Curso_de_Estatística_Parte_1.ipynb

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   "source": [
    "***\n",
    "# <font color=green size=10>CURSO DE ESTATÍSTICA - PARTE 1</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cVgsZ_E3jQKg"
   },
   "source": [
    "# <font color=green>1 CONHECENDO OS DADOS</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TDjqYYNojQKh"
   },
   "source": [
    "## <font color=green>1.1 Dataset do projeto</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Pesquisa Nacional por Amostra de Domicílios - 2015\n",
    "\n",
    "A <b>Pesquisa Nacional por Amostra de Domicílios - PNAD</b> investiga anualmente, de forma permanente, características gerais da população, de educação, trabalho, rendimento e habitação e outras, com periodicidade variável, de acordo com as necessidades de informação para o país, como as características sobre migração, fecundidade, nupcialidade, saúde, segurança alimentar, entre outros temas. O levantamento dessas estatísticas constitui, ao longo dos 49 anos de realização da pesquisa, um importante instrumento para formulação, validação e avaliação de políticas orientadas para o desenvolvimento socioeconômico e a melhoria das condições de vida no Brasil."
   ]
  },
  {
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   },
   "source": [
    "### Fonte dos Dados\n",
    "\n",
    "https://ww2.ibge.gov.br/home/estatistica/populacao/trabalhoerendimento/pnad2015/microdados.shtm"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Variáveis utilizadas\n",
    "\n",
    "> ### Renda\n",
    "> ***\n",
    "\n",
    "Rendimento mensal do trabalho principal para pessoas de 10 anos ou mais de idade.\n",
    "\n",
    "> ### Idade\n",
    "> ***\n",
    "\n",
    "Idade do morador na data de referência em anos.\n",
    "\n",
    "> ### Altura (elaboração própria)\n",
    "> ***\n",
    "\n",
    "Altura do morador em metros.\n",
    "\n",
    "> ### UF\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|11|Rondônia|\n",
    "|12|Acre|\n",
    "|13|Amazonas|\n",
    "|14|Roraima|\n",
    "|15|Pará|\n",
    "|16|Amapá|\n",
    "|17|Tocantins|\n",
    "|21|Maranhão|\n",
    "|22|Piauí|\n",
    "|23|Ceará|\n",
    "|24|Rio Grande do Norte|\n",
    "|25|Paraíba|\n",
    "|26|Pernambuco|\n",
    "|27|Alagoas|\n",
    "|28|Sergipe|\n",
    "|29|Bahia|\n",
    "|31|Minas Gerais|\n",
    "|32|Espírito Santo|\n",
    "|33|Rio de Janeiro|\n",
    "|35|São Paulo|\n",
    "|41|Paraná|\n",
    "|42|Santa Catarina|\n",
    "|43|Rio Grande do Sul|\n",
    "|50|Mato Grosso do Sul|\n",
    "|51|Mato Grosso|\n",
    "|52|Goiás|\n",
    "|53|Distrito Federal|\n",
    "\n",
    "> ### Sexo\t\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|0|Masculino|\n",
    "|1|Feminino|\n",
    "\n",
    "> ### Anos de Estudo\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|1|Sem instrução e menos de 1 ano|\n",
    "|2|1 ano|\n",
    "|3|2 anos|\n",
    "|4|3 anos|\n",
    "|5|4 anos|\n",
    "|6|5 anos|\n",
    "|7|6 anos|\n",
    "|8|7 anos|\n",
    "|9|8 anos|\n",
    "|10|9 anos|\n",
    "|11|10 anos|\n",
    "|12|11 anos|\n",
    "|13|12 anos|\n",
    "|14|13 anos|\n",
    "|15|14 anos|\n",
    "|16|15 anos ou mais|\n",
    "|17|Não determinados| \n",
    "||Não aplicável|\n",
    "\n",
    "> ### Cor\n",
    "> ***\n",
    "\n",
    "|Código|Descrição|\n",
    "|---|---|\n",
    "|0|Indígena|\n",
    "|2|Branca|\n",
    "|4|Preta|\n",
    "|6|Amarela|\n",
    "|8|Parda|\n",
    "|9|Sem declaração|"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "#### <font color='red'>Observação</font>\n",
    "***\n",
    "> Os seguintes tratamentos foram realizados nos dados originais:\n",
    "> 1. Foram eliminados os registros onde a <b>Renda</b> era inválida (999 999 999 999);\n",
    "> 2. Foram eliminados os registros onde a <b>Renda</b> era missing;\n",
    "> 3. Foram considerados somente os registros das <b>Pessoas de Referência</b> de cada domicílio (responsável pelo domicílio)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8Rw70V6ojQKk"
   },
   "source": [
    "### Importando pandas e lendo o dataset do projeto\n",
    "\n",
    "https://pandas.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8QgOpE-wjQKl"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ufRZ7TLwjQKo"
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   "outputs": [],
   "source": [
    "dados = pd.read_csv(\"dados.csv\")"
   ]
  },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UF</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Cor</th>\n",
       "      <th>Anos de Estudo</th>\n",
       "      <th>Renda</th>\n",
       "      <th>Altura</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>800</td>\n",
       "      <td>1.603808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1150</td>\n",
       "      <td>1.739790</td>\n",
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       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>880</td>\n",
       "      <td>1.760444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3500</td>\n",
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       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>150</td>\n",
       "      <td>1.690631</td>\n",
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      "text/plain": [
       "   UF  Sexo  Idade  Cor  Anos de Estudo  Renda    Altura\n",
       "0  11     0     23    8              12    800  1.603808\n",
       "1  11     1     23    2              12   1150  1.739790\n",
       "2  11     1     35    8              15    880  1.760444\n",
       "3  11     0     46    2               6   3500  1.783158\n",
       "4  11     1     47    8               9    150  1.690631"
      ]
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   ],
   "source": [
    "dados.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "E3Mx0tVqjQKs"
   },
   "source": [
    "## <font color=green>1.2 Tipos de dados</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oB4TCBFojQKt"
   },
   "source": [
    "### Variáveis qualitativas ordinais\n",
    "\n",
    "► Variáveis que podem ser ordenadas ou hierarquizardas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "NtQZNGNbjQKt",
    "outputId": "f75cd222-87e0-487c-b3cd-641c701dd6b1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(dados['Anos de Estudo'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i2mCIMyojQKv"
   },
   "source": [
    "### Variáveis qualitativas nominais\n",
    "\n",
    "► Variáveis que não podem ser ordenadas ou hierarquizardas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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     "data": {
      "text/plain": [
       "[11,\n",
       " 12,\n",
       " 13,\n",
       " 14,\n",
       " 15,\n",
       " 16,\n",
       " 17,\n",
       " 21,\n",
       " 22,\n",
       " 23,\n",
       " 24,\n",
       " 25,\n",
       " 26,\n",
       " 27,\n",
       " 28,\n",
       " 29,\n",
       " 31,\n",
       " 32,\n",
       " 33,\n",
       " 35,\n",
       " 41,\n",
       " 42,\n",
       " 43,\n",
       " 50,\n",
       " 51,\n",
       " 52,\n",
       " 53]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(dados['UF'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
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       "[0, 1]"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "sorted(dados['Sexo'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "6VwGyqxMjQK1",
    "outputId": "6f0a238e-e94a-4691-eff8-239e64db9913"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8]"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(dados['Cor'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FQhi81LsjQK5"
   },
   "source": [
    "### Variáveis quantitativas discretas\n",
    "\n",
    "► Variáveis que representam uma contagem onde os valores possíveis formam um conjunto finito ou enumerável."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "sr4JXeZkjQK5",
    "outputId": "eea4cb2d-c154-4ad1-b03f-7f5cdf06a285"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "De 13 até 99 anos\n"
     ]
    }
   ],
   "source": [
    "print('De %s até %s anos' % (dados.Idade.min(), dados.Idade.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7mMXLTukjQK-"
   },
   "source": [
    "#### <font color='red'>Observação</font>\n",
    "***\n",
    "> A variável idade pode ser classificada de três formas distintas:\n",
    "> 1. <b>QUANTITATIVA DISCRETA</b> - quando representa anos completos (números inteiros);\n",
    "> 2. <b>QUANTITATIVA CONTÍNUA</b> - quando representa a idade exata, sendo representado por frações de anos; e\n",
    "> 3. <b>QUALITATIVA ORDINAL</b> - quando representa faixas de idade."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0L2OkaQjQK7"
   },
   "source": [
    "### Variáveis quantitativas contínuas\n",
    "\n",
    "► Variáveis que representam uma contagem ou mensuração que assumem valores em uma escala contínua (números reais)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "jOW-bGyRjQK8",
    "outputId": "f3667c96-a570-4f95-818e-4bcc9cbe9403"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "De 1.339244614 até 2.028496765 metros\n"
     ]
    }
   ],
   "source": [
    "print('De %s até %s metros' % (dados.Altura.min(), dados.Altura.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d_LW5gMIjQK_"
   },
   "source": [
    "### Classificação de uma variável\n",
    "<img src='https://caelum-online-public.s3.amazonaws.com/1177-estatistica-parte1/01/img001.png' width='70%'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PNIg_4VEjQK_"
   },
   "source": [
    "# <font color=green>2 DISTRIBUIÇÃO DE FREQUÊNCIAS</font>\n",
    "***\n",
    "\n",
    "O primeiro passo em um trabalho de análise é o conhecimento do comportamento das variáveis envolvidas no estudo. Utilizando técnicas estatísticas como as análises das <b>DISTRIBUIÇÕES DE FREQUÊNCIAS</b> e <b>HISTOGRAMAS</b> podemos avaliar melhor a forma como os fenômenos em estudo se distribuem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JhpyjkEFjQLA"
   },
   "source": [
    "## <font color=green>2.1 Distribuição de frequências para variáveis qualitativas</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SsN3ikjqjQLB"
   },
   "source": [
    "### Método 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dj_v0M44X7G3"
   },
   "source": [
    "https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.Series.value_counts.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    },
    "colab_type": "code",
    "id": "cA4gUboZjQLB",
    "outputId": "960bc58d-62e2-4ca2-bb6d-df888813cb34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    53250\n",
       "1    23590\n",
       "Name: Sexo, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Sexo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    69.299844\n",
       "1    30.700156\n",
       "Name: Sexo, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Sexo.value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "iy4-ijknjQLD",
    "outputId": "609cb9bf-45b6-4d6a-ab57-f7eee496a41b"
   },
   "outputs": [],
   "source": [
    "frequencia = dados.Sexo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qfkiLcFijQLG"
   },
   "outputs": [],
   "source": [
    "percentual = dados.Sexo.value_counts(normalize = True) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t4uFMt6fjQLI"
   },
   "outputs": [],
   "source": [
    "dist_freq_qualitativas = pd.DataFrame({'Frequência' : frequencia , 'Porcentagem (%)' : percentual})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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    "colab_type": "code",
    "id": "zUGfju86jQLM"
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       "    .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>Frequência</th>\n",
       "      <th>Porcentagem (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53250</td>\n",
       "      <td>69.299844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23590</td>\n",
       "      <td>30.700156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frequência  Porcentagem (%)\n",
       "0       53250        69.299844\n",
       "1       23590        30.700156"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_freq_qualitativas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "colab_type": "code",
    "id": "kPOYfijijQLO",
    "outputId": "4af843fe-2772-4fc7-a04c-6e50d2384b58"
   },
   "outputs": [],
   "source": [
    "dist_freq_qualitativas.rename(index = {0 : 'Masculino' , 1 : 'Feminino'}, inplace = True)\n",
    "dist_freq_qualitativas.rename_axis('Sexo', axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tMS-VSk3jQLQ"
   },
   "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>Sexo</th>\n",
       "      <th>Frequência</th>\n",
       "      <th>Porcentagem (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>53250</td>\n",
       "      <td>69.299844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>23590</td>\n",
       "      <td>30.700156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sexo       Frequência  Porcentagem (%)\n",
       "Masculino       53250        69.299844\n",
       "Feminino        23590        30.700156"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_freq_qualitativas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D82ocp_djQLR"
   },
   "source": [
    "### Método 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rLbfeA_UX7HF"
   },
   "source": [
    "https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.crosstab.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d8XMZiEfjQLS"
   },
   "outputs": [],
   "source": [
    "sexo = {0 : 'Masculino',\n",
    "        1 : 'Feminino'}\n",
    "\n",
    "cor = {0 : 'Indígena',\n",
    "       2 : 'Branca',\n",
    "       4 : 'Preta',\n",
    "       6 : 'Amarela',\n",
    "       8 : 'Parda',\n",
    "       9 : 'Sem declaração'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "YLCyOKYcjQLU",
    "outputId": "7edebb2b-4a25-4b77-f78a-f47adbc8b3d9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</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>Masculino</th>\n",
       "      <td>256</td>\n",
       "      <td>22194</td>\n",
       "      <td>5502</td>\n",
       "      <td>235</td>\n",
       "      <td>25063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>101</td>\n",
       "      <td>9621</td>\n",
       "      <td>2889</td>\n",
       "      <td>117</td>\n",
       "      <td>10862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor        Indígena  Branca  Preta  Amarela  Parda\n",
       "Sexo                                              \n",
       "Masculino       256   22194   5502      235  25063\n",
       "Feminino        101    9621   2889      117  10862"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia = pd.crosstab(dados.Sexo,\n",
