HackTH_bank.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://manipulandodados.com.br/teste-chi-quadrado-pearson-tabelas-de-contingencia/\n",
    "#https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/chi-square/\n",
    "#https://en.wikipedia.org/wiki/Chi-squared_distribution\n",
    "#https://en.wikipedia.org/wiki/Contingency_table\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from statsmodels.stats import weightstats as stests\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from scipy.stats import probplot,chi2_contingency\n",
    "from scipy import stats\n",
    "from statsmodels.stats import weightstats as stests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_chi = pd.read_csv('bank-full_.csv', delimiter=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![logos](https://www.scnsoft.com/cover-pictures/data-analytics.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_chi.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos avaliar usando a informação de '**Estado Civil**'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7fc6ae8d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_chi,x='y',hue='marital')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora se já há um **Empréstimo** vigente para o cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7fc9b7748>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_chi,x='y',hue='loan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Análise por **Distribuição Etária** (faixa de idades)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1)=plt.subplots(1)\n",
    "fig.set_size_inches(12,4)\n",
    "\n",
    "age_plotting=df_chi.loc[df_chi['age'].notnull(),'age'] #sns doesn't handle nan's\n",
    "sns.distplot(age_plotting,ax=ax1)\n",
    "ax1.set_title(\"Distribution of Ages\")\n",
    "\n",
    "x=probplot(age_plotting,plot=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Definição de Hipóteses\n",
    "\n",
    "__Hipótese Nula__: $H_O$ a hipótese nula é uma afirmação geral ou posição padrão de que não há relação entre dois fenômenos medidos, ou nenhuma associação entre grupos. <p>\n",
    "     É a hipótese assumida como ___verdadeira para a construção do teste___. É a teoria, o efeito ou a alternativa que se está interessado em testar.\n",
    "\n",
    "__Hipóteses Alternativa__: $H_A$ é considerada quando a hipótese nula não tem evidência estatística ($H_O$)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![logos](https://cdn-images-1.medium.com/max/800/1*fEPOHXPQO_ZNJC4UQDXmqw.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Suposições e Condições\n",
    "\n",
    "a. Independência:\n",
    "- _Dentro dos Grupos: As observações amostradas devem ser independentes dentro de cada grupo._ \n",
    "    - Amostra aleatória / atribuição\n",
    "    - Se amostragem sem reposição, n <10% da população total  \n",
    "- _Entre Grupos: Dois grupos devem ser independentes um do outro (não pareados)_\n",
    "\n",
    "b. *Tamanho da amostra / inclinação: cada amostra deve atender à condição de falha de sucesso::*\n",
    "    $$n_{\\rm child} p_{\\rm child} \\ge 10 \\quad \\textrm{and} \\quad n_{\\rm child} (1 - p_{\\rm child}) \\ge 10$$\n",
    "    $$n_{\\rm adult} p_{\\rm adult} \\ge 10 \\quad \\textrm{and} \\quad n_{\\rm adult} (1 - p_{\\rm adult}) \\ge 10$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabela de contingência\n",
    "Em estatística , uma tabela de contingência (também conhecida como tabulação cruzada ou tabela de referência cruzada ) é um tipo de tabela em um formato de matriz que exibe a distribuição de frequência (multivariada) das variáveis. Eles são muito utilizados em pesquisa de opinião, inteligência de negócios, engenharia e pesquisa científica. Eles fornecem uma **imagem básica da inter-relação entre duas variáveis** e podem ajudar a encontrar interações entre elas.<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contingency_table :-\n",
      " y            no   yes\n",
      "marital              \n",
      "divorced   4585   622\n",
      "married   24459  2755\n",
      "single    10878  1912\n"
     ]
    }
   ],
   "source": [
    "contingency_table=pd.crosstab(df_chi[\"marital\"],df_chi[\"y\"])\n",
    "print('contingency_table :-\\n',contingency_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>y</th>\n",
       "      <th>no</th>\n",
       "      <th>yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>4585</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>24459</td>\n",
       "      <td>2755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>10878</td>\n",
       "      <td>1912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "y            no   yes\n",
       "marital              \n",
       "divorced   4585   622\n",
       "married   24459  2755\n",
       "single    10878  1912"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tabela de Frequência\n",
