Desafio-Modulo3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROJETO MÓDULO III - Data Science Aplicada\n",
    "\n",
    "***\n",
    "\n",
    "## Estatística com python para análise de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## O PROJETO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2vw\">PRECISAMOS FALAR SOBRE BULLYING ESCOLAR </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://i.ibb.co/FDCHGbL/Whats-App-Image-2021-01-08-at-18-59-40.jpg\" alt=\"Whats-App-Image-2021-01-08-at-18-59-40\" width=\"800\" height=\"662\" data-load=\"full\" style=\"\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "**BULLLYING**\n",
    "\n",
    "A prática do bullying consiste em um conjunto de violências que se repetem por algum período. Geralmente são agressões verbais, físicas e psicológicas que humilham, intimidam e traumatizam a vítima. Os danos causados pelo bullying podem ser profundos, como a depressão, distúrbios comportamentais e até o suicídio.\n",
    "\n",
    "Fonte: <a href=\"https://brasilescola.uol.com.br/sociologia/bullying.htm\"> Brasil Escola</a>\n",
    "\n",
    "***\n",
    "\n",
    "\n",
    "**A ANÁLISE**\n",
    "\n",
    "O objetivo da análise é entender quais as principais características dos alunos que sofrem bullying nas depêndencias escolares.\n",
    "\n",
    "Os dados foram disponibilizados pelo IBGE, a partir da pesquisa amostral 'Pesquisa Nacional de Saúde do Escolar - PeNSE' que tem como objetivo coletar dados relacionados a fatores de risco e proteção à saúde dos adolescentes (disponível no endereço eletrônico: <a href=\"https://www.ibge.gov.br/estatisticas/sociais/educacao/9134-pesquisa-nacional-de-saude-do-escolar.html?=&t=o-que-e\"> IBGE</a>)\n",
    "\n",
    "***\n",
    "\n",
    "**COMO ESTE NOTEBOOK ESTÁ ORGANIZADO**\n",
    "\n",
    "Ao longo de todo o notebook há seções com breves explicações de conceitos estatísticos, os quais serviram como base para análise dos dados e formulação das conclusões.\n",
    "\n",
    "***\n",
    "\n",
    "**BREVES CONCLUSÕES** \n",
    "\n",
    "(retiradas das análises do notebook - Maiores detalhes das análises e conclusões se encontram nas seções a seguir)\n",
    "<img src=\"https://i.ibb.co/HqJDBNP/Data-Science.jpg\" alt=\"Data-Science\" width=\"900\" height=\"516\" data-load=\"full\" style=\"\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importação das libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import          pandas                 as pd\n",
    "import          matplotlib.pyplot      as plt\n",
    "import          seaborn                as sns\n",
    "import          numpy                  as np\n",
    "\n",
    "from scipy.stats    import normaltest\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importação dos Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ANOPESQ</th>\n",
       "      <th>PAIS</th>\n",
       "      <th>REGEOGR</th>\n",
       "      <th>VB00004</th>\n",
       "      <th>VB01001</th>\n",
       "      <th>VB01002</th>\n",
       "      <th>VB01003</th>\n",
       "      <th>VB01004</th>\n",
       "      <th>VB01005</th>\n",
       "      <th>VB01006</th>\n",
       "      <th>...</th>\n",
       "      <th>VE01P28A</th>\n",
       "      <th>VE01P68</th>\n",
       "      <th>VE01P69</th>\n",
       "      <th>VE01P70</th>\n",
       "      <th>VE01P71</th>\n",
       "      <th>VE01P72</th>\n",
       "      <th>V0041</th>\n",
       "      <th>aluno</th>\n",
       "      <th>escola</th>\n",
       "      <th>turma</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 296 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ANOPESQ  PAIS  REGEOGR  VB00004  VB01001  VB01002  VB01003  VB01004  \\\n",
       "0     2015    76        1        1        2        1       13        7   \n",
       "1     2015    76        1        1        2        1       14        4   \n",
       "2     2015    76        1        1        1        4       13        6   \n",
       "\n",
       "   VB01005  VB01006  ...  VE01P28A  VE01P68  VE01P69  VE01P70  VE01P71  \\\n",
       "0        7        1  ...         1        1        1        1        1   \n",
       "1        7        1  ...         1        1        1        1        1   \n",
       "2        7        1  ...         1        1        1        1        1   \n",
       "\n",
       "   VE01P72  V0041  aluno  escola  turma  \n",
       "0        1      1      1       1    296  \n",
       "1        1      1      2       1    296  \n",
       "2        1      1      3       1    296  \n",
       "\n",
       "[3 rows x 296 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('PENSE_AMOSTRA2_ALUNOESCOLA.CSV', sep=';')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seleção das colunas\n",
    "colunas =['VE01P70', 'REGEOGR', 'V0008', 'VB01003', 'VB07004', 'VB07006', 'VB07007', 'VB07008', 'VB07009',\n",
    "         'VB07010', 'VB12001', 'VB12002', 'VB12003', 'VB11006', 'VB11007', 'VB11001', 'VB11002', 'VB11003',\n",
    "         'VB11004A', 'VB11005']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando somente as colunas que serão utilizadas para análise\n",
    "df = df[colunas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "colunas = ['politica_contra_bu', 'regiao_geo', 'tipo_escola', 'idade', 'preocupacao_resp', 'amigos_trataram_bem',\n",
    "          'amigos_trataram_mal', 'motivo_bul', 'praticou_bu', 'ja_sofreu_bul', 'sentido_sozinho',\n",
    "          'sem_dormir', 'qt_amigos', 'imagem_corporal', 'sentimento_corpo', 'corpo', 'fazendo_corpo', 'remedio_perder_peso',\n",
    "          'remedio_perder_peso_sem_medico', 'remedio_ganhar_peso']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renomeando as colunas\n",
    "df.columns = colunas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ENTENDIMENTO DOS DADOS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesta seção, será criada uma variável contendo uma tabela com a quantidade de registros em cada uma das colunas.\n",
    "Esta forma de visualização tem como objetivo entender quais são os dados, analisando o dataset como um todo, poupando tempo e possibilitando um entendimento geral dos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contando e agrupando\n",
    "table = df.apply(pd.Series.value_counts)\n",
    "\n",
    "# Alterando o tipo de dado para object e os valores nuloes (nan) para a palavra 'None'\n",
    "table = table.astype(object).replace(np.nan, 'None')\n",
    "\n",
    "# Substituindo a palavra 'None' para vazio (a fim de facilitar a visualização)\n",
    "table = table.replace('None', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>politica_contra_bu</th>\n",
       "      <th>regiao_geo</th>\n",
       "      <th>tipo_escola</th>\n",
       "      <th>idade</th>\n",
       "      <th>preocupacao_resp</th>\n",
       "      <th>amigos_trataram_bem</th>\n",
       "      <th>amigos_trataram_mal</th>\n",
       "      <th>motivo_bul</th>\n",
       "      <th>praticou_bu</th>\n",
       "      <th>ja_sofreu_bul</th>\n",
       "      <th>sentido_sozinho</th>\n",
       "      <th>sem_dormir</th>\n",
       "      <th>qt_amigos</th>\n",
       "      <th>imagem_corporal</th>\n",
       "      <th>sentimento_corpo</th>\n",
       "      <th>corpo</th>\n",
       "      <th>fazendo_corpo</th>\n",
       "      <th>remedio_perder_peso</th>\n",
       "      <th>remedio_perder_peso_sem_medico</th>\n",
       "      <th>remedio_ganhar_peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>9250</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14732</td>\n",
       "      <td>3188</td>\n",
       "      <td>300</td>\n",
       "      <td></td>\n",
       "      <td>2493</td>\n",
       "      <td>1345</td>\n",
       "      <td>9147</td>\n",
       "      <td>403</td>\n",
       "      <td>2905</td>\n",
       "      <td>7584</td>\n",
       "      <td>5805</td>\n",
       "      <td>5701</td>\n",
       "      <td>642</td>\n",
       "      <td>7102</td>\n",
       "      <td>4608</td>\n",
       "      <td>952</td>\n",
       "      <td>6169</td>\n",
       "      <td>1262</td>\n",
       "      <td>1108</td>\n",
       "      <td>1321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1762</td>\n",
       "      <td>3465</td>\n",
       "      <td>9009</td>\n",
       "      <td></td>\n",
       "      <td>2476</td>\n",
       "      <td>1470</td>\n",
       "      <td>3541</td>\n",
       "      <td>241</td>\n",
       "      <td>13579</td>\n",
       "      <td>8533</td>\n",
       "      <td>3584</td>\n",
       "      <td>4736</td>\n",
       "      <td>988</td>\n",
       "      <td>6604</td>\n",
       "      <td>6920</td>\n",
       "      <td>3261</td>\n",
       "      <td>4688</td>\n",
       "      <td>15107</td>\n",
       "      <td>15243</td>\n",
       "      <td>15007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>3276</td>\n",
       "      <td>3072</td>\n",
       "      <td></td>\n",
       "      <td>3608</td>\n",
       "      <td>3195</td>\n",
       "      <td>2550</td>\n",
       "      <td>705</td>\n",
       "      <td></td>\n",
       "      <td>387</td>\n",
       "      <td>4580</td>\n",
       "      <td>4149</td>\n",
       "      <td>2056</td>\n",
       "      <td>1969</td>\n",
       "      <td>1807</td>\n",
       "      <td>8822</td>\n",
       "      <td>2674</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>3207</td>\n",
       "      <td>4175</td>\n",
       "      <td></td>\n",
       "      <td>2818</td>\n",
       "      <td>4779</td>\n",
       "      <td>670</td>\n",
       "      <td>1110</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1601</td>\n",
       "      <td>1374</td>\n",
       "      <td>12803</td>\n",
       "      <td>726</td>\n",
       "      <td>2326</td>\n",
       "      <td>2951</td>\n",
       "      <td>2859</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>3420</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>5070</td>\n",
       "      <td>5677</td>\n",
       "      <td>545</td>\n",
       "      <td>160</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>920</td>\n",
       "      <td>519</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>714</td>\n",
       "      <td>411</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>119</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>4489</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>62</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2072</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2634</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2561</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2133</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2425</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2270</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1537</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>563</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>361</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>91</td>\n",
       "      <td>90</td>\n",
       "      <td>103</td>\n",
       "      <td>79</td>\n",
       "      <td>72</td>\n",
       "      <td>52</td>\n",
       "      <td>66</td>\n",
       "      <td>77</td>\n",
       "      <td>67</td>\n",
       "      <td>155</td>\n",
       "      <td>181</td>\n",
       "      <td>159</td>\n",
       "      <td>166</td>\n",
       "      <td>187</td>\n",
       "      <td>205</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    politica_contra_bu regiao_geo tipo_escola idade preocupacao_resp  \\\n",
       "-1                                                                     \n",
       " 1               14732       3188         300                   2493   \n",
       " 2                1762       3465        9009                   2476   \n",
       " 3                           3276        3072                   3608   \n",
       " 4                           3207        4175                   2818   \n",
       " 5                           3420                               5070   \n",
       " 6                                                                     \n",
       " 7                                                                     \n",
       " 9                  62                                                 \n",
       " 11                                            2072                    \n",
       " 12                                            2634                    \n",
       " 13                                            2561                    \n",
       " 14                                            2133                    \n",
       " 15                                            2425                    \n",
       " 16                                            2270                    \n",
       " 17                                            1537                    \n",
       " 18                                             563                    \n",
       " 19                                             361                    \n",
       " 99                                                               91   \n",
       "\n",
       "    amigos_trataram_bem amigos_trataram_mal motivo_bul praticou_bu  \\\n",
       "-1                                                9250               \n",
       " 1                 1345                9147        403        2905   \n",
       " 2                 1470                3541        241       13579   \n",
       " 3                 3195                2550        705               \n",
       " 4                 4779                 670       1110               \n",
       " 5                 5677                 545        160               \n",
       " 6                                                 119               \n",
       " 7                                                4489               \n",
       " 9                                                                   \n",
       " 11                                                                  \n",
       " 12                                                                  \n",
       " 13                                                                  \n",
       " 14                                                                  \n",
       " 15                                                                  \n",
       " 16                                                                  \n",
       " 17                                                                  \n",
       " 18                                                                  \n",
       " 19                                                                  \n",
       " 99                  90                 103         79          72   \n",
       "\n",
       "    ja_sofreu_bul sentido_sozinho sem_dormir qt_amigos imagem_corporal  \\\n",
       "-1                                                                       \n",
       " 1           7584            5805       5701       642            7102   \n",
       " 2           8533            3584       4736       988            6604   \n",
       " 3            387            4580       4149      2056            1969   \n",
       " 4                           1601       1374     12803             726   \n",
       " 5                            920        519                             \n",
       " 6                                                                       \n",
       " 7                                                                       \n",
       " 9                                                                       \n",
       " 11                                                                      \n",
       " 12                                                                      \n",
       " 13                                                                      \n",
       " 14                                                                      \n",
       " 15                                                                      \n",
       " 16                                                                      \n",
       " 17                                                                      \n",
       " 18                                                                      \n",
       " 19                                                                      \n",
       " 99            52              66         77        67             155   \n",
       "\n",
       "    sentimento_corpo corpo fazendo_corpo remedio_perder_peso  \\\n",
       "-1                                                             \n",
       " 1              4608   952          6169                1262   \n",
       " 2              6920  3261          4688               15107   \n",
       " 3              1807  8822          2674                       \n",
       " 4              2326  2951          2859                       \n",
       " 5               714   411                                     \n",
