relacionados à via ou ao ambiente, as chances de óbito são 36,14% maiores para aspectos relacionados ao comportamento do condutor e 170,70% maiores para outros aspectos não identificados.</font></p>\n",
    "\n",
    "<p style='text-align: justify;'><font size=2.85> Vale ressaltar que, algumas variáveis, ao nível de significância de 5%, não apresentaram diferenças estatisticamente relevantes, sendo elas: os tipos de acidentes classificados como colisão transversal e outros, pistas múltiplas, outros traçados de vias, região Norte e aspectos relacionados ao veículos.</font></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise da adequação do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T09:44:07.457096Z",
     "start_time": "2022-02-15T09:44:07.429129Z"
    }
   },
   "outputs": [],
   "source": [
    "X = model.fittedvalues\n",
    "Y_deviance = model.resid_deviance\n",
    "Y_person = model.resid_pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T10:51:56.998813Z",
     "start_time": "2022-02-15T09:44:07.458460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axL = plt.subplots(figsize=(10, 6))\n",
    "suptitle = 'Verificação de suposição'\n",
    "title = 'Resíduos dos desvio em relação aos valores ajustados'\n",
    "\n",
    "sns.regplot(X, \n",
    "            Y_deviance, \n",
    "            ax= axL, \n",
    "            color='black', \n",
    "            scatter_kws={'s': 5}, \n",
    "            line_kws={'color':'r', 'alpha':1, 'lw':2}, \n",
    "            lowess=True)\n",
    "\n",
    "axL.text(x=-0.01, y=1.10, s=suptitle, color='black', fontsize=18, weight='bold', ha='left', va='bottom', transform=axL.transAxes)\n",
    "axL.text(x=-0.01, y=1.05, s=title, color='#646464', fontsize=16, alpha=0.9, ha='left', transform=axL.transAxes)\n",
    "axL.set_xlabel('Valores preditores', fontsize=12, x=0.089)\n",
    "axL.set_ylabel('Resíduos desvio', fontsize=12, y=0.798)\n",
    "sns.despine()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-15T12:01:32.457122Z",
     "start_time": "2022-02-15T10:51:56.999987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axR = plt.subplots(figsize=(10, 6))\n",
    "suptitle = 'Verificação de suposição'\n",
    "title = 'Resíduos Studentized Pearson em relação aos valores ajustados'\n",
    "\n",
    "sns.regplot(X, \n",
    "            Y_person,\n",
    "            ax= axR,\n",
    "            color='black', \n",
    "            scatter_kws={'s': 5},\n",
    "            line_kws={'color':'r', 'alpha':1, 'lw':2}, \n",
    "            lowess=True)\n",
    "\n",
    "axR.text(x=-0.01, y=1.10, s=suptitle, color='black', fontsize=18, weight='bold', ha='left', va='bottom', transform=axR.transAxes)\n",
    "axR.text(x=-0.01, y=1.05, s=title, color='#646464', fontsize=16, alpha=0.9, ha='left', transform=axR.transAxes)\n",
    "axR.set_xlabel('Valores preditores', fontsize=12, x=0.11)\n",
    "axR.set_ylabel('Resíduos de Pearson', fontsize=12, y=0.81)\n",
    "sns.despine()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> <p style='text-align: justify;'>Parece que os gráficos se aproximam da linha horizontal com 0 interceptação, o que sugere que não há inadequação significativa do modelo.<font size=2.85></font></p>"
   ]
  }
 ],
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