    "Quando se está a analisar um conjunto de dados, começa-se por considerar as diferentes categorias ou classes, e para cada uma delas calcula-se a sua **frequência absoluta obtendo-se a distribuição de frequências do conjunto de dados**. Esta distribuição de frequências é representada na forma de uma tabela, a que se dá o nome de tabela de frequências. Uma vez que existe alguma especificidade na fase da definição das classes, conforme o tipo dos dados a analisar, far-se-á essa distinção a seguir, nas indicações para a construção da tabela de frequências."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed Values :-\n",
      " [[ 4585   622]\n",
      " [24459  2755]\n",
      " [10878  1912]]\n"
     ]
    }
   ],
   "source": [
    "#Observed Values\n",
    "Observed_Values = contingency_table.values \n",
    "print(\"Observed Values :-\\n\",Observed_Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected Values :-\n",
      " [[ 4597.86012254   609.13987746]\n",
      " [24030.37552808  3183.62447192]\n",
      " [11293.76434938  1496.23565062]]\n"
     ]
    }
   ],
   "source": [
    "b=stats.chi2_contingency(contingency_table)\n",
    "Expected_Values = b[3]\n",
    "print(\"Expected Values :-\\n\",Expected_Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree of Freedom:- 2\n"
     ]
    }
   ],
   "source": [
    "no_of_rows=len(contingency_table.iloc[0:3,0])\n",
    "no_of_columns=len(contingency_table.iloc[0,0:2])\n",
    "df11=(no_of_rows-1)*(no_of_columns-1)\n",
    "print(\"Degree of Freedom:-\",df11)\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square statistic:- 196.4959456560396\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2\n",
    "chi_square=sum([(o-e)**2./e for o,e in zip(Observed_Values,Expected_Values)])\n",
    "chi_square_statistic=chi_square[0]+chi_square[1]\n",
    "print(\"chi-square statistic:-\",chi_square_statistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "critical_value: 5.991464547107979\n"
     ]
    }
   ],
   "source": [
    "critical_value=chi2.ppf(q=1-alpha,df=df11)\n",
    "print('critical_value:',critical_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "#p-value\n",
    "p_value=1-chi2.cdf(x=chi_square_statistic,df=df11)\n",
    "print('p-value:',p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Teste **Chi-squared** <p> \n",
    "O teste é aplicado quando você tem duas variáveis ​​categóricas de uma única população. <p>\n",
    "    É usado para determinar se existe uma associação significativa entre as duas variáveis.\n",
    "\n",
    "Por exemplo, em uma **pesquisa eleitoral**, os eleitores podem ser classificados por gênero (masculino ou feminino) e por preferência de voto (democrata, republicano ou independente). Poderíamos usar um teste do qui-quadrado para independência para determinar se o gênero está relacionado à preferência de voto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usos do **Chi-Square**<p>\n",
    "A distribuição de qui-quadrado tem muitos uso nas estatística, incluindo:\n",
    "\n",
    "- Estimativa do intervalo de confiança para um desvio padrão da população de uma distribuição normal de um desvio padrão da amostra.\n",
    "- Independência de **dois critérios de classificação** de variáveis qualitativas .\n",
    "- Relacionamentos entre **variáveis categóricas** ( tabelas de contingência ).\n",
    "- Estudo de variância da amostra quando a distribuição subjacente é normal.\n",
    "- Testes de desvios de diferenças entre frequências esperadas e observadas (tabelas unidirecionais).\n",
    "- O teste do qui-quadrado (um teste de qualidade do ajuste )."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![logos](http://static1.squarespace.com/static/55ca1b41e4b0ee7818882387/55ca1be1e4b0b9f988aa7944/5b0598111ae6cf654a519751/1527094554753/UPSC-Result-2016-6.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.Calculando chi-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Significance level:  0.05\n",
      "Degree of Freedom:  2\n",
      "chi-square statistic: 196.4959456560396\n",
      "critical_value: 5.991464547107979\n",
      "p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "print('Significance level: ',alpha)\n",
    "print('Degree of Freedom: ',df11)\n",
    "print('chi-square statistic:',chi_square_statistic)\n",
    "print('critical_value:',critical_value)\n",
    "print('p-value:',p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rejeitar H0,Existe uma relação entre duas variáveis categóricas\n",
      "Rejeitar H0,Existe uma relação entre duas variáveis categóricas\n"
     ]
    }
   ],
   "source": [
    "if chi_square_statistic>=critical_value:\n",
    "    print(\"Rejeitar H0,Existe uma relação entre duas variáveis categóricas\")\n",
    "else:\n",
    "    print(\"Reter H0,Não há relação entre duas variáveis categóricas\")\n",
    "    \n",
    "if p_value<=alpha:\n",
    "    print(\"Rejeitar H0,Existe uma relação entre duas variáveis categóricas\")\n",
    "else:\n",
    "    print(\"Reter H0,Não há relação entre duas variáveis categóricas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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