       " 6                                                             \n",
       " 7                                                             \n",
       " 9                                                             \n",
       " 11                                                            \n",
       " 12                                                            \n",
       " 13                                                            \n",
       " 14                                                            \n",
       " 15                                                            \n",
       " 16                                                            \n",
       " 17                                                            \n",
       " 18                                                            \n",
       " 19                                                            \n",
       " 99              181   159           166                 187   \n",
       "\n",
       "    remedio_perder_peso_sem_medico remedio_ganhar_peso  \n",
       "-1                                                      \n",
       " 1                            1108                1321  \n",
       " 2                           15243               15007  \n",
       " 3                                                      \n",
       " 4                                                      \n",
       " 5                                                      \n",
       " 6                                                      \n",
       " 7                                                      \n",
       " 9                                                      \n",
       " 11                                                     \n",
       " 12                                                     \n",
       " 13                                                     \n",
       " 14                                                     \n",
       " 15                                                     \n",
       " 16                                                     \n",
       " 17                                                     \n",
       " 18                                                     \n",
       " 19                                                     \n",
       " 99                            205                 228  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a tabela com a qt. de dados\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## POLÍTICA CONTRA BULLYING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "Em 2016 entrou em vigor a Lei nº 13.185 que classifica o bullying como intimidação sistemática, quando há violência física ou psicológica em atos de humilhação ou discriminação. A classificação também inclui ataques físicos, insultos, ameaças, comentários e apelidos pejorativos, entre outros. \n",
    "\n",
    "Fonte: <a href=\"http://portal.mec.gov.br/component/tags/tag/34487#:~:text=A%20Lei%20n%C2%BA%2013.185%2C%20em,e%20apelidos%20pejorativos%2C%20entre%20outros.\">Ministério da Educação</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://i.ibb.co/TcQGPp3/Captura-de-Tela-2021-01-07-a-s-22-13-32.png\" alt=\"Captura-de-Tela-2021-01-07-a-s-22-13-32\" width=\"540\" height=\"281\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como analisado abaixo, a maior parte dos alunos responderam afirmativamente a pergunta:\n",
    "\n",
    "**A escola tem alguma política, norma ou regra escrita que proíba bullying nas suas dependências?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Qt</th>\n",
       "      <th>Perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14732</td>\n",
       "      <td>88.982846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1762</td>\n",
       "      <td>10.642667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>62</td>\n",
       "      <td>0.374487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Qt       Perc\n",
       "1  14732  88.982846\n",
       "2   1762  10.642667\n",
       "9     62   0.374487"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando e agrupando\n",
    "q = df['politica_contra_bu'].value_counts()\n",
    "p = df['politica_contra_bu'].value_counts(normalize = True) * 100\n",
    "\n",
    "# Criando um DataFrame\n",
    "t = pd.DataFrame({'Qt' : q, 'Perc' : p})\n",
    "t "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Qt</th>\n",
       "      <th>Perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>262</td>\n",
       "      <td>14.869467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>252</td>\n",
       "      <td>14.301930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>250</td>\n",
       "      <td>14.188422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>237</td>\n",
       "      <td>13.450624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>216</td>\n",
       "      <td>12.258797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>211</td>\n",
       "      <td>11.975028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>178</td>\n",
       "      <td>10.102157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>92</td>\n",
       "      <td>5.221339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>64</td>\n",
       "      <td>3.632236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Qt       Perc\n",
       "12  262  14.869467\n",
       "15  252  14.301930\n",
       "14  250  14.188422\n",
       "13  237  13.450624\n",
       "11  216  12.258797\n",
       "16  211  11.975028\n",
       "17  178  10.102157\n",
       "18   92   5.221339\n",
       "19   64   3.632236"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando os alunos que responderam 'nao' para política contra bullying e\n",
    "# calculando a qt. e o percentual por idade\n",
    "\n",
    "q = df[df['politica_contra_bu'] == 2]['idade'].value_counts()\n",
    "p = df[df['politica_contra_bu'] == 2]['idade'].value_counts(normalize = True) * 100\n",
    "\n",
    "#Criando dataframe\n",
    "t = pd.DataFrame({'Qt' : q, 'Perc' : p}).sort_values(by='Perc', ascending=False)\n",
    "t "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Qt</th>\n",
       "      <th>Perc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2372</td>\n",
       "      <td>16.101005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2324</td>\n",
       "      <td>15.775183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2171</td>\n",
       "      <td>14.736628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2041</td>\n",
       "      <td>13.854195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1883</td>\n",
       "      <td>12.781700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1856</td>\n",
       "      <td>12.598425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1322</td>\n",
       "      <td>8.973663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>466</td>\n",
       "      <td>3.163182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>297</td>\n",
       "      <td>2.016020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Qt       Perc\n",
       "12  2372  16.101005\n",
       "13  2324  15.775183\n",
       "15  2171  14.736628\n",
       "16  2041  13.854195\n",
       "14  1883  12.781700\n",
       "11  1856  12.598425\n",
       "17  1322   8.973663\n",
       "18   466   3.163182\n",
       "19   297   2.016020"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando os alunos que responderam 'sim' para política contra bullying e\n",
    "# calculando a qt. e o percentual por idade\n",
    "\n",
    "q = df[df['politica_contra_bu'] == 1]['idade'].value_counts()\n",
    "p = df[df['politica_contra_bu'] == 1]['idade'].value_counts(normalize = True) * 100\n",
    "\n",
    "# Criando dataframe\n",
    "t = pd.DataFrame({'Qt' : q, 'Perc' : p}).sort_values(by='Perc', ascending=False)\n",
    "t "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "ax = sns.countplot(data=df, x='idade' ,hue='politica_contra_bu')\n",
    "\n",
    "plt.title('QT. DE ALUNOS AGRUPADOS POR RESPOSTA REF POLÍTICA CONTRA BULLYING',\n",
    "               pad = 20,\n",
    "         fontsize = 13, color ='dimgrey', fontweight = 'bold')\n",
    "\n",
    "ax = sns.despine(bottom = True, left=True)\n",
    "ax = plt.xlabel('IDADE', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "ax = plt.ylabel('QTDADE', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "ax = plt.legend(labels=['Sim', 'Não', 'Não informado'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quase 90% dos alunos responderam que existem políticas, regras ou normas que proíbem bullying nas dependências da escola.\n",
    "\n",
    "A maior quantidade de alunos que não responderam a pergunta se concentra nas maiores idades\n",
    "\n",
    "Alunos que reponderam \"sim\" se concentram nos 12, 13, 15 anos, seguido por 16 e 11 anos.\n",
    "\n",
    "Vale lembrar que se trata de **quantidades**, ou seja, na amostra, pode haver maior quantidade de alunos em determinadas idades, o que, logicamente, altera a visualização do gráfico.\n",
    "\n",
    "***\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MÉDIA, MEDIANA E DESVIO PADRÃO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MÉDIA**: A média é provavelmente a estatística mais conhecida e utilizada. De forma bastante resumida, concentra a posição média dos valores. É mais indicada quando os números estão distribuidos de maneira mais uniforme, uma vez que são bastante suscetíveis à outliers.\n",
    "\n",
    "**MEDIANA**: A mediana é o valor que se concentra exatamente no meio do conjunto de dados.\n",
    "\n",
    "1 . Para realizar o cálculo, caso o conjunto de dados seja ímpar, basta pegar o número que se encontra exatamente no meio.\n",
    "\n",
    "2 . Caso o conjunto de dados possuir um número par, basta calcular a média dos dois números que estiverem exatamente no meio, conforme exemplo a seguir:\n",
    "\n",
    "<img class=\"alignnone wp-image-13633\" src=\"https://blog.professorferretto.com.br/wp-content/uploads/2017/05/calculo-de-mediana-com-sequencia-par.png\" alt=\"Exemplo de cálculo da mediana com sequência de elementos par\" width=\"601\" height=\"293\">\n",
    "\n",
    "Fonte: <a href=\"https://blog.professorferretto.com.br/moda-media-e-mediana-medidas-de-tendencia-central/.\"> BLOG DO FERRETTO </a>\n",
    "\n",
    "\n",
    "\n",
    "**DESVIO PADRÃO** O desvio padrão é bastante utilizado para identificar a variabilidade. Aproximadamente, o desvio padrão será a distância média do centro, que neste caso, centro seria a média.\n",
    "\n",
    "O desvio padrão da população toda é representado pela letra grega σ. Já o desvio padrão da amostra é representado pela letra S.\n",
    "\n",
    "<img class=\"XqHOTb IGEbUc\" alt=\"\\sigma={\\sqrt {\\frac {\\sum(x_{i}-{\\mu})^{2}}{N}}}\" src=\"https://www.gstatic.com/education/formulas2/-1/en/population_standard_deviation.svg\" role=\"img\" data-atf=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MOTIVO DO BULLYING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para exemplificar o conceito de média e mediana, serão analisados os motivos de bullying respondidos pelos alunos"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VB07008\tNOS ÚLTIMOS 30 DIAS, qual o motivo/causa de seus colegas terem te esculachado, zombado, zoado, caçoado, mangado, intimidado ou humilhado?\n",
    "\n",
    "-1\tPulo no questionário\n",
    "1\tA minha cor ou raça\n",
    "2\tA minha religião\n",
    "3\tA aparência do meu rosto\n",
    "4\tA aparência do meu corpo\n",
    "5\tA minha orientação sexual\n",
    "6\tA minha região de origem\n",
    "7\tOutros motivos/causas\n",
    "99\tNão informado![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Copiando o dataframe original (evitar erros)\n",
    "a = df.copy()\n",
    "\n",
    "# Criando dicionario com as respostas\n",
    "rotulos = {-1 : 'Pulou', 1:'Cor/Raca', 2:'Religiao', 3: 'Aparencia Rosto', 4: 'Aparencia Corpo',\n",
    "          5: 'Orientacao sexual', 6: 'Região origem', 7: 'Outros', 8: 'Não informado'}\n",
    "\n",
    "# Traduzinho o dicionario\n",
    "a['motivo_bul'] = a['motivo_bul'].map(rotulos)\n",
    "\n",
    "# Plotando e configuando o gráfico\n",
    "ax = (a['motivo_bul'].value_counts()).plot(kind='bar')\n",
    "\n",
    "ax = plt.title('MOTIVOS DE BULLYING',\n",
    "               pad = 20,\n",
    "         fontsize = 13, color ='dimgrey', fontweight = 'bold')\n",
    "\n",
    "ax = sns.despine(bottom = True, left=True)\n",
    "ax = plt.xlabel('MOTIVOS', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "ax = plt.ylabel('QUANTIDADE', color = 'grey', labelpad = 20, fontsize = 8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A grande maioria dos alunos pulam o motivo de bullying.\n",
    "\n",
    "A aparencia do corpo e do rosto são os dois principais motivos de bullying que foram possíveis identificar.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  BULLYING POR APARÊNCIA - MÉDIA DE IDADE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando uma variável para filtrar os dados que possuem a palavra 'aparencia' na coluna 'motiv_bul'\n",
    "b = a['motivo_bul'].str.contains('Aparencia')\n",
    "\n",
    "# Criando uma coluna na variável 'a' conforme variável 'b'\n",
    "a['aparencia'] = b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.832506887052341"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Média de idade das crianças que sofrem bullying por conta da aparência\n",
    "media = a[a['aparencia'] == True]['idade'].mean()\n",
    "media "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mediana de idade das crianças que sofrem bullying por conta da aparência\n",
    "mediana = a[a['aparencia'] == True]['idade'].median()\n",
    "mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9863206518330716"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a['aparencia'] == True]['idade'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HISTOGRAMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O histograma é uma forma de visualização bastante útil para entender como os dados estão distribuídos, ou seja, representa a frequência do conjunto de dados em classes.\n",
    "\n",
    "A base de cada retângulo representa uma classe. A altura de cada retângulo representa a quantidade ou a frequência absoluta com que o valor da classe ocorre no conjunto de dados para classes uniformes ou a densidade de frequência para classes não uniformes. (Wikipédia)\n",
    "\n",
    "O conjunto de dados pode ter diversos comportamentos, dependendo da sua distribuição e frequência:\n",
    "\n",
    "1 . Caso os dados apresentem valores muito discrepante na parte superior do conjunto de dados, o histograma ficará distorcido para a direita. A média será maior do que a mediana.\n",
    "\n",
    "2 . Caso os dados apresentem valores muito discrepantes na parte inferior do conjunto de dados, o histograma ficará distorcido para esquerda. A média será menor do que a mediana.\n",
    "\n",
    "3 . Caso os dados sejam simétricos, representando uma normalidade, a média e a mediana terão aproximadamente o mesmo valor.\n",
    "\n",
    "<img border=\"0\" data-original-height=\"386\" data-original-width=\"549\" src=\"https://3.bp.blogspot.com/-ieYxAgEBaxU/WvSQXoLhpqI/AAAAAAAAEeM/3lSn5M_VXYgoWUz6YOGguurvX7DcORkwACEwYBhgL/s1600/assime2.JPG\">\n",
    "\n",
    "Fonte: <a href=\"http://soniavieira.blogspot.com/2018/05/por-conta-de-umapergunta-sobre-questao_83.html.\"> Sonia Vieira </a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "ax = sns.histplot(a[a['aparencia'] == True]['idade'], bins = 9, kde = True, element=\"step\")\n",
    "\n",
    "ax = plt.title('IDADE ALUNOS QUE SOFRERAM BULLYING DEVIDO APARÊNCIA',\n",
    "               pad = 20,\n",
    "         fontsize = 13, color ='dimgrey', fontweight = 'bold')\n",
    "\n",
    "ax = sns.despine(bottom = True, left=True)\n",
    "ax = plt.xlabel('IDADE', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "ax = plt.ylabel('QUANTIDADE', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "\n",
    "plt.axvline(x=media, linewidth=2, color='red', linestyle='--', label ='Média')\n",
    "plt.axvline(x=mediana, linewidth=2, color='orange', linestyle='--', label ='Mediana')\n",
    "\n",
    "plt.legend()\n",
    "ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BOX PLOT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O Box Plot é um método de visualização de estatística descritiva.\n",
    "Com ele, é possível visualizar a mediana, quartis e outliers.\n",
    "\n",
    "O box plot dá uma idéia da posição, dispersão, assimetria, caudas e dados discrepantes. A posição central é dada pela mediana e a dispersão por dq. As posições relativas de q1, q2, q3 dão uma noção da assimetria da distribuição. Os comprimentos das caudas são dados pelas linhas que vão do retângulo aos valores remotos e pelos valores atípicos.\n",
    "(Bussab e Morettin, Estatística básica, Ed. Saraiva, 2010)\n",