    "                         dados.Cor)\n",
    "frequencia.rename(index = sexo, inplace = True)\n",
    "frequencia.rename(columns = cor, inplace = True)\n",
    "frequencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "nrDtcf5gjQLV",
    "outputId": "6138e377-ca89-4606-8f22-084729b89637"
   },
   "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>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</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>Masculino</th>\n",
       "      <td>0.333160</td>\n",
       "      <td>28.883394</td>\n",
       "      <td>7.160333</td>\n",
       "      <td>0.305830</td>\n",
       "      <td>32.617126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>0.131442</td>\n",
       "      <td>12.520822</td>\n",
       "      <td>3.759761</td>\n",
       "      <td>0.152264</td>\n",
       "      <td>14.135867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor        Indígena     Branca     Preta   Amarela      Parda\n",
       "Sexo                                                         \n",
       "Masculino  0.333160  28.883394  7.160333  0.305830  32.617126\n",
       "Feminino   0.131442  12.520822  3.759761  0.152264  14.135867"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentual = pd.crosstab(dados.Sexo,\n",
    "                         dados.Cor,\n",
    "                         normalize = True) * 100\n",
    "percentual.rename(index = sexo, inplace = True)\n",
    "percentual.rename(columns = cor, inplace = True)\n",
    "percentual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "_MHwBGcdp6dl",
    "outputId": "d2674b4f-214e-4b08-de79-13e443d41d41"
   },
   "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>Cor</th>\n",
       "      <th>Indígena</th>\n",
       "      <th>Branca</th>\n",
       "      <th>Preta</th>\n",
       "      <th>Amarela</th>\n",
       "      <th>Parda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</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>Masculino</th>\n",
       "      <td>1081.710938</td>\n",
       "      <td>2925.744435</td>\n",
       "      <td>1603.861687</td>\n",
       "      <td>4758.251064</td>\n",
       "      <td>1659.577425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>2464.386139</td>\n",
       "      <td>2109.866750</td>\n",
       "      <td>1134.596400</td>\n",
       "      <td>3027.341880</td>\n",
       "      <td>1176.758516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Cor           Indígena       Branca        Preta      Amarela        Parda\n",
       "Sexo                                                                      \n",
       "Masculino  1081.710938  2925.744435  1603.861687  4758.251064  1659.577425\n",
       "Feminino   2464.386139  2109.866750  1134.596400  3027.341880  1176.758516"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentual = pd.crosstab(dados.Sexo,\n",
    "                         dados.Cor,\n",
    "                         aggfunc = 'mean',\n",
    "                         values = dados.Renda)\n",
    "percentual.rename(index = sexo, inplace = True)\n",
    "percentual.rename(columns = cor, inplace = True)\n",
    "percentual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eaXQUxnXjQLa"
   },
   "source": [
    "## <font color=green>2.2 Distribuição de frequências para variáveis quantitativas (classes personalizadas)</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wxsSD-VVjQLb"
   },
   "source": [
    "### Passo 1 - Especificar os limites de cada classe\n",
    "\n",
    "Utilizar a seguinte classificação:\n",
    "\n",
    "<b>A</b> ► Acima de 20 SM\n",
    "\n",
    "<b>B</b> ► De 10 a 20 SM\n",
    "\n",
    "<b>C</b> ► De 4 a 10 SM\n",
    "\n",
    "<b>D</b> ► De 2 a 4 SM\n",
    "\n",
    "<b>E</b> ► Até 2 SM\n",
    "\n",
    "onde <b>SM</b> é o valor do salário mínimo na época. Em nosso caso <b>R$ 788,00</b> (2015):\n",
    "\n",
    "<b>A</b> ► Acima de 15.760\n",
    "\n",
    "<b>B</b> ► De 7.880 a 15.760\n",
    "\n",
    "<b>C</b> ► De 3.152 a 7.880\n",
    "\n",
    "<b>D</b> ► De 1.576 a 3.152\n",
    "\n",
    "<b>E</b> ► Até 1.576\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Z08790GUjQLc",
    "outputId": "4a7bafb3-8440-42ee-fc60-a29080527059"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "5q2V5-0FjQLe",
    "outputId": "4d3c65eb-f1b0-454d-eb93-09c82314e311"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qt5F5w2UjQLg"
   },
   "outputs": [],
   "source": [
    "classes = [0, 1576, 3152, 7880, 15760, 200000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DKPIGOLLjQLi"
   },
   "outputs": [],
   "source": [
    "labels = ['E', 'D', 'C', 'B', 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IQhlvrQUjQLk"
   },
   "source": [
    "### Passo 2 - Criar a tabela de frequências"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uy3fH4IxX7Hc"
   },
   "source": [
    "https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.cut.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "colab_type": "code",
    "id": "N-wZ_2UajQLm",
    "outputId": "62245a56-ed43-4344-d2fa-bbad51c61c55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E    49755\n",
       "D    16700\n",
       "C     7599\n",
       "B     2178\n",
       "A      608\n",
       "Name: Renda, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequencia = pd.value_counts(pd.cut(x = dados.Renda,\n",
    "                                    bins = classes,\n",
    "                                    labels = labels,\n",
    "                                    include_lowest = True))\n",
    "frequencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "colab_type": "code",
    "id": "WrZ2m2GajQLo",
    "outputId": "363ba010-04c1-4f23-a08a-f5431372fcf4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E    64.751432\n",
       "D    21.733472\n",
       "C     9.889381\n",
       "B     2.834461\n",
       "A     0.791255\n",
       "Name: Renda, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentual = pd.value_counts(pd.cut(x = dados.Renda,\n",
    "                                    bins = classes,\n",
    "                                    labels = labels,\n",
    "                                    include_lowest = True),\n",
    "                            normalize = True) * 100\n",
    "percentual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "btKoxJDujQLp",
    "outputId": "849e9644-dcf1-4c0f-88c8-9bd00f24a53b"
   },
   "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",
       "        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>Frequência</th>\n",
       "      <th>Porcentagem (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>49755</td>\n",
       "      <td>64.751432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>16700</td>\n",
       "      <td>21.733472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7599</td>\n",
       "      <td>9.889381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2178</td>\n",
       "      <td>2.834461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>608</td>\n",
       "      <td>0.791255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frequência  Porcentagem (%)\n",
       "E       49755        64.751432\n",
       "D       16700        21.733472\n",
       "C        7599         9.889381\n",
       "B        2178         2.834461\n",
       "A         608         0.791255"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_freq_quantitativas_personalizadas = pd.DataFrame(\n",
    "    {'Frequência' : frequencia, 'Porcentagem (%)' : percentual}\n",
    ")\n",
    "dist_freq_quantitativas_personalizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "uTMEJXUHjQLs",
    "outputId": "ce17e408-9d56-44b2-fb26-a4037df3af00"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Frequência</th>\n",
       "      <th>Porcentagem (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>608</td>\n",
       "      <td>0.791255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2178</td>\n",
       "      <td>2.834461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7599</td>\n",
       "      <td>9.889381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>16700</td>\n",
       "      <td>21.733472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>49755</td>\n",
       "      <td>64.751432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frequência  Porcentagem (%)\n",
       "A         608         0.791255\n",
       "B        2178         2.834461\n",
       "C        7599         9.889381\n",
       "D       16700        21.733472\n",
       "E       49755        64.751432"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_freq_quantitativas_personalizadas.sort_index(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ViJYvvjujQLu"
   },
   "source": [
    "## <font color=green>2.3 Distribuição de frequências para variáveis quantitativas (classes de amplitude fixa)</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "02ve2BVojQLv"
   },
   "source": [
    "### Importando bibliotecas\n",
    "\n",
    "http://www.numpy.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hSIsHKuJjQLw"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s9UGNaMzjQLy"
   },
   "source": [
    "### Passo 1 - Difinindo o número de classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p268tvNHjQLy"
   },
   "source": [
    "#### Regra de Sturges\n",
    "\n",
    "# $$k = 1 + \\frac {10}{3}\\log_{10}n$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "wgLH6gGqjQLz",
    "outputId": "0cac691b-d35f-40a2-e1e7-31cfa0a5698a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76840"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = dados.shape[0]\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uTuVep-8jQL0"
   },
   "outputs": [],
   "source": [
    "k = 1 + (10/3) * np.log10(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "988IDFNOjQL2",
    "outputId": "9de2b02d-c804-487c-b95e-3f5386860fd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.285291187298853"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "B_Y66_KFjQL4",
    "outputId": "b93f7e32-c1f3-46b0-86d2-4575bab25906"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = int(k.round(0))\n",
    "k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5eO-RhaljQL6"
   },
   "source": [
    "### Passo 2 - Criar a tabela de frequências"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sVVA-jvDjQL7"
   },
   "outputs": [],
   "source": [
    "frequencia = pd.value_counts(\n",
    "    pd.cut(\n",
    "        x = dados.Renda,\n",
    "        bins = k,\n",
    "        include_lowest = True,\n",
    "    ),\n",
    "    sort = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "colab_type": "code",
    "id": "-ylxSip1jQL9",
    "outputId": "d362e353-a3c9-4721-efd3-2a8ff7c85ab5"
   },
   "outputs": [],
   "source": [
    "percentual = pd.value_counts(\n",
    "    pd.cut(\n",
    "        x = dados.Renda,\n",
    "        bins = k,\n",
    "        include_lowest = True,\n",
    "    ),\n",
    "    sort = False,\n",
    "    normalize = True\n",
    ") * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 576
    },
    "colab_type": "code",
    "id": "n9kkJ0WTjQL_",
    "outputId": "994669b3-9a10-424c-adab-6f934f87fd45"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Frequência</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>(-200.001, 11764.706]</th>\n",
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       "      <td>1.330036</td>\n",
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       "    <tr>\n",
       "      <th>(23529.412, 35294.118]</th>\n",
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       "      <td>0.219938</td>\n",
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       "    <tr>\n",
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       "      <td>0.001301</td>\n",
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       "    <tr>\n",
       "      <th>(188235.294, 200000.0]</th>\n",
       "      <td>3</td>\n",
       "      <td>0.003904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Frequência  Porcentagem (%)\n",
       "(-200.001, 11764.706]          75594        98.378449\n",
       "(11764.706, 23529.412]          1022         1.330036\n",
       "(23529.412, 35294.118]           169         0.219938\n",
       "(35294.118, 47058.824]            19         0.024727\n",
       "(47058.824, 58823.529]            16         0.020822\n",
       "(58823.529, 70588.235]             5         0.006507\n",
       "(70588.235, 82352.941]             4         0.005206\n",
       "(82352.941, 94117.647]             1         0.001301\n",
       "(94117.647, 105882.353]            6         0.007808\n",
       "(105882.353, 117647.059]           0         0.000000\n",
       "(117647.059, 129411.765]           1         0.001301\n",
       "(129411.765, 141176.471]           0         0.000000\n",
       "(141176.471, 152941.176]           0         0.000000\n",
       "(152941.176, 164705.882]           0         0.000000\n",
       "(164705.882, 176470.588]           0         0.000000\n",
       "(176470.588, 188235.294]           0         0.000000\n",
       "(188235.294, 200000.0]             3         0.003904"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_freq_quantitativas_amplitude_fixa = pd.DataFrame(\n",
    "    {'Frequência' : frequencia, 'Porcentagem (%)' : percentual}\n",
    ")\n",
    "dist_freq_quantitativas_amplitude_fixa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Js9iLwNkjQME"
   },
   "source": [
    "## <font color=green>2.4 Histograma</font>\n",
    "***\n",
    "\n",
    "O <b>HISTOGRAMA</b> é a representação gráfica de uma distribuição de frequências. É uma gráfico formado por um conjunto de retângulos colocados lado a lado, onde a área de cada retângulo é proporcional à frequência da classe que ele representa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VzZEXDKX3PUH"
   },
   "source": [
    "### Importando a biblioteca\n",
    "\n",
    "https://seaborn.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PYRaBCnxjQME"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "colab_type": "code",
    "id": "GAX3aCa6jQMG",
    "outputId": "e9da92bc-7d3e-4c46-e4e1-eeed3d853fef"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeb00012\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distribuição de Frequências - Altura'}, xlabel='Metros'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(dados.Altura, kde = False)\n",
    "ax.figure.set_size_inches(10, 5)\n",
    "ax.set_title('Distribuição de Frequências - Altura', fontsize = 18)\n",
    "ax.set_xlabel('Metros', fontsize = 14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 429
    },
    "colab_type": "code",
    "id": "wiQPnuMojQMJ",
    "outputId": "5deacde5-bca6-49cd-d054-02e250708859"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distribuição de Frequências - Altura - KDE'}, xlabel='Metros', ylabel='Density'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(dados.Altura, kde = True)\n",
    "ax.figure.set_size_inches(10, 5)\n",
    "ax.set_title('Distribuição de Frequências - Altura - KDE', fontsize = 18)\n",
    "ax.set_xlabel('Metros', fontsize = 14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "3fySBW7qjQMK",
    "outputId": "43604d3b-3b8a-42e9-eb74-5dd1eab2a00b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dados.Altura.hist(bins = 50, figsize = (10, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "0HcLhLYtjQMM",
    "outputId": "45a291b3-4b7a-4ea8-c135-a873ad231c02"
   },
   "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>Frequência</th>\n",
       "      <th>Porcentagem (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>49755</td>\n",
       "      <td>64.751432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>16700</td>\n",
       "      <td>21.733472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>7599</td>\n",
       "      <td>9.889381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2178</td>\n",
       "      <td>2.834461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>608</td>\n",
       "      <td>0.791255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frequência  Porcentagem (%)\n",
       "E       49755        64.751432\n",
       "D       16700        21.733472\n",
       "C        7599         9.889381\n",
       "B        2178         2.834461\n",
       "A         608         0.791255"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_freq_quantitativas_personalizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "colab_type": "code",
    "id": "IQYnbxoS-kjQ",
    "outputId": "fddf3891-eea9-4097-ae5a-5fb60e28b159"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_freq_quantitativas_personalizadas['Frequência'].plot.bar(width = 1, color = 'blue', alpha = 0.2, figsize = (10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uIb17Es3jQMP"
   },
   "source": [
    "# <font color=green>3 MEDIDAS DE TENDÊNCIA CENTRAL</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Wrya4suEX7H-"
   },
   "source": [
    "## DataFrame de exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "jj7a21flX7H_",
    "outputId": "772a6f47-ed2a-4d49-9db0-bd0944270891"
   },
   "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>Matérias</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Beltrano</th>\n",
       "      <th>Sicrano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Matemática</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Matérias    Fulano  Beltrano  Sicrano\n",
       "Matemática       8      10.0      7.5\n",
       "Português       10       2.0      8.0\n",
       "Inglês           4       0.5      7.0\n",
       "Geografia        8       1.0      8.0\n",
       "História         6       3.0      8.0\n",
       "Física          10       9.5      8.5\n",
       "Química          8      10.0      7.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data = {'Fulano': [8, 10, 4, 8, 6, 10, 8],\n",
    "                          'Beltrano': [10, 2, 0.5, 1, 3, 9.5, 10],\n",
    "                          'Sicrano': [7.5, 8, 7, 8, 8, 8.5, 7]},\n",
    "                 index = ['Matemática',\n",
    "                          'Português',\n",
    "                          'Inglês',\n",
    "                          'Geografia',\n",
    "                          'História',\n",
    "                          'Física',\n",
    "                          'Química'])\n",
    "df.rename_axis('Matérias', axis = 'columns', inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kcHJFW-YjQMP"
   },
   "source": [
    "## <font color=green>3.1 Média aritmética</font>\n",
    "***\n",
    "\n",
    "É representada por $\\mu$ quando se refere à população e por $\\bar{X}$ quando se refere à amostra\n",
    "\n",
    "# $$\\mu = \\frac 1n\\sum_{i=1}^{n}X_i$$\n",
    "\n",
    "onde \n",
    "\n",
    "$n$ = número de observações (registros)\n",
    "\n",
    "$X_i$ = valor da i-ésima observação (registro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "QW4JPcBmX7IA",
    "outputId": "45810cfa-fba2-4bf3-b571-eddd9d8ebd09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.714285714285714"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(8 + 10 + 4 + 8 + 6 + 10 + 8) / 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "LuT8WNy5X7IB",
    "outputId": "1fde4b44-b250-41d9-ff02-70c9a0e05aca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.714285714285714"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Fulano.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "BRMZcmMEjQMQ",
    "outputId": "5f3334d9-0f5f-40e2-e2b7-191e0bc3a5d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000.3831988547631"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "id": "CGgJbsK0HBQc",
    "outputId": "35b8a334-5f7d-4f17-ec85-2e10ec6ae5ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sexo\n",
       "0    2192.441596\n",
       "1    1566.847393\n",
       "Name: Renda, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.groupby(['Sexo'])['Renda'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9geu3UcKjQMS"
   },
   "source": [
    "## <font color=green>3.2 Mediana</font>\n",
    "***\n",
    "\n",
    "Para obtermos a mediana de uma conjunto de dados devemos proceder da seguinte maneira:\n",
    "1. Ordenar o conjunto de dados;\n",
    "2. Identificar o número de observações (registros) do conjunto de dados ($n$);\n",
    "3. Identicar o elemento mediano:\n",
    "\n",
    "> Quando $n$ for ímpar, a posição do elemento mediano será obtida da seguinte forma:\n",
    "\n",
    "\n",
    "# $$Elemento_{Md} = \\frac{n+1}2$$\n",
    "\n",
    "> Quando $n$ for par, a posição do elemento mediano será obtida da seguinte forma:\n",
    "\n",
    "\n",
    "# $$Elemento_{Md} = \\frac{n}2$$\n",
    "\n",
    "4. Obter a mediana:\n",
    "\n",
    "> Quando $n$ for ímpar:\n",
    "\n",
    "\n",
    "# $$Md = X_{Elemento_{Md}}$$\n",
    "\n",
    "> Quando $n$ for par:\n",
    "\n",
    "\n",
    "# $$Md = \\frac{X_{Elemento_{Md}} + X_{Elemento_{Md}+1}}2$$\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e0IuYdJFjQMT"
   },
   "source": [
    "### Exemplo 1 - n ímpar\n",
    "\n",
    "<img src='https://caelum-online-public.s3.amazonaws.com/1177-estatistica-parte1/01/img002.png' width='40%' style=\"float:left\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    },
    "colab_type": "code",
    "id": "3dXjdRFlX7IH",
    "outputId": "d032eb05-bd06-4c1f-817d-f19abedb42bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Matemática     8\n",
       "Português     10\n",
       "Inglês         4\n",
       "Geografia      8\n",
       "História       6\n",
       "Física        10\n",
       "Química        8\n",
       "Name: Fulano, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano = df.Fulano\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    },
    "colab_type": "code",
    "id": "cU91DJ-yjQMV",
    "outputId": "928ea4b6-51e4-433e-9140-412596b3b42a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inglês         4\n",
       "História       6\n",
       "Matemática     8\n",
       "Geografia      8\n",
       "Química        8\n",
       "Português     10\n",
       "Física        10\n",
       "Name: Fulano, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano = notas_fulano.sort_values()\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "z9bOnYKf1NOd",
    "outputId": "a1d69cc0-145e-4ec9-9504-32b220cf4e5c"
   },
   "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>index</th>\n",
       "      <th>Fulano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inglês</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>História</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Matemática</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Geografia</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Química</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Português</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Física</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  Fulano\n",
       "0      Inglês       4\n",
       "1    História       6\n",
       "2  Matemática       8\n",
       "3   Geografia       8\n",
       "4     Química       8\n",
       "5   Português      10\n",
       "6      Física      10"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano = notas_fulano.reset_index()\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "u3nfPEFjjQMW",
    "outputId": "a683f234-d732-4dc0-9e3e-eac66a6f904d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = notas_fulano.shape[0]\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "trC12MHxjQMY",
    "outputId": "917c6720-daa2-45be-a72d-3e0d63c67fd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elemento_md = (n + 1) / 2\n",
    "elemento_md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "NKccffcLjQMZ",
    "outputId": "46c1c33a-100b-4aca-e876-f3589b699b48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index     Geografia\n",
       "Fulano            8\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano.loc[elemento_md - 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "ody5mfXLjQMb",
    "outputId": "d43b24b8-1d80-4780-a059-720cdc073353"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Fulano.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hQHyjfLkjQMe"
   },
   "source": [
    "### Exemplo 2 - n par\n",
    "\n",
    "<img src='https://caelum-online-public.s3.amazonaws.com/1177-estatistica-parte1/01/img003.png' width='50%' style=\"float:left\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "id": "Jkx1WZenjQMe",
    "outputId": "2482f6a4-b2c1-4751-865c-827ac57bba01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Matemática    10.0\n",
       "Inglês         0.5\n",
       "Física         9.5\n",
       "História       3.0\n",
       "Química       10.0\n",
       "Português      2.0\n",
       "Name: Beltrano, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_beltrano = df.Beltrano.sample(6, random_state = 101)\n",
    "notas_beltrano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "id": "D1LUpvZkYsHq",
    "outputId": "d23d2d1c-bf17-4273-c441-092e04ec1ea8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inglês         0.5\n",
       "Português      2.0\n",
       "História       3.0\n",
       "Física         9.5\n",
       "Matemática    10.0\n",
       "Química       10.0\n",
       "Name: Beltrano, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_beltrano = notas_beltrano.sort_values()\n",
    "notas_beltrano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "PuiScCf0-vh9",
    "outputId": "569288f3-b67d-4932-91cb-8c66685163c6"
   },
   "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>index</th>\n",
       "      <th>Beltrano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inglês</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Português</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>História</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Física</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Matemática</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Química</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  Beltrano\n",
       "0      Inglês       0.5\n",
       "1   Português       2.0\n",
       "2    História       3.0\n",
       "3      Física       9.5\n",
       "4  Matemática      10.0\n",
       "5     Química      10.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_beltrano = notas_beltrano.reset_index()\n",
    "notas_beltrano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "6wB1UztBjQMf",
    "outputId": "cbb15019-56a3-4c87-8b77-6e04371c3182"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = notas_beltrano.shape[0]\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "tEBVXl7CjQMh",
    "outputId": "499b28c7-6de7-40f4-cdd9-5fef5c5c084a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metade = int (n / 2) - 1\n",
    "metade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index       História\n",
       "Beltrano         3.0\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elemento_md1 = notas_beltrano.iloc[metade]\n",
    "elemento_md1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "N-jSUjZSjQMi",
    "outputId": "1c265766-278c-4615-aec9-5c1f53cb2a25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index       Física\n",
       "Beltrano       9.5\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elemento_md2 = notas_beltrano.iloc[metade + 1]\n",
    "elemento_md2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.25"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mediana = (elemento_md1.Beltrano + elemento_md2.Beltrano) / 2\n",
    "mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "IevBBtrRjQMj",
    "outputId": "6f299f8c-f0c1-4e44-9a0f-cd72834145ba"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.25"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_beltrano.Beltrano.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nK27TxrkjQMl"
   },
   "source": [
    "### Obtendo a mediana em nosso dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "dJznpnjUjQMl",
    "outputId": "f4728447-b763-4116-9076-9ac9c4d19d75"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "W4a5PVYdjQMm",
    "outputId": "3e8a9233-4eb6-4684-9f96-ce79e53ffa7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.quantile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CmCu6BXQjQMo"
   },
   "source": [
    "## <font color=green>3.3 Moda</font>\n",
    "***\n",
    "\n",
    "Pode-se definir a moda como sendo o valor mais frequente de um conjunto de dados. A moda é bastante utilizada para dados qualitativos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "BHibMzw8X7Id",
    "outputId": "6b9375f3-1c22-4866-a40b-c28be3a01047"
   },
   "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>Matérias</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Beltrano</th>\n",
       "      <th>Sicrano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Matemática</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Matérias    Fulano  Beltrano  Sicrano\n",
       "Matemática       8      10.0      7.5\n",
       "Português       10       2.0      8.0\n",
       "Inglês           4       0.5      7.0\n",
       "Geografia        8       1.0      8.0\n",
       "História         6       3.0      8.0\n",
       "Física          10       9.5      8.5\n",
       "Química          8      10.0      7.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "colab_type": "code",
    "id": "xICDpQguX7Ik",
    "outputId": "7863773e-e0d3-4fa4-d565-deb63cf0e82b"
   },