    "\n",
    "<img alt=\"Box Plot | Simply Psychology\" src=\"https://www.simplypsychology.org/boxplot.jpg\" data-noaft=\"1\" jsname=\"HiaYvf\" jsaction=\"load:XAeZkd;\" style=\"width: 435px; height: 223.158px; \">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SENTIMENTO EM RELAÇÃO AO CORPO"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VB11007\tComo você se sente em relação ao seu corpo?\n",
    "\n",
    "1\tMuito satisfeito(a)\n",
    "2\tSatisfeito(a)\n",
    "3\tIndiferente\n",
    "4\tInsatisfeito(a)\n",
    "5\tMuito insatisfeito(a)\n",
    "99\tNão informado![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando somente os alunos que sofreram bullying pela aparência do corpo\n",
    "a = df[df['motivo_bul'] == 4]\n",
    "\n",
    "# Dicionário com as respostas\n",
    "l = {1:'1 Muito satisfeito', 2:'2 Satisfeito', 3:'3 Indiferente', 4:'4 Insatisfeito', \n",
    "      5:'5 Muito insatisfeito', 99:'6 Não Informado'}\n",
    "\n",
    "# Traduzinho o dicionario\n",
    "a['sentimento_corpo'] = a['sentimento_corpo'].map(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(13, 5))\n",
    "ax = sns.boxplot(data = a, x ='sentimento_corpo', y='idade', hue='sentimento_corpo', \n",
    "                 palette = 'crest', linewidth = 0.5, dodge=False, order=l.values())\n",
    "\n",
    "sns.despine(bottom = True, left=True)\n",
    "\n",
    "plt.title('BOX PLOT - SSENTIMENTO EM RELAÇÃO AO CORPO POR IDADE')\n",
    "plt.xlabel('SENTIMENTO EM RELACAO AO CORPO', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "plt.ylabel('IDADE', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "\n",
    "# \"Movendo\" a legenda\n",
    "plt.legend(loc=(1.03, -0.1), borderaxespad=0)\n",
    "\n",
    "ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CORRELAÇÃO E CAUSALIDADE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confundindo com frequência, correlação e causalidade são dois termos diferentes.\n",
    "\n",
    "**Correlação:** Observa-se se duas variáveis numéricas possuem alguma tipo de relação, ou seja, uma possível existência de ligação. Quando a variável X aumenta, a Y também aumenta (e vice-versa). Exemplo: Aumento de temperatura x consumo de sorvete - Quanto maior a temperatura, maior o consumo de cerveja.\n",
    "\n",
    "\n",
    "***\n",
    "\n",
    "**Interpretando os gráficos de dispersão**\n",
    "- Se os dados formarem uma linha ascendente, conforme você se movimenta da esquerda para a direita, isso indica a existência de uma relação linear (ou proporcional) positiva. Conforme x aumenta (movendo-se uma unidade para a direita), y aumenta (movendo-se para cima) um determinado valor.\n",
    "\n",
    "\n",
    "- Se os dados formarem uma linha descendente conforme você se movimenta da esquerda para a direita, isso indica a existência de uma relação relação linear (ou proporcional) negativa. Significa que conforme x aumenta (movimentando-se uma unidade para a direita), y diminui (movimentando-se para baixo) uma determinada quantia.\n",
    "\n",
    "- Se os dados não se parecerem com nenhum tipo de linha (nem mesmo vagamente), isso indica que não existe nenhum tipo de relação linear entre as variáveis.\n",
    "\n",
    "Rumsey, Deborah. Estatística Para Leigos (p. 469). Alta Books. Edição do Kindle. \n",
    "\n",
    "***\n",
    "\n",
    "O exemplo bastante claro e até \"engraçado\" é a relação entre a temperatura do ambiente e a quantidade de cricrilar dos grilos. A proposta é que você deve contar a quantidade de vezes que um grilo cricila por 15 seg, somar 40 e saberá a temperatura do ambiente. (Lembre-se de converter para graus Celsius)\n",
    "<br>\n",
    "<br>\n",
    "<img loading=\"lazy\" data-attachment-id=\"23157\" data-permalink=\"https://wohaliterapias.wordpress.com/grilo-verde/\" data-orig-file=\"https://wohaliterapias.files.wordpress.com/2020/01/grilo-verde.jpg\" data-orig-size=\"400,300\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"grilo-verde\" data-image-description=\"\" data-medium-file=\"https://wohaliterapias.files.wordpress.com/2020/01/grilo-verde.jpg?w=300\" data-large-file=\"https://wohaliterapias.files.wordpress.com/2020/01/grilo-verde.jpg?w=400\" src=\"https://wohaliterapias.files.wordpress.com/2020/01/grilo-verde.jpg?w=400\" alt=\"\" class=\"wp-image-23157\" width=\"400\" height=\"300\" srcset=\"https://wohaliterapias.files.wordpress.com/2020/01/grilo-verde.jpg 400w, https://wohaliterapias.files.wordpress.com/2020/01/grilo-verde.jpg?w=150 100w, https://wohaliterapias.files.wordpress.com/2020/01/grilo-verde.jpg?w=300 200w\" sizes=\"(max-width: 600px) 100vw, 600px\">\n",
    "\n",
    "Neste exemplo, parece existir uma relação linear positiva entre o número de vezes que um grilo crica e a temperatura externa. Ou seja, quanto mais o grilo cricrilar, maior é a temperatura. Rumsey, Deborah. Estatística Para Leigos (p. 469). Alta Books. Edição do Kindle. \n",
    "\n",
    "Você pode saber clicando aqui: <a href=\"https://www.tecmundo.com.br/ciencia/18444-voce-pode-medir-a-temperatura-de-um-ambiente-ouvindo-os-grilos-la-fora.htm\"> Você pode medir a temperatura de um ambiente ouvindo os grilos lá fora </a>\n",
    "\n",
    "\n",
    "***\n",
    "\n",
    "**Causalidade:** Diferentemente da correlação, causalidade se trata da relação entre um evento A e um evento B, ou seja, quando ocorrer o A, o evento B será impactado.\n",
    "\n",
    "Para entender a diferença entre correlação e causalidade de forma clara, tomando como exemplo a grilo e a temperatura. Trata-se de uma correlação, como foi falado. Não pode ser considerado uma causalidade, uma vez que não é porquê o grilo crica que a temperatura se eleva. A variação de temperatura tem outras origens que não estão relacionadas aos grilos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANALISANDO A CORRELAÇÃO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dando continuidade na análise e observar a correlação entre as variáveis, primeiramente, serão selecionadas as variáveis alvo.\n",
    "\n",
    "O objetivo é entender o comportamento dos alunos que sofrem bullying devido a aparência do corpo (primeiro motivo identificado na pesquisa - 1 pulou e 2 classificado como 'outros'), assim, serão consideradas as colunas:\n",
    "\n",
    "- amigos_trataram_bem e amigos_trataram_mal\n",
    "\n",
    "- sentido_sozinho\n",
    "\n",
    "- sem_dormir\n",
    "\n",
    "- qt_amigos\n",
    "\n",
    "- imagem_corporal\n",
    "\n",
    "- sentimento_corpo\n",
    "\n",
    "- fazendo_corpo\n",
    "\n",
    "- remedio_perder_peso\n",
    "\n",
    "- remedio_perder_peso_sem_medico\n",
    "\n",
    "- remedio_ganhar_peso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# colunas selecionadas\n",
    "c = ['motivo_bul','amigos_trataram_bem', 'amigos_trataram_mal', 'sentido_sozinho','sem_dormir', 'qt_amigos', \n",
    "     'imagem_corporal', 'sentimento_corpo', 'corpo', 'fazendo_corpo', 'remedio_perder_peso', \n",
    "     'remedio_perder_peso_sem_medico', 'remedio_ganhar_peso']\n",
    "\n",
    "# 'Filtrando' o dataframe com as colunas selecionadas\n",
    "c = df[c]\n",
    "\n",
    "# Selecionando somente os alunos que sofreram bullyng devido a aparência do corpo\n",
    "c = c[c['motivo_bul'] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>motivo_bul</th>\n",
       "      <th>amigos_trataram_bem</th>\n",
       "      <th>amigos_trataram_mal</th>\n",
       "      <th>sentido_sozinho</th>\n",
       "      <th>sem_dormir</th>\n",
       "      <th>qt_amigos</th>\n",
       "      <th>imagem_corporal</th>\n",
       "      <th>sentimento_corpo</th>\n",
       "      <th>corpo</th>\n",
       "      <th>fazendo_corpo</th>\n",
       "      <th>remedio_perder_peso</th>\n",
       "      <th>remedio_perder_peso_sem_medico</th>\n",
       "      <th>remedio_ganhar_peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>80</td>\n",
       "      <td></td>\n",
       "      <td>267</td>\n",
       "      <td>276</td>\n",
       "      <td>49</td>\n",
       "      <td>401</td>\n",
       "      <td>140</td>\n",
       "      <td>120</td>\n",
       "      <td>278</td>\n",
       "      <td>128</td>\n",
       "      <td>105</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>173</td>\n",
       "      <td>459</td>\n",
       "      <td>214</td>\n",
       "      <td>304</td>\n",
       "      <td>91</td>\n",
       "      <td>468</td>\n",
       "      <td>283</td>\n",
       "      <td>173</td>\n",
       "      <td>551</td>\n",
       "      <td>979</td>\n",
       "      <td>998</td>\n",
       "      <td>1013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>317</td>\n",
       "      <td>405</td>\n",
       "      <td>342</td>\n",
       "      <td>330</td>\n",
       "      <td>184</td>\n",
       "      <td>162</td>\n",
       "      <td>194</td>\n",
       "      <td>239</td>\n",
       "      <td>214</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1110</td>\n",
       "      <td>317</td>\n",
       "      <td>135</td>\n",
       "      <td>182</td>\n",
       "      <td>146</td>\n",
       "      <td>786</td>\n",
       "      <td>77</td>\n",
       "      <td>329</td>\n",
       "      <td>449</td>\n",
       "      <td>63</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>221</td>\n",
       "      <td>111</td>\n",
       "      <td>105</td>\n",
       "      <td>54</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>160</td>\n",
       "      <td>128</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   motivo_bul  amigos_trataram_bem amigos_trataram_mal sentido_sozinho  \\\n",
       "1                               80                                 267   \n",
       "2                              173                 459             214   \n",
       "3                              317                 405             342   \n",
       "4        1110                  317                 135             182   \n",
       "5                              221                 111             105   \n",
       "99                               2                                       \n",
       "\n",
       "   sem_dormir qt_amigos imagem_corporal  sentimento_corpo  corpo  \\\n",
       "1         276        49             401               140    120   \n",
       "2         304        91             468               283    173   \n",
       "3         330       184             162               194    239   \n",
       "4         146       786              77               329    449   \n",
       "5          54                                         160    128   \n",
       "99                                    2                 4      1   \n",
       "\n",
       "   fazendo_corpo remedio_perder_peso remedio_perder_peso_sem_medico  \\\n",
       "1            278                 128                            105   \n",
       "2            551                 979                            998   \n",
       "3            214                                                      \n",
       "4             63                                                      \n",
       "5                                                                     \n",
       "99             4                   3                              7   \n",
       "\n",
       "   remedio_ganhar_peso  \n",
       "1                   90  \n",
       "2                 1013  \n",
       "3                       \n",
       "4                       \n",
       "5                       \n",
       "99                   7  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando se as respostas são ordinais (é possível classificá-las? ex: meses do ano) \n",
    "tabela = c.copy()\n",
    "\n",
    "# Contando e agrupando\n",
    "tabela = tabela.apply(pd.Series.value_counts)\n",
    "\n",
    "# Alterando o tipo de dado para object e os valores nuloes (nan) para a palavra 'None'\n",
    "tabela = tabela.astype(object).replace(np.nan, 'None')\n",
    "\n",
    "# Substituindo a palavra 'None' para vazio (a fim de facilitar a visualização)\n",
    "tabela = tabela.replace('None', '')\n",
    "\n",
    "tabela"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ATENÇÃO**\n",
    "\n",
    "Conforme dicionário de dados, foram analisadas as respostas para identificar quais poderiam ser consideradas ordinais.\n",
    "\n",
    "Exemplo:\n",
    "\n",
    "VB07007\tNos últimos 30 dias, com que frequência algum dos seus colegas de escola te esculacharam, zoaram, mangaram, intimidaram ou caçoaram tanto que você ficou magoado, incomodado, aborrecido, ofendido ou humilhado?\n",
    "1\tNunca\n",
    "2\tRaramente\n",
    "3\tÀs vezes\n",
    "4\tNa maior parte do tempo\n",
    "5\tSempre![image.png](attachment:image.png)\n",
    "\n",
    "Entende-se que quanto maior for o número de resposta, pior. Ou seja, considerando que a pergunta se refere a quantas vezes o aluno foi esculachado/zoado/humilhado, etc., a resposta 1 (que seria nunca) é melhor do que a 5 (que é sempre).\n",
    "\n",
    "Assim, analisando todas as outras variáveis, foram selecionadas novamente as variáveis onde as respostas podem ser classificadas da mesma forma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    329\n",
       "2    283\n",
       "3    194\n",
       "5    160\n",
       "1    140\n",
       "Name: sentimento_corpo, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando as variáveis\n",
    "var = c[['amigos_trataram_mal', 'sentido_sozinho', 'sem_dormir', 'sentimento_corpo']] \n",
    "\n",
    "# Verificando que existem respostas com 99, o que pode distorcer os resultados. Estas resposta (apenas 4) serão\n",
    "# excluidas do dataframe\n",
    "# Selecionando os índices que possuem resposta 99\n",
    "r = var[var['sentimento_corpo'] == 99].index\n",
    "\n",
    "# Excluindo os indices (variável r)\n",
    "var.drop(r, axis=0, inplace = True)\n",
    "\n",
    "# Verificando se está correto\n",
    "var['sentimento_corpo'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COEFICIENTE DE CORRELAÇÃO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O coeficiente de correlação mede a força e a direção entre duas variáveis. \n",
    "\n",
    "A força desta relação vai variar conforme a similariedade dos dados em uma linha reta, podendo ser positiva ou negativa.\n",
    "\n",
    "**Interpretando a correlação**\n",
    "\n",
    "- A correlação r sempre é um valor entre -1 e +1.  \n",
    "- A correlação exatamente igual a -1 indica a existência de uma relação linear negativa perfeita.  \n",
    "- A correlação próxima a -1 indica a existência de uma relação linear negativa forte.  \n",
    "- A correlação próxima a 0 significa a não existência de relações lineares.  \n",
    "- A correlação próxima de +1 indica a existência de uma relação linear positiva forte.\n",
    "- A correlação exatamente igual a +1 indica a existência de uma relação linear positiva perfeita.\n",
    "- A ordem dos valores de x e y podem se alterar dentro do conjunto de dados e a correlação ainda permanecerá a mesma.\n",
    "\n",
    "Rumsey, Deborah. Estatística Para Leigos (p. 475 - 477). Alta Books. Edição do Kindle. \n",
    "\n",
    "**Níveis de correlação**\n",
    "\n",
    "<img loading=\"lazy\" data-attachment-id=\"325\" data-permalink=\"https://segredosdaestatistica.wordpress.com/semanas/semana-5-correlacoes/correlacao-2/\" data-orig-file=\"https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg\" data-orig-size=\"640,418\" data-comments-opened=\"1\" data-image-meta=\"{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}\" data-image-title=\"correlação\" data-image-description=\"\" data-medium-file=\"https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg?w=300\" data-large-file=\"https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg?w=593\" class=\"alignnone size-full wp-image-325\" src=\"https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg?w=593&amp;h=387\" alt=\"correlação\" width=\"593\" height=\"387\" srcset=\"https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg?w=593&amp;h=387 593w, https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg?w=150&amp;h=98 150w, https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg?w=300&amp;h=196 300w, https://segredosdaestatistica.files.wordpress.com/2016/04/correlac3a7c3a3o.jpg 640w\" sizes=\"(max-width: 593px) 100vw, 593px\">\n",
    "\n",
    "Fonte: <a href=\"https://segredosdaestatistica.wordpress.com/semanas/semana-5-correlacoes/\"> Segredos da Estatística </a>\n",
    "***\n",
    "\n",
    "**Exemplos de ângulo de inclinação conforme coeficiente de relação** \n",
    "\n",
    "<img alt=\"Fichier:Correlation examples2.svg\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/d/d4/Correlation_examples2.svg/506px-Correlation_examples2.svg.png\" decoding=\"async\" width=\"506\" height=\"231\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/d/d4/Correlation_examples2.svg/759px-Correlation_examples2.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/d/d4/Correlation_examples2.svg/1012px-Correlation_examples2.svg.png 2x\" data-file-width=\"506\" data-file-height=\"231\">\n",