   "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>Matérias</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Beltrano</th>\n",
       "      <th>Sicrano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Matérias  Fulano  Beltrano  Sicrano\n",
       "0              8      10.0      8.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    2\n",
       "3    3\n",
       "4    4\n",
       "5    4\n",
       "6    5\n",
       "7    6\n",
       "8    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo = pd.Series([1, 2, 2, 3, 4, 4, 5, 6, 6])\n",
    "exemplo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "x9lIRFESX7Il",
    "outputId": "44945487-3723-422d-e720-57fd216be8e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exemplo.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YwhBwz-JjQMr"
   },
   "source": [
    "### Obtendo a moda em nosso dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "mC8vvYk-jQMr",
    "outputId": "bb3e196d-2447-4596-917f-5bdf34067dd9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    788\n",
       "Name: Renda, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "Sdosy_vSX7Iw",
    "outputId": "949a3b5a-b978-40ab-eed0-872fb425d0f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.568128\n",
       "1    1.671225\n",
       "2    1.681659\n",
       "3    1.692977\n",
       "4    1.708163\n",
       "5    1.708370\n",
       "6    1.753842\n",
       "7    1.779073\n",
       "8    1.796462\n",
       "Name: Altura, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Altura.mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_-0gjQWFjQMu"
   },
   "source": [
    "## <font color=green>3.4 Relação entre média, mediana e moda</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0S7pa_jLjQMu"
   },
   "source": [
    "<img src='https://caelum-online-public.s3.amazonaws.com/1177-estatistica-parte1/01/img004.png' width='80%'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b7ZchyFsjQMu"
   },
   "source": [
    "### Avaliando a variável RENDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "d8WQQ0PdjQMv",
    "outputId": "da495910-b7ab-4d66-f187-a8d6ff6b00f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeb00012\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Renda', ylabel='Density'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(dados.query('Renda < 20000').Renda)\n",
    "ax.figure.set_size_inches(10, 5)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "krws6f_SjQMw",
    "outputId": "22ef1fae-c11c-468c-fa4a-f19fbf932411"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "788"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Moda = dados.Renda.mode()[0]\n",
    "Moda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "_Y7KpB25jQMx",
    "outputId": "66d5c523-420c-47a3-a5d7-964d29545c02"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mediana = dados.Renda.median()\n",
    "Mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "gQ0e56VajQMy",
    "outputId": "796f4faa-7171-4322-89e9-fd62b950a463"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000.3831988547631"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Media = dados.Renda.mean()\n",
    "Media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "nX6lnCBujQM0",
    "outputId": "d4bd80b4-9eee-4ad2-f41d-29c12362580e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Moda < Mediana < Media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FnLQAxSijQM1"
   },
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WazchssNjQM1"
   },
   "source": [
    "### Avaliando a variável ALTURA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "41uJsCk9jQM2",
    "outputId": "3f7aec0c-e6b0-4629-d235-8061ce3b25c4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeb00012\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Altura', ylabel='Density'>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(dados.Altura)\n",
    "ax.figure.set_size_inches(10, 5)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "EkgerGuXjQM3",
    "outputId": "2966d112-76a5-4286-923c-bb01f02c6acf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.568128\n",
       "1    1.671225\n",
       "2    1.681659\n",
       "3    1.692977\n",
       "4    1.708163\n",
       "5    1.708370\n",
       "6    1.753842\n",
       "7    1.779073\n",
       "8    1.796462\n",
       "Name: Altura, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Moda = dados.Altura.mode()\n",
    "Moda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Jj6MFUXbjQM4",
    "outputId": "ef153a71-fa7b-4165-b23c-136157955825"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6993247325"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mediana = dados.Altura.median()\n",
    "Mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "84X-3AzvjQM5",
    "outputId": "c2b94040-6ef1-46d1-cac1-58c8f6ba0d53"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6995124540575812"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Media = dados.Altura.mean()\n",
    "Media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PJIMB8i8jQM6"
   },
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eS4I2H7jjQM7"
   },
   "source": [
    "### Avaliando a variável ANOS DE ESTUDO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "id": "NPCcjXqPjQM8",
    "outputId": "e811449e-866e-4986-a20b-8f2d44ddb437"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeb00012\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Anos de Estudo', ylabel='Density'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PYx1SyMoVEdkLkysiIiJyKb0dEmh1tnLV0uuYiIgAJldERETkYmzJlV/vkitrx8DyBh1EdgwkIjtgckVEREQupaLePpWrYF8VBAAtBjPqdUY7REZEnk7y5Grp0qVISEiARqNBcnIytm3bdsF9165di6uuugohISHw9/dHamoqNmzY0G6/NWvWYOjQoVCr1Rg6dCjWrVvnyJdAREREfcg65yqkF80sAEAhlyHIx3IMNrUgInuQNLlavXo1Hn30UTzzzDPIyMjAlClTMHPmTBQUFHS4/9atW3HVVVdh/fr1SE9Px2WXXYbrr78eGRkZtn127tyJ2bNnY86cOThw4ADmzJmD2267Dbt37+6rl0VEREQOYhZFVDbap3IFgO3YiciuBFHCQcYpKSkYM2YM3n33Xdu2pKQk3HjjjVi8eHGXjjFs2DDMnj0bf/vb3wAAs2fPRl1dHX788UfbPjNmzEBgYCBWrVrVpWPW1dVBq9WitrYW/v7+3XhFRERE1B0rd3f8heqF1DTp8a8NJyAXBDx/wzDIZUKvnv+nwyXYmlWBCf2D8MWfUnt1LCJyT93JDSSrXOn1eqSnp2P69Olttk+fPh07duzo0jHMZjPq6+sRFBRk27Zz5852x7z66qs7PaZOp0NdXV2bGxERETkf65DAIB9VrxMrgGtdEZF9SZZcVVRUwGQyISwsrM32sLAwlJaWdukYr776KhobG3HbbbfZtpWWlnb7mIsXL4ZWq7XdYmJiuvFKiIiIqK+U29qw926+lVWItWMgkysisgPJG1oIQttvnURRbLetI6tWrcLzzz+P1atXIzQ0tFfHXLRoEWpra223wsLCbrwCIiIi6iv2asNuZU3S6lqMaDGY7HJMIvJcCqmeODg4GHK5vF1FqaysrF3l6XyrV6/G/Pnz8dVXX+HKK69sc194eHi3j6lWq6FW2+dDmoiIiByn0k4LCFt5KeVQK2TQGc04Xd2EAaF+djkuEXkmySpXKpUKycnJSEtLa7M9LS0NEydOvODjVq1ahbvuugsrV67Etdde2+7+1NTUdsf8+eefOz0mERERuYZyO61xZSUIAgK9LdWrwupmuxyTiDyXZJUrAFi4cCHmzJmDsWPHIjU1FR988AEKCgqwYMECAJbhekVFRVixYgUAS2I1d+5cvPHGG5gwYYKtQuXl5QWtVgsAeOSRRzB16lS88sormDVrFr799lts3LgR27dvl+ZFEhERkV0YTWbUNBkA2G/OFQAEeitRWteC00yuiKiXJJ1zNXv2bCxZsgQvvvgiRo0aha1bt2L9+vWIi4sDAJSUlLRZ8+r999+H0WjEgw8+iIiICNvtkUcese0zceJEfPHFF1i2bBkuueQSLF++HKtXr0ZKSkqfvz4iIiKyn8pGPUQAaoUMvmr7fT8c2LqQ8OmqJrsdk4g8k6TrXDkrrnNFRETUN7qzztWR4lp8vrsA0YFeeODSAXaL4bdTFfjhUAmuHRGBd34/xm7HJSL34BLrXBERERF1R3WjZY0r6xwpewn0VgIATlezckVEvcPkioiIiFxCdet8K7snVz5saEFE9sHkioiIiFxCTVNr5cpHadfjBnhZkquqRj0adUa7HpuIPAuTKyIiInIJjqpceank0Cgtl0RFNaxeEVHPMbkiIiIil1DdWrkK8LJv5QoAgloTNs67IqLeYHJFRERETq9Zb4LOaAYABNi5cnXuMQurWLkiop5jckVEREROz1q18lHJoVLY//KFHQOJyB6YXBEREZHTq7HOt/Kxf9Xq3OOeZsdAIuoFJldERETk9Bw53wo42ySjkJUrIuoFJldERETk9Gxt2B0w3+rc47JyRUS9weSKiIiInJ61DXuAo4YFts65qmkyoL7F4JDnICL3x+SKiIiInJ6tcuWgYYFqpfycphasXhFRzzC5IiIiIqfn6MoVAEQHegNgckVEPcfkioiIiJxai8GEZoMJgOMqVwAQE+QFgO3YiajnmFwRERGRU7O2YfdSyqFWyh32PKxcEVFvMbkiIiIip3a2U6DjqlYAEB1oqVwVVrFyRUQ9w+SKiIiInJptjSsHtWG3imHlioh6ickVEREROTXrsMC+qlxxzhUR9RSTKyIiInJqfVW5impNrupajKht5lpXRNR9TK6IiIjIqVXbKleOTa68VQoE+1qeg9UrIuoJJldERETk1GpslSvHDgsEgCjOuyKiXmByRURERE5LbzSjUd+6xpWDK1cAOwYSUe8wuSIiIiKnZa1aqRUyeKkct8aVVXSAJbkqrmlx+HMRkfthckVEREROq6/mW1lFaDUAgJJaDgskou5jckVEREROq7qPFhC2irBVrphcEVH3MbkiIiIip2Vd48rRbditIrWtyVUthwUSUfcxuSIiIiKn1feVK8uwwIoGHfRGc588JxG5DyZXRERE5LRq+mgBYat+PiqoFDKIInCmjtUrIuoeJldERETktM4OC+ybypUgCLamFpx3RUTdxeSKiIiInJLRZEa9zgig7ypXwLkdA1m5IqLuYXJFRERETqmuxZJYKWQCfPpgjSurs00tWLkiou5hckVEREROyTrfSuulhCAIffa81qYWJVxImIi6qUfJVW5urr3jICIiImqjttky30rbR/OtrCJb17riQsJE1F09Sq4GDBiAyy67DJ999hlaWvitDhEREdlfTWtyFeDVd/OtgHOGBbJyRUTd1KPk6sCBAxg9ejQef/xxhIeH47777sOePXvsHRsRERF5sNrWToFar76tXNmGBbJyRUTd1KPkavjw4XjttddQVFSEZcuWobS0FJMnT8awYcPw2muvoby83N5xEhERkYepabaucdXHyVVr5aq6yYBmvalPn5uIXFuvGlooFArcdNNN+PLLL/HKK68gOzsbTzzxBKKjozF37lyUlJTYK04iIiLyMLW2YYF9m1z5axS27oSsXhFRd/Qqudq3bx8eeOABRERE4LXXXsMTTzyB7Oxs/PrrrygqKsKsWbPsFScRERF5mBqJhgUKgoAIW1MLzrsioq5T9ORBr732GpYtW4YTJ07gmmuuwYoVK3DNNddAJrPkagkJCXj//fcxZMgQuwZLREREnqHFYILOaAbQ990CActCwqfKGlBcw8oVEXVdj5Krd999F3fffTf++Mc/Ijw8vMN9YmNj8dFHH/UqOCIiIvJM1k6BXko51Iq+W0DYytoxkJUrIuqOHiVXaWlpiI2NtVWqrERRRGFhIWJjY6FSqTBv3jy7BElERESexdopsK+bWVixYyAR9USP5lwlJiaioqKi3faqqiokJCT0OigiIiLybNZOgX0938qKa10RUU/0KLkSRbHD7Q0NDdBoNL0KiIiIiEiqNa6sWLkiop7o1rDAhQsXArB00fnb3/4Gb29v230mkwm7d+/GqFGj7BogEREReR6p2rBbWde6KmHlioi6oVvJVUZGBgBL5erQoUNQqVS2+1QqFUaOHIknnnjCvhESERGRx7E2tNB6qy6yp2NEtlau6nVG1LcY4KeRJskjItfSreRq06ZNAIA//vGPeOONN+Dv7++QoIiIiMizWStXUg0L9FYpoPVSorbZgJLaFiZXRNQlPZpztWzZMiZWRERE5BBmUZS8WyBgWesKANe6IqIu63Ll6uabb8by5cvh7++Pm2++udN9165d2+vAiIiIyDM16owwiSIEAP4SVowiA7xwvLSea10RUZd1ObnSarUQBMH2dyIiIiJHqGmtWvlpFJDLBMniYOWKiLqry8nVsmXLOvw7ERERkT3ZOgVK1MzCKjKAa10RUff0aM5Vc3MzmpqabD/n5+djyZIl+Pnnn+0WGBEREXmmGombWVhZK1dc64qIuqpHydWsWbOwYsUKAEBNTQ3Gjx+PV199FbNmzcK7775r1wCJiIjIs9Q26QFIt8aVlW2tK865IqIu6lFytX//fkyZMgUA8PXXXyM8PBz5+flYsWIF3nzzTbsGSERERJ7l7BpX0iZX1rWuimuaIYqipLEQkWvoUXLV1NQEPz8/AMDPP/+Mm2++GTKZDBMmTEB+fn63jrV06VIkJCRAo9EgOTkZ27Ztu+C+JSUluPPOOzF48GDIZDI8+uij7fZZvnw5BEFod2tp4bdORERErsA250riylV467BAndGM6tYmG0REnelRcjVgwAB88803KCwsxIYNGzB9+nQAQFlZWbfWv1q9ejUeffRRPPPMM8jIyMCUKVMwc+ZMFBQUdLi/TqdDSEgInnnmGYwcOfKCx/X390dJSUmbm0aj6d6LJCIiIklY17jSeknb0EKtkCPY1xIDOwYSUVf0KLn629/+hieeeALx8fFISUlBamoqAEsVa/To0V0+zmuvvYb58+fjnnvuQVJSEpYsWYKYmJgLztuKj4/HG2+8gblz53baDl4QBISHh7e5ERERkfMzmsyo1xkBSD8sEOC8KyLqnh4lV7fccgsKCgqwb98+/PTTT7btV1xxBV5//fUuHUOv1yM9Pd1W9bKaPn06duzY0ZOwbBoaGhAXF4fo6Ghcd911yMjI6HR/nU6Hurq6NjciIiLqe3UtlsRKIRPgo5JLHA07BhJR9/QouQKA8PBwjB49GjLZ2UOMHz8eQ4YM6dLjKyoqYDKZEBYW1mZ7WFgYSktLexoWhgwZguXLl+O7777DqlWroNFoMGnSJGRlZV3wMYsXL4ZWq7XdYmJievz8RERE1HM1rZ0CtV5KCIJ0Cwhbca0rIuqOLi8ifK7Gxka8/PLL+OWXX1BWVgaz2dzm/pycnC4f6/wPTlEUe/VhOmHCBEyYMMH286RJkzBmzBi89dZbF+xkuGjRIixcuND2c11dHRMsIiIiCdQ6SadAK1aunMvK3R3Py5fCnSmxUodATqhHydU999yDLVu2YM6cOYiIiOhRMhQcHAy5XN6uSlVWVtaumtUbMpkM48aN67RypVaroVar7facRERE1DM1tk6B0jazsIporVyVsHJFRF3Qo+Tqxx9/xA8//IBJkyb1+IlVKhWSk5ORlpaGm266ybY9LS0Ns2bN6vFxzyeKIjIzMzFixAi7HZOIiIgc42ynQOeoXEW2Vq6KWbkioi7oUXIVGBiIoKCgXj/5woULMWfOHIwdOxapqan44IMPUFBQgAULFgCwDNcrKirCihUrbI/JzMwEYGlaUV5ejszMTKhUKgwdOhQA8MILL2DChAkYOHAg6urq8OabbyIzMxPvvPNOr+MlIiIix6pptsy5CnCWYYGtlaszdS0wm0XIZNLPAyMi59Wj5Orvf/87/va3v+GTTz6Bt7d3j5989uzZqKysxIsvvoiSkhIMHz4c69evR1xcHADLosHnr3l1bqv39PR0rFy5EnFxccjLywMA1NTU4E9/+hNKS0uh1WoxevRobN26FePHj+9xnERERNQ3bHOunKRyFeanhkwADCYRFQ06hPpz3UwiurAeJVevvvoqsrOzERYWhvj4eCiVbT8A9+/f3+VjPfDAA3jggQc6vG/58uXttomi2OnxXn/99S63gyciIiLnUtNknXPlHMmVQi5DqJ8GpXUtKK5tYXJFRJ3qUXJ144032jkMIiIi8nQtBhN0RksHYmfpFggAEQGW5KqkphmjYgKkDoeInFiPkqvnnnvO3nEQERGRh7N2CvRSyqFWSL+AsFWk1gsZqEFxLTsGElHneryIcE1NDf773/9i0aJFqKqqAmAZDlhUVGS34IiIiMhzWDsFOkszC6vIgNa1rmrYMZCIOtejytXBgwdx5ZVXQqvVIi8vD/feey+CgoKwbt065Ofnt+nuR0RERNQV1k6BztLMwipC27rWFStXRHQRPapcLVy4EHfddReysrKg0Zyd2Dlz5kxs3brVbsERERGR53C2Na6srJUrrnVFRBfTo+Rq7969uO+++9ptj4qKQmlpaa+DIiIiIs9jbcPuLJ0CrWyVqxpWroiocz1KrjQaDerq6tptP3HiBEJCQnodFBEREXkea0MLrbdK4kjaimitXJXVt8BgMkscDRE5sx4lV7NmzcKLL74Ig8HyISgIAgoKCvD000/jd7/7nV0DJCIiIs/gbAsIWwX7qKGUCzCLwJk6Vq+I6MJ6lFz95z//QXl5OUJDQ9Hc3Ixp06ZhwIAB8PPzw0svvWTvGImIiMjNmUXRabsFymQCwrWtHQPZ1IKIOtGjboH+/v7Yvn07Nm3ahPT0dJjNZowZMwZXXnmlveMjIiIiD9CoM8IkihAA+GucK7kCLPOuCquaUcx27ETUiW4nV2azGcuXL8fatWuRl5cHQRCQkJCA8PBwiKIIQRAcEScRERG5sZrWqpWfRgG5zPmuJSJZuSKiLujWsEBRFHHDDTfgnnvuQVFREUaMGIFhw4YhPz8fd911F2666SZHxUlERERuzFnnW1lFBFg7BrJyRUQX1q3K1fLly7F161b88ssvuOyyy9rc9+uvv+LGG2/EihUrMHfuXLsGSURERO7N2ikwwMk6BVpZK1fFrFwRUSe6VblatWoV/vKXv7RLrADg8ssvx9NPP43PP//cbsERERGRZ6ht0gNwvjWurGxrXXEhYSLqRLeSq4MHD2LGjBkXvH/mzJk4cOBAr4MiIiIiz3J2jSsnTa5a17riQsJE1JluJVdVVVUICwu74P1hYWGorq7udVBERETkWaxzrpy1chXZWrmqbNSjxWCSOBoiclbdSq5MJhMUigtP05LL5TAajb0OioiIiDyLdY0rrZdzzrkK8FZCo7RcNpVy3hURXUC3GlqIooi77roLarW6w/t1Op1dgiIiIiLPYTSZUa+zfDnrrMMCBUFApNYLORWNKK5tRnywj9QhEZET6lZyNW/evIvuw06BRERE1B11LZbESiET4KOSSxzNhUUEaJBT0ch5V0R0Qd1KrpYtW+aoOIiIiMhD1bR2CtR6KSEIzreAsBU7BhLRxXRrzhURERGRvdU6eadAK+taV0WsXBHRBTC5IiIiIknZFhB20mYWVlGBlspVcQ0rV0TUMSZXREREJKmznQKdu3IVFeANAChickVEF8DkioiIiCRV02yZcxXg5MMCrZWroupmiKIocTRE5IyYXBEREZGkbHOunLxyFdE656rZYEJ1a7WNiOhcTK6IiIhIUjVN1jlXzp1caZRyBPta1vosqubQQCJqj8kVERERSaZZb4LOaAYABHg7d0ML4JyhgZx3RUQdYHJFREREkqluXePKRyWHSuH8lyXRAUyuiOjCnP9TjIiIiNyWNbkK9HH+qhXQtqkFEdH5mFwRERGRZKyNIQJdYEggAETZKldNEkdCRM6IyRURERFJprqxtXLl5G3YraI4LJCIOqGQOgAiIiLyXK42LDAygMMCPZFZFPHr8TLojWZMGhDs9MsGkHSYXBEREZFkalxtWGDrnKvqJgOa9EZ4q3gp5e7Mooh1+4uQXlANANiZU4lx8UG4IikUYf4aiaMjZ8NhgURERCQJURRR1Vq5CnCRYYFaLyX81JaEqphDA92eKIr4LrMY6QXVkAlAdKAXTGYRu3IqMeVfm5B29IzUIZKTYXJFREREkqhpMkDfusaVq1SugLPVq9McGujWRFHE/w6WYE9eFQQAtyTH4P5piZg/OQGxQd7QG8144fsjMJjMUodKToTJFREREUnCmpz4qRVQyl3nkoRNLTzDjuxK7MypBADcPCYao2ICIAgCEkN8MX9yAoJ91Thd3Yy1+09LHCk5E9f5JCMiIiK3UlhtaWfuKkMCrbjWlfszmUVsP1UBAJg5PBzJcYFt7lfKZVgwrT8A4O1Np1i9IhsmV0RERCSJ063Jlat0CrRi5cr9nSitQ22zAd4qOSb079fhPr9PiUOwrwqFVc1Yl1HUxxGSs2JyRURERJIorLIkJ6403wpg5coT7MqpAgCMjQu64JBVL5Uc901NBAC8/SurV2TB5IqIiIgkYatcuVhyFcnKlVsrr9fhVHkDBAApCUGd7vv7CbHo56NCQVUTvmH1isDkioiIiCRibWgR6ONac66iW5OrM3UtrFa4od25liYWg8P9Ljpk1VulwJ+mnp17ZeT54PGYXBEREVGfE0XxbHLlYpWrYF81VHIZzCJQWtsidThkR3qjGftbFwu+0Fyr881JjYPWS4n8yiak51c7MjxyAUyuiIiIqM9VNurRbDBBABDg5VqVK5lMQGSABgCHBrqbzMIatBjM6OejwoBQ3y49xlulwBVDQgEAvxwvc2R45AIUUgdARETkCCt3F0gdgs2dKbFSh+B0bGtcaRRQuNAaV1ZRgV7Iq2xiUws3IooidrWua5WSEASZIHT5sVckhWFtRhE2Hj2Dv1yT5KgQyQW43qcZERERubzCKtdsZmHFduzup7SuBaV1LVDIBCTHdd7I4nxTBwVDKReQU9GInPIGB0VIroDJFREREfW5s80sXDW58gbAduzu5GRpPQBgQKgvvFTybj3WT6NESoJljtYvxzg00JMxuSIiIqI+d7YNu2vNt7KyrXXFypXbOHHGklwNCvPr0eOvSLLMu9p47IzdYiLXw+SKiIiI+lyhi3YKtLI2tChmcuUWmvUmFLQOVR3cw+TqyqQwAMC+/GrUNhnsFhu5FiZXRERE1OdslSsXHRYYbR0WWNMMURQljoZ6K6usHmYRCPVT9/icjAnyxqAwX5jMIjaf5NBAT8XkioiIiPqUKIq2uUquWrkK12ogCIDOaEZFg17qcKiXTvZySKDVFa3Vq42cd+WxmFwRERFRnyqv10FnNEMmAFoXW+PKSqWQIcLfMjTQOpyMXJNZFHHyjKXD3+Dw3iVXV7bOu9p8ogwGk7nXsZHrYXJFREREfco63ypC6wW5rOtrCTmb2H6WoYEFVY0SR0K9UVLTggadESqFDHGt/6Y9NSomEEE+KtS3GLE3r8pOEZIrkTy5Wrp0KRISEqDRaJCcnIxt27ZdcN+SkhLceeedGDx4MGQyGR599NEO91uzZg2GDh0KtVqNoUOHYt26dQ6KnoiIiLrLOt/K2nHPVcUF+QAA8itZuXJlJ87UAQAGhPhCIevdpbFcJuCywZbqFVuyeyZJk6vVq1fj0UcfxTPPPIOMjAxMmTIFM2fOREFBQYf763Q6hISE4JlnnsHIkSM73Gfnzp2YPXs25syZgwMHDmDOnDm47bbbsHv3bke+FCIiIuoi6xpX0S6eXFkrV0yuXNuJ1vWtejsk0OrSwSEAgF05lXY5HrkWSZOr1157DfPnz8c999yDpKQkLFmyBDExMXj33Xc73D8+Ph5vvPEG5s6dC61W2+E+S5YswVVXXYVFixZhyJAhWLRoEa644gosWbLEga+EiIiIuiqvwjKMLjaod0OwpBZnS644LNBVNeqMtmS/t80srMbFBwEAjpXUoUFntMsxyXVIllzp9Xqkp6dj+vTpbbZPnz4dO3bs6PFxd+7c2e6YV199da+OSURERPaT15qMJAT7SBxJ71iHBbKhhevKKquHCCBCq7Fbc5VwrQbRgV4wi0BGQbVdjkmuQ7LkqqKiAiaTCWFhYW22h4WFobS0tMfHLS0t7fYxdTod6urq2tyIiIjIMXIrLMmIqydX1mGBFQ16VihclLVLoL2qVlbW6tXePCZXnkbyhhaC0LZLkCiK7bY5+piLFy+GVqu13WJiYnr1/ERERNSx+hYDKhp0AIB4F0+utF5KBHhbqh0FnHflkqxV1MQQX7seNzkuEACQns+OgZ5GsuQqODgYcrm8XUWprKysXeWpO8LDw7t9zEWLFqG2ttZ2Kyws7PHzExER0YXltVatgn1V8Ne45hpX54oLYjt2V1XTpEdNkwEyAYgJsm9zFWvlKqOghutdeRjJkiuVSoXk5GSkpaW12Z6WloaJEyf2+Lipqantjvnzzz93eky1Wg1/f/82NyIiIrK/nArLMKz4fq5dtbKK7cd27K7K+m8WofWCWiG367EHhvrCX6NAk96EYyWcbuJJFFI++cKFCzFnzhyMHTsWqamp+OCDD1BQUIAFCxYAsFSUioqKsGLFCttjMjMzAQANDQ0oLy9HZmYmVCoVhg4dCgB45JFHMHXqVLzyyiuYNWsWvv32W2zcuBHbt2/v89dHREREbeW5yXwrK2vlKp9NLVyOdUhgfC8XDu6ITCYgOS4Qm06UY19eNS6JDrD7c5BzkjS5mj17NiorK/Hiiy+ipKQEw4cPx/r16xEXFwfAsmjw+WtejR492vb39PR0rFy5EnFxccjLywMATJw4EV988QX++te/4tlnn0ViYiJWr16NlJSUPntdRERE1DHbBa2bJFfWphacc+V6rJWrOAdVUcfGB1mSq/wq3D05wSHPQc5H0uQKAB544AE88MADHd63fPnydttEUbzoMW+55RbccsstvQ2NiIiI7CyndY2r/m6SXFmHN+ZzzpVLadabcKauBcDZ9crs7dyOgfZo2EauQfJugUREROQ5rAsIu0vlynphXlzTwsYFLqSgqgkigH4+Kvg5qLHKJdFaKOUCyut1KKxqdshzkPNhckVERER9orpRj9pmAwD3aWgR6qeGRimDySyiqJoX0K4iv3V4qqOGBAKARinHiCgtAGBvHluyewrJhwUSEXXVyt0FF9+pj9yZEit1CORAlQ06VDXqEeyrhtZbCRmH89iFdUhghFYDL5V9u7NJRRAExAZ54+SZBuRXNblNRc7dObKZxbnGxQdhf0EN9uVX4XfJ0Q59LnIOTK6IiIhaiaKIbVkV+PloKcytU3wVMgGh/mpcPSwcA0P9pA3QxdmGBLpJ1coqNsgHJ880oKCyEUCI1OHQRRhNZpxurTI6+ly0Lia8L6/aoc9DzoPDAomIiGCZ4P757gL8dMSSWAV6KyGXCTCaRRTXtODTnfm25IB6xt06BVpZ511xrSvXUFTTDKNZhI9Kjn6+Koc+lzW5yiprQHWj3qHPRc6BlSsiIvJ4FfU6LN+Zh6pGPeQyAdddEoHx8UEwi0BNkx7/O1iCE2fqsWJXHu6d0h8RWi+pQ3ZJ7tYp0MqWXHGtK5dwbgt2R3fw6+erRv8QH+SUNyLzdA0uGxzq0Ocj6bFyRUREHs1kFvHF3gJUNeoR4K3EfVP7IyWhHwRBgFwmoJ+vGneMj0VckDdaDGYs/82ShFH3uVunQKvYIK515Ur6ar6V1cjWBYQPFtb2yfORtJhcERGRR/vtVAWKa1vgpZRjwbRERAe2v+BSKWSYmxqPcH8N6nVGLPstF3oj2253hyiKtuQqIbhvLmr7Stw5a111ZT1Oko5ZFB2+ePD5Lom2dAw8eLqmT56PpMXkioiIPFZlgw6/HD8DALhmRAT8O1nvxkslx12T4qH1UqKyUY8d2RV9FaZbKK/XoVFvgkwAYoLcK7mKCvCCTABaDGaU1eukDoc6UV6vQ7PBBKVcQGRA3wzvtSZXB07XMvn2AEyuiIjII4miiG8yi2AwiUgM8cGY2ICLPsZfo8TVw8IAAFuzytGsNzk4SveR21q1igr0glrhHm3YrVQKme1CnU0tnJt1SGBMkDfksr5ZYmFohBZymYCKBh1K61r65DlJOkyuiIjII2UU1CC7vBEKmYAbR0V1eWL7JdEBCPNXo8VgxtascgdH6T5y3bQNu9XZjoHsKOnMrMlvX56HXio5BoVZlnE4wHlXbo/JFREReZxmvQk/HCoBAFyZFIZ+vuouP1YmCLgqKRwAsCO7AvUtBofE6G5yK92zU6BVbJDldRWwY6BTsya/cX3UzMJqJOddeQwmV0RE5HF251ai2WBCqJ8akwYEd/vxSRF+iAn0gsEkYvMJVq+6wl07BVpZL9bzOCzQadU2G1DdZIAAILaDxjWONMKWXLFy5e6YXBERkUcxmMz4LbsSADBtUEiP5l0IgoDpwyzVqz25VVwctAtybZ0C3TO5SgzxBQBklzVIHAldiLVqFRGggVrZt/P+bO3YT9ewqYWbY3JFREQeJT2/Go06IwK8lbik9YKnJxJDfJEY4gOTKGLzyTL7BeiGzOaz7a/dNbkaGNqaXJU3wGTmxbMzyuvjFuznGhzuB5VChroWI5ueuDmF1AEQEfWWwWRGdnkDSmpbUFLbgjN1LQjwUmJ0bACGRmihUvB7JLIwmUVsa21CMWVAcK+7hV0+JAzZ5TnILKzBzOER0PTxt+Gu4nR1M3RGM1RyGaL6qP11X4sJ8oZKIYPOaMbp6iZJLuCpc/mV0jVVUcplGBrhj8zCGhw4XeO2w2OJyRURubiyuhZ8vrsA5Q1t15Ypr9chq6wBakUxRkRpcdXQMPh1soYReYbDRbWobjLAWyVHclxQr48X388bIX5qlNfrkFlYgwn9+9khSvdzvLQOADAg1BcKuXt+2SGXCUgM8cWxkjpknWlgcuVkWgwmlNZa2qD3dTMLq0uitcgsrMHB07WYNSpKkhjI8dzzE46IPMLB0zVYuiUb5Q06+KjkGBmtxYxh4ZiXGofLh4Qi0FsJndGMffnVeGfTKZyu5lAMTyaKIractFStJiYG26WiKQgCxsdbkrS9eVWcS3EBx0vrAQBDwv0kjsSxrEMDszjvyukUVDVBBBDko+p0sXBHsg5DPsSmFm6NlSuSzMrdBVKHYHNnSqzUIVA3mEURPx4qsTUl6B/sg9vHx8JXffYjbXC4Py4fEoq8ykZ8m1GM8gYdPtiagxtHR2FMbKBUoZOETp5pQGldC1RyGSb0733Vymp0TAA2HClFSW0LimqaEd3HXchcwQlrchXhKclVvcSR0PnybEMCpfv/aW3Hfri4Fiaz2GeLGFPfYuWKiFzOr8fLbInV1IEh+OOkhDaJlZVMENA/2Bf3X5qIIeF+MJpFfJ1+Gj8eKmGFwQP9ll0BABgXHwhvlf2+W/RWKzA8ynLRtCe3ym7HdSfHWocFDg73lzgSxxoYZkmuTrFy5XTyJWxmYdU/xBc+Kjma9CaeI26MyRURuZSTZ+qx6bilM9vNo6MwY3j4Rb/90yjl+MOEOFw2OBQAsO1UBX4+esbhsZLzqGzQ4VRZAwQAqYndX9fqYsa1Dg08eLoWLQaT3Y/vyloMJtsaV0luPixwQKjl9Z0qa4CZHQOdhtFsRmGVNbmSrnIllwkY1vpFzAEuJuy2mFwRkcuoadJj9d5CiADGJwRhbHzXh3bJBAFXDQ3Dja2TiLecLLfNvyH3tyfPUlEaGOaLIB+V3Y8f388bIb5q6E1mXjSdJ+tMA8wiEOitRIifWupwHCqunzeUcgFNehOKa5ulDodaFVc3w2gW4a2SI8RX2nPQOjSQ867cF5MrInIJeqMZK/cUoNlgQlSAF64dEdGj44xPCMKM1sVfNxwpxe7cSnuGSU7IaDIjPb8aAJCS4JhufoIgYFzC2cYWdJa1U+CQcH8IgnvPMVHKZbZ1vNjUwnlY17eK7+cj+Tl4yTmLCZN7YnJFRC7h5R+P43R1M7yUctw5PhbKXrRznjooBNMGhQAAvsss5i85N3e4uA5NehO0XkoMCnPcsLQxMQGQywQU11gaW5CFtZnFYDcfEmg1sHVoYDaTK6dhXd9KyiGBViNbk6tjJfXQG83SBkMOweSKiJze4aJaLNuRCwC4NTkagXYY1jV9aBhSEoIgAvgq/TRyW+eEkPvZ01qdHBsf6NDuXN5qBZIiLA0bDhTWOOx5XI21DXuSm3cKtEq0dgw8w+TKGZhFEflVZytXUosJ8kKAtxJ6k9lW1SX3wuSKiJya2Szib98ehihaFmAcEmGfbmOCIOD6kZEYGuEPk1nEZ7vyUVbXYpdjk/M4U9eCvMomyARgrB0WDb6YUecM+TGzIyWAs8mVu3cKtGI7dudSUa9Dk94EpVxARIBG6nAgCAJGtDa1OMh5V26JyRURObV1GUXYX1ADb5UcM4f3bJ7VhcgEAbPHxSAm0AvNBhM+2ZmH+haDXZ+DpGVtjT4k3B9aL8cvHDoo3BdeSjnqWoyshgKoaNChokEHQQAGtbYpd3fWduxZZQ1c8sEJWFuwRwd6QyFzjsvekZx35dac4ywjIupAXYsBi388DgB4+IqBDrk4VsplmJMaj34+KlQ3GfDJzjy20nYTeqMZGYWtjSzsuGhwZxQyGYZHWSo0mRwaaJtvFRfkbde1xZxZQrAPZAJQ32JEWb1O6nA8njMsHny+S6JZuXJnTK6IyGm9sTELFQ069A/2wd2TEhz2PL5qBe6aGA9vlRzFNS1YsTOfE43dwMHTNWgxmBHko0JiSN9VTUbGBAAAjhTXwmDy7PPoWIl18WDPmG8FAGqF3Da3h/OupHc2uZJ+vpWVtWPgyTP1aNIbpQ2G7I7JFRE5pawz9Vi+Iw8A8PwNw6BSOPbjqp+vGn+clAC1Qoa8ykas3JMPo9mzL4xdnXVtq/HxQZD1Yfvl+H4+0Hop0WIw2yo3nsr6+od4yHwrqwGcd+UUapsNqG4yQAAQE+Q8latwrQahfmqYReBIMZtauBsmV0TklP614QRMZhFXDQ3D1Na26Y4WFeCFeanxUMoFnDzTgC/3FsJk5pwJV3TodC1OVzdDLhMwJi6wT59bJgi2hUI9fUHh47bkynMqV0DbeVckHWsL9gitBhqlXOJo2jq73hWHBrobJldE5HTS86uRdvQMZALw1Iwhffrc8cE++ENKHOQyAYeL6/DlvkIYPXxolytauScfADA80h++6r6f62MdGni8tB7Nes+cw2cyizh5pjW5slOXT1dhXevqFIcFSsrazCLOiYYEWo20zbuqkTYQsjsmV0TkVERRxCs/WZpY3JocYxte05cGhvnh9nExkAsCDhXVYjmbXLiUuhYDvs0sBgCMT+gnSQzh/pZhPyaziCPFnvnNdF5lI3RGMzRKGWKdaEhWX7B+bp0sq2fHQAk50+LB5xvBphZui8kVETmVLSfLsSe3CiqFDI9cOVCyOIZFajFvYjxUChlyyhvx4bYc1LFNu0v4NqMITXoTQv3UknUIEwQBo1qrV5ke+s20db7V4DA/hy7e7IwSQ3whCEBNkwGVjXqpw/FILQYTSmotaxc6Y+XKOiwwt6IRtc383eJOmFwRkdMwm0W88tMJAMC81DhEBnhJGs+AUF/cO6U/fNQKlNS24L3N2SisapI0JuqcKIr4fHcBAGB8QhCEPmxkcT7rWja55Y0orfW8BarPLh7sWfOtAMBLJbdV66wdE6lvFVQ1QQQQ6K3skzXuuivIR4WYIMvvuMNFrF65EyZXROQ0vj9YjGMldfBTK/DApQOkDgeApcnFgqn90c9HhZpmA97fmo2tJ8thZqMLp7S/oBrHS+uhUcowOqZvG1mcL9BHhbggb4gA/newWNJYpHCotWKX5GHzrayGR3HYl5TynbAF+/ms1StPb3zjbphckSSMJjPMHIdO59AbzXj155MAgPum9Uegj0riiM7q56vGg5cNwPAoLcwi8NORUty1fC/KuUCo0/lsl6VqdcPISHippO8OZm1s8U1mkbSB9DGzWcT+ghoAQHIfd2t0FtaGBYeYXEkir7WZhVMnV9YEvJDniDvxjOXSyWmIooj3t+bgjY1ZaDaYIBcEKOQCQvzUuGl0FCK00g4DI+ms3leIgqomBLeuN+VsNEo57hgXg30hvvj+YDG2nizHla9twTPXJOHWsdGSDj8ji7L6FluF6PcpcU6xfsyIKC3+d7AYh4vqcKqsQZIGLVLIqWhAbbMBGqXMYytXI6ICALAbnBSMZjNOV1s7BTpfMwurs+3YaySNg+yLlSvqM7XNBvzp03S8/ONxNLd2XjOJInRGM05XN+O9LdksjXuoJr0Rb/6SBQB4+IoB8JGgdXZXCIKAcQlBePCyARgW6Y/aZgOeXHMQd3y4C7kVjVKH5/FW7i6AwSRiTGyArWIkNR+1wtaW+zsPql7tz68BYLl4VMo981JjeJQ/BAEorm1hlbuPFde0wGAS4a2SI8RPLXU4FzQiWstzxA155ice9bkjxbW44e3tSDt6Biq5DC/dNBx/vSYJT80YgkeuGIiBob4wmESs3luI9YdKuHCrh1n2Wx7K63WICfLC7eNipQ7nosL8Nfj2wUn4yzVDoFHKsCunClcv2Yp3Np2C3sg1saSgN5ptQwLvcrLK5yjb0MBij2nLnZ5fDcBzhwQCgJ9Gif7BliFph4pqpA3Gw+SWW9YXi+vn49SjCnzVCiSGWKrZPEfcB5Mrcriyuhbc+eFu5Fc2ISrAC1/fn4rfp8TBW62A1kuJMH8N5k2Mx7RBIQCA7acq8HV6ocdchHi66kY93tucDQB4/KrBUClc42NJIZfhT1MTkfbYNEwZGAy90Yx/bziB69/ajv0F1VKH53F+OFSMigYdwvzVmDk8XOpw2kiK8Ie3So6CqiZkFNZIHU6fSG/9P5Ac67nJFXC2YySbWvSt7HLLSILEEOedb2V1SevcvAOcd+U2XOMqhlyWKIr46zeHUdtswLBIf/zw8GTbGONzyQQBVw8Lxx3jYyETgAOnaz3mIsTTvbclG/U6I4aE++GGkZFSh9NtMUHeWHH3eCyZPQpBPiqcOFOP3727A//431HojFx4uC+Ioohlv+UBAOZMiHO6YWgqhQzTh4YBAL7LdP+ugbVNBpwqs1QORscGSBuMxC7hQrF9zmgyI6/Smlw5/xxHW1MLTotwG845sYHcxg+HSvDz0TNQyAT859aRCPDuvAPciCgtKhrCkHb0DL4/UIz4fj4IcqKucWRfJbXNWL4jDwDw1IwhkLnQQqMrW9dSOtcD0xKx/nAJ9hfU4L/bc7H+cAluHxuLYAeP+b8zxfmHUjpSRmENDp6uhUohwx3jnfO9mDU6Ct9kFuN/B4vx12uToHCyBNCe9hdaqlYJwT7o5+u88136wohzKleiKDr1EDV3UVDdBKNZhK9agVAnnm9ldUnrsOFDRTxH3IX7frqT5CobdHju2yMAgAcvG9DljlHTBoUgrp83dEYzvtxXyPlXbuzfG05AZzRjXHwgLh0cInU4veatVuCW5BjMmRAHb5UcxTUteHvTKezP5zBBR1reWrWaNTLSaS/mJw8IRj8fFSoa9Nh+qkLqcBzKer57etUKAIZF+kMuE1DRoEOJBy4kLYWc1iGB/UOce76V1dAIfyhkAioa9CjmOeIWmFyRw7zw/VFUNuoxOMwPD17W9QVhZYKA25JjoFbIUFDVhC0nyxwYJUkls7AGa/dbuqc9c+1Ql/gl2FVJEf546PKBSAj2gd5kxtf7T2P9oRKu7eYApbUtWH+oBAAwb2K8tMF0QimX4dpLIgAA37r50EA2szhLo5RjUJilWySHBvaN7NYhqQNcYEggYDlHBoe3niOcDuEWmFyRQ2w6XobvDhRDJgD/vvWSbjcpCPRR2ebf/Hq8DCW1zY4IkyQiiiJe+N5S1bx5TJStm5o70XopMX9yAq5ICgVgadSyem8hDCZ2E7SnD7bmwGgWMT4hCMNb5y44q1mjogAAG46UolnvnvPxjCYzDrReIDK5srAtJsxucA6nM5pQ2Lq+VX8XSa6Ac5paMAF3C0yuyO5EUcTrG08CAOZPTuiwgUVXjIoJwLBIf5hF4KfDpXaMkKT2bWYxMgpq4K2S46kZQ6QOx2FkgoArhoThtrHRkAsCDhXVYtlvuWjSG6UOzS2U1bfg8935AICHLu96dVwqY2IDEBPkhSa9CWnHzkgdjkOcOFOPRr0Jvues7+XpRrCpRZ/Jr2yCWQQCvZUuNV/bep3EBNw9MLkiu9uRXYmDp2uhUcqwYFpij48jCAJmDo+AXBCQVdaArLJ6O0ZJUmnSG/Hyj8cBWObihflrJI7I8UbFBOKuSfFQK2TIq2zCf7cxwbKH/27Lhc5oxujYAEweECx1OBclCAJmjbRUr77NcM8Fhc+dbyV3oQY1jjTyvKYW5DjZretbuVLVCmjbVdLMeeYuj8kV2d17WyxrFs0eG9PryeVBPiqk9A8CYKlecc6K63tvSw5K61oQHeiF+ZOda7FXR0oM8cV9UxPhp1agtK4Fy37LQ4vBPYeG9YXKBh0+3WmpWj18xUCXmbN342jLcOctJ8tR3aiXOBr7219QAwAY4+HrW51rUJgfVHIZapsNKKhqkjoct5ZT7jot2M81KMwPaoUM9S1GWxt5cl1MrsiuDp2uxbasCshlAu6Z0t8ux7xscCjUChlKaltsY/nJNWWXN9iS72euSYJGKZc4or4VrtXg7skJ8FbJUVTTjE925EFv5BysnvhwWy6aDSZcEq3FpYNcp9PkgFA/DIv0h9Es4ofWRhzuxNrMYgznW9moFDIkRVq65XJooOM06Y0orrHMz+7vAosHn0spl2EozxG3weSK7Oq9rZYL5+sviUBMkLddjumjVmBa68VT2rEzbAjgokxmEf/31QHojWZMHRSCGcPDpQ5JEmH+Gtw9KQEapQz5VU1YsSuP53Q3VTXqsWJnHgDgEReqWlnNGmWpXn2b6V5DA8vqW1BQ1QRBgFs2qekNLhTreLkVjRABhPip4a9RSh1Ot507fJRcG5Mrspu8ikb82PpN7H29mGvVkYmJwfDXKFDTZMCunEq7Hpv6xrLfcrG/oAa+agVevnmEy10Q21NkgBfumpgAlUKGnPJGfJV+mkNeu+Hj7blo0pswLNIflw8JlTqcbrthZBQEAdibV43T1e4zTGzLiXIAlrWdtF6ud3HrSNY5NRmtwybJ/rJtQwJdq2pldXbeVY20gVCvMbkiu/lgWw7MInDp4JAuLxjcVSqFDFcmhQGwzFXQGTlXxZXkVjTi3xtOAAD+ck0SIgO8JI5IerFB3pgzIQ5yQcDholpsYEfMLimuacZH23MBuNZcq3OFazWYkNAPAPDdAfdZ8+rX45Y1CS8fEiZxJM5nQn/Lv3dmYQ0adGxm4winWte36h/sWvOtrKwdAw8X18LI0QwuTfLkaunSpUhISIBGo0FycjK2bdvW6f5btmxBcnIyNBoN+vfvj/fee6/N/cuXL4cgCO1uLS1c9dqRqhv1+Dr9NADgfjtXraxGxwain48KTXoTdudUOeQ5yP7MZhFPfn0AOqMZkwcE447xMVKH5DQSQ3xx8xhL97htpypYle2Cl9YfQ7PBhPEJQZg+1HUv4q1DA9fuL3KLDnJ6oxlbT1oqV1e4YDXR0WKCvBEb5A2jWcSeXP4/t7eKBh0qGnSQCwIGhLpmctU/2Ae+agVaDGZktSaK5JokTa5Wr16NRx99FM888wwyMjIwZcoUzJw5EwUFBR3un5ubi2uuuQZTpkxBRkYG/vKXv+Dhhx/GmjVr2uzn7++PkpKSNjeNxv3bPUtpXUYR9EYzhkb4Y3xCkEOeQy4TcOlgyy/tbacq2AjARXy4LQd786rhrZJjsYcPB+zI6NhAW1X2+wPFOFZSJ3FEzmtHdgV+OFgCmQA8f/0wlz6XrrkkAt4qOU6VNWCXG3xZtCe3Co16E4J91Rjh5Is5S2XyQMtyAduzmFzZ2/HWz82EYB+XbZQkkwkYHmVtalEjbTDUK5ImV6+99hrmz5+Pe+65B0lJSViyZAliYmLw7rvvdrj/e++9h9jYWCxZsgRJSUm45557cPfdd+M///lPm/0EQUB4eHibGzmOKIpYvbcQAHDH+BiHXvCMiglAoLcSjToj9ua5/gWJu9txqgKv/GRZ0+qZa5Ps1uTE3Vw2OARj4wIhAvhib4FbzcOxF6PJjBe+OwoA+MOEOFtnLVflr1HixtGWquVnu/Iljqb3fjluWRT58iEhkHF9qw5Z12Lbfqpc4kjcz7FSyzqYQyJce+FqNrVwD5IlV3q9Hunp6Zg+fXqb7dOnT8eOHTs6fMzOnTvb7X/11Vdj3759MBgMtm0NDQ2Ii4tDdHQ0rrvuOmRkZHQai06nQ11dXZsbdV1mYQ1OnKmHWiHDDaOiHPpccpmASwdZqldbT5azy5oTK65pxp9XZcAsAjePicKd42OlDslpCYKAWaOiMDDUFwaTiBU7891yDaTe+HRXPk6cqUegtxILrxokdTh2MWdCHABgw5FSnKlz3aHroijil2Ocb3Uxqf37QRCAk2caUObC/97OpllvQn7r2lBDwl37SxfrvKsDrFy5NMmSq4qKCphMJoSFtf0gDgsLQ2lpxxO7S0tLO9zfaDSioqICADBkyBAsX74c3333HVatWgWNRoNJkyYhKyvrgrEsXrwYWq3WdouJ4ZyQ7rBWra4dEdEnHaJGxwVA66VEvc6IfaxeOaUWgwn3f5aOqkY9hkb44583cTjgxchlAu4YH4twfw0adEYs35mHZj0btwBAeb0Or6WdBAA8cfVgBHirJI7IPpIi/DEuPhBGs4hVezoeDu8KsssbUVDVBJVcZhv6Ru0F+qgwPNIyZPK37AqJo3EfJ87UwywCYf5qBPm49mfDmLgAAMDR4jo2PnFhkje0OP+CSxTFTi/COtr/3O0TJkzAH/7wB4wcORJTpkzBl19+iUGDBuGtt9664DEXLVqE2tpa262wsLCnL8fjNOiMtm5Xs8f1TVKqkMls615tOVnOrjpORhRFPPftERw4XYsAbyXen5PssmPg+5pGKce8ifHw1yhQXq/DZ7vzPf78FkURT605iPoWI4ZF+uP2ce5VAZ2TGg8AWLm7wGUr8ZtauwSm9A+Cr1ohcTTObdIAzruyN+s8VVevWgFAhNYLUQFeMItARkG11OFQD0mWXAUHB0Mul7erUpWVlbWrTlmFh4d3uL9CoUC/fv06fIxMJsO4ceM6rVyp1Wr4+/u3uVHX/HCwGE16ExKCfRzWyKIjY+MC4a9RoK7FiHR+ADmV19JOYvW+QsgE4K07RnOeVTdpvZSYNzEeaoUMuRWNWJvhHt3kemr5jjz8erwMKoUMr942EnI3m88zY1g4gn1VKKvXIe3oGanD6RHrfCt2Cbw467yr305VePT/a3sxms3IKrPMt0oKd+35VlbWa6m9eby2cVWSJVcqlQrJyclIS0trsz0tLQ0TJ07s8DGpqant9v/5558xduxYKJUdD0cTRRGZmZmIiIiwT+DUxhetQwJnj3NsI4vzKeQyTLVWr06Uw2h2zW983c17W7Lx1q+nAAAvzBqOKQNDJI7INUVovXDn+FjIBMucxo3HXPOiu7eOFtdh8frWhijXJLnFN9PnUylktmrcip150gbTA7XNBttFIOdbXdzY+ECoFDKU1rUgu5zttnsrr6IJLQYzfNQKRLvJF3lj4wMBAHtzOe3BVUk6LHDhwoX473//i48//hjHjh3DY489hoKCAixYsACAZbje3LlzbfsvWLAA+fn5WLhwIY4dO4aPP/4YH330EZ544gnbPi+88AI2bNiAnJwcZGZmYv78+cjMzLQdk+znRGk9MgpqoJAJtrV6+tK4eMsQlJpmAzK56r3kPt2Vj5d/tFwIPzVjiG2yPvXMwDA/3NjaIGbTiXKPm1/YrDfhoVX7oTeZcWVSKOamuu/5dGeKJZHelVOFk2fqpQ6nW7aeLIfJLGJgqC9i+7nHxa0jaZRyjI+3VCa2Z3HeVW8dL20dEhjmB5mbzOu1nh8ZhdUuO1TY00maXM2ePRtLlizBiy++iFGjRmHr1q1Yv3494uIsv0RLSkrarHmVkJCA9evXY/PmzRg1ahT+/ve/480338Tvfvc72z41NTX405/+hKSkJEyfPh1FRUXYunUrxo8f3+evz919uc9StboiKRShfn2/jphSLsPU1snTm1t/wZM0vtxbiGe/OQwAePCyRNx/qWMWkvY0Y+ODcNlgS/Xvm8wiZLnYhXdPiaKI5787guzyRoT6qfGvW0a6dUOUyAAvXNW6IPJ7m7MljqZ7fm4dynh5EocEdpVt3tUpzrvqDVEUz863cvEW7OdKDPFFgLcSLQYzDhexJbsrkryhxQMPPIC8vDzodDqkp6dj6tSptvuWL1+OzZs3t9l/2rRp2L9/P3Q6HXJzc9tVpF5//XXk5+dDp9OhrKwMGzZsQGpqal+8FI9iNJnxbWYRAODWZOm6K45P6AdvlRxVjXq2LpWAKIp4b0s2nlxzEAAwLzUOT0wfLHFU7uXKpDCMigmAWQQ+312AHA8YSvTulmys3lcIQQBenz3K5TuAdcWfLxsIwJJEu8q/cXWjHhuOWOZBXzOcQ++7yjrvaldOpcc3rOmNsnodqpsMUMgEDAj1lTocu5HJBIyNswwN3Md5Vy5J8uSKXNO2rApUNOjRz0eFaYOlm1ejUshs83o2HS+DmROE+4zZLOKf64/ZhgLeN60/nr9hmFtXGKQgCJZhtwNDfaE3mfHJzjzbBG53tCb9NP710wkAwN+uG2r7lt/djYjW4vIhoTCLwNubTkkdTpes2X8aeqMZwyL9cUm0VupwXMbQSH8EeCvRoDMio7BG6nBc1qHWqk5iiC/UCvfqSDuudWjgHg8bDu4umFxRj6zZfxoAcP3ISCjl0p5GExKC4KWUo7JRz1XN+4jOaMITXx3Ah9tyAViaDSyamcTEykEUMhn+MCEOg8P8YDCJ+HRnPk6Uut9i51tPluOp1irofVP744+TEiSOqG89coWlevVtZjHyKholjqZzoihiZevaXHemxPL/fjfIZQIuG2wZRvm/1qVMqHvMomhrVT4yJkDaYBxgbGtytS+vil0lXRCTK+q2uhaDrWXw78ZESxwNoFbKbQtXbjpexrlXDlbRoMMf/rsbazOKIJcJePXWkbh3an+pw3J7SrkMv58Qi6ER/jCaRXy2qwCZbvSt9/6Catz/WTqMZhGzRkXiqRlDpA6pz42MCcBlg0NgMotOX73alVOFnPJG+KjkmDWq7xsaubobRkUCAP53sIRNC3ogr7IR1U0GqBUyDI1wvy6iI6K0UCtkqG4ysKukC2JyRd3246ES6IxmDAj1xfAo5/hQS+3fD15KOcobdFx4z4GOFtdh1tu/YW9eNfw0Cnx81zj8Lln6BNtTKGQy3DE+FiOitDCJIr7cV4j1h0pcft7GlpPl+P2Hu9GoN2FiYj/8+5aRkLnZelZd9ciVgwAA6zKKkF/pvNUra9XqhlFRXDi4B6YMCEY/HxUqG/XYfopdA7sro7VD8IgoLVQK97uUVSlkGNVakeN6V67H/c5Icrg1+y2NLG4eE+U0Q0E0SjkubZ379cvxMn4T6AA/HS7BLe/tQFFNMxKCffDNg5MwbRDXseprcpmA2eNibO/99lMVmPvxHlQ26CSOrGe+P1CMez7Zi2aDCVMHheC/88a65cVSV42KCcC0QZbqlXXNOGdT2aDDT4dLAAC/T4mVOBrXpJDLcN0lliYg32YUSRyNa9Ebz3bRGx0bKHE0jmNbTJjrXbkcz/0NRj1SWNWEPblVEATY1uBxFhP694PWS4naZgN25bDFrb0YTWb8c/0xLPhsP5r0JkweEIxvHpiExBD36c7kamSCgKuHheOO8bFQyWXYkV2Ja9/cjp9bO7e5AlEUsWJnHh7+IgMGk4jrLonAf+eOhbeKVZBHrrTMvVqz/zQOOOHQz6/TT8NgEnFJtBbDo9jIoqdmjbb8Dv356Bk06Y0SR+M6jpbUQmc0I9BbiTg3XlvNOu9qbz6TK1fD32LULd+0fsM2MbEfIgO8JI6mLaVchiuTQrFmfxE2nyjH2LggeKncq4NQXyurb8Hs93cht3Vy/ZQBwZg+LBw/HCqRODICLENiQv3U+O5AMXIrGvGnT9Nx1dAwvHDDMKf7/3muZr0Jf/3msK0xzu9TYvHirOGQe+hQwPONiQ3ETaOjsC6jCIvWHsJ3f54EhcSNg6zMZhGrrI0sxrNq1RujYwIQ188b+ZVNSDt6hnPXumh/65DA0bGBbrNwcEfGxAZAJgCFVc0orW1BuLbv1xOlnnGOT2tyCaIoYm1rcnXTaOecZzM6NhChfmo0G0zYllUudTgubWd2Ja57cztyKxqhUljm+swcEcELYCcT5q/B+oen4IFLE6GQCUg7egZXvrYFr6edRE2TXurw2skpb8BNS3/Dmv2nIROA/7t6MP5xIxOr8z1zbRK0XkocLanD8h15Uodj8/PRM8irbIKvWoHrR0ZKHY5LEwQBs1rfw284NLBLSmqbkV1mafAwxo2HBAKAn0aJpNZmHbtzORrHlTC5oi7LKKxBbkUjvJRyzBgeLnU4HZIJAqYPDQMA/JZdgboWg8QRuR6jyYzX0k7izv/uQlm9DiF+ajxwaSJGcPiP0/JSyfHkjCH44eEpGBsXiCa9CW/8koVJL/+KxeuPoay+ReoQbRWPG97+DcdL6xHsq8Zn96TgwcsGOM3cTWcS7KvGopmWjomvpZ1EcU2zxBFZ5rq8/OMxAMBdE+Phw0YWvWYdGrg1q8Jl5032pW8yiiECiO/n7RGLi1vX+duWxaYnroSfjNRl61obWcwYHu7U3aGSIvwRG+SNgqom/HS4FLeNjZE6JJdRXNOMR7/ItC1ceNvYaAyNcM9uTO5ocLgfvrwvFesPl+CdTdk4VlKH97fm