    "\n",
    "Fonte: <a href=\"https://fr.wikipedia.org/wiki/Fichier:Correlation_examples2.svg\"> Wikipédia </a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando a matiz de correlação\n",
    "correlacao = var.corr()\n",
    "\n",
    "mask = np.triu(np.ones_like(correlacao, dtype=bool))\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "sns.heatmap(correlacao, annot=True, square = True, mask = mask, cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'DIFICULDADES PARA DORMIR')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fkvTB9RkaAABA7yEGAwAA3aRm4knS6yQ9RdIWSbdLemEQBL+7LqMCAADoXcRgAACga9RLPM0GQTAnaS4Mw4NBEHxxvQYFAADQw4jBAABA1zDnXNUrwjC8RdI9kkzS9tjvLgiCJ63bCFur+sECAIBuYRs9gLUiBgMAAB2oZgxWM/HUpXrqYAEA6EEdn3jqUsRgAAB0t5oxWL3m4u9XjSAhCIKrWzAoAAAAlCEGAwAA3aRej6fXr9soAAAAUEQMBgAAuka9xNOP1LnuulYPBAAAAJKIwQAAQBdJ1bnuLyT9iqTR6G+L/QAAACAZxGAAAKBr1Kt4ukDSkyU9T9IPS/q6pI8GQfD9dRgXAABAryIGAwAAXWPFVe3CMMxIepqkN0v6QhAEv7seA0sIK6oAANDduqYqiBgMAAB0kFWtavcTkp4laUjSlyU9IwiCk60fGwAAAIqIwQAAQDepWfEUhmFB0iFJ09FFTj6D5YIgeNL6DK/l+LYNAIAuUyg4zS7mNbuY17bNAx1f8UQMBgAAOlDNGGzFqXZdpqcOFgCAbjW3mNfsgk82zecKKsYzD9q+ueMTT12KGAwAgO7W/FQ7SQrD8DJJCoLgpjAM+yW9XNJLgiB4QmvHBwAAUNtivqCzC/lSwqnQ5V+cEYMBAIBuUa/H0wH5jFU2DMM5SedL+rikZ6zT2AAAQI/KF6fPRcmmxXxho4e0bojBAABAN6lX8XRJEARPlqQwDP9P0t4gCGbWZ1gAAKCXOOc0n/NVTbOLec0v5jd6SBuJGAwAAHSNes3FvyPp1yWlJL1N0m8UrwuC4MvrMbgEdHddPgAAHWQhV1hW1dSK6XPd0OOJGAwAAHSgVfV4+qSkJ8V+f2L0u5Nf2hcAAKBhuXyhtPrc3EJBuULvTJ9rEjEYAADoGiuuaheG4W5JOyQdD4JgYl0GZTYo6SuSBuSTYx9zzgVl21wp6Z8l3RVd9Ann3JtW2DXftgEAsE4KxT5Ni76iaSGXfKKpGyqeiojBAABAB6kZg9WbaneRpL+RdEzSUUljknZJ+qUgCO6qeqMWMTOTtMk5d8bMMpK+JunVzrkbYttcKem3nHPPbmLXBD0AACTEOae5xaWqpvXo0+Sc05H7ZjV+dFLjE1N630sf1fGJJ2IwAADQgVY11e6vJL0yCII7iheEYXiJpL+W9MzWja2S89mwM9GfmeiHgAUAgDZSbAg+W2wInitopUrqtSo4p7tOzWh8YkoHJyZ1aGJK959dTPQ+NwAxGAAA6BqpOtcNxwMeSQqC4HZJQ8kOyTOztJl9R9I9kr7gnPtGlc0ea2YHzexzZnZ5jf1cY2Y3mtmNBw4cSHLIAAB0vflcXlNnF3X31Jy+f+9ZHZuc1f1nFzS3mE8k6ZQvON1y97Q+cuMR/f4nD+t5f/Xf+qXrvqV3fvkOfeW2U6WkU3Yooydesq3l979BiMEAAEDXqFfxdG8Yho8NguDrxQvCMHycpPuSH5bknMtLeriZbZX0STPb45w7HNvk25IeEJWCXyXpU5IuqbKfA5KK0Q7f2AEA0IR4Q/DZhbzyhWQ/ShdyBd124rQOTvipc4ePTmu2ypS98zb3a//YVu0fy2rfWFYXnjssP0usKxCDAQCArlEv8fQKSe8Iw/AP5b/xukDScUm/vB4DK3LOTZrZ9ZKeIelw7PLp2O+fNbO/NrNtzrlT6zk+AAC6iXOulGSaXYeG4HOLed10fFrjE1Man5jSTcenq97njpFB7d+d1b6xrdo3ltWu7GA3JZrKEYMBAICu0ciqdilJ2ySdCoJgXdY9NrPtkhajgGdI0uclvdU595nYNjsknXDOOTN7tKSPyX/7Vu+A+LYNAIAyc7FEU9J9mmbmczp8zCeZDh6Z0m0nTitXpYrqwnOHtS+qZto3mtX5I4MN7b/LVrUjBgMAAJ1iVavapSX9iqSr5BtLLkr6jKR3B0GQ6DI1ZrZP0j9ISsv3ofqIc+5NZvYKSXLOvcfMflXSKyXlJM1K+g3n3H+vsGuCHgBAz1vI+elzxYRTIcFE09Tsog4fnSo1A7/jnjMqzzOZpAdu36T9UTXT3tGszt3Uv6r764bEEzEYAADoQKta1S6QD3SeEwTBYhiGGUmvjS5/Q2vHt5xzblzSI6pc/p7Y7++S9K4kxwEAQDfIF5zOLuR8smmhoFwhueKZ+2YWND4xqYPR1Lm7Ts1UbJMy6ZILtmjfaLaUaBoZyiQ2pg5EDAYAALpGvcTT44MgeGrxjyAIFiW9JQzDLyU/LAAAsFrOOc0tFkrJpiT7NJ2YnitVM41PTGni/tmKbfpSpkt3btHe0az2796qy3eNaLi/XgjS84jBAABA16gX9dUqiaZUGgCANjOfW+rTNLeYTJ8m55yOTs6WGoEfnJjUien5iu36+1K6fNeI9kWJpkt3bNFAJt3y8XQxYjAAANA16iWefjgMw6+UXWaS9iQ4HgAA0IBcvqCzi3nNRcmmfJUG3WtVcE7fv/esxqNqpvGJKd07s1Cx3XB/Wnt2jZRWnHvoji3KpFMtH08tKTMNZFIa6Oua5BYxGAAA6BorrmrXZXrqYAEA3aNQcJpd9Emm2YW8FvOtnz6XLzjdefJMKck0PjGp6blcxXYjg33aG/Vn2je2VQ8+f7PSqfXr6Z1JpzSQSWkwk9ZAX0XCqeObi3cpYjAAALrbqpqLAwCADVLs01RMNs0vtn4xs1y+oNtOnCn1Zzp8dEozC5X3c85wplTNtH8sq4u2bVLK1ie/U6xmGuxLl6qa1jPJBQAAgLUh8QQAQJuYz/lV584u5hLp07SQK+jm49Ol/kw3HZvWXJXG4+dvGShVM+0by2r3OUOydUo09UcVTMVkU3/f+k3ZAwAAQOs1lXgKw3CPpOcHQfD/JTQeAAB6xmLeVzQl1adpdiGvw8emSlPnbrl7Wov5yvsY3Tqk/WNLU+d2ZAdbOo5a0inTQF9ag1El00BfSimqmaoiBgMAAJ1qxcRTGIZXSPoJSU+V9O+S/jPpQQEA0I1y+aWpc3MLBeUKre3TdGYup0NHp0pT5247cVrVclkXnTesfWNbtX8sq71jWW3bPNDScVRjZlE1k/8ZzKTXtQF5JyIGAwAA3aBm4ikMw7+W9EBJX5X0Pkl7gyB43XoNDACATpcvOM0l2BB88uyCxo9OafyIr2i68+SZig7OJuni8zdHFU1btXd0RFuH+1s6jmr6Uqmy3kypdZuu1+mIwQAAQDepV/GUkZSXNB/9sBoJAAB1FApOczmfZJpdzGuhSv+ktTh1Zr7Un2n8yJS+f9/Zim3SKdNDL9hc6s+0ZzSrzQPJtnQsVjMN9qU0kElrsC+lPqqZ1oIYDAAAdA2r17g0DMOUpCdJer6kp0j6hKTrgyD4j/UZXssRuAEAWmpucSnRNJ9rXUNw55zunp7ziaYjUxo/Oqljk3MV22XSpkt3jkQrzm3VZbtGNJRJt2QMtWTSxSlzbVnN1DYDWQtiMAAA0GFqxmB1E0/lwjB8lKQ3BkHwrFaMagMQ9AAA1qTYEHx2wf8UWphoOnLfrMaPTpaSTSfPzFdsN5hJ6fJd2VIz8IftGEl05TczK/VkKv6bbu8G4G09uNUiBgMAAG2uZgxWr8fTgyS9Uj5QeIekPZJeK+kHrR4dAADtqlBwml3M6+xCXnOLrevTVHBOd52aKU2dOzQxpfvPLlZst2kgrb2j2VIz8EvO35zoNLZSNVOUaGqzaqaeQAwGAAC6Sb2mD38n6fclnSPpa5LeL7+M79R6DAwAgI3gnNN8rqDZhbzOLuY1v5hvyX7zBafb7zldqmY6dHRKZ+ZzFdtlhzLRtLms9o5m9aDtmxOrMIr3ZipWNNGbqS0QgwEAgK5RL/GUD4LgvyQpDMM/CILgD9ZpTAAArKvFfKFU0dSq6XMLuYJuO3G6VNF0+Oi0Zqsksc7b3K/9USPw/WNZXXjucGIVRqw01zGIwQAAQNeo2eMpDMO7JX1efp7e02O/uyAIrl63EbYW/QUAAMoXnOZaPH1ubjGvm45Pa3xiSuMTU7rp+HTVVe12Zge1dzTq0bR7q3ZlBxNL/vSX9WbK9EY1U8dn0ojBAABAB2q+x5Okx8R+f33sdwIHAEBHcc5pbjFqCt6i6XMz8zkdPjZVSjTdevdp5QqVH5G7zxnS/t2+omnfaFbnjwyu+b6rSadMA31pDWai1eb6Ukq1dxNw1EYMBgAAukbDq9qFYfg4ST8h6YogCJ6Q6KiSQ8AGAD1ibjGaOreY19xiQc2s4lrN1OyiDh+dKk2du+OeMyrPM5mkB27fVJo6t3c0q3M39a/pfqtJmWkgk1J/2jcB70+nEl3ZrsN0XbaNGAwAAHSAmjFYval2fZKeKun5ki6Q9BD5xpa3JTHCdULQAwBdajHvK5rmFnyyKV+l+qgZ980saHxiUgejiqa7Ts1UbJMy6ZILtmjfaLaUaBoZyqzpfqvp70stq2YiyVRXxyeeiMEAAEAHWtVUu5OSPiopDILgaBiGn+vwgAcA0EXyBeenzrWoT9OJ6bnStLmDE5OauH+2Ypu+lOnSnVu0L6pounzXiIb7632UNq84ZW4g1p+JKXM9hxgMAAB0jXrR8o/Lf9P2njAMb5S0aX2GBABApWKfprMLOc0u5qs27m5mX0cnZ5clmk5Mz1dsN9CX0mW7RkrNwC/bOaKBTHoth1Ehk/YJJqqZEEMMBgAAukZDPZ7CMHyEfG+BR0k6EQTBS5MeWEIo8waADuGc03yu4Cuacmvr01RwTt+/96zGJyZLyaZ7ZxYqthvuT2vPrpFSRdNDd2xp6UpwKbNlq8xRzZSIrnpAicEAAECHaH6qXRiGV0gKJPVLmpT0uiAI/iAMw4e0fHgAAGipIfjcYkFzi3kVVployhec7jx5ppRkOnR0SlOzixXbjQz2ae9oVvt2b9X+sawu3r5Z6RYmgorVTAOZlAapZkKDiMEAAEA3qTfV7i8kvSAIgmNhGD4s+vsqegwAAFplPpfX3ELUFHwNiaZcvqBbT5wuJZoOH53SzEK+YrtzhjOlFef2jWV10bZNSllrEk3FleYG+5YSTVQzYZWIwQAAQNeol3g6GwTBMUkKguCWMAwH1mlMMrNBSV+RNCA/xo8554KybUzSX0q6StJZSS9zzn17vcYIAGjeQm4pyTS3hpXnFnIF3Xz3tMaP+P5MNx2b1lyVnk/nbxmIkky+omnsnCFZixJNmXTKJ5iiKXMDfa3t/YSeRgwGAAC6Rs0eT2EY3iLpnuJ2krbLr7LigiB4UqKD8gHNJufcGTPLSPqapFc7526IbXOVpFfJBz2PkfSXzrnHrLBr+gsAwDoqrjx3diGnuYWCcoXVNQSfXcjr8LGpUkXTLXdPazFfeUofO2eo1Ah839hW7cgOrvUQJFVWMw30pVs6JQ8t1fFPDDEYAADoQDVjsIaai28kMxuWD3pe6Zz7Ruzy90q63jn3oejvWyVd6Zw7Xmd37X2wANDhiivPFZNNq1157sxcToeOTml8YlIHJ6Z024nTqlYcddF5w6Wpc3vHstq2uTWFIf1RBRO9mTpSxyee2gUxGAAAaELzzcU3mpmlJX1L0oMl/VU84ImMSjoS+3siuqxe0AMAaLGFaOW52UX/s5ovNCbPLpSqmcYnpnTnyTMV/0tNmXTx9s3aN5bV/rGt2juaVXY4s+bxp1PLV5rrT7PSHHobMRgAAGiltk08Oefykh5uZlslfdLM9jjnDsc2qfa/gor/7ZjZNZKukaT3vve9uuaaa5IYLgD0jFy+UEoyrXb63MnT81GSaVLjE1P6/n1nK7ZJp0wPvWBLqRH4ntGsNg+s7WPLzKJqplQp0ZRJU80ExBGDAQCAVqoZwYdhmAmCYDH2949KSkn69yAI1q1c2jk3aWbXS3qGpHjQMyFpd+zvMUnHqtz+gKQDxT8TGiYAdK18wWkuSjTNLuS1mG8u0eSc093TcxqfmNLBI1MaPzqpY5NzFdtl0qbLdo6UmoFftmtEQ5m1NezOpFOlxt++N1OqZc3FgaQQgwEAgG5S76vjL4VheFUQBGfCMHx3dNlJSS+S9LIkB2Vm2yUtRgHPkKSnSXpr2WaflvSrZvZh+caWUyv0FgAANKBQcJrL5UvT55rt0+Sc05H7ZjV+dNInmiamdPLMfMV2g5mULt+VjabOZfWwHSNr6qVUbAA+0Lc0bY4G4OhQxGAAAKBr1Es8FaKAZ1DS04MgeLAkhWH4H+swrp2S/iHqMZCS9BHn3GfM7BWS5Jx7j6TPyq+mcof8Ur4vX4dxAUDXiTcEn13Ma34x39TtC87prpMzOhibOjc5u1ix3aaBtPaO+mqm/WNZXXL+ZvWtYZobDcDRxYjBAABA16iXeEqFYfhAST8q6Uuxy4eSHZLknBuX9Igql78n9ruT9CtJjwUAuo1zTvOxhuDzuUJTDcHzBafb7zldmjp3+NiUTs/lKrbbOpQp9WfaN7ZVD9y2adUVSOmUaaAvrcFYRRMNwNHFiMEAAEBbKhSc8s4pX3AqFP8tqO6iP/UST6+S9CeSZiT9jiSFYfhgSZ9r5aABAMmbz/lG4LOLec0t5lVoItG0kCvo1rtPl6bOfffYtGarVEWdt7lf+8e2lpJNDzh3eNX9lPqjqXI0AEePIgYDAACJq5VEKl7mXOz66PJaX1ivNvF0bxAEPylJYRj2SVIQBHeEYfixNRwXAGAdrGXlubnFvG46Pl1ade6m46er9nnamR30SabRrPbt3qpd2cFVJZrMrFTJNBhNm6OaCT2OGAwAADTFRQmivFuePCqULms8idRq9RJP10l6SvT752O/vzP2OwCgDeQLrrTq3NxicyvPzczndPjYVGnq3G0nTitXqPwQuvDcYe0dzWr/bp9sOn9kcFVjTadMg5m0BllpDqiFGAwAgB5XTCTlYtVIpZ9YcqkQbbNeSaTVqJd4sgZ+BwBsgGKiaS5KNjWTaJqaXdShianS1Lk7T55ReZ7JJD1w2ya/4tzurdo7mtW5m/pXNdZMOuUbgEfJJpqAAysiBgMAoMtUrUjKL5/qlitWKEV/d4t6iadzwjB8inyQE/9963oMDACwJF9wPskUJZuqTX2r5b6ZBY1PTEarzk3prlMzFdukTLrkgi3aF1U07dmV1chQ7XnatZiZ78/Ul9JAJq3BvtSaVq4DehQxGAAAHSBfWJ40ik9ti1coFSuWelW9xNMnJT2xyu+fSnJAAIC1JZpOTM9F/ZmmdHBiUhP3z1Zs05cyPWzHFu3f7ZuBX75rRMP99T4SqmPaHJAIYjAAADZAvnxam1uqQIonk9a7R1Kns5UeqDAML5R0gaTjQRBMrMuoksOrAkBbyuULmssVmp4655zT0cnZZYmmE9PzFdsN9KV02a4R3wh8LKvLdo5oIJNuepyZdDRlLsNqc2hbXZP5JAYDAGB14pVGpdXa3NIqboWyHkm9XpHUCg/avrlmDFYz8RSG4cWS/k7S/0k6LmlU0oWSfiEIgv9LYJzrgVcSgLZQXHVubrHQVDNw55y+d+/Z0opz4xNTundmoWK74f609oxmS4mmh+7Y0nSSKGXmezNF1UysNocO0fEvUmIwAAAq5fKFiils8cqkXJ4E0kaql3iqN6/irZJeGQTBTcULwjC8VNKfSnpB64YHAN1vtavO5QtOd548U6poOnR0SlOzixXbjQz2ac9oVvujZuAXb9+sdJNJomI1UzHJRBNwYMMQgwEAekJ58qjYbDtXKKhQkHKFQul6dK56iaeReMAjSUEQ3ByG4ZaExwQAHa9QcJrLRYmmXEHzi/mGbpfLF3TbiTM6GFUzHT46pZmFytueM5zR/jHfn2nfWFYXbdukVBO9lYpNwIeK0+aoZgLaCTEYAKBjFQrR6mxR9VFxpbbiZbnCUoKJHkm9oV7iaUsYhg8qu8wkEfQAQBnnnOYW/fS52agZeCMfpAu5gm4+Pl2aOvfdY9Oaq9JI/PwtA9o3li0lm8bOGWqqiXd82txgJq2BvhSJJqB9EYMBANpGrURSqfG2o+E26quXeLpF0h9UufzmhMYCAB1lLlpxrtirqZEP2dmFvL57bEoHo6lzt9w9rcV85e3GzhnSvtGs9kbJph3ZwabGlkmnfKIpSjIN9DXfSBzAhiEGAwAkJt54u3yKG4kkFFuEnJ3PaWYhr7MLOZ1dyGtm3v8+s+CvO7uQ18xCTmejyz/yisfV3GfNxFMQBC8vvywMw3MkPbs1hwMAnWUhV2wI7qfQFRr4ED4zl9Oho76a6eDElG47cVrVpqhfdN6w9o1t1f4xn2zatnmg4XGZmQYzPrlU/LfZ/k4A2gcxGACgUS5ehVSWMFqWXGLltq7mnNNi3pUSQTNRsqg8aeR/L15XPZE0t9hYL9pm1Kt4kiSFYbhT0vMlPVPSfZL+q+WjAIA2VFx5bnYxr7mFgnKFlU/Ck2cXSo3AxyemdOfJMxVLOZmki8/frP1jWe0b26p9o1llhzMNj6svlVq22txAX6qpaXcAOgMxGAD0nmUVRy42ra00tU3LKpSoRupsBec0G0sCxRNDpWqjeNKomCyqkkjKJZhUTKdMm/rT2jTQp039fRrqT2vTQFrD/X3a1J/WUH/92RU1E09hGL5W0pMkHZX0cUkDQRC8tKWjB4A2UmwIfnbBVzQ1svLcydPzpf5M4xNT+v59Zyu2SadMD71gs08yjWW1ZzSrzQMr5v0l+WomP1UupYFMWoN9KfWlWW0O6GbEYADQPZyrnTTKL0so+SQTiaTOsJgvLCWLYhVGM/N5zS76xFBF9dF8TmcX82UVSY0tQLRag5mUNvX3abg/reEBnyQajv7eNBBdvuyy9NL2/X0aHkhrOONXu17LF931/ufzdPlv1z4v6WuSXrPqewGANpQvuGV9mhaqNPWOc87p7uk5jU9M6eCRKY0fndSxybmK7TJp02U7R6IV57bqsl0jGso01mOp2JtpoC9dSjhRzQT0HGIwAGhD5dVIxWltBRclj2LJpUJsehvag3NOc7nC8t5F8zWqiBZymi3vaxRLJFXr0doqKVMpKbSUBFpKBMUv89vFt1leldQu7TesXkY16ifwXEk/JukKSW+SdH0QBHetz/Bajnc90MNy+YLmcoVSsqmRRNOR+2Y1fnTSJ5ompnTyzHzFdoOZlC7flS31Z7p0x4j6+1auSipWMw1m6M0EtFBXvImIwQAgWVUrkVitrS3lStVFyxND8aTR2RUSScXrk2xxNdCXqqgeGo5VDy2rJhpYmqK2eaBv2e069YvnB23fXHPQdRNPcWEYbpJ0laTnBUHw4haNbb1xpgB6SDHRNLvgE00rTZ0rOKe7Ts2UqpkOTUzp/rOLFdttGkhr72i21Az8kvM3NzT9LZ2yUgPw4mpznfihArS5rntTEYMBQH21KpGKl7lSRZKoRFonzjkt5Aplq6KVNbVeVk1UbRU1/+/8Cl8Wr4VJZdPOfIJoqD+tzcVeRsUpZ7Hfl01HiyqNer0dRr3EU90mI2EY/rik50m6QNJxSZ/q4IAHQJcrLv1ZXHVupURTvuB0+z2nS9VMh45O6cx8rmK77FBG+8aypWbgD9y2qaHKpL5UyieZ+ovT5hqbbgcAxGAAehWVSBurGE+XJ4HKE0M+aVR9VbRi0ijJ6qJM2pYSQpm+5dVFUWKomDRaXnm0PHk0kEkpxRfBiatZ8RSG4a9J2iPpDyUdkzQq6XclHQ6C4J3rNsLW4owEdJFmK5oWcgXdevdpjR/1jcAPH53W7GJlQ7/zNvdrf1TNtG8sqwvPHW6oMimTXpo2N5hJK9Pj33oAG6Tjo0diMADdZFkVUulflRprl6/cRiXS6izkCsuSRDMLuaXV0apMOVtWbRRrel0tNm6l4f7yaiI/7Wx4WWVR9SRR/G/i7Paz2oqn50t6ShAExXf+XWEY/j9JX5DUqUEPgA62kCtoLueTTPOLhRUTTXOLed10fLq06txNx09X7eu0MztYagS+byyrXdnBhhJN/aX+TGkNZejPBKBliMEAtKVS1VFsRbZlVUnxy6PfUVvB+YVuljW1Lp+OtqyaKJZAWrYyWrLNrtMpW1oNbSAd9SbyPYriTbCHSo2tl5pcl6aqRQknqot6U73EUy4W8EiSgiDIh2GY3ARLAIhZyBU0u5jX/GJec4sF5Qr1Tz8z8zkdPjZVWnXuthOnlasS8Fx47nCUaMpq32hW548MrjiW8kbgg31ppUg0AUgGMRiAxBSKSSHn5JyWVSG52O8FV9kviSltXi5fWLbKWXyKWXk10WysEfZSImkpgZTkIzrYl6pSTbSUJNpc3tcoszyR5KexpdVPX1KsUb3E094wDK8ru8wkXZ7geAD0sPlcXnMLS1VNK31LNjW7qMNHp3Rwwk+du+OeMxVzyU3Sg7Zv0t7RrPbv3qq9o1mdu6l/xbH0pVIaiBJMA5kUjcABrCdiMAANWampdnkFUjG51Iucc6UWDTPzy1dGW2pwXda7qCyBVPx9pZWR1yJlKiWCSqujFSuMljW19pdtjm83EJu+1t9HNT7aRr3E02NqXP62JAYCoPcUp8wVG4KvNKf/vpkFjU9M6uCEr2q669RMxTYpky45f4tvBr47qz27shoZyqw4lvi0ucG+VM+vSgFgQxGDAT0qX3DKFQoVTbWLFUrl09t6IYmUL7iqDaxnY72L4tVHPkm0tFLa0tS1ZJtd9/elSgkfX00U71VUTAxF1UX9y5NF8X/5shPdqGZz8VrCMPx8EAQ/mtB4ktb9Z2agjcUrmmYXVk40nZiei5JMvqJp4v7Zim0yadPDdmwp9We6fNeIhvvrLtiplFmpmmkw4z/gmTYHdI2ufTMTgwGdp1pF0rIf55TLd18/JOecFnKFilXR4kmg8obXMzV6F80tJlddZFpqdl3sTVTsXbS8X1F5BVJf2cppab60RM9bbXPxWhIP6Mxst6TrJO2QVJB0wDn3l2XbXCnpnyXdFV30Cefcm5IeG4DGxXs0za4wdc45p6OTs1EjcD997sT0fMV2A30pXbZrJFpxbqsu3bFFA5l03XGkUxZVMjFtDkBHIwYDNki8qXax2sj/vpRAKhRUsXpbpyk4F00pW5qGtpQQWlr5bKaiEXZlcqlan81W6UtZlabW1RNCw2WrosV7Gg1maHaN7mZmMklmkslUfLmb+S/j45cv+z26bSq6TBa7Tdn+ir/XUzPxFIbhU6qNW9I5qz3oJuQk/aZz7ttmtkXSt8zsC865m8q2+6pz7tnrMB4ADSitOrewcjPwgnP6/r1nS9VM4xNTundmoWK74f609uwaKVU0PXTHlhWXT82ko/5MUbKpv49voAB0DmIwoPWWehwtJYjiPY8Kzm/jXDHBFGvC3QEJpMV8YXlCKNb0unIq2lIiaaYsyTS7mE90nEOZdKlhdamaKEogDZVdVqo6KiaMYtVHxHbYCMUvruOJF0W/F/8tT/SULi/eUJXJnYp9Wuw2WkoAxW8rqWqiaNm+2iipWq/i6Yk1Lv+XJAYS55w7Lul49PtpM7tZ0qik8qAHwAZqJtGULzjdefJMqZrp0MSUpudyFduNDPZp72hWe8ey2j+2VQ8+f3Pdxohm5vsz9aU0QH8mAN2BGAyIKcQaYxdiK7HFE0fVVmJzbinh1I6cc5pbLFRUFpU3vJ6tWn0U63O0kNNiPrljTJmi1c+KlUNL09HKexct61kUWxWt2PSa6qLeFE/GSJXJk/Jt4skUrZRcWcrmxPZfuV35/arK5fFEj5Vvw2t3TZru8bTezOwiSV+RtMc5Nx27/EpJH5c0IemYpN9yzn23yu2vkXSNJL33ve/9oWuuuSb5QQNdqplEUy5f0K0nTpeqmQ4fndLMQuW3aOcMZ7RvbGs0dS6ri7ZtqhuUmJkGY/2ZBjNMmwOwDCeEFiEGw1oUCk5OS4mgeBIoXnGUX5ZI8smY8r/bTS5fWN6XqKwnUXmfovLqo3hz7CSLqQb7UjWriYZj09A2LWuCXdxuaapaPy0KOkK9KVXx64pTpaoldGolc6pdX6/qph0rbrAuaj7hNRNPYRj+hyobQeYlvS4Igm+2bmy1mdlmSf8p6S3OuU+UXTciqeCcO2NmV0n6S+fcJSvssv0+uYA21kyiaSFX0M3Hp3Uwmjp307FpzVVZavb8LQPaF/Vn2j+W1dg5Q3U/lOjPBKBJHX+CIAbDeqpVTeS0NAVNsUqiYjKp05JFzjnN5wrLehAtVQwtXy1teTXR8kbYMwt5LVSJb1rFpGVVQ/HEkE8a1V4VrTgdrVhhVK9iHK3RaLKn0R46qRq3U/l+yu+b2BjtofnEUzVhGJ4j6VNBEPxIK0ZVj5llJH1G0r875/68ge2/J+mHnXOn6mzWfp+CQBuJrzo3t0Iz8NmFvL57bCpadW5Kt9w9XbXMe+ycIe0b9dVMe8ey2jEyWPfDMZNOlSqZBjPpFfs5AUCZroy+icF6W0ViR/7valPO4gkkV0ogLa88iv/d7vIFtzTVLF4xFEsElSeNlnoa+STT7KL/N8nqov6+1LJkUHnSKF5NVLqufOW0gT4N8gVb02omeMouT5mqVvk00zy5mBhKkewBqqn5pmhqVbsgCO4PwzC5FH/E/Lv4byXdXCvgMbMdkk4455yZPVpSStK9SY8N6BbFb/7mFn0109xivm4AemYup0NHpzQ+MamDE1O67cTpqgHcRecNlxqB7xvLatvmgbrj6O8rJprozwQAtRCDtQ8Xr/KRT+IUE0GK/pb8ZaUkUGHp7+LtCv6Gy/YRrx4qrzzqJM45LeZdZe+i+aWVz86WXbaUWFqeSJpbTL66aKhaZVGpoXW1CqNY/6Losl79oqxW0qe4WlZ8Wpf/J+q702TiR4qvwLV0vymquoCO0MyqdgOSHi/pjkRH5D1e0kskHTKz70SXvU7ShZLknHuPpBdKeqWZ5STNSnqR67RPZWAdFRtYzi3mNZfLa36xUDfRNHl2odSfaXxiSneePFPxdbVJuvj8zdoXNQLfOzqircP9dcdRbABeTDZRBg4AyxGDNSdeDVQ+DWxZ8qYQqxTSUjKnOPBiIqgQ31fBVSSFulnB+eqi8uqhs7EpZ8v7GS2fhhZPJOUSLC9Kp0ybyqqHhuOVQ8v6GFU2uN4UJZkGM93Z7LqYzCkmatIpq6j4qZjSFUvqpMyWkjzLEkFR0ih2GwBoRDOr2s1JukHSvyY3HM859zWtUCrvnHuXpHclPRagU+ULLqpmymsuV9BCrlA3YD55ej5KMvkeTd+/72zFNumU6SEXbNb+qKJpz2hWmwdqn0bKG4EP9KX4ZgoAVtZ1MdiZ+dyyfkHl1T/OLU8ElVcRFZNIUmV1EKTFfMEniRbLK4ziK6MVp6XFp6JVJpKSNJhJRUmfpcbV5dPRSiumDaSXTUkbHliqOsqkraOTHuWJoXjVTzE5FE/2FK+z1NLl5YmhFBVAANpYvcTT+yX1BUFwW/GCMAwfIukBkr6X8LgANGk+ly9NnZtfLGgxX7s03Tmnu6fnND4xpYNHpjR+dFLHJucqtsukTZfuHIlWnNuqy3aNaCiTrrnfYqJpKLOUaOrkwBAANkjXxWD3TFd+xvQ655zmcgWdnS9PAsWSRFE1Ua2G18XLqvVYbJWUqVRZtKk0/WypgXW1ptbxpFG831GnVjk3mihK2fIeQOnUUuVQ+fUA0EvqJZ7eJellZZfdL+k6Sc9MakAAVlbszzS/WNDsYl7zufqNwJ1zOnLfrMaPTvpE08SUTp6Zr9huMJPS5buyUaIpq4ftGFF/X+2eBSkzDZBoAoBWIwZrY/mCq9nUujwhVG1VtPhqakk2ux7oS1WueNZfNv2s9HtsilpZr6P+DvpsX1YdZL76Z2nqmCoTRFEVUa3pZSSKAKA16iWehoMguCd+QRAEJ8MwHEx4TADKFAouWmmu4FeeW6w/ba7gnO46NVOqZjo0MaX7zy5WbLdpIK29o76aaf9YVpecv7luc+90yqIm4GkNZFIkmgAgGcRgLVb8wqZ6U+ulBFJ8NbRllUixy+ZyCTe7HkhrOOMTQ6WG1tFl5U2ti6uibR5YXok03J/uuMU6SomelI830mb+35QtqzhiihkAdJ56iafFMAy3B0FwsnhBGIYXSMolPyygt+XyBc2VVpzLa2GFIDdfcLr9ntOlqXOHj03p9FzlWzU7lCmtNrd/bKseuG1T3bL3vlRKg5mUbwieSWmgr/Y0OwBAyxCDRfIFp9nF2kmg5dVE+eqVSFGSKcnqokzaaiaEhuJT1EoVSEvbLDXF7tNgprO+0KnXxDq+5Hwqqj5KlSWQSr+TNAKArlYv8fRaSZ8Mw/DLko5LGpX0I5JetR4DA3pJLu+nzBVXnavXn0mSFnIF3XbitA5GjcAPH53W7GJlQ9DzNvdr32hW+3f7iqYLzx2uG9CmU+anzfX7qqZ60+wAAInpuhjs4JHJiobX1VZFK08azS0mV10kadkUtE3Rymfx1dCGyldKi62eFt+m0z8vU7HqIl9hVDnVrFiFlEqpVI3USUkyAMDGsXrTdcIwzEh6rKSdko5JuiEIgsr5Op2DpU/QFhZyhWjq3MqNwCVpbjGvm45PR6vOTemm49NVq6B2Zgejiia/6tyu7OCKiabi1LnBfiqaAHSFrvifcLfFYBf97r+2LAZLp2zZFLRNdRJC5ZcNDyxVHw31p5XqksRJMXHkq4p8Yii+MlppStqypBEJJABAS9X8MKmZeArD8Odr3SgIgr9rwaA2AoknbIj5XF5zC0v9mXKF+ommmfmcDh+bKk2du+3EaeWqzBG48Nzh0tS5faNZnT9Sv/1HfOrcUKbzv6EFgCo6/n/Q3RiDXfS7/+oG+1JVqomWEkGbBvo0VLYqWmm7WNIok+7OREm8f1E6Pi0t+j1dljgqXk/iCADQJmp+GNWbalc5bwfAivIF5yuZoh5NC7mCCnUqCyVpanZRh49OlabO3XHPmYpeFCbpQds3laqZ9o5mde6m/rr7zaRTGsikNBglmjId1mgUAHpU18VgX/j1J9XtKdgtyhtkx6erFRNE6dRSb6PiNr3w2AAAele9xNPZIAg+KklhGF4SBMHt6zQmoKPM55aSTI1Mm5Ok+2YWND4xqYPR1Lm7Ts1UbJMy6ZILtkQ9mrLasyurkaFM3f0WE01DmbQGSTQBQKfquhis0xIr8YTQst+jpFK8OikVSyZReQQAQKV6iadXSvpo9Pt7JT0l+eEA7a24HPNc1Ah8PpdXvoFlck5Mz/lpc1FF08T9sxXbZNKmh16wRft3+4qmy3eNaLi/3lvUJ5oGoxXnhjKdt3QyAKAqYrAWMjP1Rf2P+mokkcoTTQAAoHXq/68W6HGFgk80+RXnfGVTvYb8kk9OHZ2cLTUCPzgxqRPT8xXbDfSldNmukdKqc5fu2KKBTP3m3v19S9VMg5k0wTEAoOesVI1UeRmflQAAbKR6zcVvkXSPfGuZ7bHfXRAET1q3EbYWzcVRVy5fWKpoyhU0v7hym42Cc/r+vWc1HlUzjU9M6d6ZhYrthvvT2jPqm4DvG8vqoTu21J0KZ2bq70tpsC+loX6/8hzBMwCsqONPlN0Yg/3fyTPLYjCzyoqj8lXWiomjYrNtkkgAALS15le161I9dbBYWbGKaT76t5H+TPmC050nz5SSTOMTk5qey1VsNzLYp72jWe3bvVX7x7K6ePvmuhVKZqaBvqVG4AN9KQJsAGgeJ842dHYh54oJpj5WYQMAoBs1n3gKw/D9qpGoCYLg6taMa92ReOph5f2Z5hbzK642J/kqqFtPnC4lmg4fndLMQmUl1DnDGe2PVpzbv3urHnDesFJ1AuuUmV9xri+toX6faCIQB4A16/gTKTEYAADoQDVjsHo9nl6fwECAdeOcKyWY5nJ+xblGEk0LuYJuPj5d6s9007FpzeUqK6HO3zLgk0xRsmnsnKG6iaOU2VI1UyZFogkAUAsxGAAA6Bp1p9qFYfhwSc+RtEPScUn/EgTBwfUZWiL4tq2L5Qsuqmby/ZkWGmgELkmzC3kdPjZVqmi65e5pLeYrbze6dUj7x3x/pn27t2rHyGDd/aZTPtE02JfWYH9KA331G4cDAFqiKzL6xGAAAKDDrGqq3YskvVjS2yQdkzQq6TWSPhQEwYdbP8Z1QdDTRRZyBc3nlqbNNdKfSZLOzOV06KivZhqfmNJtJ06rUOWV8cBtm0qNwPeOZbVt80Dd/ZJoAoC20PGJJ2IwAADQgVY11e4aSVcFQTAX/X1bGIZfl/RZSZ0a9KBDFfszzS8WNJfzVU35atmiKibPLsQagU/pzpNnKqLflEkXb9/sq5nGtmrfaFbZ4Uzd/canzpFoAgC0EDEYAADoGvUST/lYwCNJCoJgLgzDldeXB9YoX3DLqpnmG5w2J0knT8+XVpsbn5jS9+87W7FNOmV66AVbokRTVntGs9o8UO/tIPWlUhrMpDSQSft/STQBAJJBDAYAALpGvf9pXxKG4ZvKLjNJD05wPOhRq50255zT3dNzOnhkqtQM/PjUXMV2mbTpsp0jpUbgl+4a0VCmfuKoL5XSYH+qVNWUSadWdWwAADSJGAwAAHSNeomnl9a4/ItJDAS9Yy3T5pxzOnLfrMaPTpaSTSfPzFdsN5hJ6fJd2VIz8IftGFF/X/3EUSad0kAm5afOkWgCAGwcYjAAANA16q5q14V66mDbRXG1uflc89PmCs7prpMzOhibOjc5u1ix3aaBtPaO+v5M+8eyuuT8zepbIXFUrGgaiiqaVtoeANAROr65eJciBgMAoLutqrk40DTnnBbyBc0t+qlz84uFhqfNST5Jdfs9p0vVTIeOTunMfK5iu+xQRvvGfEXT3tGsHrR9s9Kp+v/XKK06FyWaVqqAAgAAAAAAa0PiCWsSbwJeTDQVmqiiW8gVdOvdp0tT5757bFqzi5W9U8/b3F/qz7R/LKsLzx2WWf1Ek5lpMDZ1bnCFnk4AAAAAAKC1SDyhKfO52JS5JquZJGluMa+bjk9r/MiUxo9O6qbjp7WQq9zHzuxgtOKcTzbtyg42lGga6IumzvWnNdCXWvE2AAAAAAAgOW2ZeDKz3ZKuk7RDUkHSAefcX5ZtY5L+UtJVks5Keplz7tvrPdZuVii4UpJpbhXVTJI0M5/T4WN+2tz4xJRuvfu0clUaiV947nCUaMpq32hW548MNrT//liiabAvrdQK0+0AAEBtxGAAAKDV2jLxJCkn6Tedc982sy2SvmVmX3DO3RTb5pmSLol+HiPp3dG/WKWFXKGUYJrP5atWIq1kanZRh4/6JNPBiUndcc8ZleeZTNIDt28qTZ3bO5rVuZv6G9p/Jp3yPZr6fZ+mlfo6AQCAphCDAQCAlmrLxJNz7rik49Hvp83sZkmjkuJBz3MlXef88mg3mNlWM9sZ3RYrWGtvpqL7ZhY0PjGpgxNTOjQxpf87NVOxTcqkSy7Yon2j2VKiaWQo09D+0ynzPZqiRFOGlecAAEgMMRgAAGi1tkw8xZnZRZIeIekbZVeNSjoS+3siumxZ0GNm10i6RpLe+9736pprrklsrO1srb2Zik5Mz5WqmcYnpjRx/2zFNn0p08N2bPGNwHdv1eW7RjTc39hLLWVWWnVusD+lgT4aggMAsBGIwQAAQCu0deLJzDZL+rik1zjnpsuvrnKTipId59wBSQdqXd+N4r2Ziv+upprJOaejk7Ol/kwHJyZ1Ynq+YruBvpQu3Tmi/VGi6dIdWzTQ4Apy5SvP0RAcAICNRwwGAABapW0TT2aWkQ94Puic+0SVTSYk7Y79PSbp2HqMrd3EezPNLeZXXc1UcE7fv/esxqNqpvGJKd07s1Cx3XB/WntGs6Wpcw/dsaXhKXBmttQQPJPWYIZEEwAA7YQYDAAAtFJbJp6i1VL+VtLNzrk/r7HZpyX9qpl9WL6h5VQv9BYoFNxSkmkNvZkk3+fpzpNnSkmm8YlJTc/lKrYbGezT3tGs9u3eqv1jWV28fXNTTb0z6ZSG+1l5DgCAdkcMBgAAWs3cKpMWSTKzJ0j6qqRD8kv5StLrJF0oSc6590SB0bskPUN+Kd+XO+duXGHX7XewK1jIFUpNwNdSzSRJi/mCbjtxupRoOnx0SjML+YrtzhnOlFac2zeW1UXbNinVRFVSfOW5wb6U+mgIDgBYP3y7sQbEYAAAYJVqxmBtmXhKUFsfbKtWmitayBV08/HpUiPwm45Nay5Xmbg6f8uA9u/eqr2jWe0fy2rsnKGmpr9l0imfZMqQaAIAbDgST+2prWMwAACwZjVjsLacatcLnPMNwOdzS0mmtVQzSdLsQl6Hj02VKppuuXtai/nKOG/snCHfn2m3r2raMTLY1P3096WWVp7LpJuadgcAAAAAAHoHiad1kssXNJcraD5aaW4+V9Baq83OzOV06OhUqaLpthOnVaiyy4vOG142de68zQNN3U+xGfggiSYAAAAAANAEEk8JKFUzxRqA5wprq2aSpMmzC7FG4FO68+SZirr1lEkXb98cJZm2at9oVtnhTFP3Q6IJAAAAAAC0AomnFljM+8bf87liA3C35momSTp5er602tz4xJS+f9/Zim3SKdNDL9hSqmbaM5rV5oHmntZ4M/AhEk0AAAAAAKBFSDw1qVBwpb5MxSbg+Wrz25rknNPd03M6eMRXMx2cmNTxqbmK7TJp02U7R7RvLKv9Y1t16a4RDWXSTd1XXyqlwX5f1TSUSdMMHAAAAAAAJILE0woWypJMC1VWhVsN55yO3Der8aOTpWTTyTPzFdsNZlK6fJdfbW7fWFYP2zGi/r7mEkVmpsFMSsOZPg32pzTQ11yiCgAAAAAAYDVIPMUUq5nmSg3AW1PNJEkF53TXyRkdjE2dm5xdrNhu00Bae0ezpWbgl5y/eVUVScU+TcP9fRrMpGTG9DkAAAAAALC+ejrxtJBbav7dymomScoXnG6/57SfNndkSoeOTunMfK5iu61DmVJ/pv1jW3XRtk2r6rGUTpmfOtfP9DkAAAAAANAeeirxNLeYj35aW80k+STWrXefLk2d++6xac0u5iu2O29zf6maad9YVg84d3jV1UiDUY+moX6/+hwAAAAAAEA76anE06kz8y2rappbzOum49MaPzKl8aOTuun46ar73pkdjJJMW7V/LKud2cFVJ5r6UikN9ac1HFU1pVh9DgAAAAAAtLGeSjytxcx8ToePTZWmzt124rRyVSqmLjx3OJo2l9Xe0azOHxlc9X2aWWnluaH+dNNNxQEAAAAAADYSiacapmYXdWjCVzONT0zpjnvOqDzPZJIetH2T9kVT5/aOZnXupv413S9NwQEAAAAAQLcg8RS5b2ZB4xOT0apzU7rr1EzFNimTLrlgi/ZH/Zn2jma1ZTCzpvulKTgAAAAAAOhWPZt4OjE9FyWZfEXTxP2zFdv0pUwP27FF+3f7iqbLd41ouH9tD5mZaaAvpeGoIThNwQEAAAAAQLfqqcTTZw4e07d/MKmDE5M6MT1fcf1AX0qX7RrRvlFf0XTZzhENtCAxlEn7RNNQf1qDfTQFBwAAAAAAvaGnEk9//G+3Lvt7uD+tPbtG/Ipzu7N6yAVblGnBVLe+VEqD/alSY3CmzwEAAAAAgF7UU4mnkcE+7R3Nat/urdo/ltXF2zcr3YLqo3TKStPmhjKsPgcAAAAAACBJ5pxbeasu8YP7Zlwuv/bjTZmVkkyD/SkN9NGnCQCANsF89vbUOwEnAAC9qWYM1lMVTykzrSbuMTMNZlIa7PN9mgb6UjIjrgUAAAAAAKinpxJPzejvi3o09fvKJhJNAAAAAAAAzSHxFOnvSy1Nn8ukW9L7CQAAAAAAoJf1bOIpk05pqH+pITiJJgAAAAAAgNbqqcTTloGM0sNGogkAAAAAAGAd9NSqdmJFFQAAuh3fLLUnYjAAALpbzRgstZ6jAAAAAAAAQO9oy8STmf2dmd1jZodrXH+lmU2Z2Xeinzes9xgBAAC6DTEYAABotXbt8XStpHdJuq7ONl91zj17fYYDAADQE64VMRgAAGihtqx4cs59RdJ9Gz0OAACAXkIMBgAAWq0tE08NeqyZHTSzz5nZ5bU2MrNrzOxGM7vxwIED6zk+AACAbkQ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      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "\n",
    "ax = plt.subplot(2, 2, 1)\n",
    "ax = sns.regplot(data =var, x='sentido_sozinho', y='sem_dormir', truncate=False, marker='')\n",
    "\n",
    "sns.despine(bottom = True, left=True)\n",
    "\n",
    "plt.title('RETA DE REGRESSÃO: ALUNOS COM DIFICULDADE PARA DORMIR x SENTIDO SOZINHO')\n",
    "plt.xlabel('SE SENTINDO SOZINHO', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "plt.ylabel('DIFICULDADES PARA DORMIR', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "\n",
    "####\n",
    "\n",
    "ax2 = plt.subplot(2,2,2)\n",
    "ax2 = sns.regplot(data =var, x='amigos_trataram_mal', y='sem_dormir', truncate=False, marker='')\n",
    "\n",
    "sns.despine(bottom = True, left=True)\n",
    "\n",
    "plt.title('ALUNOS COM DIFICULDADE PARA DORMIR x TRATAMENTO DE AMIGOS')\n",
    "plt.xlabel('AMIGOS TRATAM MAL', color = 'grey', labelpad = 20, fontsize = 8)\n",
    "plt.ylabel('DIFICULDADES PARA DORMIR', color = 'grey', labelpad = 20, fontsize = 8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quando comparadas as variáveis: Alunos que se sentem sozinhos x Dificuldades para dormir e Amigos que tratam mal x dificuldades para dormir, com coeficientes de correlação 0,46 e 0,10, observa-se a inclinação positiva muito muito maior na primeira reta.\n",
    "\n",
    "Conforme a matriz de correlação acima, a relação mais forte está na primeira relação em: Se_sentindo_sozinho x Dificuldades_para_dormir, ou seja, observando a reta, os alunos que declararam que se sentem sozinhos, tendem a ter mais dificuldades para dormir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COMPARANDO OS NÚMEROS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### POLÍTICA CONTRA BULLYING NAS ESCOLAS"
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VB07009\tNOS ÚLTIMOS 30 DIAS, você esculachou, zombou, mangou, intimidou ou caçoou algum de seus colegas da escola tanto que ele ficou magoado, aborrecido, ofendido ou humilhado?\n",
    "1\tSim\n",
    "2\tNão\n",
    "99\tNão informado![image-3.png](attachment:image-3.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VB07010\tVocê já sofreu bullying?\n",
    "1\tSim\n",
    "2\tNão\n",
    "3\tNão sei o que é bullying\n",
    "99\tNão informado![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Politica_Sim</th>\n",
       "      <th>Politica_Nao</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>46.178387</td>\n",
       "      <td>41.770715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nao</th>\n",
       "      <td>51.377953</td>\n",
       "      <td>53.745743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nao Sei o que é Bullying</th>\n",
       "      <td>2.131415</td>\n",
       "      <td>4.143019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nao informado</th>\n",
       "      <td>0.312245</td>\n",
       "      <td>0.340522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Politica_Sim  Politica_Nao\n",
       "Sim                          46.178387     41.770715\n",
       "Nao                          51.377953     53.745743\n",
       "Nao Sei o que é Bullying      2.131415      4.143019\n",
       "Nao informado                 0.312245      0.340522"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variáveis 'separando' se na escola há política contra bullying\n",
    "politica_sim = df[df['politica_contra_bu'] == 1]\n",
    "politica_nao = df[df['politica_contra_bu'] == 2]\n",
    "\n",
    "# Calculando o percentual\n",
    "a = politica_sim['ja_sofreu_bul'].value_counts(normalize =True) * 100\n",
    "b = politica_nao['ja_sofreu_bul'].value_counts(normalize = True) * 100\n",
    "\n",
    "# Renomeando Index\n",
    "indice = {\n",
    "    1 : 'Sim',\n",
    "    2 : 'Nao',\n",
    "    3: 'Nao Sei o que é Bullying',\n",
    "    99 : 'Nao informado'}\n",
    "\n",
    "# Renomeando indice\n",
    "politica = pd.DataFrame({'Politica_Sim' : a, 'Politica_Nao' : b}).sort_index().rename(index=indice)\n",
    "\n",
    "\n",
    "politica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quantidade de alunos que sofreram bullying não diferete muito entre as escolas com e sem políticas contra bullying.\n",
    "\n",
    "Em escolas que não possuem políticas de ações para lidar com o Bullying, observa-se que, percentualmente, a quantidade de alunos que declararam não conhecer o termo \"Bullying\" é o dobro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Com Politica</th>\n",
       "      <th>Sem Politica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>17.607928</td>\n",
       "      <td>16.855846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nao</th>\n",
       "      <td>81.984795</td>\n",
       "      <td>82.463110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nao informado</th>\n",
       "      <td>0.407277</td>\n",
       "      <td>0.681044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Com Politica  Sem Politica\n",
       "Sim               17.607928     16.855846\n",
       "Nao               81.984795     82.463110\n",
       "Nao informado      0.407277      0.681044"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variáveis 'separando' se na escola há política contra bullying\n",
    "politica_sim = df[df['politica_contra_bu'] == 1]\n",
    "politica_nao = df[df['politica_contra_bu'] == 2]\n",
    "\n",
    "# Calculando o percentual\n",
    "a = politica_sim['praticou_bu'].value_counts(normalize =True) * 100\n",
    "b = politica_nao['praticou_bu'].value_counts(normalize = True) * 100\n",
    "\n",
    "# Renomeando Index\n",
    "indice = {\n",
    "    1 : 'Sim',\n",
    "    2 : 'Nao',\n",
    "    3: 'Nao Sei o que é Bullying',\n",
    "    99 : 'Nao informado'}\n",
    "\n",
    "# Renomeando indice\n",
    "praticou = pd.DataFrame({'Com Politica' : a, 'Sem Politica' : b}).sort_index().rename(index=indice)\n",
    "\n",
    "\n",
    "praticou"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observa-se que mais de 80% dos alunos declaram que não esculachou/zombou/mangou/intimidou ou caçoou de colegas de forma que estes se sentissem magoados, entretanto, nota-se divergência nos percentuais quando comparamos a quantidade de alunos que declararam que sofreram Bullying."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### PREOCUPAÇÃO DOS PAIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando dicionario \"traduzinho\" cada resposta\n",
    "trad = {1: 'Nunca',\n",
    "       2: 'Raramente', \n",
    "       3: 'As vezes',\n",
    "       4: 'Na maior parte do tempo',\n",
    "       5: 'Sempre',\n",
    "       99: 'Não informado'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>sentido_sozinho</th>\n",
       "      <th>Nunca</th>\n",
       "      <th>Raramente</th>\n",
       "      <th>As vezes</th>\n",
       "      <th>Na maior parte do tempo</th>\n",
       "      <th>Sempre</th>\n",
       "      <th>Não informado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preocupacao_resp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nunca</th>\n",
       "      <td>4.385117</td>\n",
       "      <td>1.993235</td>\n",
       "      <td>3.944189</td>\n",
       "      <td>2.482484</td>\n",
       "      <td>2.210679</td>\n",
       "      <td>0.042281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raramente</th>\n",
       "      <td>2.856970</td>\n",
       "      <td>3.551583</td>\n",
       "      <td>5.055569</td>\n",
       "      <td>2.367722</td>\n",
       "      <td>1.105339</td>\n",
       "      <td>0.018120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>As vezes</th>\n",
       "      <td>6.462914</td>\n",
       "      <td>5.182411</td>\n",
       "      <td>7.332689</td>\n",
       "      <td>2.053636</td>\n",
       "      <td>0.730853</td>\n",
       "      <td>0.030201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Na maior parte do tempo</th>\n",
       "      <td>5.774342</td>\n",
       "      <td>5.055569</td>\n",
       "      <td>4.463639</td>\n",
       "      <td>1.226142</td>\n",
       "      <td>0.483209</td>\n",
       "      <td>0.018120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sempre</th>\n",
       "      <td>15.426432</td>\n",
       "      <td>5.822662</td>\n",
       "      <td>6.801160</td>\n",
       "      <td>1.528147</td>\n",
       "      <td>1.020778</td>\n",
       "      <td>0.024160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Não informado</th>\n",
       "      <td>0.157043</td>\n",
       "      <td>0.042281</td>\n",
       "      <td>0.066441</td>\n",
       "      <td>0.012080</td>\n",
       "      <td>0.006040</td>\n",
       "      <td>0.265765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sentido_sozinho              Nunca  Raramente  As vezes  \\\n",
       "preocupacao_resp                                          \n",
       "Nunca                     4.385117   1.993235  3.944189   \n",
       "Raramente                 2.856970   3.551583  5.055569   \n",
       "As vezes                  6.462914   5.182411  7.332689   \n",
       "Na maior parte do tempo   5.774342   5.055569  4.463639   \n",
       "Sempre                   15.426432   5.822662  6.801160   \n",
       "Não informado             0.157043   0.042281  0.066441   \n",
       "\n",
       "sentido_sozinho          Na maior parte do tempo    Sempre  Não informado  \n",
       "preocupacao_resp                                                           \n",
       "Nunca                                   2.482484  2.210679       0.042281  \n",
       "Raramente                               2.367722  1.105339       0.018120  \n",
       "As vezes                                2.053636  0.730853       0.030201  \n",
       "Na maior parte do tempo                 1.226142  0.483209       0.018120  \n",
       "Sempre                                  1.528147  1.020778       0.024160  \n",
       "Não informado                           0.012080  0.006040       0.265765  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando uma tabela cruzada\n",
    "crosstab = pd.crosstab(df['preocupacao_resp'], df['sentido_sozinho'], normalize=True) * 100\n",
    "\n",
    "# renomeando indices e colunas\n",
    "crosstab.rename(index = trad, inplace= True)\n",
    "crosstab.rename(columns = trad, inplace = True)\n",
    "\n",
    "# Visualizando tabela\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GU0REREQSSjWcIiIiIpJQKV7DmdrpsIiIiIikPdVwioiIiKQ7NamLiIiISEKleJO6Ek4RERGRdKcaThERERFJKNVwioiIiEhCqYZTRERERBJKCaeIiIiIJFSGmtRFREREJJFUwykSrYL8fIZfdgZ1t2zIudffVmKZ6b/+yG1XDOCMgUPYdb+uSY4wNc3Jns1tQ69lwfx5mBmHHdWXXv1OiDqsyOWuXsXwK88hN3c1BQX57L5fN3qdcFaRMsuWLubxu4cxZ/ZMKlfegtMuupYmLVpFFHE05uTM5u6br2fh/HlYhnHwEX3o2ff4ImWmfvsVN193CVmNtwZg707dOPaUs6MINzJzsmdzx7DrWDB/HhlmHHJkH446pujnbMwHb/Pyc08CULV6Nc677Fpatt4hgmiT45MJ4xkxfBgF+QUc3ecYzjhrQJH17s6IW4Yxcfw4qlarytBhw9lp5zZr1ufn53Ncvz40ysri/gcfBuDO20cwbuwYKleuTJOmzRhy0y3Url07qceVcBo0JMlkZg7c6e6Xha8HAjXdfXCkgUVozFsv0bhpC1YuX1bi+oL8fF576kF23mXPJEeW2jIyMznrgoFst8NOLF+2jAvOOJZd9tib5ttWrMSpuEqVq3D5zfdTtVp18vLyuOWKAbTbbR9a7dh2TZm3Rz1Fs5bbccF1I5j193Sefeh2Lr/5/gijTr7MzExOP/dSWm2/E8uXL+OyAcfTYfe9aFYs8d653S5cP/zeiKKMXmZmJmeedxmtdwjO00VnHMcuu+9Ns5jPWdZW2zD8/seoVas2X02ayH23DuWukc9GGHXi5Ofnc/OwITz8yBNkZWVxfP++dOnajVatW68pM3HCeGb8NZ033/2AqVO+46Yhg3nuhZfWrH/umadp2bIVS5ctXbNs733248KLL6NSpUrcdcdtPPbIw1xy2eXJPLTES/EaztSOTspjFdDbzBpEHUgqWDA3h2lffcp+PXqut8zYt19ml326UKtOvSRGlvq2bNCQ7XbYCYDqNWrQtHlL5s3JiTiq6JkZVatVByA/L4/8/DwoVrHw74w/2anD7gBs1bQFc3NmsWjBvGSHGqn6Wzak1fbh9VO9Bk2ab8v8uXMijir11G/QkNY7rD1PTVu0ZN7cop+zndt1pFatoDZuhzbtmTcnO+lxJsu0qVNo2rQ5TZo2pXKVKhxy2OGMHTO6SJkxH4+m55G9MDPad+jIkiWLmRP+bsqePZsJ48dydJ++RbbZd7/9qVQpqGNr36EjOdmzk3NAyWRWvkeSKOHc/OQBI4FLiq8wsyfNrG/M66Xh/13MbKyZvWxmP5nZc2bBVWhme5jZp2b2nZl9YWa1zKyFmU0ws2/Cx77JOrgN9fKj93D0Kedi6/lQLZw3h8mTxnPAIb2SG1iamT3rH37/9Sd2aNMu6lBSQkF+PjdccBIXn3gobTruSasd2hZZ33Tb7fjm07EA/PHz98zLmc2CeRU32cqe9S9//Poz2+/Udp11P/8whYvO6MeNV5zHjD9/jyC61JE96x/++OUndth5/Z+zD956ld322j+JUSVXTnY2jbdqvOZ1o6wssrOLJtg5OdlkNV5bJiurMTlhmVuH38wll11ORsb605vXXvkf+x3QaRNHngIso3yPsnZr9riZ5ZjZtBLWDTQzj6eSSwnn5ukB4AQzq7MB2+wCXAzsDLQE9jOzKsCLwEXu3gE4EFgB5AA93H1XoD9QYnuYmQ0ws6/M7Ku3Rj1d7oMpr6lffkLNuvVo1nrH9ZZ56dF7OPqUc8jIzExiZOllxfLl3HTtZZx94eXUqFEz6nBSQkZmJjfe9wx3PPkGf/7yAzOnF02UDjvmZJYtW8INF5zE6Ldeolmr7cnIqJjX2Irlyxlxw0DOPH8g1YtdP62235FHXniHex4bxeG9j+Xm69b5nlxhrFi+nGHXDeSsCy9f5zwV+u6bL/ng7dc47ZyLkhxd8ji+zrJ1Kgy85DLjxo6hfv367Nxm3S82hR55+CEyK2Vy+BFHbnSsKSdxNZxPAoes+3bWFOgBzIhnJ+rDuRly98Vm9jRwIUGCGI8v3H0mgJlNBloAi4BZ7v5l4X7D9TWA+82sI5APbL+eOEYS1LYy+qe56/6GSLDff5zC1C8m8v3Xn5G3ejUrli/jiTtv5LRLb1hTZsZvP/HY7cHrZYsXMe3rz8jIzKTj3pvht99yyMvLZei1l9L1oMPYv8uBUYeTcqrXrMUO7XZl2jeTigwKqla9BmdcfD0QDHC44oyjaRgOjKlI8vJyGX7DQDofeCj7dOq+zvrYxGr3vQ/g4btuYfHCBdSuW7G6t+Tl5XLzdZfRtcdh7Nd53fME8Odvv3DviBsZctsD1K5TN7kBJlFWVmNmz1rb3J2TnU2jRo2KlGmU1Zjs2WvLZGfPpmGjRnz4wfuMHfsxEyeMZ9WqVSxbtpSrrxzILSNuB+CN115l/LixjHzsyfW2eqW1BPXhdPfxZtaihFV3AVcAr8ezHyWcm6+7gW+AJ2KW5RHWaodN5lVi1q2KeZ5PcG0YlPB1M2iuzwY6hPtbuamC3pR6nXwOvU4+B4Bfpn7DR689XyTZBBj6yMtrnj99z0203X0/JZshd+euWwbTrHlL+hx7ctThpIzFixZQKbMS1WvWYvWqlfww+UsO7XtSkTLLly6hyhZVqVS5MuPff53t2+xCteo1Ioo4Gu7OfbfeSNNm23JUv5NKLLNg3lzq1t8SM+OXH6dR4E6tzTiZKom7c8/wG2naYluOPrbk85STPYth113GZdfdxDbNmic5wuRq07YdM2ZMZ+bMv8lqlMV777zNLbfdUaRMl67deOH/nuWQww5n6pTvqFmzFg0bNuKiSy7joksuA+DLLz7nqScfX5NsfjJhPE889giPPfUs1apVS/pxJUUSk2gzOxL4x92/izd5V8K5mXL3+WY2CjgDeDxcPB3YDRgFHAVULmM3PwFbm9ke7v6lmdUiqDGtA8x09wIzOwVIq7bC8e++CkCnQ4+OOJLU9v