4OPfcnHV0DDcNjYGUwaGOLRKtHJ3QbttpXUt+DajCPlVTQCA+H4+uH18DPIqmpBX0X5/srhtbAy+Tj+NffnVeO67I/hw7lhJ4/l0Vz7yKpsQ7KvGgksTJY3FXSSG+GJElBaHimrxw6ESzE2Nlzokp2U2i/gqvRCA+1etrKYODMEHW3Ow9WQ5RFHkF1EugldM1CU6ownfH7QsdnjTaOceFy4IAq4dEQEBQGZhDU6VcY2IixFFEV/tK8TVS7ZiT14VfNUKvHH7KPzrlpFMrFyMTCbguksisf7hyfho3liMiQ2AwSRi/aFS3LVsLya/8iv+uf4Y9hdUw+zgNeHqWwxYf6gEb/+ahfyqJqjkMlwzPBzzJyfAX6N06HO7A5lMwD9vHmEb7vmdhAvO1jTp8eYvWQCAJ6YPcuov2FzNrNY1r75OP80FYzuRduwMcsoboVbIPKaRytj4QHgp5Sir1+F4ab3U4VAX8aqJumTT8XLUNBkQ6qe2lamdWUyQN1L6WzrtfJtZxNbsnThT14L5n+zD/319EPUtRoyMCcD/HprMydUuThAEXJEUhrUPTML6h6fgronx0HopUVLbgg+25uDmpTsw8eVf8dy3h7HpeBma9Sa7PXd1ox7fZhbh3xtOYPupCphFS0X50SsHYrKDK2fuZlCYH+5vrRI99fVBHCupkySON37JQm2zAUPC/XArRwPY1Y2jo6BWyHDwdC12ZHNuTUdEUcTSzdkALJ2BNUrPaFalUcqRmtgPgGU9QHIN/OqJumRdhqWr102jo1zmwmj60HAcKa5DZaMeW06W48qkMKlDcipGkxmf7y7Aqz+fQF2LESq5DI9dNQj3Tklwms5kZB9DI/3x/A3D8PTMIdh0vAzrD5fi12NnUFrXgk925uOTnflQKWQYHx+ECf2DMDo2ECNjArpVnSitbUHasTNYsSMP2eUNsBbFYgK9cNmQUAwJd44Fx13RI1cMRGZhDbZlVeBPn+7Ddw9ORmAfzjfJKW/ApzvzAQB/vXaoy/wOcBXBvmrcMT4Wy3fk4a1fs1ziC8y+tjO7EgcKa6BWyDzu/Zk6MBi/Hi/DlhPlWDCNw3FdAZMruqjqRj1+PV4GALhpjOtUMzRKOa4dEYEv9hZiy8lyjIwOQIifWuqwnML2rAq8+L8jOHnGMmRyRJQWr942EoPC/CSOjBxJo5Rj5ogIzBwRgRaDCduzKvDL8TJsPVmOoppmbD9Vge2nLBOnZYJlPkh8sA9ig7wRG+QNP40CMkGAIAA6oxn5lY3IrWhEdlkjTpxpO2RlQKgvpg0KQf9gH84T6CWFXIa37hiNG97+DQVVTXhw5X6suHt8n3wJYjSZ8ey3h2E0i7h8SCgmD/SsC9u+8qep/fH57nzsyqnCvrwq2xpHZPHuFkvVava4GI8bkjptcCjw/VHsy69Co87IRjIugP9CdFH/O1gMg0nE0Ah/l/v2eUSUFvsLqnHyTAO+ySzC/MkJbr0uRmdEUcTu3Cq8uznbNrwg0FuJx6cPxu3jYlitcmEdNZHoqhFRWgyP9EdFgx5ZZfUoqGpCQVUTapoMyCprQFYX5ywKsAzHHRrhj6ER/gjmFxl2FeCtwodzx+Kmpb9hR3Yl/vHDMTx3/VCHJ64v/3gcv52qhJdSjr9ck+TQ5/JkkQFeuCU5Gqv2FOLtTaew/I/jpQ7JaRw6XYttWRWQywTcO6W/x3XOi+/nbWvStTO7ElcO5SgcZ8fkii5qTWuXwJtdqGplJQgCbhgZhTd+OYncikb8erzM44YHGkxm/HLsDN7bkoPMwhoAgFwmYM6EODx65UAEeLt/O1vqnCAICPFTI8RPjYmto07qWgwoqWlBdZMeVY2Wm8FkhghLoi4TBAT5qBDsq0awrwqRAV7wY5MKhxoc7ofXbhuJBZ/tx/IdeTCZRbxwwzDIHDRMb036afx3ey4A4D+3jsSAUF+HPA9ZLJiWiNV7C7H5RDkOna7FiGjPaNpwMUs3nwIAzBoZiZggb4mj6XuCIGDqoGB8tqvAMsWByZXTY3JFncopb0BmYQ3kMgE3jHLNBSODfFS4cVQUvko/jU3HyxAb5O32w9/MZkuV6rsDxfjxcAlqmizrfakUMtyaHI17p/RHfLCPxFGSM/PXKOEfzmTJ2cwYHoG/3zgcf/v2MD7dlY+aZgNevdX+XT33F1Rj0dpDAICHLh+Aay+JsOvxqb24fj6YNSoK6zKK8PamLLw/R9rW+87gVFkDfjpSCgAe3f5/2qBQfLarAFuz2NTCFTC5ok6ta101fsrAYIT6aSSOpudGxwYiv7IJe/Kq8OW+Qvz5sgFuVbExm0WcKm/AzuxK7MyuxO7cSlQ3nV1AOdhXjdnjonHXxATOOyNycXMmxCHAS4mFX2bi+wPFqG02YOnvx9htLkrWmXos+DQdepMZVw0Nw2NXDrLLceniHrg0Ed9kFmHDkTM4WlyHoZGuNRTfnkRRxD9+OApRBK4aGub2X4p2JjWxH5RyAfmVTciraOSXo06OyRVdkNFkxlf7LF0Cbx4TLXE0vXftJRE4XdOE4poWrNpTgHun9odC5nrzjERRRHFtCw4U1uDA6RocLKzFoaJaNOiMbfbz0ygwc3g4bhgZhQn9gzinisiNXD8yElovJe77NB1bT5bj6te3YvHNIzB1UEivjvu/g8V48uuDaNKbMDjMD6/PHuWwYYfU3sAwP1wzIgI/HCzB02sPYu39Ez32s3v9oVJsPlEOlVyGp2cOkTocSfmqFUiOC8SunCpsOVnO5MrJMbmiC9p8ohyldS0I9Fbi6mGuP8ZXKZfhzvFxeHtTFgqrm/F1+mncmhzj9G2FTWYRx0rqsCO7ArtzqnDgdA0qGvTt9tMoZRgbF4TUxH6Y0L8fLonWQumhv5SJPMHUQSFYeW8KHlqVgdPVzZj78R78bkw0nr0uqdPKfEcNUExmERuOlNq6RfYP8cHvkqPxXaZ0Cxd7qr9dNxTbTpbj4OlafLAtBw9cOkDqkPpcXYsBL3x/BABw/6WJSAzhfL9pg0KxK6cKW0+WY97EeKnDoU4wuaILWrXH8gv4luRoqBXusWBfkI8Ks8fG4tNdeTh4uhYCgFuSnWtBzJW7C9BiMOFEaT2OFNciu7wRzYa2C7zKBCDcX4OoQG/EBHohKtALoX4aW6J4orQeJ7iaO5HbGx0biA2PTsV/fj6B5TvysGb/afx8pBQ3jYnCnSmxF+3wqjOakFFQg105lSir1wEApg0KwZVJYU7/xZO7CvPX4G/XD8MTXx3AkrQsXJnkeUPiXt1wAmX1OiQE+9gW0fZ0lw4OwSs/Hcf2UxVo0Bk9riW9K+G/DHWouKYZm05Y1ra6fXysxNHY1+BwP9wxPhar9hTgwOlaAMCdKbGSX0gYTGakHT2DFTvzkFXWAJN1FVYAaoUMCcE+6N+65lBEgBerUkQEAPBRK/Dc9cNw3SWRWLT2IE6eacCKnflYsTMfo2ICMD4hCP2DfdA/xBc+ajmOldShptmAM3WW4cU6oxmA5XPmluRoDItklzqp/W5MFNYfKsGvx8vwxFcHPGp44IHCGqzYZVm0+qUbh0OjdI8vd3trSLgf+gf7IKeiEWlHS3HTaNefruGumFxRh77cVwizCEzoH+SW5fhhkdo2CdajqzPxr99dAi9V33+IF1Y1YdWeAny57zQqGnS27cG+KgyP1CIpwh+RAV6SJ39E5NyS4wLx0yNT8Vt2BVbtKcDPR84gs7DGtgTDhfTzUWFC/34YExsoyWcgtScIAhbfPAJXvbYFB0/X4r0t2fjz5QOlDsvhWgwmPL32EEQRuGl0FCYO4KLVVoJg6dq8ZGMWvs0sZnLlxJhcUTsms4jVewsBAHe4WdXqXMMitbh9XCy+2FuA7w8U43hJHd66c3SfLJRsNJnxy/EyrNxtaa0qthapgn3VGBbpj5ExAQjzUzt8gVAici8ymYApA0MwZWAIyut1+PloKbLONCCnohE55Q1o1pugUcqh9VIiwFuJQWF+GBDq67GLqzuzMH8Nnrt+GB7/6gBeTTuJuH4+uH6kay6J0hVms4jHvzqAYyV1CPRW4plruWj1+W4YaUmutmVVoLJBh36+7P7rjJhcUTtbTpahpNbayCJc6nAcaniUFncpE/D9wWJklTVg1tu/4a/XDcUfUmIdktgU1zTji72FWL23AGfqzlapJg8Ixp0psbhqaJitQyMRuY+Omkj0BQECBoX5edycnZ6S6t+pI3emxOLmMVHYl1+NVXsK8NjqTPiqFbhsSKjUoTnEG79k4YeDJVDKBbz3h2QEM3Fop3+IL0ZEaXGoqBbrD5dizoQ4qUOiDjC5onZW7rZUrX43JtojxjoPCPXFj49MwRNfHcDmE+V49pvDWJN+Gg9fMQCXDQ7tdZKlN5qx9WQ5Vu0pwKYTZbBOperno8ItY6Nxx7hYtlUlIqJ2BEHAP24cjgadEd8fKMaCz9Kx4u7xSOnfT+rQ7Oq7A8V445csAMBLN45wu9dnTzeMjMSholp8l1nE5MpJMbmiNoprmvHr8TMA3K+RRWeCfdX4eN44fPxbLv694QQyC2tw9/J9GB7ljz9NTcS0QSHQeim7fDyd0YSd2ZX44WAJNhwpRV3L2TWoUvv3w50psZg+LMxtujASEZFjyGUCXrttJBp1Rvx6vAzzP9mHD+Yku818pN05lXjiqwMAgD9N7Y/bxjlXB19nc93ICPzzx2PYm1eNoppmRAV4SR0SnYfJFbWx7LdcWyOLAaHu18iiMzKZgHum9McNoyLx3225+GxXPg4X1eHhVRmQywSMjgnA1EEh6B/igxBfNYL91NAo5ahu1KO6SY+KBh0Ona7D/oJqHC2ug95kth07xE+NWSMjcUdKrFs2CCEiIsdRymVY+vsxmPfxHuzOrcLvP9qNhy8fiIevGOjSzY6+zSzC/311EHqTGVcmheKpGZ69WHBXRGi9MD4+CLtzqyzVzGlsVe9smFyRTW2zAav2WIYE3jfVc/+zhvpp8JdrkrBgWiKW/ZaLHw6VIKe8Efvyq7Evv7obx1FjxvBwXDsiAmPjg1z6FyAREUlLo5Rj+R/H4/nvjmD1vkK88UsWduVU4o3bRyNcq5E6vG4RRRFv/3oKr6adBABcPSwMS2aP5u/JLpo1Kgq7c6vwXSaTK2fE5IpsVu4uQIPOiEFhvrh0cIjU4UguyEeFx6cPxuPTB6Owqglbs8qxK6cKZ2pbUN6gQ0W9Ds0GEwJ9VAjyViHAW4nB4X4YExuIMbGBiAnyYrc/IiKyGy+VHK/ccglSE/vhmXWHsDu3Cle8uhnzJsbjnin9EeSjkjrEi6ptMuD5749gXUYRAODeKQl4emYSE6tumDk8HM99dxhHS+pwqqweA0LZsMaZMLkiAJY5Qst+ywUA/GlqIpOC88QEeeP3KXH4fQonjxIRkbRuHB2FS6K1eGx1Jg6crsXSzdlYviMPc1Pjcfu4GKdskmQ2i/g6/TRe/uk4qhr1kAnAC7OGsylDDwT6qDBtUAg2HivDF3sK8dfrhkodEp2DyRUBAL7NKEZZvQ7h/hrc4MbraBAREbmD/iG++ObBSUg7egZLNmbhaEkd3tuSjfe2ZGNIuB+uHhaOy4eEYkiEn6TNk0xmEdtPVWDJxpPIKKgBAAwM9cU/bhzOroC98PsJcdh4rAyr9hTgoSsGdqvpFjkWkyuC2Szig205AIC7J8dDpZBJHFHfc6a1TYiIiLpCEARMHxaOq4aGYeOxMnyyIw87cypxvLQex0vr8cYvWVDKBQwO98OIKC0Sgn0QE+iNmCBvRAV4QeulhKwLw/G6+ztSFEVUNeqRWViD9Pxq1DQbAAAqhQxXDAnFxMRgZJc3Iru8sUevm4BLB4VgcJgfTpypx8rdBbj/Us69chZMrgi/Hi/DqbIG+KkVuMOD2q8TERG5A0EQcNXQMFw1NAw1TXpsPFaGDUdKsTevCjVNBhwuqsPhorp2j1PIBPTzVSHYV3325qdCsI8a/l4K+GuU8PdSoqi6GRqlDF5KOZStX8CKIiBCRLPehPoWI+pbDKhs1KOgqgkFVU2oP2cJEo1ShtExgZjazWVN6MIEQcC9U/vjia8OYNlvubh7cjyXd3ESTK48nCiKeGvTKQDAnRNi4afhhx4REZGrCvBW4ZbkaNySHA1RFHG6uhmHi2pxtKQO+ZVNKKxuQmFVEyoa9DCaRZyp0+FMnc7uccgEIL6fD8bGB2FYpD+Ucs8bFeNoN4yMxH82nEBpXQu+zSzGbWO5RpgzYHLl4b4/WIIDhTXwVskxf3KC1OEQERER7D9cPULrhQitFybAMs/JaDajUWdCQ4sRDToDGnRG1LcY0aAzolFnRIvBjBajCS0GE1oMZjQbTNAbze2OKxcE+GkU8NUooPVSIjrQG7Gtww49cZpBX1IpZPjjpHgs/vE4Ptyag1uTo9mQzAkwufJgLQYTXvnxOABgwbREhPq51joZRERE1DMKmQxaL1nrMD2vLj3GZBZhMJkhAIAACBCglAu8oJfQHSmxeOvXU8gqa8DmE+W4bEio1CF5PH6l4MGW78hDUU0zwv01uHdKf6nDISIiIicmlwnQKOVQK+VQK+RQKWRMrCTmr1HijvGW4YDvbsmGKIoSR0RMrjxUZYMO7/xqmWv1f1cPhpeKkyCJiIiIXM3dkxOgksuwJ7cKPx4ulTocj8fkykO98UsW6nVGDI/yx02jo6QOh4iIiIh6IELrhQXTLCOQXvj+COpbDBJH5NmYXHmg46V1+Lx1ouwz1wzt0hoXREREROScHrhsAOL7eeNMnQ6v/nxS6nA8GpMrD9NiMOGRVZkwmUVcPSwMqYlcHZ2IiIjIlWmUcvzjxhEAgE925uHg6RppA/JgTK48zMs/HseJM/UI9lXjpZtGSB0OEREREdnB5IHBuHFUJEQR+Mu6QzCa2rfOJ8djcuVBfj1+Bst35AEA/nPrJQj2VUsbEBERERHZzTPXDoW/RoHDRXV4e9MpqcPxSEyuPERZfQv+76uDAIC7JyXg0sFcB4GIiIjInYT4qfHsdUMBAEs2ZmFdxmmJI/I8TK48QIvBhIdXZaCyUY8h4X54csZgqUMiIiIiIge4dWwM7ptq6R745NcHseNUhcQReRYmV25OZzRhwWfp2JVTBR+VHG/dMRoaJde0IiIiInJXT80YgmsviYDBJOK+T9NxorRe6pA8BpMrN2YwmfHQygxsPlEOjVKGj+8ah4FhflKHRUREREQOJJMJePXWkRgfH4R6nRFzPtqNzMIaqcPyCEyu3JTRZMbCLw/g56NnoFLI8N+545DSn23XiYiIiDyBRinHB3OTMSjMF2X1Otz23k6s3lsgdVhuj8mVGzpd3YTZH+zC9weKoZQLeO8PYzB5YLDUYRERERFRHwrwVuHr+yfiqqFh0JvMeGrNISxaewgtBpPUobktJldu5qfDJbjmjW1Iz6+Gn1qBd3+fjMuHhEkdFhERERFJwF+jxPt/SMb/XT0YggCs2lOAK17dgjXpp2Eyi1KH53YUUgdA9lFQ2YQlv5zE2v1FAICRMQF46/bRiO3nLXFkRERERCQlmUzAg5cNwPAoLZ78+gCKaprx+FcH8OG2HDx21SBcMSQUCjlrLvbA5MrF5VU04p1Np7A2o8j27cN90/rjiemDoeR/EiIiIiJqNW1QCDY/cRmW78jDu5tP4XhpPe77NB3BvipcPzISN42OwogoLQRBkDpUlyWIoihpPXDp0qX497//jZKSEgwbNgxLlizBlClTLrj/li1bsHDhQhw5cgSRkZF48sknsWDBgjb7rFmzBs8++yyys7ORmJiIl156CTfddFOXY6qrq4NWq0VtbS38/f17/NocpaimGRsOl2LDkVLszauCtaI7dVAIHrliIJLjAqUNsItW7uakSiIiIqLeujMlttuPqWnS470tOfhqXyEqG/W27SF+aqQkBCGlfz+MjQtEYogvVArP/sK+O7mBpJWr1atX49FHH8XSpUsxadIkvP/++5g5cyaOHj2K2Nj2J0lubi6uueYa3Hvvvfjss8/w22+/4YEHHkBISAh+97vfAQB27tyJ2bNn4+9//ztuuukmrFu3Drfddhu2b9+OlJSUvn6JvdagM+JocR0OFdXiSFEtDhXVIqusoc0+lw4OwcNXDMSYWNdIqoiIiIhIWgHeKjw9cwgenz4I27LKsS6jGD8fKUV5vQ7/O1iC/x0sAQAoZAISgn0wKMwPsf28ERnghagADcL8NQj0VkHrpYS3Ss5qVytJK1cpKSkYM2YM3n33Xdu2pKQk3HjjjVi8eHG7/Z966il89913OHbsmG3bggULcODAAezcuRMAMHv2bNTV1eHHH3+07TNjxgwEBgZi1apVXYrLmSpXj395AGv2n26zTRCAcXFBuHp4OKYPDUNMkGvOq2LlioiIiKj3elK56kiLwYTMwhrszqnC7txKHDxdiwad8aKPU8oFaL1UCPBWQuulhL9GAS+VHGqFHGqFDBql5U+1Qga1Ug6ZIEAuQ+uflpvt74IAmezs/VcPC4dGKbfL6+spl6hc6fV6pKen4+mnn26zffr06dixY0eHj9m5cyemT5/eZtvVV1+Njz76CAaDAUqlEjt37sRjjz3Wbp8lS5ZcMBadTgedTmf7uba2FoDljZTagAABIWoTkiL8MTTCH0Mj/TEiSot+vurWPYxOEWdPNDVytXAiIiKi3rLnteDQYCWGBofhj+PDIIoiSmtbcKq8AdllDSipbUFxbTNKaltQXqdDXYsBBpMIHYCypkaUVdotDJtNT0xDiJ/G/gfuBuv725WalGTJVUVFBUwmE8LC2rYJDwsLQ2lpaYePKS0t7XB/o9GIiooKREREXHCfCx0TABYvXowXXnih3faYmJiuvhyH2yd1AERERETklO6VOgAHGrBE6gjOqq+vh1ar7XQfybsFnj8+UxTFTsdsdrT/+du7e8xFixZh4cKFtp/NZjOqqqrQr18/txg/WldXh5iYGBQWFko+zNFT8D3ve3zPpcH3ve/xPe97fM+lwfe97/E975goiqivr0dkZORF95UsuQoODoZcLm9XUSorK2tXebIKDw/vcH+FQoF+/fp1us+FjgkAarUaarW6zbaAgICuvhSX4e/vz/8ofYzved/jey4Nvu99j+953+N7Lg2+732P73l7F6tYWUnWV1GlUiE5ORlpaWlttqelpWHixIkdPiY1NbXd/j///DPGjh0LpVLZ6T4XOiYREREREZE9SDoscOHChZgzZw7Gjh2L1NRUfPDBBygoKLCtW7Vo0SIUFRVhxYoVACydAd9++20sXLgQ9957L3bu3ImPPvqoTRfARx55BFOnTsUrr7yCWbNm4dtvv8XGjRuxfft2SV4jERERERF5BkmTq9mzZ6OyshIvvvgiSkpKMHz4cKxfvx5xcXEAgJKSEhQUnG3XnZCQgPXr1+Oxxx7DO++8g8jISLz55pu2Na4AYOLEifjiiy/w17/+Fc8++ywSExOxevVql1zjyl7UajWee+65dkMfyXH4nvc9vufS4Pve9/ie9z2+59Lg+973+J73nqTrXBEREREREbkLyeZcERERERERuRMmV0RERERERHbA5IqIiIiIiMgOmFwRERERERHZAZMrN7F06VIkJCRAo9EgOTkZ27Zt63T/LVu2IDk5GRqNBv3798d7773XR5G6vsWLF2PcuHHw8/NDaGgobrzxRpw4caLTx2zevBmCILS7HT9+vI+idm3PP/98u/cuPDy808fwHO+9+Pj4Ds/bBx98sMP9eZ5339atW3H99dcjMjISgiDgm2++aXO/KIp4/vnnERkZCS8vL1x66aU4cuTIRY+7Zs0aDB06FGq1GkOHDsW6desc9ApcU2fvu8FgwFNPPYURI0bAx8cHkZGRmDt3LoqLizs95vLlyzs8/1taWhz8alzDxc71u+66q917N2HChIsel+f6hV3sPe/ofBUEAf/+978veEye5xfH5MoNrF69Go8++iieeeYZZGRkYMqUKZg5c2abNvbnys3NxTXXXIMpU6YgIyMDf/nLX/Dwww9jzZo1fRy5a9qyZQsefPBB7Nq1C2lpaTAajZg+fToaGxsv+tgTJ06gpKTEdhs4cGAfROwehg0b1ua9O3To0AX35TluH3v37m3znlsXaL/11ls7fRzP865rbGzEyJEj8fbbb3d4/7/+9S+89tprePvtt7F3716Eh4fjqquuQn19/QWPuXPnTsyePRtz5szBgQMHMGfOHNx2223YvXu3o16Gy+nsfW9qasL+/fvx7LPPYv/+/Vi7di1OnjyJG2644aLH9ff3b3Pul5SUQKPROOIluJyLnesAMGPGjDbv3fr16zs9Js/1zl3sPT//XP34448hCEKbJY46wvP8IkRyeePHjxcXLFjQZtuQIUPEp59+usP9n3zySXHIkCFttt13333ihAkTHBajOysrKxMBiFu2bLngPps2bRIBiNXV1X0XmBt57rnnxJEjR3Z5f57jjvHII4+IiYmJotls7vB+nue9A0Bct26d7Wez2SyGh4eLL7/8sm1bS0uLqNVqxffee++Cx7ntttvEGTNmtNl29dVXi7fffrvdY3YH57/vHdmzZ48IQMzPz7/gPsuWLRO1Wq19g3NTHb3n8+bNE2fNmtWt4/Bc77qunOezZs0SL7/88k734Xl+caxcuTi9Xo/09HRMnz69zfbp06djx44dHT5m586d7fa/+uqrsW/fPhgMBofF6q5qa2sBAEFBQRfdd/To0YiIiMAVV1yBTZs2OTo0t5KVlYXIyEgkJCTg9ttvR05OzgX35Tluf3q9Hp999hnuvvtuCILQ6b48z+0jNzcXpaWlbc5ltVqNadOmXfDzHbjw+d/ZY6hztbW1EAQBAQEBne7X0NCAuLg4REdH47rrrkNGRkbfBOgmNm/ejNDQUAwaNAj33nsvysrKOt2f57r9nDlzBj/88APmz59/0X15nneOyZWLq6iogMlkQlhYWJvtYWFhKC0t7fAxpaWlHe5vNBpRUVHhsFjdkSiKWLhwISZPnozhw4dfcL+IiAh88MEHWLNmDdauXYvBgwfjiiuuwNatW/swWteVkpKCFStWYMOGDfjwww9RWlqKiRMnorKyssP9eY7b3zfffIOamhrcddddF9yH57l9WT/Du/P5bn1cdx9DF9bS0oKnn34ad955J/z9/S+435AhQ7B8+XJ89913WLVqFTQaDSZNmoSsrKw+jNZ1zZw5E59//jl+/fVXvPrqq9i7dy8uv/xy6HS6Cz6G57r9fPLJJ/Dz88PNN9/c6X48zy9OIXUAZB/nf5MsimKn3y53tH9H26lzf/7zn3Hw4EFs37690/0GDx6MwYMH235OTU1FYWEh/vOf/2Dq1KmODtPlzZw50/b3ESNGIDU1FYmJifjkk0+wcOHCDh/Dc9y+PvroI8ycORORkZEX3IfnuWN09/O9p4+h9gwGA26//XaYzWYsXbq0030nTJjQpgHDpEmTMGbMGLz11lt48803HR2qy5s9e7bt78OHD8fYsWMRFxeHH374odMLfp7r9vHxxx/j97///UXnTvE8vzhWrlxccHAw5HJ5u29pysrK2n2bYxUeHt7h/gqFAv369XNYrO7moYcewnfffYdNmzYhOjq624+fMGECv+npIR8fH4wYMeKC7x/PcfvKz8/Hxo0bcc8993T7sTzPe87aEbM7n+/Wx3X3MdSewWDAbbfdhtzcXKSlpXVateqITCbDuHHjeP73UEREBOLi4jp9/3iu28e2bdtw4sSJHn3G8zxvj8mVi1OpVEhOTrZ18bJKS0vDxIkTO3xMampqu/1//vlnjB07Fkql0mGxugtRFPHnP/8Za9euxa+//oqEhIQeHScjIwMRERF2js4z6HQ6HDt27ILvH89x+1q2bBlCQ0Nx7bXXdvuxPM97LiEhAeHh4W3OZb1ejy1btlzw8x248Pnf2WOoLWtilZWVhY0bN/boSxlRFJGZmcnzv4cqKytRWFjY6fvHc90+PvroIyQnJ2PkyJHdfizP8w5I1UmD7OeLL74QlUql+NFHH4lHjx4VH330UdHHx0fMy8sTRVEUn376aXHOnDm2/XNyckRvb2/xscceE48ePSp+9NFHolKpFL/++mupXoJLuf/++0WtVitu3rxZLCkpsd2ampps+5z/nr/++uviunXrxJMnT4qHDx8Wn376aRGAuGbNGilegst5/PHHxc2bN4s5OTnirl27xOuuu0708/PjOd4HTCaTGBsbKz711FPt7uN53nv19fViRkaGmJGRIQIQX3vtNTEjI8PWle7ll18WtVqtuHbtWvHQoUPiHXfcIUZERIh1dXW2Y8yZM6dNd9jffvtNlMvl4ssvvyweO3ZMfPnll0WFQiHu2rWrz1+fs+rsfTcYDOINN9wgRkdHi5mZmW0+53U6ne0Y57/vzz//vPjTTz+J2dnZYkZGhvjHP/5RVCgU4u7du6V4iU6ns/e8vr5efPzxx8UdO3aIubm54qZNm8TU1FQxKiqK53ovXOzzRRRFsba2VvT29hbffffdDo/B87z7mFy5iXfeeUeMi4sTVSqVOGbMmDZtwefNmydOmzatzf6bN28WR48eLapUKjE+Pv6C/6moPQAd3pYtW2bb5/z3/JVXXhETExNFjUYjBgYGipMnTxZ/+OGHvg/eRc2ePVuMiIgQlUqlGBkZKd58883ikSNHbPfzHHecDRs2iADEEydOtLuP53nvWdvXn3+bN2+eKIqWduzPPfecGB4eLqrVanHq1KnioUOH2hxj2rRptv2tvvrqK3Hw4MGiUqkUhwwZwgT3PJ2977m5uRf8nN+0aZPtGOe/748++qgYGxsrqlQqMSQkRJw+fbq4Y8eOvn9xTqqz97ypqUmcPn26GBISIiqVSjE2NlacN2+eWFBQ0OYYPNe752KfL6Ioiu+//77o5eUl1tTUdHgMnufdJ4hi6yxvIiIiIiIi6jHOuSIiIiIiIrIDJldERERERER2wOSKiIiIiIjIDphcERERERER2QGTKyIiIiIiIjtgckVERERERGQHTK6IiIiIiIjsgMkVERF5pOeffx6jRo2SOgy7ueuuu3DjjTdKHQYRkUdjckVERHa3Y8cOyOVyzJgxQ+pQHOquu+6CIAjtbl193fHx8ViyZIljgyQioj6jkDoAIiJyPx9//DEeeugh/Pe//0VBQQFiY2OlDslhZsyYgWXLlrXZplarJYqGiIikxMoVERHZVWNjI7788kvcf//9uO6667B8+fI292/evBmCIOCXX37B2LFj4e3tjYkTJ+LEiRNt9nv33XeRmJgIlUqFwYMH49NPP21z//PPP4/Y2Fio1WpERkbi4Ycf7jSul19+GWFhYfDz88P8+fPR0tLSbp9ly5YhKSkJGo0GQ4YMwdKlSy/6etVqNcLDw9vcAgMDLxrnpZdeivz8fDz22GO2ipd1//OHKy5ZsgTx8fG2n00mExYuXIiAgAD069cPTz75JERRbPMYnU6Hhx9+GKGhodBoNJg8eTL27t170ddDREQ9x+SKiIjsavXq1Rg8eDAGDx6MP/zhD1i2bFm7C38AeOaZZ/Dqq69i3759UCgUuPvuu233rVu3Do888ggef/xxHD58GPfddx/++Mc/YtOmTQCAr7/+Gq+//jref/99ZGVl4ZtvvsGIESMuGNOXX36J5557Di+99BL27duHiIiIdonThx9+iGeeeQYvvfQSjh07hn/+85949tln8cknn/T4vegszrVr1yI6OhovvvgiSkpKUFJS0uXjvvrqq/j444/x0UcfYfv27aiqqsK6deva7PPkk09izZo1+OSTT7B//34MGDAAV199Naqqqnr8eoiI6CJEIiIiO5o4caK4ZMkSURRF0WAwiMHBwWJaWprt/k2bNokAxI0bN9q2/fDDDyIAsbm52XaMe++9t81xb731VvGaa64RRVEUX331VXHQoEGiXq/vUkypqaniggUL2mxLSUkRR44cafs5JiZGXLlyZZt9/v73v4upqakXPO68efNEuVwu+vj4tLm9+OKLXYozLi5OfP3119tse+6559rEJYqi+Prrr4txcXG2nyMiIsSXX37Z9rPBYBCjo6PFWbNmiaIoig0NDaJSqRQ///xz2z56vV6MjIwU//Wvf13w9RARUe+wckVERHZz4sQJ7NmzB7fffjsAQKFQYPbs2fj444/b7XvJJZfY/h4REQEAKCsrAwAcO3YMkyZNarP/pEmTcOzYMQDArbfeiubmZvTv3x/33nsv1q1bB6PReMG4jh07htTU1Dbbzv25vLwchYWFmD9/Pnx9fW23f/zjH8jOzu70NV922WXIzMxsc3vwwQd7FGdX1NbWoqSkpE38CoUCY8eOtf2cnZ0Ng8HQ5j1UKpUYP3687T0kIiL7Y0MLIiKym48++ghGoxFRUVG2baIoQqlUorq6us1cJKVSafu7db6R2Wxut+3c41i3xcTE4MSJE0hLS8PGjRvxwAMP4N///je2bNnS5rhdZX3eDz/8ECkpKW3uk8vlnT7Wx8cHAwYM6PC+nsQpk8naDaM0GAxdfSkAYHt8Z+8hERHZHytXRERkF0ajEStWrMCrr77apopz4MABxMXF4fPPP+/ysZKSkrB9+/Y223bs2IGkpCTbz15eXrjhhhvw5ptvYvPmzdi5cycOHTp0wePt2rWrzbZzfw4LC0NUVBRycnIwYMCANreEhIQux92RzuJUqVQwmUxt9g8JCUFpaWmbBCszM9P2d61Wi4iIiDbxG41GpKen234eMGAAVCpVm/fQYDBg3759bd5DIiKyL1auiIjILv73v/+huroa8+fPh1arbXPfLbfcgo8++gh//vOfu3Ss//u//8Ntt92GMWPG4IorrsD333+PtWvXYuPGjQCA5cuXw2QyISUlBd7e3vj000/h5eWFuLi4Do/3yCOPYN68eRg7diwmT56Mzz//HEeOHEH//v1t+zz//PN4+OGH4e/vj5kzZ0Kn02Hfvn2orq7GwoULLxirTqdDaWlpm20KhQLBwcEXjTM+Ph5bt27F7bffDrVajeDgYFx66aUoLy/Hv/71L9xyyy346aef8OOPP8Lf37/N63n55ZcxcOBAJCUl4bXXXkNNTY3tfh8fH9x///34v//7PwQFBSE2Nhb/+te/0NTUhPnz53fp34CIiHpAyglfRETkPq677jpbw4nzpaeniwDE9PR0W0OL6upq2/0ZGRkiADE3N9e2benSpWL//v1FpVIpDho0SFyxYoXtvnXr1okpKSmiv7+/6OPjI06YMKFNg4yOvPTSS2JwcLDo6+srzps3T3zyySfbNY74/PPPxVGjRokqlUoMDAwUp06dKq5du/aCx5w3b54IoN1t8ODBXYpz586d4iWXXCKq1Wrx3F/J7777rhgTEyP6+PiIc+fOFV966aU2DS0MBoP4yCOPiP7+/mJAQIC4cOFCce7cubaGFqIois3NzeJDDz0kBgcHi2q1Wpw0aZK4Z8+eTt8jIiLqHUEUO+iPS0RERERERN3COVdERERERER2wOSKiIiIiIjIDphcERERERER2QGTKyIiIiIiIjtgckVERERERGQHTK6IiIiIiIjsgMkVERERERGRHTC5IiIiIiIisgMmV0RERERERHbA5IqIiIiIiMgOmFwRERERERHZAZMrIiIiIiIiO/h/xcT0HkiQQloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(dados['Anos de Estudo'], bins = 17)\n",
    "ax.figure.set_size_inches(10, 5)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "PjfvPYUajQM9",
    "outputId": "477cf5fc-d11a-4e6f-dc45-dade49eff315"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    12\n",
       "Name: Anos de Estudo, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Moda = dados['Anos de Estudo'].mode()\n",
    "Moda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "VF_Bj8x2jQM-",
    "outputId": "70b0abb2-902e-4f80-b8c7-fe0f8c0e3140"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mediana = dados['Anos de Estudo'].median()\n",
    "Mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "ohqUZ5RsjQNA",
    "outputId": "286e2b23-ef56-453f-e92a-2c73887c1af4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.469664237376367"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Media = dados['Anos de Estudo'].mean()\n",
    "Media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "AwM8UvApjQNC",
    "outputId": "91f0c84b-13ff-43e1-cac0-46ce0527ddec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(Moda) > Mediana > Media"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vcaIo45UjQND"
   },
   "source": [
    "# <font color=green>4 MEDIDAS SEPARATRIZES</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f4XrApNxjQNE"
   },
   "source": [
    "## <font color=green>4.1 Quartis, decis e percentis</font>\n",
    "***\n",
    "\n",
    "Há uma série de medidas de posição semelhantes na sua concepção à mediana, embora não sejam medidas de tendência central. Como se sabe, a mediana divide a distribuição em duas partes iguais quanto ao número de elementos de cada parte. Já os quartis permitem dividir a distribuição em quatro partes iguais quanto ao número de elementos de cada uma; os decis em dez partes e os centis em cem partes iguais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "id": "VmbdUO-ujQNE",
    "outputId": "d7a091db-3901-45ae-95c9-2d1a6b840cb6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25     788.0\n",
       "0.50    1200.0\n",
       "0.75    2000.0\n",
       "Name: Renda, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.quantile([0.25, 0.5, 0.75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "gyiJDAMbjQNF",
    "outputId": "565e6c38-11b6-4030-c05e-c1e74f69b2b9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i / 10 for i in range(1, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "EpRKboV5jQNG",
    "outputId": "dfe7892e-35d2-4b99-a635-68a04967b84f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1     350.0\n",
       "0.2     788.0\n",
       "0.3     800.0\n",
       "0.4    1000.0\n",
       "0.5    1200.0\n",
       "0.6    1500.0\n",
       "0.7    1900.0\n",
       "0.8    2500.0\n",
       "0.9    4000.0\n",
       "Name: Renda, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.quantile([i / 10 for i in range(1, 10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LQW--GapjQNI",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01        0.0\n",
       "0.02        0.0\n",
       "0.03        0.0\n",
       "0.04       50.0\n",
       "0.05      100.0\n",
       "         ...   \n",
       "0.95     6000.0\n",
       "0.96     7000.0\n",
       "0.97     8000.0\n",
       "0.98    10000.0\n",
       "0.99    15000.0\n",
       "Name: Renda, Length: 99, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Renda.quantile([i / 100 for i in range(1, 100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "colab_type": "code",
    "id": "pO2HCo1WUE5F",
    "outputId": "1d51695f-6cf2-4a2e-dae1-738ee842e617"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeb00012\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distribuição de Frequências Acumulada'}, xlabel='Anos', ylabel='Acumulado'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot(dados.Idade,\n",
    "                  hist_kws = {'cumulative': True},\n",
    "                  kde_kws = {'cumulative': True})\n",
    "ax.figure.set_size_inches(14, 6)\n",
    "ax.set_title('Distribuição de Frequências Acumulada', fontsize=18)\n",
    "ax.set_ylabel('Acumulado', fontsize=14)\n",
    "ax.set_xlabel('Anos', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "GGHVrQA7jQNI",
    "outputId": "39bcdcfa-d7e3-40bc-f1d1-9e46a2237912"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1    28.0\n",
       "0.2    33.0\n",
       "0.3    36.0\n",
       "0.4    40.0\n",
       "0.5    43.0\n",
       "0.6    47.0\n",
       "0.7    51.0\n",
       "0.8    55.0\n",
       "0.9    61.0\n",
       "Name: Idade, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.Idade.quantile([i / 10 for i in range(1, 10)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6gIeZO_CjQNJ"
   },
   "source": [
    "## <font color=green>4.2 Box-plot</font>\n",
    "***\n",
    "\n",
    "O box plot dá uma idéia da posição, dispersão, assimetria, caudas e dados discrepantes (outliers). A posição central é dada pela mediana e a dispersão por $IIQ$. As posições relativas de $Q1$, $Mediana$ e $Q3$ dão uma noção da simetria da distribuição. Os comprimentos das cauda são dados pelas linhas que vão do retângulo aos valores remotos e pelos valores atípicos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VeiTE_gvjQNK"
   },
   "source": [
    "<img src='https://caelum-online-public.s3.amazonaws.com/1177-estatistica-parte1/01/img005.png' width='65%'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "colab_type": "code",
    "id": "pBY2EWkzjQNK",
    "outputId": "eaef866d-66ed-4f6e-b0de-3e9a8bfa4b50"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Altura'}, xlabel='Metros'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot( x = 'Altura', data = dados, orient = 'h')\n",
    "ax.figure.set_size_inches(12, 4)\n",
    "ax.set_title('Altura', fontsize=18)\n",
    "ax.set_xlabel('Metros', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "colab_type": "code",
    "id": "AEhX7-ggsuiY",
    "outputId": "b484ac10-4c32-42bd-db4c-053898fa034c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Altura'}, xlabel='Metros', ylabel='Sexo'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot( x = 'Altura', y = 'Sexo', data = dados, orient = 'h')\n",
    "ax.figure.set_size_inches(12, 4)\n",
    "ax.set_title('Altura', fontsize=18)\n",
    "ax.set_xlabel('Metros', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "colab_type": "code",
    "id": "2ZPrRov1suSF",
    "outputId": "6b176c56-cee6-4570-f288-5adfa84bc022"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Renda'}, xlabel='R$'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot( x = 'Renda', data = dados, orient = 'h')\n",
    "ax.figure.set_size_inches(12, 4)\n",
    "ax.set_title('Renda', fontsize=18)\n",
    "ax.set_xlabel('R$', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "colab_type": "code",
    "id": "CSwlKUrrsuFi",
    "outputId": "895356d3-f6ab-4659-f821-f1bee28ae21a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Renda'}, xlabel='R$'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot( x = 'Renda', data = dados.query('Renda < 10000'), orient = 'h')\n",
    "ax.figure.set_size_inches(12, 4)\n",
    "ax.set_title('Renda', fontsize=18)\n",
    "ax.set_xlabel('R$', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "colab_type": "code",
    "id": "IDrzBcJ_jQNL",
    "outputId": "af4f0ff0-8db8-4550-f230-3508561dd8a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Renda'}, xlabel='R$', ylabel='Sexo'>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot( x = 'Renda', y = 'Sexo', data = dados.query('Renda < 10000'), orient = 'h')\n",
    "ax.figure.set_size_inches(12, 4)\n",
    "ax.set_title('Renda', fontsize=18)\n",
    "ax.set_xlabel('R$', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "colab_type": "code",
    "id": "V3I-2KsbjQNM",
    "outputId": "4ba82684-e7cf-4ca8-eec3-21df2008f5ad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Anos de Estudo'}, xlabel='Anos'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4xje+Ee3atYuVK1dGeXl5DBgwIMaNGxdTp07d7OOLTj/99Lj77rvjoosuil69ekWLFi1i2bJlUV5eHl/60pfiF7/4Rbz22mv5p1xvi1NOOSXmzJkT5513XrRt2zbWrl0b7dq1i+9///sxd+7c6NatW73bd+vWLV566aW4++6749RTT4327dvHJ598EmvXro1u3brFWWedFePGjYvnn39+m2ertm7duno/vqj6srHHH388rrvuujjqqKOiU6dO8emnn0ZExH777RcXXXRRzJo1K38GfWMXXHBB3HPPPdG3b99o3rx5LF68ON59993N3vipd+/e8eKLL+b3W1VVVey1114xdOjQmDdvXhx00EH1/kw7ut/PPffceO2112LIkCGx7777xpo1a6KoqCh69+4dP/vZz+LVV1/N/zcMQNpyWVbPO0sAAABAI+OMLgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkpWh7N6yqqor33nsvysvLI5fL7cyZAAAAYDNZlsWKFSuiQ4cO0aRJ3edttzt033vvvejUqdP2bg4AAADbZdGiRdGxY8c6r9/u0C0vL8/fQUVFxfbeDAAAAGyV5cuXR6dOnfI9WpftDt3qpytXVFQIXQAAAHaZLb181ptRAQAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKKCj0AQCFkWRarV68u9BhAA5VlWaxZsyYiIkpKSiKXyxV4op2ntLQ0qZ8HoDZCF9gtrV69Ok4++eRCjwGwy02ZMiXKysoKPQbA58pTlwEAAEiKM7rAbm9l7/Mja+LXIbCRDeuifP6EiIhY0eu8iKbFBR5ox+Sq1see8+4v9BgAu4xHdsBuL2tS1OgfxAKfo6bFjf53RFboAQB2MU9dBgAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABISlGhB/g8ZVkWq1evjoiI0tLSyOVyBZ4IAACgYUmxm5I+o7t69eo4+eST4+STT84fOAAAAP5Pit2UdOgCAACw+xG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUooKPcDnKcuy/NerV68u4CRAQ1Pjd8JGvysAkuQxEVCPjX8vZIk8Ltrq0F2zZk2sWbMm//3y5cs/l4F2po3nPeusswo4CdCgVa2PiGaFngLg81O1Pv+lx0RAfdasWRPNmzcv9Bg7bKufujxy5Mho0aJF/tKpU6fPcy4AAADYLlt9Rve6666LH/7wh/nvly9f3uBjt6SkJP/1gw8+GKWlpQWcBmhIVq9e/X9nNZok/SoOgBq/5zwmAja18eOijRuqMdvqR3clJSWN7ofO5XL5r0tLS6OsrKyA0wAN1ka/KwCS5DERsJVyiTwu8q7LAAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgAAAEkRugAAACRF6AIAAJAUoQsAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUooKPcDnqbS0NKZMmZL/GgAAgJpS7KakQzeXy0VZWVmhxwAAAGiwUuwmT10GAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApRYUeAKDQclXrIyv0EEDDsmFd7V83Urmq9YUeAWCXErrAbm/PefcXegSgASufP6HQIwCwjTx1GQAAgKQ4owvslkpLS2PKlCmFHgNooLIsizVr1kRERElJSeRyuQJPtPOUlpYWegSAz53QBXZLuVwuysrKCj0G0IA1b9680CMAsJ08dRkAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIStH2bphlWURELF++fKcNAwAAAHWp7s/qHq3LdofuihUrIiKiU6dO23sTAAAAsM1WrFgRLVq0qPP6XLalFK5DVVVVvPfee1FeXh65XG67B2TbLV++PDp16hSLFi2KioqKQo9DHRynhs8xahwcp8bBcWr4HKPGwXFqHBynwsmyLFasWBEdOnSIJk3qfiXudp/RbdKkSXTs2HF7N2cnqKio8A+rEXCcGj7HqHFwnBoHx6nhc4waB8epcXCcCqO+M7nVvBkVAAAASRG6AAAAJEXoNkIlJSXxk5/8JEpKSgo9CvVwnBo+x6hxcJwaB8ep4XOMGgfHqXFwnBq+7X4zKgAAAGiInNEFAAAgKUIXAACApAhdAAAAkiJ0AQAASIrQbURGjhwZRx55ZJSXl0fbtm3jzDPPjP/+7/8u9FjUY+TIkZHL5WLYsGGFHoVN/P3vf48LLrgg2rRpE82bN4/evXvH7NmzCz0WG1m/fn38y7/8S3Tr1i3Kyspin332iZ///OdRVVVV6NF2W0899VScdtpp0aFDh8jlcvHQQw/VuD7LsvjpT38aHTp0iLKysjj66KPjtddeK8ywu7H6jtO6devi2muvjUMOOST22GOP6NChQwwaNCjee++9wg28m9rSv6eNDRkyJHK5XPz617/eZfOxdcfoL3/5S5x++unRokWLKC8vj6985Svxt7/9bdcPy2aEbiPy5JNPxtChQ+PPf/5zTJ06NdavXx8nnHBCfPLJJ4UejVrMmjUr7rjjjjj00EMLPQqb+Oijj6JPnz5RXFwcU6ZMiQULFsSvfvWraNmyZaFHYyOjRo2K2267LW655Zb4y1/+EjfccEPceOONcfPNNxd6tN3WJ598Er169Ypbbrml1utvuOGGGD16dNxyyy0xa9asqKysjOOPPz5WrFixiyfdvdV3nFatWhVz5syJH/3oRzFnzpyYNGlSvPHGG3H66acXYNLd25b+PVV76KGH4oUXXogOHTrsosmotqVj9Ne//jX69u0bPXr0iJkzZ8b8+fPjRz/6UZSWlu7iSamNjxdqxJYuXRpt27aNJ598Mvr161focdjIypUr4/DDD49bb701/vVf/zV69+7tr7ANyIgRI+LZZ5+Np59+utCjUI9TTz012rVrF2PHjs0v+8Y3vhHNmzePe+65p4CTERGRy+XiwQcfjDPPPDMiPjub26FDhxg2bFhce+21ERGxZs2aaNeuXYwaNSqGDBlSwGl3X5sep9rMmjUrvvSlL8W7774bnTt33nXDkVfXcfr73/8eX/7yl+Oxxx6LU045JYYNG+ZZYgVS2zE677zzori42P+TGihndBuxZcuWRURE69atCzwJmxo6dGiccsopcdxxxxV6FGoxefLkOOKII+Jb3/pWtG3bNg477LC48847Cz0Wm+jbt29Mnz493njjjYiImD9/fjzzzDPx9a9/vcCTUZuFCxfG+++/HyeccEJ+WUlJSfTv3z+ee+65Ak7GlixbtixyuZxntTQwVVVVMXDgwBg+fHgcfPDBhR6HTVRVVcUjjzwS+++/f5x44onRtm3b+PKXv1zvU9DZtYRuI5VlWfzwhz+Mvn37Rs+ePQs9DhuZMGFCzJ49O0aOHFnoUajD22+/HWPGjInu3bvHY489Fpdffnn84Ac/iLvvvrvQo7GRa6+9Ns4///zo0aNHFBcXx2GHHRbDhg2L888/v9CjUYv3338/IiLatWtXY3m7du3y19HwrF69OkaMGBHf/va3o6KiotDjsJFRo0ZFUVFR/OAHPyj0KNRiyZIlsXLlyvi3f/u3OOmkk+Lxxx+Ps846K84+++x48sknCz0eEVFU6AHYPt///vfj5ZdfjmeeeabQo7CRRYsWxVVXXRWPP/6412c0YFVVVXHEEUfE9ddfHxERhx12WLz22msxZsyYGDRoUIGno9rEiRPj3nvvjfvuuy8OPvjgmDdvXgwbNiw6dOgQgwcPLvR41CGXy9X4PsuyzZbRMKxbty7OO++8qKqqiltvvbXQ47CR2bNnx29+85uYM2eOfz8NVPUbI55xxhlx9dVXR0RE796947nnnovbbrst+vfvX8jxCGd0G6Urr7wyJk+eHDNmzIiOHTsWehw2Mnv27FiyZEl88YtfjKKioigqKoonn3wyfvvb30ZRUVFs2LCh0CMSEe3bt4+DDjqoxrIDDzzQuyQ2MMOHD48RI0bEeeedF4ccckgMHDgwrr76as+WaKAqKysjIjY7e7tkyZLNzvJSeOvWrYtzzjknFi5cGFOnTnU2t4F5+umnY8mSJdG5c+f844l33303rrnmmujatWuhxyMi9tprrygqKvJ4ogFzRrcRybIsrrzyynjwwQdj5syZ0a1bt0KPxCaOPfbYeOWVV2osu+iii6JHjx5x7bXXRtOmTQs0GRvr06fPZh/N9cYbb0SXLl0KNBG1WbVqVTRpUvPvsU2bNvXxQg1Ut27dorKyMqZOnRqHHXZYRESsXbs2nnzyyRg1alSBp2Nj1ZH75ptvxowZM6JNmzaFHolNDBw4cLP3+TjxxBNj4MCBcdFFFxVoKjbWrFmzOPLIIz2eaMCEbiMydOjQuO++++K//uu/ory8PP9X8xYtWkRZWVmBpyMiory8fLPXTO+xxx7Rpk0br6VuQK6++ur42te+Ftdff32cc8458eKLL8Ydd9wRd9xxR6FHYyOnnXZa/PKXv4zOnTvHwQcfHHPnzo3Ro0fHxRdfXOjRdlsrV66Mt956K//9woULY968edG6devo3LlzDBs2LK6//vro3r17dO/ePa6//vpo3rx5fPvb3y7g1Luf+o5Thw4d4pvf/GbMmTMn/vjHP8aGDRvyjydat24dzZo1K9TYu50t/Xva9A8QxcXFUVlZGQcccMCuHnW3taVjNHz48Dj33HOjX79+MWDAgHj00Ufj4YcfjpkzZxZuaP5PRqMREbVexo8fX+jRqEf//v2zq666qtBjsImHH34469mzZ1ZSUpL16NEju+OOOwo9EptYvnx5dtVVV2WdO3fOSktLs3322Sf753/+52zNmjWFHm23NWPGjFr/PzR48OAsy7Ksqqoq+8lPfpJVVlZmJSUlWb9+/bJXXnmlsEPvhuo7TgsXLqzz8cSMGTMKPfpuZUv/njbVpUuX7KabbtqlM+7utuYYjR07Nttvv/2y0tLSrFevXtlDDz1UuIGpwefoAgAAkBRvRgUAAEBShC4AAABJEboAAAAkRegCAACQFKELAABAUoQuAAAASRG6AAAAJEXoAgAAkBShCwAAQFKELgBspUGDBkUul4vKyspYv359occBAOogdAFgKyxfvjz+8Ic/RC6Xiw8++CAeeeSRQo8EANRB6ALAVrj//vtj1apVcc0110Qul4uxY8cWeiQAoA5CFwC2wtixY6NZs2Zx3XXXRZ8+feJPf/pTLF68eLP1crlcHH300bF06dK4+OKLo23btlFWVhZf+cpXYubMmbXe9t/+9re45JJLYu+9945mzZpFx44d45JLLolFixZttu7ixYvjqquuiu7du0dZWVm0bt06DjnkkLjiiiti+fLlO/vHBoBGKZdlWVboIQCgIXvllVfi0EMPjbPOOismTZoUd955Z1x22WUxcuTIGDFiRI11c7lc9OrVK1atWhUVFRVx1FFHxZIlS2LixInRtGnTmD17dvTs2TO//ptvvhl9+/aNJUuWxGmnnRYHH3xwvPbaa/Hwww9H27Zt49lnn4399tsvIiJWrVoVPXv2jHfeeSdOOOGEOPTQQ2Pt2rXx9ttvx7Rp02LBggXRtWvXXblrAKBBEroAsAXDhg2L3/zmNzFp0qQ466yzYtmyZVFZWRmdOnWKN954o8a6uVwuIiKuuOKKuPnmm6NJk8+ePDV27Ni49NJLY8iQIXHbbbfl1z/22GPjiSeeiNtvvz0uu+yy/PI77rgjhgwZEscee2xMmzYtIiIefvjhOP300+Pqq6+O0aNH17jfFStWRElJSTRr1uxz2QcA0Jh46jIA1GPt2rVx7733RqtWreKUU06JiIgWLVrEGWecEW+++WY89dRTm22zxx57xKhRo/KRGxExePDgKCoqilmzZuWXLVq0KJ544ok46KCD4rvf/W6N2/jud78bBx54YEyfPn2zpzCXlZVtdp/l5eUiFwD+P6ELAPV46KGH4h//+Eece+65NUJy0KBBERExbty4zbbp3r177LnnnjWWFRUVRbt27eLjjz/OL5s7d25ERPTv3z9/JrhaLpeLfv36RUTE/PnzIyKiX79+UVlZGSNHjoxTTjklbr311nj55ZfDk7MAoCahCwD1qA7ZgQMH1lh+4oknRmVlZfznf/7nZm8C1aJFi1pvq6ioKDZs2JD/vnq7du3a1bp+ZWVlREQsW7Ysf7vPP/98DBw4MJ5//vkYOnRo9OrVKzp37hy33nrrdvx0AJAmoQsAdVi0aFFMnTo1IiL69OkTuVwufykqKor3338/Vq1aFRMmTNiu26+oqIiIiA8++KDW66uXV68XEdG1a9e46667YunSpTF37twYNWpUZFkWQ4cOjfvvv3+75gCA1BQVegAAaKjGjx8fVVVV0bdv3zjggAM2u37t2rVxzz33xNixY2u8kdTW6t27d0REPPXUU5FlWY2nL2dZFk8//XSN9TbWtGnT6N27d/Tu3Tu++tWvRr9+/WLy5Mlx/vnnb/McAJAaoQsAtciyLMaPHx+5XC7uvvvu6NatW63rvfrqq/Hiiy/Gq6++WuNjg7ZG586dY8CAATFjxowYN25cXHLJJfnrxo0bF6+99locc8wx0alTp/x9lZeXR5cuXWrcTvWZ39repAoAdkdCFwBqMX369HjnnXdiwIABdUZuRMRFF10Uc+fOjbFjx8ZNN920zfczZsyY6Nu3b3z3u9+Nhx9+OA466KBYsGBBTJ48Ob7whS/EmDFj8utOmzYtrrnmmujTp0/06NEj2rRpE2+//XZMnjw5ysrK4vvf//52/awAkBqv0QWAWowdOzYiIi6++OJ61/v2t78dzZo1i3vvvTfWrl27zfdzwAEHxEsvvRQXXnhhvPjii3HjjTfGiy++GBdeeGHMmjUr9t9///y6J554Ylx55ZWxcuXKmDRpUtx0003x0ksvxXnnnRezZ8+Oww8/fJvvHwBSlMt8JgEAAAAJcUYXAACApAhdAAAAkiJ0AQAASIrQBQAAIClCFwAAgKQIXQAAAJIidAEAAEiK0AUAACApQhcAAICkCF0AAACSInQBAABIitAFAAAgKf8P7gp6fFOIaigAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot( x = 'Anos de Estudo', data = dados, orient = 'h')\n",