2Ubxn93lu0aLUd557SD4BTz76APfc9IOLIorVo/lweu2soBQX5eIGzxwHd6bjn/ox55xUAuh7Wm3//ns6jd95IRmYmWzdtwWkXXRtx1Mn349TJjP3gbZq33I6Lz+gPwIlnnc+ccLDGoUcdw6fjPuLdN14iMzOTKlWqMnDQLZtnzVMpfpg6mY/ff4sWLbfj/NOCz9kpAy5Yc54O63UMzz8xksWLFvLgnTcDkJlZiXse/b/IYk6kSpUqcfW1gzhnwJkUFOTT6+g+tG69HaNefB6Afv2P44BOnZk4fhxHHNqDqlWrMeSmm8vc7y3DhrI6dzX/OfM0ANp16MD1NwxJ6LEkXTlrOM1sABA799TIsIVyfeWrA9cCB23Q+3gJfSEkfZnZUnevGT7PAv4EbnX3weHr1wlqJUcDF7h7TTPrAgx09yPC7e4HvnL3J81sD+A+oBpBsnkgsBXwP2A5MKZwP6XFFUWTejpq2UB9JOPx74J4e4rIljW3iDqEtFAps2IluuXVpP5mWjuYAFUrFZ+/IrGqHXZPuf7OrnjnojLjDJvU33L3tmbWjiCHWB6ubgL8C+zp7usd/q8azs1MbOLn7tlA9WKv944pfnW4fCwwNqbc+THPvyy2DcCvQPvi+xEREZGIJKl1wN2nAms61prZdGB3d59b2nYapS4iIiKS7hI3LdLzwGfADmY208zOKE94quEUERERSXeJG6V+XBnrW8SzHyWcIiIiIukuxQfcKeEUERERSXcpfi91JZwiIiIi6U41nCIiIiKSUKrhFBEREZGEUg2niIiIiCRSqt+lSwmniIiISJpTwikiIiIiiZXa+aYSThEREZF0pxpOEREREUkoJZwiIiIiklBKOEVEREQkoZRwioiIiEhipXa+qYRTREREJN2phlMEyKpZNeoQ0sLcJauiDiEt5KzQeYpXw1pbRB1CWqhdTX8ORRJJnzARERGRNKcaThERERFJKCWcIiIiIpJQSjhFREREJLFSO99UwikiIiKS7lTDKSIiIiIJpYRTRERERBJKCaeIiIiIJFZq55tKOEVERETSnWo4RURERCShlHCKiIiISEIp4RQRERGRhEr1hDMj6gBEREREZCNZOR9l7dbscTPLMbNpMctuM7OfzGyKmb1qZnXL2o8SThEREZE0Z2blesThSeCQYss+BNq6e3vgF+DqsnaihFNEREQkzSUq4XT38cD8Yss+cPe88OUkoElZ+1HCKSIiIpLmyptwmtkAM/sq5jFgA9/6dODdsgpp0JCIiIhIBeXuI4GR5dnWzK4F8oDnyiqrhFNEREQk3SV5kLqZnQIcAXR3dy+rvBJO2azNzZnNvcMHsXDBPMwy6HH40RzR5/giZZYuWcwDt93I7H9nUqXKFpx3+SCabds6ooijsXr1Km4aeDZ5uavJz89nzwO60+ekdVtVfvjua559+E7y8/KoVacu1932cATRRm/4uf3Zomo1MjIyycjM5IIRRSsHcv75i5cfGM4/f/7KwcedSacjj40o0ujMyZnNXcOuZ8H8eViGcUjPPhzZ9/gSy/7y4/dcfu7JXHHDcPbr0iPJkUbvlhuv49OJ46lXrz5Pj3ptnfV/Tf+DW268nl9++oGzzr2Q4046LflBJtEnE8YzYvgwCvILOLrPMZxxVtHfRe7OiFuGMXH8OKpWq8rQYcPZaec2AAy67mrGjxtL/fpb8srrb63Z5qcff+SmITewetUqMitlcs11g2nXvn1SjyvRkjktkpkdAlwJdHb35fFso4QzAmaWD0wlOP9/Aie5+8JIgyqDmV3j7jdHHceGyszM5NT/XELL7XdixfJlXP6fE+mw2940bdFyTZn//d/jbNt6B64ccgczZ/zJo/eOYPDt/40w6uSrXLkK14x4kKrVqpOXl8fQy86iw+770HqndmvKLFu6hCcfuJUrbrqHBo0as2jh/FL2uPkbMPhuatSuW+K66jVr0/P0C/nhi4nJDSqFZGZmcvp5l9J6+51YvnwZl5x1PB1334tmLVoVKZefn89TD9/DLnvsE1Gk0Tu0Zy969z+eYYOuKXF97dp1uGjgVUwY+3GSI0u+/Px8bh42hIcfeYKsrCyO79+XLl270ar12kqAiRPGM+Ov6bz57gdMnfIdNw0ZzHMvvATAUb16c9zxJ3Lt1VcW2e9dd97Gf849j/0P6MyE8eO4+87beOzJZ5J5aAmXqITTzJ4HugANzGwmcAPBqPQtgA/D953k7v8pbT8aNBSNFe7e0d3bEoz8Oi/eDc0sqi8JJf8mTHH1tmxIy+13AqBa9Ro0ab4t8+fmFCkz868/aLfLHgA0abYtObP/ZeH8eUmPNUpmRtVq1QHIz8sjLy8Piv3y+nTM++yxbxcaNGoMQJ269ZMeZ7qoWaceTVvvREalivudvv6WDWkdfvaqV69B0+bbMm/OnHXKvfXKC+zbuTt16lXc66njrrtTu3ad9a6vV39LdmrTjkoV4HqaNnUKTZs2p0nTplSuUoVDDjucsWNGFykz5uPR9DyyF2ZG+w4dWbJkMXPmBL/Xd9t9D2rXWfdcGsbSpcsAWLpkCQ0bNkr8wSRZAkepH+fuW7l7ZXdv4u6PuXtrd28a5jIdy0o2QQlnKvgM2AbAzPY0s0/N7Nvw/x3C5aea2Utm9ibwgZnVNLPRZvaNmU01s6PCci3CiVgfNbNpZvacmR1oZp+Y2a9mtmdYrkY4keuX4XsdFfM+r5jZe2H5W8Plw4FqZjbZzJ4Ll51oZl+Eyx42s8ykn7kNlDP7X/787Se226ltkeUtWm7PpAljAPj1p2nMyZ7NvGJJaUVQkJ/PNeeewLnHHky7Xfek9Y5Fz9Psf2awbOkSbrr8P1x3/slM+OjtiCKNngGP3TSQ+644i88/fCPqcFJe9qx/+f3Xn9lh56LX1Lw5OXw24WMOObJvRJFJqsnJzqbxVo3XvG6UlUV2dnbRMjnZZDVeWyYrqzE5xcoUd8VV13DX7bdyUPfO3HH7CC685NJNG3gKSOA8nJuEEs4IhUlad6DwL9ZPQCd33wUYBMQ2Ye8DnOLu3YCVwNHuvivQFbjD1l41rYF7gPbAjsDxwP7AQNbWUl4LfOzue4Tb32ZmNcJ1HYH+QDugv5k1dferWFsre4KZ7RSW2c/dOwL5wAklHN+aqRZeeu7xcp+nTWHFiuXcNvhyTjt3INVr1Cyy7ujjTmXZ0sVcNuA43nn1RbbdbgcyM1M+f97kMjIzufnB57j32bf4/ecf+Hv670XWF+Tn8+dvPzFw6F1cOexeXvu/x5k186+Ioo3WOTc9wIW3Pspp197KZ++/xh8/fBd1SClrxfLl3DJoIGddsO5n75H7buPUsy+qkJ83KZmz7tiTdZKiEsanlJU4jXrxeS6/8mo+GD2Oy6+8msHXX7tRcaakBN1paFPZ/OvnU1M1M5sMtAC+JpixH6AO8JSZbQc4UDlmmw/dvbDTnAE3m1knoICghjQrXPenu08FMLPvgdHu7mY2NXw/gIOAI81sYPi6KtAsfD7a3ReF2/8ANAf+LhZ/d2A34MvwQ14NWKdKMHaqhWkzl5Y5gi1R8vJyuW3w5RzQ/VD2PqDbOuur16jJ+VcMBoLO6Oec0JNGjbdOcpSpo0bNWuzUflemfPUZTWP629Vr0Ij2tetQtWo1qlatxo5tOzLjj1/ZqknzCKONRu36DYCg6bzNngcw87cfablzh4ijSj15ebncMmggXQ48lH07dV9n/a8//8BtQ64CYPGihXw9aSIZmZXY54CuyQ5VUkRWVmNmz5q95nVOdjaNGhVt/m6U1Zjs2WvLZGfPpmGj0pvI33z9Va68OkgyDzr4UG4cdN0mjDo1JLO2sjxUwxmNFWHNYHOgCmv7cA4FxoR9O3sSJIKFlsU8PwFoCOwW7ic7puyqmHIFMa8LWPsFw4A+MX0vmrn7jyVsn0/JX0oMeCpm+x3cfXDZh5187s6Dtw+lSbNtOfKYE0sss2zpEnJzcwH46J1X2bn9ruvUxGzuFi9cwLKlSwBYvWol0779gq2bFk0kd9unEz9/P5n8/DxWrVzJ7z9/z9bNto0i3EitXrmCVSuWr3n+63dfktW04p2Hsrg79464kabNt6VX/5NKLPPYi2/z2Ivv8NiL77Bv5wM555KrlWxWcG3atmPGjOnMnPk3uatX8947b9O5a9GKgi5du/HmG6/h7kz5bjI1a9Yqs09mw0aN+OrLLwD44vNJNGveIlGHEJlUb1JXDWeE3H2RmV0IvG5mDxHUcP4Trj61lE3rADnunmtmXQkS1w3xPnCBmV0Q1n7u4u7flrFNrplVdvdcYHQY813unmNm9YFa7p5y7as/TZvMuA/fptm2rblswHEAHH/GeczNCb4dH9yzLzP/+pN7RwwiIyODps1bcu7AQVGGHImF8+fy8B03UpBfgHsBe3U6kF32OoDRb/8PgO6H92GbZtvSfrd9uPqcE8gwo8shRxWpAa0olixawDO3BbUjBfn5dNz/QHbYZS8mffA6AHsfdBRLFszjvqvOZtWKZZhlMPHtl7n0rqeoWr1GabverPwwdTJjPnibFi2348Iz+gNw8lnnMyc7+OwdetQxUYaXUgZfcznffv0lixYupPdh3Tl9wLnBwD2gV9/+zJs7l7NO7s+yZUvJsAxeev5Znhn1OjVqbn5fjCtVqsTV1w7inAFnUlCQT6+j+9C69XaMevF5APr1P44DOnVm4vhxHHFoD6pWrcaQm9b2Prty4KV89eUXLFy4gB7dOnHOeRfQu88xDBo8lFuH30x+Xh5VttiCQYOHRHWICZPiFZxYHHN1yiZmZkvdvWbM6zeBUcBvwFPAHOBjgumSWpjZqcDu7n5+WL4B8CZBk/tkYD/g0HB3b4U1pJjZk+Hrl82sReE6M6sG3A3sS1BbOd3djyjhfd4Cbnf3sWY2AjgS+Cbsx9mfYFqEDCAXOM/dJ63vmKNsUk8nK3Lzow4hLcxcsiLqENJGm0a1ow4hLdStUbnsQkLtajpP8apaKblTsW93+Xvl+jv7622HJCVOJZySFEo446OEMz5KOOOnhDM+Sjjjo4QzfslOOLe/onwJ5y+3JifhVJO6iIiISJpL9UFDSjhFRERE0lyK55tKOEVERETSXUZGamecSjhFRERE0pxqOEVEREQkoVK9D6cmfhcRERGRhFINp4iIiEiaS/EKTiWcIiIiIuku1ZvUlXCKiIiIpDklnCIiIiKSUCmebyrhFBEREUl3quEUERERkYRK8XxTCaeIiIhIulMNp4iIiIgkVIrnm0o4RURERNKdajhFREREJKFSPN9UwikiIiKS7lTDKQK8/3tO1CGkheZ1q0UdQlr4YubiqENIGx22qht1CGmhcmZG1CGIbJQUzzeVcIqIiIiku1Sv4Yz7K52Z1TazWokMRkREREQ2nFn5HmXv1x43sxwzmxazrL6ZfWhmv4b/1ytrP2UmnGa2u5lNBaYA08zsOzPbrewQRURERCQZzKxcjzg8CRxSbNlVwGh33w4YHb4uVTw1nI8D57p7C3dvDpwHPBFPhCIiIiKSeImq4XT38cD8YouPAp4Knz8F9CprP/EknEvcfULMG08ElsSxnYiIiIikMDMbYGZfxTwGxLFZlrvPAgj/b1TWBvEMGvrCzB4Gngcc6A+MNbNdwzf6Jo59iIiIiEiClHfQkLuPBEZu2mjWFU/C2TH8/4Ziy/clSEC7bcqARERERGTDJHmUeraZbeXus8xsK6DMuQ/LTDjdvesmCU1EREREEiLJsyK9AZwCDA//f72sDeIZpX5ROCWSmdmjZvaNmR208bGKiIiIyKaQqFHqZvY88Bmwg5nNNLMzCBLNHmb2K9AjfF2qeJrUT3f3e8zsYIJOoacRjFL/II5tRURERCTBElXD6e7HrWdV9w3ZTzwJZ+EhHAY84e7fWapPZy8iIiJSgaR6ahZPwvm1mX0AbAtcHd5tqCCxYYmIiIhIvFI834wr4TyDYKT6H+6+3My2JGhWFxEREZEUkJHiGWc8E787sDNwYfi6BlA1YRGJiIiIyAZJ1J2GNpV4Es4HgX2Awk6jS4AHEhaRiIiIiGyQBN5LfZOIp0l9L3ff1cy+BXD3BWZWJcFxiYiIiEicMlK7RT2uhDPXzDIJmtYxs4Zo0JCIiIhIytgcRqnfC7wKNDKzYUBf4LqERiWyCa1avpTxT9/N/H/+wszofMolZLXaac36f3+ewvsP3EjtBo0BaLHrvux2xAlRhRuZ287rzxZVq2MZGWRkZnLe8KK31p084UPGv/48AFtUrcaRZ17CVi1aRxFqpJbkzOSrp29b83r5vNnseMjxtOp81Jpls6ZN4qd3nwPLwDIyadfrTLZsuXMU4UZmTvZsbr/pWhbMn4eZceiRfenVr+jn6u+//uTOmwfx2y8/cspZF9D3+FMiijb5PvtkAnfddgsFBfkc2asvJ59+VpH1zz71GO+/8xYA+fn5TP/zD979eCJVq1bjnDNOZvXq1eTn59HtwIM465wLojiEpPhkwnhGDB9GQX4BR/c5hjPOGlBkvbsz4pZhTBw/jqrVqjJ02HB22rkNq1at4rSTTyB39Wry8vPpcdDBnHv+het5l81DiuebpSecZpYB/AlcQTDBpwG93P3HJMQmITM7GngF2Mndf4o6nnTz6Yv/pWmb3enxn+vIz8slb/WqdcpstV1bDrngxgiiSy1n3HAXNWrXLXFdvUZbcdbge6hWsxY/f/s5r428g3Nufii5AaaAWo2a0HXgPQB4QT7v33gaW7Xbp0iZhtt1oHGbvTAzFv37J189fSvdr6pY5yozM5Ozzh9I6x12YvnyZVx4+rHsssfeNN+21ZoytWrX5j8XX8ln48dEGGny5efnc/vwm7j3oUdplJXFaSf054DOXdm21dovcCeecgYnnnIGABPGjeGF556mTp26uDv3j3yc6tVrkJeby4DTT2Sf/TrRtn2HqA4nYfLz87l52BAefuQJsrKyOL5/X7p07Uar1mvP08QJ45nx13TefPcDpk75jpuGDOa5F16iSpUqPPr4U1SvUYPc3FxOPel49j+gE+07dIzseBLNSO2Ms9RBQ+5eANzh7j+5+wPufr+SzUgcB0wEjo06kHSzesUyZv8yjR32PxiAzEqV2aJ6zYijSk/Nd2hLtZq1AGi23c4smjcn4oiiN+fXKdTYsjHV6zcqsrzSFtXWNG/lr14FKf6HIBHqN2hI6x2CloTq1WvQtEVL5s3NKVKmbr0t2WGntlSqFE9j2+bjh2lTadK0Gds0aUrlylXocfChjB/78XrLf/jeO/Q45DAgaDatXr0GAHl5eeTl5W22l9e0qVNo2rQ5TZo2pXKVKhxy2OGMHTO6SJkxH4+m55G9MDPad+jIkiWLmTMnJzhPNYqfp830RIUyrHyPpMUXR5kPzKyP7i4UDTOrCexHMB/qsTHLtzKz8WY22cymmdkBxbY71MxGxbzuYmZvhs8PMrPPzOwbM3vJzGqa2e7hviab2VQzK+yz28rM3jOzr81sgpntGC4/Jnzf78xsfBJORbksnjubqrXqMO7JO/nf0PMY9/Td5K5auU657D9+5OUh5/LuPdcz/9+/Iog0eobxxLDLeeDKAXzx0Zullv3q47fZfpc9kxRZ6vrn2/Fss0unEtf9O+UzRg8/h0mPDGGXYzfvpryyZM/6h99/+Ykddm4XdSgpYU5ONo2yGq953SirMXPm5JRYduWKFUz6dAJdu/dYsyw/P5+T+h/Nod33Z8+996Vtu82vdhMgJzubxlvFnqcssrOzi5bJySar8doyWVmNyQnL5Ofn06/3UXQ9YF/23mdf2m+GtcCxUn2UejwJ56XAS8AqM1tsZkvMbHGC45K1egHvufsvwHwz2zVcfjzwvrt3BDoAk4tt9yGwt5nVCF/3B140swYEfXAPdPddga+AS939K3fvGO7vPeD2cLuRwAXuvhswkGCaLIBBwMHu3gE4chMe7ybl+fnMnfEbO3c+nD7XP0DlKlWZ/N6oImUaNGvF8bc8Rd9BD9KmW08+eHBIRNFGa8DQ+zl/xCOccs0IPn//Nf784bsSy/0x7Vu+HvMOh5xwdpIjTC0FebnM/v4Ltu64X4nrt26/D92veog9T7+GH999LsnRpY4Vy5dz07WXcfZFl1OjhloXADwYgxuXCePH0q7jrtSpU3fNsszMTJ558VXeeH8MP0ybyu+//ZqAKKNX0nlaJ0Hy9ZfJzMxk1Cuv88HH45g2dQq//vpLQuKU+JSZcLp7LXfPcPcq7l47fF27cL2ZtUlsiBXeccAL4fMXWDsf6pfAaWY2GGjn7ktiN3L3PILEsaeZVQIOB14H9iaYyP8TM5sMnAI0L9zOzPoBuwJXhbWr+wIvhWUfBrYKi34CPGlmZwGZJQVuZgPM7Csz+2rSm8+X+wRsjBr1GlCjXgMatdwRgG1325+5f/1WpEyVajWoXLUaAM3a7UlBfh4rlyxKeqxRq12/AQA169Rj5z32Z+Zv6/aemf3X77z68G2cePkwqteqk+wQU0r2T19TZ5tWVK1Vr9RyDVq1Zfm8WaxaWvG+p+fl5XLTdZfS9aDD2K/zgVGHkzIaNWpMTvbsNa9zsmfTsGGjEst+9P47HBQ2pxdXq1Ztdt19DyZ9OiEhcUYtK6sxs2fFnqdsGjUqep4aZTUme/baMtnZs2lYrEzt2rXZY8+9+HTi5nmeCm0OE7+X5ZlNsA8pQXgb0W7Ao2Y2Hbgc6G9m5u7jgU7AP8AzZnZyCbt4EegX7uPLMCk14MPC2kx339ndzwjfrw1wI3Csu+cTXB8LY8p2dPedANz9PwQ1pU2ByWGsRbj7SHff3d1337vnccVXJ0X1OvWpWa8hC2fPBOCfHydTb+tmRcosXzQfD78l5/z5M17gbFGz9jr72pytXrmCVSuWr3n+25SvyGq2bZEyC+dm89zt19P3/GtosHXTKMJMKf98M4Ftdi25OX3pnH/XXFMLZ/5OQV4eVWrUSmZ4kXN37r5lME2bt6T3sSX9eqq4dmrTlr9n/MW//8wkN3c1H77/Lgd06bpOuaVLlvDt11/SqUu3NcsWzJ/PkiXBl5eVK1fy5eef0bxFy6TFnkxt2rZjxozpzJz5N7mrV/PeO2/TuWu3ImW6dO3Gm2+8hrsz5bvJ1KxZi4YNGzF//nwWL157niZ99ikttt08z1OhDLNyPZJlU/TUVt/OxOkLPO3ua9ouzWwcsL+ZzQD+cfdHwmbzXYGni20/FngMOIsg+QSYBDxgZq3d/Tczqw40AbIJalBPdvc5AO6+2Mz+NLNj3P2lsB9ve3f/zsxaufvnwOdm1pMg8ZyXmNOwcfY97hw+fuxWCvJyqdVgK7qcegk/jHsbgJ07H84fX0/kx3FvY5mZVKpche4Drkr5+cw2taWLFvDc7dcDUJCfT/v9u7N9x734/IPXAdjroKP4+OWnWL50MW88ehdAiVMnVRR5q1eR88tkOhxz7pplf376LgDb7nsos6Z8xt9ffYxlViKzchV2P/mKCndNfT/lW0a//xYtWm3Heaf2A+CUsy9gTvYsAA7v1Y/58+Zy4ZnHsXzZMjIyMnjtpWd5+NlXN/um90qVKjHwymu56NyzKCgo4IijjqZlq+145aWgMav3MUF3/bFjPmLPvfejWrXqa7adO3cOQwddTX5BAV5QQPceh7B/py5RHEbCVapUiauvHcQ5A86koCCfXkf3oXXr7Rj1YtBi1q//cRzQqTMTx4/jiEN7ULVqNYbcdDMAc+fkcN01V1FQkE9BgXPQwYfQuYSkfnOS6r9izEvo/7BBOzD7JuwLKJuYmY0Fhrv7ezHLLgR2IkgcLwdygaUEieKfJezjfuBUoJG7Lw+XdQNGAFuExa4D6gH3AX8UbuvuHc1sW+Ahgqb0ysAL7j7EzF4BtiP4wjEauNhLuZjuGPfHxl1oFUTzutWiDiEtfDGz4jVPl9d/9mxediGhXo3KUYeQFqpVKbEHlZSgaqXkVsj1feKbcv2dffm0XZMSZ8WaiyLNuHuXEpbdG/PyqTj2cT5wfrFlHwN7lFB8nf2FSewhJSzvXdZ7i4iISHKkeg3npkg4V2+CfYiIiIhIOSWzP2Z5xJVwmtmRBANUAMa5+5pJ+tx970QEJiIiIiLxSe10M46E08xuAfYECieSu9DM9nX3qxMamYiIiIjEJdUHJsZTw3k40DG8zSVm9hTwLaCEU0RERCQFJPM2leURbx/OusD88HnFnu1ZREREJMVsDjWctwDfmtkYgi4CnVDtpoiIiEjKSPF8s+yE092fD+eD3IMg4bzS3WeXvpWIiIiIJMvmUMMJsBKYDVQFtjez7cNbK4qIiIhIxNK+D6eZnQlcRHD7w8nA3sBnBPfnFhEREZGIpXoNZ0YcZS4iaE7/y927ArsAcxIalYiIiIjEzcr5KHO/ZpeY2fdmNs3MnjezquWJL56Ec6W7rwzfdAt3/wnYoTxvJiIiIiKbXoZZuR6lMbNtgAuB3d29LZAJHFue+OLpwznTzOoCrwEfmtkC4N/yvJmIiIiIbHoJbFGvBFQzs1ygOuXMAeMZpX50+HRwODVSHeC98ryZiIiIiKQOMxsADIhZNNLdRwK4+z9mdjswA1gBfODuH5TnfcpsUjezvc2sVvjG44AxBP04RURERCQFmFm5Hu4+0t13j3mMjNlnPeAoYFtga6CGmZ1Ynvji6cP5ELA05vWycJmIiIiIpACz8j3KcCDwp7vPcfdc4BVg3/LEF08fTnN3L3zh7gVmFu/8nSIiIiKSYGUNACqnGcDeZladoEm9O/BVeXYUTw3nH2Z2oZlVDh8XAX+U581EREREZNNLRA2nu38OvAx8A0wlyBtHlrrResRTU/kf4F7gOsCB0RTtXCpSpi2rq1I8HpkpPnFvqjiwZf2oQ0gb1bfIjDqEtFAp1W/TIlKGRE387u43ADds7H7iGaWeQznnXBIRERGRxIunyTpK8dzasipwBtCG4F7qALj76QmMS0RERETitDnc2vIZoDFwMDCO4J7qSxIZlIiIiIjEL8PK90hafHGUae3u1wPL3P0p4HCgXWLDEhEREZF4pXrCGc9Ijtzw/4Vm1haYDbRIWEQiIiIiskFSvUk9noRzZDjT/PXAG0DN8LmIiIiIpIBUn2ghnlHqj4ZPxwEtExuOiIiIiGyoFK/gjGuU+pbAYGA/gnk4JwBD3X1eYkMTERERkXgk6E5Dm0w8g4ZeAHKAPkBfYC7wYiKDEhEREZH4ZZTzkSzx9OGs7+5DY17fZGa9EhSPiIiIiGygFK/gjCu5HWNmx5pZRvjoB7yd6MBEREREJD4ZZuV6JC2+OMqcDfwfsDp8vABcamZLzGxxIoMTERERkfQXzyj1WskIRERERETKJ9Wb1OMZpd6ppOXuPn7ThyMiIiIiGyrt5+EELo95XhXYE/ga6JaQiERERERkg6T6tEjxNKn3jH1tZk2BWxMWkYiIiIhskBTPN+Oq4SxuJtB2UwciIiIiIuWT9k3qZnYfwR2GIBjV3hH4LoExiYiIiMgGMFI744ynhvOrmOd5wPPu/kmC4hHZ5B68+ESqVK2GZWSQkZnJaUMfLLJ+5fJlvPnQcBbPy6EgP5+9DutL+86HRBRtdIaf258tqlYjIyOTjMxMLhgxssj6nH/+4uUHhvPPn79y8HFn0unIYyOKNHoF+fncNvBM6mzZkP9cV7SH0a9Tv2HkLVezZaOtAOiwT2cO7X9aFGFGaviQ6/hs4njq1avPky++ts76D999i/97+jEAqlWrzqVXXU/r7XdMcpTR+fSTCdw+4mYKCgrodXRfTj3jrBLLfT9tKqeddCw333onB/Y4GIDnnnmS1195Gcxovd323DDkZrbYYotkhp9Qn0wYz4jhwyjIL+DoPsdwxlkDiqx3d0bcMoyJ48dRtVpVhg4bzk47twFg0HVXM37cWOrX35JXXn9rzTY//fgjNw25gdWrVpFZKZNrrhtMu/btk3pciZb2NZzAy8BKd88HMLNMM6vu7stL28jMHLjT3S8LXw8Earr74I2MOS5m9h9gubs/nYz3W08M17j7zRuxfRdgoLsfEWf5jsDW7v5Oed9zc3X8tbdTvVadEtd98+HrNNimGcdcNpTlixfy8OWn02a/7mRWqpzkKKM3YPDd1Khdt8R11WvWpufpF/LDFxOTG1QKGvvWS2Q1ac7KFSX/Gmy1c4d1EtGK5tAjetG73/HcfMM1Ja7fauttuPfhJ6lVuw6TPpnA7TffyH+ffD7JUUYjPz+fETcP5YGHHyMrK4uTj+9Hpy5dadmq9Trl7rv7Dvbed781y3Kys3nx/55l1KtvUbVqVa66/BI+eO8deh51dLIPIyHy8/O5edgQHn7kCbKysji+f1+6dO1Gq9Zrz83ECeOZ8dd03nz3A6ZO+Y6bhgzmuRdeAuCoXr057vgTufbqK4vs9647b+M/557H/gd0ZsL4cdx952089uQzyTy0hEv1hDOeid9HA9ViXlcDPopju1VAbzNrUJ7ANpa7/3dDkk0zK09/1vXty8wsAyj5N23idAQOS/J7pj8zVq1YgbuzeuUKqtaoRUZGZtRRpZyaderRtPVOZFTaZB+VtLRgbg7ff/UZ+/ToWXbhCqzDrrtTq3bJX/IA2nbYZc36Nu3aMycnO1mhRe77aVNo2rQZTZo0pXLlKhx0yGGMG/vxOuVefP5Zuh3Yg/r1tyyyPD8/n1WrVpKXl8fKFSto2LBRskJPuGlTp9C0aXOaNG1K5SpVOOSwwxk7ZnSRMmM+Hk3PI3thZrTv0JElSxYzZ04OALvtvge166x73RnG0qXLAFi6ZMlmdc4KmVm5HskST8JZ1d2XFr4In1ePY7s8YCRwSfEVZtbTzD43s2/N7CMzyyqhzKlm9pqZvWlmf5rZ+WZ2abjNJDOrH5Y7y8y+NLPvzOx/ZlY9XD44rFXFzDqG20wxs1fNrF64fKyZ3Wxm44CLir3/YDN7xsw+NrNfzeyscHlNMxttZt+Y2VQzOypc3sLMfjSzB4FvgMeAamY22cyeC8ucaGZfhMseNrN1shozO8TMfjKziUDvmOX1w/MxJTyW9sW2qwIMAfqH++9vZjXM7PHw/HwbE2u853asmd1tZp+a2TQz2zOeWFKOGS8Mv4onrjuXbz9e966su/U4inn/zuC+84/l0asH0OOkc7GMeD4amxcDHrtpIPddcRaff/hG1OGkrFceu5ejTjmn1ClI/vx5GrdcfAoPDrmMWTP+SGJ06ent119hr333jzqMpMnJySGrceM1rxs1yiInu2jCnZOdzdiPP6LPMUW7rjTKyuLEU07jiIO7c8iBnahZq1aRGtB0l5OdTeOtYs5NVhbZxc9NTnaR85eV1Xid81fcFVddw12338pB3Ttzx+0juPCSSzdt4Ckgw8r3SFp8cZRZZma7Fr4ws92AFXHu/wHgBDMr/nVjIrC3u+9CcKvMK9azfVvgeIK5P4cRNJHvAnwGnByWecXd93D3DsCPwBkl7Odp4Ep3bw9MBW6IWVfX3Tu7+x0lbNceOBzYBxhkZlsDK4Gj3X1XoCtwh639irAD8LS77+LupwEr3L2ju59gZjsB/YH93L0jkA+cEPtmZlYVeAToCRwANI5ZfSPwbXgM14THtIa7rwYGAS+G7/kicC3wsbvvEcZ6m5nV2IBzC1DD3fcFzgUejyeWmOMZYGZfmdlXY1/9v5KKJMVJg+7i9GEP0e/yYXzz0RvM+GlKkfV/Tv2KrOatuOD+Fzh92H/54On7WbV8WUTRRuecmx7gwlsf5bRrb+Wz91/jjx80NrC4aV9+Qs06dWnWev19DZu02oEhI1/m6rufovNhfXnklmQ3dKSXb776grffeIWzz9/8EoD1cl9nUfGapjtuu4ULLr6MzMyi9RKLFy9i3JiPeeOdD3nvw3GsWLGCd97afL4gOmWfm3jOX3GjXnyey6+8mg9Gj+PyK69m8PXXblScqcisfI9kiadt7GLgJTP7N3y9FUHiVCZ3X2xmTwMXUjRJbQK8aGZbAVWAP9ezizHuvgRYYmaLgDfD5VMJkkGAtmZ2E1AXqAm8H7uDMNmt6+7jwkVPAS/FFHmxlEN43d1XACvMbAxBcvY2cLMFd2AqALYBCmto/3L3SevZV3dgN+DL8INRDcgpVmZH4E93/zWM/VmgsLf0/kAfAHf/2My2NLM67r6olPgPAo4srOklmLi/Wfg8nnML8Hz4nuPNrLaZ1Y03FncfSVDLzZNfzlj3N0SS1KoX9OqoUace2++2H7N+/5lmO649xCnj3mefnsdiZtRvvA11GzZm3qy/2bpVxRnAAFC7fnCeatapR5s9D2Dmbz/ScucOEUeVWv74aSrTvvyEH76eRG7ualYuX8ZTdw3hlEsGrSlTrXqNNc/b7L4Pox6+g6WLF1JzPX1jK7Lff/2Z224axK33/Jc6detGHU7SNMrKInv27DWvc3KyadioaBPvj99P45orLwNg4YKFfDJhPJUyM8nLy2PrbbahXv36AHTtfiBTvvuWw444MnkHkEBZWY2ZPSvm3GRn06jYuWmU1bjI+cvOnr3O+Svuzddf5cqrgyTzoIMP5cZB123CqFNDqk/8XmYNp7t/SZAInUNQy7WTu3+9Ae9xN0GtY42YZfcB97t7O+BsgkSoJKtinhfEvC5gbbL8JHB+uK8bS9nX+pRWlVU8SXKCWsmGwG5hTWV2zHuWti8DngprHzu6+w7rGUC1vsSspCuprCTOgD4x79nM3X8M18Vzbkt6Dy9nLJFYvXIFq8KBHatXruDPaV/ToEmLImVqN2jE9O+/BWDZogXMm/U3dcMRxhVF8fP063dfktV024ijSj1HnvQfhj72Kjc+8jKnXTaY7dvvViTZBFi8YB4e1sBM/+UH3AuosZ4BaxVZ9uxZXH/FxVx74y00bd4i6nCSauc27fh7xl/8M3Mmubmr+eC9d+jUuWuRMm+8+xFvvjuaN98dTfceB3HltYPo0u1AGjfeimlTvmNl2O/8y88n0WLbVhEdyabXpm07ZsyYzsyZf5O7ejXvvfM2nbsWvbFhl67dePON13B3pnw3mZo1a5XZJ7Nho0Z89eUXAHzx+SSabYbXXKo3qcczD2dlgmSz8J7qY83sYXfPjecN3H2+mY0iSDoLm2TrAP+Ez0/ZsJDXUQuYFcZ5Qsx+C99/kZktMLMD3H0CcBIwroT9lOQoM7uFIFnuAlwFHAPkuHuumXUFmpeyfa6ZVQ7P1WjgdTO7y91zwn6Stdz9r5jyPwHbmlkrd/8dOC5m3fjw+IZaMHp9rrsvLvZ+S8LzUeh94AIzu8Dd3cx2cfdv4zz2Qv2BMWa2P7AoPJ/xxJISli1eyCt3DwaCqWx23rcrrTrswTejgwrdXbv3ZL9eJ/DWw7fx6FVn4UDX/meud0T75mrJogU8c1vwjb8gP5+O+x/IDrvsxaQPXgdg74OOYsmCedx31dmsWrEMswwmvv0yl971FFWr1yht1xXCxPdeA2D/Q3rx7adjmfjeq2RkZlKlyhacOvDGpHbMTxU3Xns5k7/+kkULF9L38O6cNuBc8vLyADiqT3+eevQhFi1axF0jbgIgs1ImI58eFWXISVOpUiUuv/o6LjjnTPILCjiyV29atd6Ol0e9AEDffuufcqxt+w5073EwJxzbh8zMTHbYcSd69+2XrNATrlKlSlx97SDOGXAmBQX59Dq6D61bb8eoF4MZDPr1P44DOnVm4vhxHHFoD6pWrcaQm9ZOBnPlwEv56ssvWLhwAT26deKc8y6gd59jGDR4KLcOv5n8vDyqbLEFgwYPieoQEybVf82Yl9AXokgBs0eBygRN0RAkbPnufmYZ2y1195rh8yyCZvNb3X1wOHjlLoLkcBKwh7t3Kbb9qcDu7n5++Hp6+Hpu7DozO4egD+hfBM3Btdz9VDMbDCx199stmC7ovwSDnf4ATnP3BWY2lmDaodi5RgvffzCwNdCKoBn6Vnd/xIJR92+G52QysB9waLjZW+7eNmYfI4AjgW/Cfpz9gasJapZzgfOKN8Gb2SEEtcJzCfq6tnX3I8IE9QlgW2A5MMDdpxTbtj5BklkZuAV4I9zXvgS1ktPDfcV7bscS9OnsDNQGTnf3L+KJpbgom9TTSZ0tqkQdQlqoUVmzCMSr/TZ1ow4hLdSoomsqHpUrVbwBleVVtVJyZ2J/4JPp5fo7e95+LUqNM+xK9yjB2A8nyAU+29D3iSfh/C4ckFPqss1NbMIadSxRKS0h31BKOOOjhDM+Sjjjp4QzPko446OEM37JTjgf/LR8Cee5+5aZcD4FTHD3R8MZcaq7+8INfZ94rpx8M1vTQcTMWhKMsBYRERGRzZSZ1SboUvkYBDPilCfZhPhGqV9O0IfvD4Jm2ebAZn+ftmTdESmVFe/mICIiIqkpQQOAWgJzgCfMrAPwNXCRu2/w3IFlJpzuPtrMtiOYY9KAn9x9VRmbiYiIiEiSlHdaJDMbwNopGAFGhtMaQpAn7gpc4O6fm9k9BAOor9/Q94lnlHp14FKgubufZWbbmdkO7v7Whr6ZiIiIiGx65R2lHjtndglmAjPd/fPw9csECecGi6cP5xPAaoK77RS++U3leTMRERER2fQyzMr1KI27zwb+NrMdwkXdgR/KE188fThbuXt/MzsufPMVMbdyFBEREZGIJTAzuwB4Lhyh/gflHMcTT8K52syqEd5JJhyxrj6cIiIiIikiURNWuftkYPeN3U88CecNwHtAUzN7jmCi81M39o1FREREZNNI9cbnUhNOM8sA6gG9gb0JRqlf5O5zkxCbiIiIiMQhtdPNMhJOdy8ws/PdfRTwdpJiEhEREZENUN5pkZIlnib/D81soJk1NbP6hY+ERyYiIiIicbFyPpIlnj6cpxMMGDq32PKWmz4cEREREdlQKV7BGVfCuTNBsrk/QeI5AfhvIoMSERERkfil9aCh0FPAYuDe8PVx4bJ+iQpKREREROKXqGmRNpV4Es4d3L1DzOsxZvZdogISERERkQ2T6jWc8STE35rZ3oUvzGwv4JPEhSQiIiIiG2JzGDS0F3Cymc0IXzcDfjSzqYC7e/uERSebjXMG3Bp1CGnhgZFXRB1CWvhhzuqoQ0gbOzeuE3UIaWFlXkHUIaSFypVSveG24kr1Gs54Es5DEh6FiIiIiGy2ykw43f2vZAQiIiIiIuWT6nXP8dRwioiIiEgK2xya1EVEREQkhaV2uqmEU0RERCTtpXgFpxJOERERkXSXkeJ1nEo4RURERNKcajhFREREJKFMNZwiIiIikkiq4RQRERGRhFIfThERERFJKNVwioiIiEhCKeEUERERkYTSoCERERERSaiM1M43lXCKiIiIpDvVcIqIiIhIQqkPp4iIiIgkVKrXcGZEHYCIiIiIpC4zyzSzb83srfLuQzWcUiH894YTOLRTW+bMX8Lux9wMwLVnH8bpvfdlzoKlANxw/xu8P/GHKMOM1EMXn0iVqtXIyMggIzOTU4Y+WGT9quXLePOh4Syel0NBfj57HtaX9p0PiSjaaK1evpRPn7uXhf/+hQH7nnQxDVvutHb9imVMfOJ2li2YQ0FBPm0O7E3rfXpEF3AEbrvpeiZ9Mp669erz2P+9us76yV9/yaArLqTx1tsAsH+X7px8xjnJDjNl3XzjdXw6YRz16tfnmVGvRx1OUn0yYTwjhg+jIL+Ao/scwxlnDSiy3t0ZccswJo4fR9VqVRk6bDg77dyG2bNmce3VVzBv3lzMMuh7TD9OOOmUIts+9cRj3Hn7rYyd+Bn16tVP5mElXIIHDV0E/AjULu8OlHCmKTO7FjgeyAcKgLPd/fNoo0pdz7w5if++OI5Hh55cZPl9z47h7mdGRxRV6jnu2tupXqtOieu++fB1GmzTjL6XDWX54oU8cvnptNmvO5mVKic5yuh98dJIttl5N7qcdQ35ebnkr15VZP3P496izlZN6XbuDaxcsojXbhzAtnt0qVDn6uDDj+KovscxYsi16y3TtuOu3HzHA0mMKn0c1rMXffodz003XB11KEmVn5/PzcOG8PAjT5CVlcXx/fvSpWs3WrVuvabMxAnjmfHXdN589wOmTvmOm4YM5rkXXiKzUiYDr7iKnXZuw7JlSzn2mD7svc9+a7adPWsWn336KVtttXVER5dYiWpSN7MmwOHAMODS8u5HTeppyMz2AY4AdnX39sCBwN9Jeu/MZLzPpvbJN78zf9HyqMNIb2asXrECd2f1yhVUrVGLjIy0vBw2yuoVy8n5bRqt9z0IgMxKlalSvWaxUkbuyuBc5a5awRYV8Fy132V3atcu+cuLlK3jrrtTu07FO3/Tpk6hadPmNGnalMpVqnDIYYczdkzRSoExH4+m55G9MDPad+jIkiWLmTMnh4YNG7HTzm0AqFGjJi1btiQnJ3vNdreNuIVLLrscS/XRNeVkVt6HDTCzr2IeA4rt+m7gCoLKrXJTDWd62gqY6+6rANx9LoCZ7QbcCdQE5gKnuvssMxsLfAvsBjQETgauBtoBL7r7dWbWAngP+BzYBfgFONndl5vZdOBx4CDgfjObD9wIbAH8Dpzm7kuTcNyb3H+O7cTxR+zJNz/M4Ko7X2HhkhVRhxQZM2PU8KvAjI7dDqdjt8OLrN+1x1G8cucgHjj/WFavXM5R51+HZVS876xL585ii5p1+PSZu5g/80+2bNaaPY45m8pbVF1TZscuR/DxQ0N4+eqTyF21gk6nX1khz1VZfpj6HWed2IctGzTkPxcOpEXL1mVvJJu1nOxsGm/VeM3rRllZTJ0ypWiZnGyyGq8tk5XVmJzsbBo2bLRm2T//zOSnH3+kXfsOAIz9eDSNshqxw447JvgIolPeNNrdRwIjS9yn2RFAjrt/bWZdyvkWgGo409UHQFMz+8XMHjSzzmZWGbgP6OvuuxEkiMNitlnt7p2A/wKvA+cBbYFTzWzLsMwOwMiw1nQxcG7M9ivdfX/gI+A64EB33xX4ivVUscd+a8qb+/0mOvRN55GXJrBzz8HsdexwZs9dzPBLe0cdUqROGHQXpw57iGMuH8Y3H73B3z8V/SX/59SvaNS8Fefd/wKnDfsvHz59P6uWL4so2ugUFBQw/+/f2P6Aw+h5zX1UqlKVaR+8VKTMvz98Q/2mLel7yzMccfV9fDHqv6xeoRr2WNvtuBPPv/YBjzz7P47udzyDrrgo6pAkBTi+zrJ1aiS99DLLly3jsosv5PKrrqFmzZqsWLGCR0b+l3PP37yvsQyzcj3KsB9wZFjx9ALQzcyeLVd85dlIohXWJu4GDADmAC8CZxMkkB+a2WSCpLBJzGZvhP9PBb5391lhDekfQNNw3d/u/kn4/Flg/5jtXwz/3xvYGfgkfJ9TgObriXOku+/u7rtXatCmnEebODnzl1BQ4Lg7j7/yCbu3LfEwKoxa9RoAUKNOPbbfbT/+/f3nIuunjnuf7XffHzOjXuNtqNOwMfNmJaUnR0qpUXdLqtdtQMNtg5qS5rvux/wZvxUp89tnH9Ks476YGbUbbU3NLbNYnF3xzlVpatSoSbXq1QHYa99O5OXlsWjhgoijkqhlZTVm9qzZa17nZGfTqFGjImUaZTUme/baMtnZs2kYlsnNzeXSiy/ksMN7cmCPoNvLzL9n8M8/M+nX+ygO7dGN7OzZHNu3N3PnzEnCESWPlfNRGne/2t2buHsL4FjgY3c/sTzxKeFMU+6e7+5j3f0G4HygD0Ei2TF8tHP3g2I2KRzVUBDzvPB1YdeK4l8bY18XVmUZ8GHM++zs7mdskoNKssYN1g62O6pbB374fVaE0URr9coVrApr4FavXMGf076mYZMWRcrUbtCIv77/FoBlixYwf9bf1G20VbJDjVy1OvWpUa8hi7JnAjDrp++os1WzImVq1G/ErJ++A2DF4gUsyv6Hmg0ar7Ovimz+vLl4WFP10/dTcS+gdp260QYlkWvTth0zZkxn5sy/yV29mvfeeZvOXbsVKdOlazfefOM13J0p302mZs1aNGzYCHdn8KBradmyJSefetqa8tttvwNjJ3zGux9+zLsffkxWVmNeePkVGjRsmOzDS6xEZJybkPpwpiEz2wEocPdfw0UdCaYrOMjM9nH3z8Im9u3dfUPaspsVbg8cB0wsocwk4AEza+3uv5lZdaCJu/9S/iNKvKduOZUDdtuOBnVr8tt7Qxn633fotNt2tN+hCe7OX7Pmc8FNz0cdZmSWL17IK3cPBqAgP5+d9+1Kyw578O3oNwHYpXtP9u11Au88fBuPXXUWAF36n7neEe2buz37nc3EJ24jPy+PWg0as+/JF/Pz+HcA2KHTYbQ/9Fg+efou3rjpXHDYrdepVK1Zsc7VTddfwXfffMmihQvp37M7p5x1Hvl5eQD07N2P8R9/wBuvjCIzM5MttqjKdUNv22wHc5THDdcMZPJXX7Jw4UKOPrQbZ5x9Hkf06hN1WAlXqVIlrr52EOcMOJOCgnx6Hd2H1q23Y9SLwe/nfv2P44BOnZk4fhxHHNqDqlWrMeSmYKq7b7/5mrfeeJ3ttt+efr2PAuCCiy/lgE6dIzueZEr0xO/uPhYYW97tzUvoCyGpLRwcdB9QF8gDfiNoXm8C3AvUIfgycbe7PxIOGhro7l+FnX4HuvsR4b7GAgMJBhm9A4wH9gV+BU6KGTS0e8zgpG7ACIJBQwDXuXthk32Jqu1yvi60ODww8oqoQ0gLsxavjjqEtHHKrs3KLiRsUVkNfvGoVVX1VPGqWim5t/754o9F5fo7u2fLOkmJU1dOGnL3rwmSwuLmAp1KKN8l5vlYYr6hFK4LR6kXuPt/Sti+RbHXHwN7bHjkIiIikgip3j6ghFNEREQk3aV4xqmEUwBw9+kEo9xFREQkzSS6D+fGUsIpIiIikuZSfcydEk4RERGRNJfi+aYSThEREZG0l+IZpxJOERERkTSnPpwiIiIiklCp3odTM92KiIiISEKphlNEREQkzaV4BacSThEREZG0l+IZpxJOERERkTSnQUMiIiIiklCpPmhICaeIiIhImkvxfFMJp4iIiEjaS/GMUwmniIiISJpTH04RERERSSj14RQB5n1xX9QhpIWMVP+NkSLco44gfeiSkk0pv0Afvvgl98OX6h91JZwiIiIi6S7FM04lnCIiIiJpTn04RURERCShUr37jBJOERERkTSX4vmmEk4RERGRtJfiGacSThEREZE0pz6cIiIiIpJQqd6HMyPqAEREREQk9ZhZUzMbY2Y/mtn3ZnZRefelGk4RERGRNJegCs484DJ3/8bMagFfm9mH7v7Dhu5INZwiIiIi6c7K+SiFu89y92/C50uAH4FtyhOeEk4RERGRNGfl/Wc2wMy+inkMKHH/Zi2AXYDPyxOfmtRFRERE0lx5Bw25+0hgZOn7tprA/4CL3X1xed5HCaeIiIhImkvUIHUzq0yQbD7n7q+Udz9KOEVERETSXQIyTjMz4DHgR3e/c2P2pT6cIiIiImmuvH04y7AfcBLQzcwmh4/DyhOfajhFRERE0lwiJn5394lsorpTJZwiIiIiaS7FbzSkhFNEREQk3enWliJJ9snECfQ64hCOPPQgHn903Zke/vzjD04+oT977tKOp594rMi6JYsXM/CSCzm656H07nkY303+NllhJ80nE8Zz5OEHc8QhPXjskXXPj7sz/OabOOKQHvQ9uic//vB9kfX5+fn069OL8889e82yO28fwVFHHELfo3ty8YXnsXhxuWbNSCmfTBzPUUccTM9De5R4Hbk7I26+iZ6H9uCYYudpcXgd9ep5CEf3PLTIdfT8c89w1BEH0/uow7nrjluTciyJtDHX06DrrqbLAfvQ+6gjimyzOV5PsHHnqrRt/++5Zzjy8IM5+sjDuev29L+mYn0ycQJH9zyEIw87iCdK+By+89ab9Ot9JP16H8mpJx7LLz//BMD0P//g2L691jwO2Hs3nnvmqWSHn2QJmPl9U3J3PeJ4AA7cEfN6IDA4fF4FeAcYDdyzgfvdHbg3jnIXEszw/1xEx98CmFbe7ZetLvBkPBavyPVu3br7z7//5QuXrfTDj+jpU378pUiZv2fN8S++/s5H3HaHP/TwI0XWXTrwcn/m/170ZasLfOGylT577sKkxF34WJHrCX0sXZnn3bp391//mOGLl63yI47o6dN+/LVImQ9Gj/XTTj/Dl68u8M+/+tZ79+lbZP3DjzzuF118qZ9x1oA1y0aPneBLVuT6ilz3m4ff6jcPvzWhx7F8dWIfS1bkebdu3f2X32f4ovA8Tf3x1yJl3v8oOE/LVhX4pC+D81S47tKBV/iz/zfKl692X7Rslc+eu8iXr3YfN/EzP+nkU3zh0lW+fLX7zFlzE34sqXw9TfzsC//mu2l+6GGHF9km2ddTMh4bc65K27bwmlq0bJWvyHWfOXtuYo9jVUHSHouWB7/Pf/rtL1+wNPx9/sMvRcp88vlX/m/OAl+6qsDf/2is9+7Tt8T97LPPvv7Ln38nNf5k/52euWCVl+eRrPhUwxm/VUBvM2tQfIW7r3b3w9y9u7tv0I3t3f0rd78wjqLnAoe5+wnx7NfMKmR3iWlTp9C0WTOaNG1K5cpVOPjQwxj78egiZepvuSVt2rWjUqWip2jp0qV88/VXHN2nLwCVK1ehVu3aSYs9GaZNnULTps2D81OlCoccdjhjxxQ9P2M+Hk3PI3thZrTv0JElSxYzZ04OANmzZzNh/Ng156jQvvvtv+Z8tu/QkZzs2ck5oAQJrqPmMdfR4etcR2PHjOaIEs5TcB19WeQ6qh1eR6NefJ7TzhhAlSpVgOBaTGcbez3ttvse1K5TZ539bm7XE2zcuSpt25defJ7Tz1x7TW2Z5tdUrGlTp9Ck+O/zYuesQ8dd11xD7dp3ILuEa+WLzz+jSdOmbL11ue7ImDZSvH5TCecGyCOYif+S4ivMrKeZfW5m35rZR2aWFS6vb2avmdkUM5tkZu1L2LaLmb0VPh9sZo+b2Vgz+8PMLgyX/xdoCbxhZpesb7/h9iPN7APg6fD1U2b2gZlNN7PeZnarmU01s/fCyVwxs0Fm9qWZTQu3t3D5bmb2nZl9BpwXE3NVM3si3M+3ZtZ1057q8svJySar8VZrXmdlNWZOTnZc2/4z82/q1avPDdddzbF9j+bGQdexYvnyRIUaiZzsbBpv1XjN60ZZWWRnFz0/wTlcWyYrqzE5YZlbh9/MJZddTkbG+n91vPbK/9jvgE6bOPLkysnJpnGRc5BFTrHrKCe7eJngPM0Mr6NB111N/769uHHQtWuuo7+mT+ebr7/ixOOO4YxTT2Ta1CnJOaAE2djrKR6bw/UEG3euStu28Jo64dhjOP2U9L+mYs3JyaZxzO/zRmVcO6+9+jL77b/utfL+u+9w8KGHJyTGVGJWvkeyKOHcMA8AJ5hZ8a/kE4G93X0X4AXginD5jcC37t4euAZ4Oo732BE4GNgTuMHMKrv7f4B/ga7uflcZ+90NOMrdjw9ftwIOB44CngXGuHs7YEW4HOB+d9/D3dsC1YDCDlVPABe6+z7FYjwPINzPccBTZlY1jmNLPC9hWZyfqLy8PH768QeO6X8cL7z8KtWqVePxxx7ZtPFFzEs4QVb8/HjJZcaNHUP9+vXZuU3b9e7/kYcfIrNSJocfceRGxxolX885iKdMfngd9et/HC++/BpVq1Xj8ceCvmf5+fksWbyYZ/5vFBdfdgVXDLy4xP2ki425nuKxuVxPsHHnqrRt8/LzWbx4Mc8+P4pLLruCyy9L72sqVkmHsb5r58svJvHaK//jwksuK7I8N3c148d+TI+DDklEiCklQfNwbjJKODeAB/cPfZqgP2WsJsD7ZjYVuBxoEy7fH3gm3PZjYMsSktXi3nb3Ve4+F8gBskooU9p+33D3FTFl33X3XGAqkAm8Fy6fStAvE6BrWEM7FegGtAn3V9fdx4VlnlnP+/8E/AVsXzxIMxtgZl+Z2VclDbpIhEZZWWTPnrXmdXb2bBo2bBTXtlmNG9MoK4t27TsAcOBBB/PTDz8kJM6oZGU1ZvastU1OOdnZNGpU9Pw0ympM9uy1ZbKzZ9OwUSMmf/sNY8d+zKE9unHlwEv58vNJXH3lwDXl3njtVcaPG8stI26PO6FIVVlZjZld5Bxkr3MdZTUuXiY4T8F11HjNddTjoEP4MbyOsrKy6HZgD8yMdu3ak2EZLFiwIAlHlBgbcz2VZXO6nmDjzlVp22ZlZdG98Jpq356MjPS+pmI1yspidszv85z1XDu//PwzQ2+4nrvufYC6desVWffJhAnsuNPObNlgnd5wm58Ub1NXwrnh7gbOAGrELLuPoJawHXA2UFjbV9KPsqyvnqtinudT8tRVpe13WUn7c/cCINfXfvUtACqFNZMPAn3D+B8J47dSYo3rEnX3ke6+u7vvfvqZA+LZZKO1aduOGTP+4p+ZM8nNXc37775Dl67d4tq2QYOGNG68FdP//AOALyZ9RstWrRIZbtIF52c6M2f+Te7q1bz3ztt0LnZ+unTtxptvvIa7M+W7ydSsWYuGDRtx0SWX8eHH43n3w48Zcfud7LHX3twy4nYgGEH7xGOPcM/9D1GtWrUoDm2TKjxP/8z8O7yO1j1Pnbt0460SzlNwHTVecx19HnMdde12IF9+MQmAv6b/SW5uLvXqFf0DmU425noqzeZ2PcHGnavStu3a/UC++Dy4pqZvBtdUrDZt2/H3X0V/n3fuUvSczZr1LwMvuYCht4ygeYtt19nHe+++XSGa09NBhRxYsjHcfb6ZjSJIOh8PF9cB/gmfnxJTfDxwAjDUzLoAc8Na0o1V4n7LWQtQmBzPNbOaQF/gZXdfaGaLzGx/D+40EDtYqfD9Pzaz7YFmwM/lO5RNq1KlSlx5zfWce/YZFOQXcNTRfWjVejteevEFAI7pfyxz587hhP59WbZ0KZaRwXPPPs3/Xn+bmjVrcuU113HNlZeTl5vLNk2bcuPQmyM+ok2rUqVKXH3tIM4ZcCYFBfn0OroPrVtvx6gXnwegX//jOKBTZyaOH8cRh/agatVqDLmp7HNwy7ChrM5dzX/OPA2Adh06cP0NQxJ6LIlUqVIlrrpmEOecfSYF+fkcFZ6nl8LzdEzheZowjp6H9qBqtWpFrpUrr7mea64cSG54HQ0ZegsAvXr34YbrrqFPryOoXLkyQ28enta1dxt7PV058FK++vILFi5cQI9unTjnvAvo3eeYze56go07V+vbFuDoo/sw6Ppr6H1UeE0NS+9rKlbh7/Pz/hP8Pj8y/H3+8qjg93nffsfyyH8fZNHChdxyU3B9ZGZm8tyL/wNgxYoVfP7ZJ1w76MbIjiGZUv2nbptLX49EM7Ol7l4zfJ4F/Anc6u6Dzewo4C6CpHMSsIe7dzGz+gT9ILcFlgMD3H1Ksf12AQa6+xFmNhhY6u63h+umAUe4+3Qzmw7s7u5z17ffErYv/jr2GNasM7ObgGOB6cDfwF/hce1GkFQvB94nqAVtG9aK/pegv2gecKm7jynt/C3P1YUWj4zN5A9Foulqip8uKdmU8gv04YtXjSrJ/fTlLMkt1w+nUa3KSYlTCackhRLO+CjhjI+upvjpkpJNSQln/JKdcM5ZkleuH07DWpWSEqea1EVERETSXYp/uVTCKSIiIpLmUjzfVMIpIiIiku5SvfuMEk4RERGRNJfMSdzLQwmniIiISJpL9RpOTfwuIiIiIgmlGk4RERGRNJfqNZxKOEVERETSnPpwioiIiEhCqYZTRERERBIqxfNNJZwiIiIiaS/FM04lnCIiIiJpTn04RURERCShUr0Pp+bhFBEREZGEUg2niIiISJpL8QpOJZwiIiIiaS/FM04lnCIiIiJpLtUHDZm7Rx2DSCTMbIC7j4w6jlSn8xQ/nav46DzFT+cqPjpPqU+DhqQiGxB1AGlC5yl+Olfx0XmKn85VfHSeUpwSThERERFJKCWcIiIiIpJQSjilIlN/n/joPMVP5yo+Ok/x07mKj85TitOgIRERERFJKNVwioiIiEhCKeEUERERkYRSwikiIiIiCaWEU0RkEzCzembWPuo4UpmZ1TKzmlHHIZsHM6tiZm3DR+Wo45HSKeEUkXVY4EQzGxS+bmZme0YdV6oxs7FmVtvM6gPfAU+Y2Z1Rx5VqzKydmX0LTAN+MLOvzaxt1HGlIjOrbmbXm9kj4evtzOyIqONKNWbWBfgVeAB4EPjFzDpFGZOUTgmnVChmtreZfWlmS81stZnlm9niqONKQQ8C+wDHha+XEPxil6LquPtioDfwhLvvBhwYcUyp6GHgUndv7u7NgMvQNDbr8wSwiuDzBzATuCm6cFLWHcBB7t7Z3TsBBwN3RRyTlEIJp1Q09xMkUb8C1YAzgfsijSg17eXu5wErAdx9AVAl2pBSUiUz2wroB7wVdTAprIa7jyl84e5jgRrRhZPSWrn7rUAugLuvACzakFJSZXf/ufCFu/8CqFk9hVWKOgCRZHP338ws093zCZpAP406phSUa2aZgAOYWUOgINqQUtIQ4H3gE3f/0sxaEnyZkaL+MLPrgWfC1ycCf0YYTypbbWbVWPvZa0VQ4ylFfWVmj7H2mjoB+DrCeKQMmvhdKhQzG0/Q5PkoMBuYBZzq7h0iDSzFmNkJQH9gV+ApoC9wvbuPijQwSUtmVg+4EdifoLZuPDA4rDmXGGbWA7gO2Bn4ANiP4HfU2CjjSjVmtgVwHkWvqQfdXcl5ilLCKRWKmTUHsgmahy8B6hD8kvot0sBSkJntCHQn+GU+2t1/jDiklGNm2wMPAVnu3jYcpX6ku6vPXQnMrA5Q4O5Loo4lFZlZBsGXu9HA3gSfvUnuPjfSwEQ2ASWcUqGYWQ1ghbsXhK8zgS3cfXm0kaUWM3vG3U8qa1lFZ2bjgMuBh919l3DZNHfXCOwYZrYH8DhQK1y0CDjd3dUEWoyZjQ8HwUgJzGwqYXeDkri7piZLUerDKRXNaIIm9aXh62oEzVb7RhZRamoT+yJMzHeLKJZUVt3dvzArMqYjL6pgUthjwLnuPgHAzPYnGI2t5GBdH5rZQOBFYFnhQnefH11IKaVwiqjzwv9j+3Cq4iCFKeGUiqaquxcmm7j7UjOrHmVAqcTMrgauAaqF00UVZlKr0TQ2JZkbDuooHODRl6BfsBS1pDDZBHD3iWamZvWSnR7+f17MMgdaRhBLynH3vwDMbD933y9m1VVm9gnBQD5JQUo4paJZZma7uvs3AGa2G7Ai4phShrvfAtxiZre4+9VRx5MGziNIxHc0s38IRl6fGG1IKekLM3sYeJ4geeoPjDWzXQEKP48C7r5t1DGkiRpmtr+7TwQws33RVFspTX04pUIJ+5K9APwbLtoK6K++ZOsys22A5sR8MXX38dFFlLrCvsEZGgxTMjMbU8pqd/duSQsmxZlZVeBcgtHXDkwA/uvuKyMNLMWElQWPEwz8BFhI0C9YX15SlBJOqXDCe+7uQNBc/JO750YcUsoxs+HAscAPQH642N39yOiiSj1mlg/cBlzt4S9TM/vG3XeNNjJJV2Y2iuDOXs+Gi44D6rn7MdFFlbrMrDZBLrMo6likdGpSl4poD6AFwfW/i5nh7k9HG1LKORrYQXPalel7gju2fWBm/cOBHborTDFmVhc4mbWfOwDc/cKIQkplOxSbF3iMmX0XWTQpzMwOJxjgWLVw4J67qw9nilLCKRWKmT0DtAImE1NzByjhLOoPgtvEKeEsXZ67X2Fm/YAJZnYypUzZUoG9A0wCpqI7VpXlWzPb290nAZjZXsAnEceUcszsv0B1oCvBjTz6Al9EGpSUSk3qUqGY2Y/Azq4Lv1Rm9j+gA8E0UmuSTtVIFWVm38bMv9mGYFBMM3evG2lgKUbdDOIX/o7aAZgRLmoG/EiQqLvmmQyY2RR3bx/zf03gFXc/KOrYpGSq4ZSKZhrQGE1dU5Y3woeU7szCJ+7+fTi/ZK/owklZz5jZWcBbFP0Co7kl13VI1AGkicLZRZab2dbAPEAj/FOYEk6paBoAP5jZFxT9w6fBMDHc/Skzq0ZQW/dz1PGksB/N7HqC83QWkAUoiVrXaoLBVdeytsuB5pYsgbv/Fd57vilF+7tq9HVRb4V9g28DviG4nh6NNCIplZrUpUIxs84lLXf3ccmOJZWZWU/gdqCKu29rZh2BIUrMizKzF4GvgZPDe6lXAz5z947RRpZazOx3YC/dE7xsZjYUOBX4nZjkXFNHrZ+ZbUFwUw+NVE9hquGUCkWJZdwGA3sCYwHcfbKZqblqXa3cvb+ZHQfg7ius2H0uBQhG8+u2g/HpR3BdrY46kFQW3m73cGJmPghnHLkzyrhk/ZRwSoUS3k6vsNagCsFI7GXuXju6qFJSnrsvKpY7qTlkXavDWs3COThboZH9JckHJocTwGsQWummAXWBnIjjSHVvAivRzAdpQwmnVCjuXiv2tZn1IqjJk6KmmdnxQKaZbQdcCHwacUypaDDwHtDUzJ4D9iNoDpWiXgsfUrZbCKZGmob6mZemiUbspxf14ZQKz8wmufveUceRSsysOsEAj4MIJjJ/Hxiq2+uty8y2BPYmOE+T1E+xZBqEFh8z+x54mGI1d+oOVJSZjQBGu/sHUcci8VHCKRWKmfWOeZkB7A50dvd9IgpJ0piZvUEw9+Yb7r4s6nhSlQahxc/Mxrl7iYMbZS0zO5rg9p8ZQC7BFz5X96jUpYRTKhQzeyLmZR4wHXjE3dVfKoaZ7Q5cw7q3IlQTVoxw1oP+BIMXvgBeBN5STXBRZvY10A0YGzNR/lR3bxdtZKnHzO4kaEp/g6JN6poWKYaZ/UEw5+1U3cgjPagPp1Qo7n5a1DGkieeAy1GH/FKFzZzjwhGz3YCzgMcB1bIUpUFo8dsl/D+2m48TXF+y1q/ANCWb6UMJp1QIZjaolNXu7kOTFkx6mOPuutNQHMK+iT0Jajp3BZ6KNqKUpEFocXL3rlHHkCZmAWPN7F2K1gRrWqQUpSZ1qRDM7LISFtcAzgC2dPeaSQ4ppZlZd+A41r2X+iuRBZWCwonf9yIYqT6KoMlYNcLFFBuEBmsHoWkKqWLMLAu4Gdja3Q81s52Bfdz9sYhDSylmdkNJy939xmTHIvFRwikVjpnVAi4iSDZHAXeoD2dRZvYssCPBhN2FCZS7++nRRZV6zOwQ4EN3z486llRmZse4+0tlLRMIa+yeAK519w5mVgn4Vv1d1wq7sDzl7idGHYvETwmnVBhmVh+4FDiBoNnzHndfEG1UqUkDOmRTMrNv3H3XspYJmNmX7r6HmX0bM8Bqsm6XWpSZvQ/01B2Z0of6cEqFYGa3Ab2BkUA7d18acUipbpKZ7ezuP0QdiKQvMzsUOAzYxszujVlVm2CWCFnXsnBu18K7V+0N6B7h65oOfBJOTbZmSjL14UxdquGUCsHMCgj6IuZRdHSs5m4rgZn9CLQC/iQ4b4XnSdMiSdzMrAPQERgCxA7cWwKMUQvDusxsV+A+oC3BbS4bAn3dfUqkgaUY9eFMP0o4RWQdZta8pOXu/leyY0llZrYfMNndl5nZiQSj1O/ReSrKzCq7e27UcaQyM9sD+NvdZ4f9Ns8G+gA/AIPcfX6kAaaosE++q9Uq9WVEHYCIpB53/ytMmlYQ1AgXPqSoh4DlYU3eFcBfwNPRhpR6lGzG5WGgsD/ivgSj+h8AFhB0BZIYZtbWzL4lqAX+3sy+NrM2Uccl66eEU0TWYWZHmtmvBE3q4wj6S70baVCpKS+cePoogprNe4BaEcck6SkzphazPzDS3f/n7tcDrSOMK1WNBC519+bu3hy4DHgk4pikFEo4RaQkQwnudPKLu28LdAc+iTaklLTEzK4GTgTeDqdrqRxxTCnLzGqZmea8LVlm2JQOweft45h1GuC7rhruPqbwhbuPJZhbWVKUEk4RKUmuu88DMswsI/zF3jHimFJRf4JBVWe4+2xgG+C2aENKPWbWLqb584ew+bNt1HGlmOcJbpP6OkFXlgkAZtYajVIvyR9mdr2ZtQgf1xG0yEiK0qAhEVmHmX0E9AJuARoAOcAe7r5vlHGlOjPbHzjO3c+LOpZUYmafEkxkPiZ83QW4WddTUeEUSFsBH7j7snDZ9kBNd/8m0uBSjJnVA24E9g8XjQdu1MwHqUsJp4isw8xqENSyZBBMlF8HeC6s9ZQYZtYROB7oR1DD8j93vz/SoFKMmX3n7h3KWiZSFjN7xt1PMrOLwj7TkibUL0REigj7Ib7u7gcS3NbyqYhDSjlhrdOxBPebnwe8SPAFvmukgaWuP8zseuCZ8PWJqPlTyme3cNq2083saYI5gtfQ9FGpSwmniBTh7vlmttzM6ri7+o6V7CeCPnY93f03ADO7JNqQUtrpBM2frxAkCOOB0yKNSNLVf4H3gJbA1xRNOD1cLilITeoisg4zG0UwSv1Dit427sLIgkohZnY0QQ3nvgR//F4AHg1H9ItIgpnZQ+5+TtRxSPyUcIrIOszslJKWu7ua12OEfV17ETStdyPofvCqu38QZVypwszepJQbBrj7kUkMRzYzYfefLGJaa919RnQRSWmUcIqIbAJmVh84Bujv7t2ijicVmFnn8GlvoDHwbPj6OGC6u18TSWCS9szsfGAwkE3Q1xyCW1y2jywoKZUSThFZh5ltRzAl0s5A1cLl7q7+UbLBzGy8u3cqa5lIvMzsN2AvzZyRPjTxu4iU5AmC+4TnAV0J7g/+TKlbiKxfQzNb82XFzLYFGkYYj6S/v9GE+GlFo9RFpCTV3H20mZm7/wUMNrMJwA1RByZp6RJgrJn9Eb5uAQyILhzZDPxBcE29TXC3LwDc/c7oQpLSKOEUkZKsNLMM4Newr9Q/QKOIY5I05e7vhd00dgwX/eTuq0rbRqQMM8JHlfAhKU59OEVkHWa2B/AjUBcYSnCnoRHu/nmUcYmISHpSwikiZTKzSgSjr5+LOhYRqbjM7G53v3h9U25pqq3UpSZ1EVnDzGoD5wHbAG8QTPx+HjAQ+A5QwikiUSocvHh7pFHIBlMNp4isYWavAwuAz4DuQD2C/lEXufvkCEOTNGZmBpwAtHT3IWbWDGjs7l9EHJqIJIkSThFZw8ymunu78HkmMBdo5u5Loo1M0pmZPUQwOXc3d9/JzOoBH7j7HhGHJiJJoiZ1EYmVW/jE3fPN7E8lm7IJ7OXuu5rZtwDuvsDMNLJYpAJRwikisTqY2eLwuQHVwtdGcNu42tGFJmksN6wxdwAza8ja2xGKlJuZ1SL43bQ06likdEo4RWQNd8+MOgbZLN0LvAo0MrNhQF/g+mhDknRmZu0I7oBWP3hpc4BT3H1atJHJ+qgPp4iIJJyZ7UgwEM2A0e7+Y8QhSRozs0+Ba919TPi6C3Czu+8bZVyyfqrhFBGRhDKzZ9z9JOCnEpaJlEeNwmQTwN3HmlmNKAOS0inhFBGRRGsT+yLsz7lbRLHI5uEPM7uetfNyngj8GWE8UoaMqAMQEZHNk5ldbWZLgPZmtjh8LAFygNcjDk/S2+lAQ+AVgv7BDYHTIo1ISqU+nCIikjBmlgE86u6nRx2LiERHTeoiIpIw7l5gZh2ijkM2L+HUWlcQdNeoWrjc3btFFpSUSk3qIiKSaJPMTHcVko1mZneFT58jGIS2LXAjMB34MqKwJA5qUhcRkYQysx+A7YG/gGWsvZFA+0gDk7RjZh+6ew8z+9rddzOzKYXXkZmNc/fOUccoJVOTuoiIJNqhUQcgm43V4f+Ft+GdZWaHA/8CTaIJSeKhGk4REUkKM2tE0f52MyIMR9KQmVV291wzOwKYADQF7gNqAze6+xuRBijrpYRTREQSysyOBO4AtiaYEqk58KO7tyl1QxHZbKhJXUREEm0osDfwkbvvYmZdgeMijknSkJkNKmW1u/vQpAUjG0Sj1EVEJNFy3X0ekGFmGeEtCTtGHJOkp2UlPADOAK6MKigpm2o4RUQk0RaaWU2CPnfPmVkOkBdxTJKG3P2OwudmVgu4iOAOQy8QdNuQFKU+nCIiklBmVgNYSTAd0glAHeC5sNZTZIOYWX3gUoJr6SngHndfEG1UUhbVcIqISEK5+zIzawzsCcwH3leyKeVhZrcBvYGRQDt3XxpxSBIn1XCKiEhCmdmZwCDgY4Jazs7AEHd/PNLAJO2YWQGwiqBLRmwCU3gzgdqRBCZlUsIpIiIJZWY/A/sW1mqa2ZbAp+6+Q7SRiUiyaJS6iIgk2kxgSczrJcDfEcUiIhFQDaeIiCSUmT0NtANeJ2gGPQr4AvgFwN3vjC46EUkGDRoSEZFE+z18FHo9/L9WBLGISARUwykiIiIiCaU+nCIiIiKSUEo4RURERCShlHCKiEjCmFmmmV0SdRwiEi0lnCIikjDunk8wKl1EKjANGhIRkYQys2EE909/EVhWuNzdv4ksKBFJKiWcIiKSUGY2poTF7u7dkh6MiERCCaeIiIiIJJT6cIqISEKZWR0zu9PMvgofd5hZnajjEpHkUcIpIiKJ9jjB/dP7hY/FwBORRiQiSaUmdRERSSgzm+zuHctaJiKbL9VwiohIoq0ws/0LX5jZfsCKCOMRkSRTDaeIiCSUmXUEniKYGsmA+cCp7v5dlHGJSPIo4RQRkaQws9oA7r446lhEJLmUcIqISEKY2Ynu/qyZXVrSene/M9kxiUg0KkUdgIiIbLZqhP/XijQKEYmcajhFREREJKFUwykiIgllZlWBM4A2QNXC5e5+emRBiUhSaVokERFJtGeAxsDBwDigCcFE8CJSQahJXUREEsrMvnX3Xcxsiru3N7PKwPvu3i3q2EQkOVTDKSIiiZYb/r/QzNoSzMfZIrpwRCTZ1IdTREQSbaSZ1QOuB94AagKDog1JRJJJTeoiIiIiklCq4RQRkYQys7rAyQTN6Gv+7rj7hRGFJCJJpoRTREQS7R1gEjAVKIg4FhGJgJrURUQkoczsG3ffNeo4RCQ6SjhFRCShzOwSYCnwFrCqcLm7z48sKBFJKjWpi4hIoq0GbgOuBQprORxoGVlEIpJUquEUEZGEMrPfgb3cfW7UsYhINDTxu4iIJNr3wPKogxCR6KhJXUREEi0fmGxmYyjah1PTIolUEEo4RUQk0V4LHyJSQakPp4iIiIgklPpwioiIiEhCKeEUERERkYRSwikiIiIiCaVBQyIiklBm1hC4EtgZqFq43N27RRaUiCSVajhFRCTRngN+BLYFbgSmA19GGZCIJJdGqYuISEKZ2dfuvpuZTXH39uGyce7eOerYRCQ51KQuIiKJlhv+P8vMDgf+BZpEGI+IJJkSThERSbSbzKwOcBlwH1AbuCTakEQkmdSkLiIiIiIJpRpOERFJCDMbVMpqd/ehSQtGRCKlGk4REUkIM7ushMU1gDOALd29ZpJDEpGIKOEUEZGEM7NawEUEyeYo4A53z4k2KhFJFjWpi4hIwphZfeBS4ATgKWBXd18QbVQikmxKOEVEJCHM7DagNzASaOfuSyMOSUQioiZ1ERFJCDMrAFYBeUDsHxsjGDRUO5LARCTplHCKiIiISELpXuoiIiIiklBKOEVEREQkoZRwioiIiEhCKeEUEUkBZtbRzA6LeX2kmV21nrKRjPY2s0fNbOcyyjxpZn2TFZOIpAdNiyQikho6ArsD7wC4+xvAG1EGVJy7nxl1DCKSnlTDKSKykcyshpm9bWbfmdk0M+tvZruZ2Tgz+9rM3jezrcKyY81shJl9YWa/mNkBZlYFGAL0N7PJ4fanmtn94TbbmtlnZvalmQ2NeV8zs9vC95xqZv1LiXErMxsf7n+amR0QLj8u3HaamY0Ilx0ZlptsZj+b2Z8xse8ePl9qZsPCY55kZlkxb9fJzD41sz8Kazs3JFYR2fwo4RQR2XiHAP+6ewd3bwu8B9wH9HX33YDHgWEx5Su5+57AxcAN7r4aGAS86O4d3f3FYvu/B3jI3fcAZscs701QM9oBOBC4rTCxLcHxwPvuXlh+spltDYwAuoX72cPMern7G2EcHYHvgNtL2F8NYJK7dwDGA2fFrNsK2B84AhhejlhFZDOjhFNEZONNBQ4May4PAJoCbYEPzWwycB3QJKb8K+H/XwMt4tj/fsDz4fNnYpbvDzzv7vnung2MA/ZYzz6+BE4zs8EEd/1ZEpYd6+5z3D0PeA7oVLiBmV0BrHD3B0rY32rgrfUcx2vuXuDuPwCFNZ8bEquIbGbUh1NEZCO5+y9mthtwGHAL8CHwvbvvs55NVoX/5xP/7+GS7tJhGxDjeDPrBBwOPBPednLx+sqbWXfgGGIS0GJyfe2dQ4ofx6qY51bsfxGpgFTDKSKykcKm6eXu/ixB8/NeQEMz2ydcX9nM2pSxmyVArfWs+wQ4Nnx+Qszy8QT9PjPNrCFBcvjFemJsDuS4+yPAY8CuwOdAZzNrYGaZwHHAuLDsg0A/d19RRtzxijtWEdn8qIZTRGTjtSPok1gA5ALnENw//F4zq0Pwu/Zu4PtS9jEGuCpsgr+l2LqLgP8zs4uA/8UsfxXYh6CfpQNXuPtsStYFuNzMcoGlwMnuPsvMrg7f24B33P11M7sB2BJ41cwg6J962Hr2G68NiVVENjO6l7qIiIiIJJSa1EVEREQkodSkLiKyGTGzdhQdyQ6wyt33iiIeERFQk7qIiIiIJJia1EVEREQkoZRwioiIiEhCKeEUERERkYRSwikiIiIiCaWEU0REREQS6v8BdoXPpkgGhmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando a tabela cruzada com o percentual\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "sns.heatmap(data = crosstab, annot=True, cmap='Blues')\n",