    "ax.figure.set_size_inches(12, 4)\n",
    "ax.set_title('Anos de Estudo', fontsize=18)\n",
    "ax.set_xlabel('Anos', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Anos de Estudo'}, xlabel='Anos', ylabel='Sexo'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot( x = 'Anos de Estudo', y = 'Sexo', data = dados, orient = 'h')\n",
    "ax.figure.set_size_inches(12, 4)\n",
    "ax.set_title('Anos de Estudo', fontsize=18)\n",
    "ax.set_xlabel('Anos', fontsize=14)\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "amloMZwVjQNN"
   },
   "source": [
    "<img src='https://caelum-online-public.s3.amazonaws.com/1177-estatistica-parte1/01/img006.png' width='80%'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aJ6v_NI9jQNN"
   },
   "source": [
    "# <font color=green>5 MEDIDAS DE DISPERSÃO</font>\n",
    "***\n",
    "\n",
    "Embora as medidas de posição forneçam uma sumarização bastante importante dos dados, elas podem não ser suficientes para caracterizar conjuntos distintos, especialmente quando as observações de determinada distribuição apresentarem dados muito dispersos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D6xVxAQ7jQNO"
   },
   "source": [
    "## <font color=green>5.1 Desvio médio absoluto</font>\n",
    "***\n",
    "\n",
    "\n",
    "# $$DM = \\frac 1n\\sum_{i=1}^{n}|X_i-\\bar{X}|$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "0sL31blV7nz3",
    "outputId": "5f44f5d3-88b4-40e2-810c-7e1e1a9bace0"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Matérias</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Beltrano</th>\n",
       "      <th>Sicrano</th>\n",
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       "      <td>7.5</td>\n",
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       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10</td>\n",
       "      <td>9.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "Matérias    Fulano  Beltrano  Sicrano\n",
       "Matemática       8      10.0      7.5\n",
       "Português       10       2.0      8.0\n",
       "Inglês           4       0.5      7.0\n",
       "Geografia        8       1.0      8.0\n",
       "História         6       3.0      8.0\n",
       "Física          10       9.5      8.5\n",
       "Química          8      10.0      7.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
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    "id": "f3pAri4G7nc4",
    "outputId": "f6800dd6-a0dd-47ae-d6d1-b7feca522389"
   },
   "outputs": [
    {
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      "text/plain": [
       "Matérias    Fulano\n",
       "Matemática       8\n",
       "Português       10\n",
       "Inglês           4\n",
       "Geografia        8\n",
       "História         6\n",
       "Física          10\n",
       "Química          8"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano = df[['Fulano']]\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "8_Q2tOTmjQNO",
    "outputId": "5c0d8214-aa63-4d0f-cfb1-f61bc171195f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.714285714285714"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nota_media_fulano = notas_fulano.mean()[0]\n",
    "nota_media_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "RWo0AhBEjQNP",
    "outputId": "19437165-543f-4cb7-885c-956eb5e31090"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zeb00012\\AppData\\Local\\Temp\\ipykernel_47184\\349761232.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  notas_fulano['Desvio'] = notas_fulano['Fulano'] - nota_media_fulano\n"
     ]
    },
    {
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       "      <td>10</td>\n",
       "      <td>2.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4</td>\n",
       "      <td>-3.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
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       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Matérias    Fulano    Desvio\n",
       "Matemática       8  0.285714\n",
       "Português       10  2.285714\n",
       "Inglês           4 -3.714286\n",
       "Geografia        8  0.285714\n",
       "História         6 -1.714286\n",
       "Física          10  2.285714\n",
       "Química          8  0.285714"
      ]
     },
     "execution_count": 103,
     "metadata": {},
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    }
   ],
   "source": [
    "notas_fulano['Desvio'] = notas_fulano['Fulano'] - nota_media_fulano\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
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    "colab_type": "code",
    "id": "OiWDTw0ljQNQ",
    "outputId": "0123646d-ad0d-457e-cc5a-035bd6a843ec"
   },
   "outputs": [
    {
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       "      <th>|Desvio|</th>\n",
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       "      <td>0.285714</td>\n",
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       "      <td>-1.714286</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
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       "      <th>Física</th>\n",
       "      <td>10</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>2.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
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      ],
      "text/plain": [
       "Matérias    Fulano    Desvio  |Desvio|\n",
       "Matemática       8  0.285714  0.285714\n",
       "Português       10  2.285714  2.285714\n",
       "Inglês           4 -3.714286  3.714286\n",
       "Geografia        8  0.285714  0.285714\n",
       "História         6 -1.714286  1.714286\n",
       "Física          10  2.285714  2.285714\n",
       "Química          8  0.285714  0.285714"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['|Desvio|'] = notas_fulano['Desvio'].abs()\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
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    {
     "data": {
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       "<AxesSubplot:>"
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     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = notas_fulano['Fulano'].plot(style = 'o')\n",
    "ax.figure.set_size_inches(12, 5)\n",
    "ax.hlines(y = nota_media_fulano, xmin = 0, xmax = notas_fulano.shape[0] - 1, colors = 'red')\n",
    "for i in range(notas_fulano.shape[0]):\n",
    "    ax.vlines(x = i, ymin = nota_media_fulano, ymax = notas_fulano['Fulano'][i], linestyle='dashed')\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 375
    },
    "colab_type": "code",
    "id": "dtIDTt6djQNV",
    "outputId": "ee4b72f8-e0d6-4f04-e445-1178a190332c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5510204081632648"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['|Desvio|'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "m4buiQWF01xt",
    "outputId": "e166623b-e7a4-44f6-ecb1-2cfc0bc43ab1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5510204081632648"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desvio_medio_absoluto = notas_fulano['Fulano'].mad()\n",
    "desvio_medio_absoluto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jSKfnKwEjQNW"
   },
   "source": [
    "## <font color=green>5.2 Variância</font>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tHD3yNqzjQNW"
   },
   "source": [
    "### Variância\n",
    "\n",
    "A variância é construída a partir das diferenças entre cada observação e a média dos dados, ou seja, o desvio em torno da média. No cálculo da variância, os desvios em torno da média são elevados ao quadrado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WrCn9hw0jQNX"
   },
   "source": [
    "### Variância populacional\n",
    "\n",
    "# $$\\sigma^2 = \\frac 1n\\sum_{i=1}^{n}(X_i-\\mu)^2$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jV5mQT_djQNX"
   },
   "source": [
    "### Variância amostral\n",
    "\n",
    "# $$S^2 = \\frac 1{n-1}\\sum_{i=1}^{n}(X_i-\\bar{X})^2$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "4tlbCQeujQNZ",
    "outputId": "21e4a6b5-8a87-4338-8f94-b02ef2ea9e07"
   },
   "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>Matérias</th>\n",
       "      <th>Fulano</th>\n",
       "      <th>Desvio</th>\n",
       "      <th>|Desvio|</th>\n",
       "      <th>(Desvio)^2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Matemática</th>\n",
       "      <td>8</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Português</th>\n",
       "      <td>10</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>5.224490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inglês</th>\n",
       "      <td>4</td>\n",
       "      <td>-3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>13.795918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geografia</th>\n",
       "      <td>8</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>História</th>\n",
       "      <td>6</td>\n",
       "      <td>-1.714286</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>2.938776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Física</th>\n",
       "      <td>10</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>5.224490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Química</th>\n",
       "      <td>8</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.081633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Matérias    Fulano    Desvio  |Desvio|  (Desvio)^2\n",
       "Matemática       8  0.285714  0.285714    0.081633\n",
       "Português       10  2.285714  2.285714    5.224490\n",
       "Inglês           4 -3.714286  3.714286   13.795918\n",
       "Geografia        8  0.285714  0.285714    0.081633\n",
       "História         6 -1.714286  1.714286    2.938776\n",
       "Física          10  2.285714  2.285714    5.224490\n",
       "Química          8  0.285714  0.285714    0.081633"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['(Desvio)^2'] = notas_fulano['Desvio'].pow(2)\n",
    "notas_fulano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "oCcAfoIOjQNa",
    "outputId": "e55249a4-6d91-4776-d89f-428f067a87ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.57142857142857"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notas_fulano['(Desvio)^2'].sum() / (len(notas_fulano) - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "EyrfPHLtjQNb",
    "outputId": "a024839f-1c4a-4a91-b892-3966d7cc8f95"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.57142857142857"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variancia = notas_fulano['Fulano'].var()\n",
    "variancia"
   ]
  },
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   "source": [
    "## <font color=green>5.3 Desvio padrão</font>\n",
    "***\n",
    "\n",
    "Uma das restrições da variância é o fato de fornecer medidas em quadrados das unidades originais - a variância de medidas de comprimento, por exemplo, é em unidades de área. Logo, o fato de as unidades serem diferentes dificulta a comparação da dispersão com as variáveis que a definem. Um modo de eliminar essa dificuldade é considerar sua raiz quadrada."
   ]
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    "### Desvio padrão populacional\n",
    "\n",
    "# $$\\sigma = \\sqrt{\\frac 1n\\sum_{i=1}^{n}(X_i-\\mu)^2} \\Longrightarrow \\sigma = \\sqrt{\\sigma^2}$$"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "D3J4MvMpjQNg"
   },
   "source": [
    "### Desvio padrão amostral\n",
    "\n",
    "# $$S = \\sqrt{\\frac 1{n-1}\\sum_{i=1}^{n}(X_i-\\bar{X})^2} \\Longrightarrow S = \\sqrt{S^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
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    "colab_type": "code",
    "id": "2di6iLFaSPda",
    "outputId": "d4b3d928-0ef9-4412-8b97-a16724770f84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1380899352993947"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(variancia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "BMTx9yKHSPON",
    "outputId": "2a045e01-b0a6-4c42-96e8-18708617687d"
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   "outputs": [
    {
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       "2.1380899352993947"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desvio_padrao = notas_fulano['Fulano'].std()\n",
    "desvio_padrao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
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    "id": "_PeQtqbdjQNh",
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       "Matérias    Fulano  Beltrano  Sicrano\n",
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    "df"
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    {
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       "Matérias\n",
       "Fulano      7.714286\n",
       "Beltrano    5.142857\n",
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     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
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    "df.mean()"
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   "execution_count": 115,
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    {
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       "Matérias\n",
       "Fulano      8.0\n",
       "Beltrano    3.0\n",
       "Sicrano     8.0\n",
       "dtype: float64"
      ]
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     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
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    "df.median()"
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   "metadata": {
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    "id": "G8vorygCjQNk",
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