    "sns.despine(bottom = True, left=True)\n",
    "plt.title('PREOCUPAÇÃO DO RESP x SE SENTINDO SOZINHO (%)')\n",
    "\n",
    "ax = ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### SE SENTINDO SOZINHO x QT. AMIGOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dicionario para \"traduzir\" as respostas\n",
    "amigos = {1 : 'Nenhum',\n",
    "         2 : '1 amigo',\n",
    "         3 : '2 amigos',\n",
    "         4: '3 ou mais',\n",
    "         99: 'Não informado'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>sentido_sozinho</th>\n",
       "      <th>Nunca</th>\n",
       "      <th>Raramente</th>\n",
       "      <th>As vezes</th>\n",
       "      <th>Na maior parte do tempo</th>\n",
       "      <th>Sempre</th>\n",
       "      <th>Não informado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>qt_amigos</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Nenhum</th>\n",
       "      <td>1.298623</td>\n",
       "      <td>0.567770</td>\n",
       "      <td>0.845615</td>\n",
       "      <td>0.531529</td>\n",
       "      <td>0.628171</td>\n",
       "      <td>0.006040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1 amigo</th>\n",
       "      <td>1.510027</td>\n",
       "      <td>1.165741</td>\n",
       "      <td>1.733511</td>\n",
       "      <td>0.881856</td>\n",
       "      <td>0.664412</td>\n",
       "      <td>0.012080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 amigos</th>\n",
       "      <td>2.778449</td>\n",
       "      <td>2.621406</td>\n",
       "      <td>3.841508</td>\n",
       "      <td>2.029476</td>\n",
       "      <td>1.123460</td>\n",
       "      <td>0.024160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 ou mais</th>\n",
       "      <td>29.415318</td>\n",
       "      <td>17.274704</td>\n",
       "      <td>21.218893</td>\n",
       "      <td>6.215269</td>\n",
       "      <td>3.140855</td>\n",
       "      <td>0.066441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Não informado</th>\n",
       "      <td>0.060401</td>\n",
       "      <td>0.018120</td>\n",
       "      <td>0.024160</td>\n",
       "      <td>0.012080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.289925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sentido_sozinho      Nunca  Raramente   As vezes  Na maior parte do tempo  \\\n",
       "qt_amigos                                                                   \n",
       "Nenhum            1.298623   0.567770   0.845615                 0.531529   \n",
       "1 amigo           1.510027   1.165741   1.733511                 0.881856   \n",
       "2 amigos          2.778449   2.621406   3.841508                 2.029476   \n",
       "3 ou mais        29.415318  17.274704  21.218893                 6.215269   \n",
       "Não informado     0.060401   0.018120   0.024160                 0.012080   \n",
       "\n",
       "sentido_sozinho    Sempre  Não informado  \n",
       "qt_amigos                                 \n",
       "Nenhum           0.628171       0.006040  \n",
       "1 amigo          0.664412       0.012080  \n",
       "2 amigos         1.123460       0.024160  \n",
       "3 ou mais        3.140855       0.066441  \n",
       "Não informado    0.000000       0.289925  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando uma tabela cruzada\n",
    "crosstab = pd.crosstab(df['qt_amigos'], df['sentido_sozinho'], normalize=True) * 100\n",
    "\n",
    "# renomeando indices e colunas\n",
    "crosstab.rename(index = amigos, inplace= True)\n",
    "crosstab.rename(columns = trad, inplace = True)\n",
    "\n",
    "# Visualizando tabela\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando a tabela cruzada com o percentual\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "sns.heatmap(data = crosstab, annot=True, cmap='Blues')\n",
    "sns.despine(bottom = True, left=True)\n",
    "plt.title('QT AMIGOS x SE SENTINDO SOZINHO (%)')\n",
    "\n",
    "ax = ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  SENTIMENTO DO CORPO x BULLYING PELA APARÊNCIA DO CORPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando somente os alunos que sofreram bullying devido a aparência do corpo\n",
    "bullying_corpo = df[df['motivo_bul'] == 4]\n",
    "não_sofreu_bullying = df[df['ja_sofreu_bul'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpo = {1 : 'Muito Satisfeito',\n",
    "         2 : 'Satisfeito',\n",
    "         3 : 'Indiferente',\n",
    "         4 : 'Insatisfeito',\n",
    "         5 : 'Muito insatisfeito',\n",
    "         99 : 'Não informado'}\n",
    "\n",
    "\n",
    "acao_corpo = {1: 'Não fazendo nada',\n",
    "             2 : 'Tentando Perder peso',\n",
    "             3 : 'Tentando Ganhar Peso',\n",
    "             4 : 'Tentando manter o mesmo peso',\n",
    "             5 : 'Não informado'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>fazendo_corpo</th>\n",
       "      <th>Não fazendo nada</th>\n",
       "      <th>Tentando Perder peso</th>\n",
       "      <th>Tentando Ganhar Peso</th>\n",
       "      <th>Tentando manter o mesmo peso</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentimento_corpo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Muito Satisfeito</th>\n",
       "      <td>4.864865</td>\n",
       "      <td>4.774775</td>\n",
       "      <td>2.072072</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.09009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satisfeito</th>\n",
       "      <td>8.018018</td>\n",
       "      <td>9.189189</td>\n",
       "      <td>6.306306</td>\n",
       "      <td>1.981982</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiferente</th>\n",
       "      <td>4.324324</td>\n",
       "      <td>8.468468</td>\n",
       "      <td>3.243243</td>\n",
       "      <td>1.441441</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insatisfeito</th>\n",
       "      <td>4.864865</td>\n",
       "      <td>17.567568</td>\n",
       "      <td>6.486486</td>\n",
       "      <td>0.720721</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muito insatisfeito</th>\n",
       "      <td>2.882883</td>\n",
       "      <td>9.549550</td>\n",
       "      <td>1.171171</td>\n",
       "      <td>0.720721</td>\n",
       "      <td>0.09009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Não informado</th>\n",
       "      <td>0.090090</td>\n",
       "      <td>0.090090</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fazendo_corpo       Não fazendo nada  Tentando Perder peso  \\\n",
       "sentimento_corpo                                             \n",
       "Muito Satisfeito            4.864865              4.774775   \n",
       "Satisfeito                  8.018018              9.189189   \n",
       "Indiferente                 4.324324              8.468468   \n",
       "Insatisfeito                4.864865             17.567568   \n",
       "Muito insatisfeito          2.882883              9.549550   \n",
       "Não informado               0.090090              0.090090   \n",
       "\n",
       "fazendo_corpo       Tentando Ganhar Peso  Tentando manter o mesmo peso  \\\n",
       "sentimento_corpo                                                         \n",
       "Muito Satisfeito                2.072072                      0.810811   \n",
       "Satisfeito                      6.306306                      1.981982   \n",
       "Indiferente                     3.243243                      1.441441   \n",
       "Insatisfeito                    6.486486                      0.720721   \n",
       "Muito insatisfeito              1.171171                      0.720721   \n",
       "Não informado                   0.000000                      0.000000   \n",
       "\n",
       "fazendo_corpo            99  \n",
       "sentimento_corpo             \n",
       "Muito Satisfeito    0.09009  \n",
       "Satisfeito          0.00000  \n",
       "Indiferente         0.00000  \n",
       "Insatisfeito        0.00000  \n",
       "Muito insatisfeito  0.09009  \n",
       "Não informado       0.18018  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando uma tabela cruzada\n",
    "crosstab = pd.crosstab(bullying_corpo['sentimento_corpo'], df['fazendo_corpo'], normalize=True) * 100\n",
    "\n",
    "# renomeando indices e colunas\n",
    "crosstab.rename(index = corpo, inplace= True)\n",
    "crosstab.rename(columns = acao_corpo, inplace = True)\n",
    "\n",
    "# Visualizando tabela\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando a tabela cruzada com o percentual\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "sns.heatmap(data = crosstab, annot=True, cmap='Blues')\n",
    "sns.despine(bottom = True, left=True)\n",
    "plt.title('SATISFAÇÃO DO CORPO EM ALUNOS QUE SOFRERAM BULLYING DEVIDO A APARÊNCIA DO CORPO')\n",
    "\n",
    "ax = ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  ALUNOS QUE NÃO SOFRERAM BULLYING\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>fazendo_corpo</th>\n",
       "      <th>Não fazendo nada</th>\n",
       "      <th>Tentando Perder peso</th>\n",
       "      <th>Tentando Ganhar Peso</th>\n",
       "      <th>Tentando manter o mesmo peso</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentimento_corpo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Muito Satisfeito</th>\n",
       "      <td>14.649010</td>\n",
       "      <td>4.675964</td>\n",
       "      <td>4.195476</td>\n",
       "      <td>7.465135</td>\n",
       "      <td>0.140630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Satisfeito</th>\n",
       "      <td>17.965546</td>\n",
       "      <td>10.230868</td>\n",
       "      <td>7.746396</td>\n",
       "      <td>9.797258</td>\n",
       "      <td>0.058596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiferente</th>\n",
       "      <td>2.906364</td>\n",
       "      <td>3.199344</td>\n",
       "      <td>1.687566</td>\n",
       "      <td>1.078167</td>\n",
       "      <td>0.011719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insatisfeito</th>\n",
       "      <td>2.695418</td>\n",
       "      <td>5.472870</td>\n",
       "      <td>2.261807</td>\n",
       "      <td>0.609399</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muito insatisfeito</th>\n",
       "      <td>0.855502</td>\n",
       "      <td>0.878941</td>\n",
       "      <td>0.292980</td>\n",
       "      <td>0.316419</td>\n",
       "      <td>0.023438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Não informado</th>\n",
       "      <td>0.082034</td>\n",
       "      <td>0.093754</td>\n",
       "      <td>0.070315</td>\n",
       "      <td>0.070315</td>\n",
       "      <td>0.468768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fazendo_corpo       Não fazendo nada  Tentando Perder peso  \\\n",
       "sentimento_corpo                                             \n",
       "Muito Satisfeito           14.649010              4.675964   \n",
       "Satisfeito                 17.965546             10.230868   \n",
       "Indiferente                 2.906364              3.199344   \n",
       "Insatisfeito                2.695418              5.472870   \n",
       "Muito insatisfeito          0.855502              0.878941   \n",
       "Não informado               0.082034              0.093754   \n",
       "\n",
       "fazendo_corpo       Tentando Ganhar Peso  Tentando manter o mesmo peso  \\\n",
       "sentimento_corpo                                                         \n",
       "Muito Satisfeito                4.195476                      7.465135   \n",
       "Satisfeito                      7.746396                      9.797258   \n",
       "Indiferente                     1.687566                      1.078167   \n",
       "Insatisfeito                    2.261807                      0.609399   \n",
       "Muito insatisfeito              0.292980                      0.316419   \n",
       "Não informado                   0.070315                      0.070315   \n",
       "\n",
       "fazendo_corpo             99  \n",
       "sentimento_corpo              \n",
       "Muito Satisfeito    0.140630  \n",
       "Satisfeito          0.058596  \n",
       "Indiferente         0.011719  \n",
       "Insatisfeito        0.000000  \n",
       "Muito insatisfeito  0.023438  \n",
       "Não informado       0.468768  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando uma tabela cruzada\n",
    "crosstab = pd.crosstab(não_sofreu_bullying['sentimento_corpo'], df['fazendo_corpo'], normalize=True) * 100\n",
    "\n",
    "# renomeando indices e colunas\n",
    "crosstab.rename(index = corpo, inplace= True)\n",
    "crosstab.rename(columns = acao_corpo, inplace = True)\n",
    "\n",
    "# Visualizando tabela\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando a tabela cruzada com o percentual\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "sns.heatmap(data = crosstab, annot=True, cmap='Blues')\n",
    "sns.despine(bottom = True, left=True)\n",
    "plt.title('SATISFAÇÃO DO CORPO EM ALUNOS QUE NAO SOFREM BULLYING')\n",
    "\n",
    "ax = ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "***\n",
    "\n",
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONCLUSÕES\n",
    "\n",
    "**1**. Quase 90% dos alunos afirmam haver regras/orientações/política, etc., nas escolas proibindo bullying, entretanto, o percentual de alunos que alegam já ter sofrido bullying difere muito poucos das escolas que não possuem tais políticas. **O quanto as escolas estão atuando de forma efetiva?**\n",
    "\n",
    "**2**. Aparência do corpo e do rosto são os dois principais motivos categorizados pelos alunos que sofreram bullying (o maior percentual de alunos não responderam, seguido pela classe \"outros\")\n",
    "\n",
    "**3**. O correlação entre as variáveis relacionadas a \"dificuldade para dormir\" x \"se sentindo sozinho\" é positiva com coeficiente de 0.46. Dentro das variáveis analisadas, foram as que apresentaram maior coeficiente de correlação.\n",
    "\n",
    "**4**. Alunos que desconhecem o termo \"Bullying\" é quase o dobro em escolas que não possuem políticas sobre o assunto em relação às que possuem.\n",
    "\n",
    "**5.** Quando perguntado aos alunos se já praticaram Bullying, a quantidade de respostas negativas é superior a 80%, inconsistente com o percentual de alunos que afirma que já sofreram Bullying (mais de 40%)\n",
    "\n",
    "**6.** Preocupação dos pais x o sentimento dos alunos em se sentirem sozinhos: \n",
    "\n",
    "**6.1** 15% dos alunos que responderam que os responsáveis **sempre** se preocupam com seus sentimentos e problemas também responderam que **nunca** se sentiram sozinhos, enquanto que aqueles que responderam que os pais/responsáveis **nunca** se preocupam são 4,4%.\n",
    "\n",
    "**6.2** O inverso também é verdadeiro. Alunos que se sentem mais sozinhos também responderam em maior quantidade que os pais/responsáveis nunca ou raramente se preocupam com seus problemas.\n",
    "\n",
    "**7.** Sentimento dos alunos em se sentirem sozinhos x Quantidade de amigos\n",
    "\n",
    "**7.1** 30% dos alunos que responderam ter 3 ou mais amigos também responderam que nunca se sentiram sozinhos, entretanto, aqueles que responderam sempre se sentirem sozinhos também tem maior quantidade de respostas com 3 ou mais amigos, ou seja, a quantidade de amigos não está relacionada a este sentimento dos alunos.\n",
    "\n",
    "**8.** Alunos que sofreram bullying devido a aparência do corpo x sentimento em relação ao corpo\n",
    "\n",
    "**8.1** Quando filtrado os alunos que responderam que já sofreram bullying devido a aparência do corpo, 18% se diz insatisfeito e que estão tentando emagrecer, enquanto os alunos que responderam que nunca sofreram bullying representa, nestas mesmas condições 5,5%.\n",
    "\n",
    "**8.2** Alunos que responderam não ter sofrido bullying estão mais concentrados em respostas \"muito satisfeito e satisfeito\" e não estão fazendo nada em relação ao corpo, enquanto os alunos que responderam que sofrem bullying devido a aparência do corpo se concentram nas classes \"insatisfeito e muito insatisfeito\" além de que, mesmo aqueles que responderam estarem satisfeitos, responderam com maior frequência que estão tentando perder peso."
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