Aula11_Atividade_ModelosProbabilísticosDiscretos.ipynb

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   "source": [
    "___\n",
    "# Atividade: <p> <center> Modelos probabilísticos discretos e Dados\n",
    "___\n",
    "\n",
    "## Aula 11\n",
    "\n",
    "**Objetivo da aula:** Ao final desta aula, o aluno deve ser capaz de especificar as distribuições de probabilidades adequadas para variáveis aleatórias discretas considerando modelos probabilísticos discretos já bem definidos na literatura estatística.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"indice\"></div>\n",
    "\n",
    "## Índice\n",
    "\n",
    "- [Teoria: Distribuição Binomial](#teoria)\n",
    "    - [Esperança e Variância](#esperanca-variancia)\n",
    "- [DETRAN](#detran)\n",
    "    - [Modelo teórico](#modelo-teorico)\n",
    "        - [Exercício 1](#ex1)\n",
    "        - [Exercício 2](#ex2)\n",
    "    - [Resultados empíricos](#resultados-empiricos)\n",
    "        - [Exercício 3](#ex3)\n",
    "        - [Exercício 4](#ex4)\n",
    "    - [Comparação: resultados empíricos X modelo teórico](#comparacao)\n",
    "        - [Opção 1: Frequências relativas X Probabilidades teóricas](#opcao1)\n",
    "        - [Opção 2: Frequência relativa acumulada X Probabilidade acumulada](#opcao2)\n",
    "        - [Exercício 5](#ex5)\n",
    "        - [Exercício 6](#ex6)\n",
    "        - [Exercício 7](#ex7)\n",
    "- [Lista de comandos utilizados neste notebook](#comandos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from scipy import stats #importa apenas as funções de estatísticas da biblioteca SciPy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"detran\"></div>\n",
    "\n",
    "# <font color='blue'>DETRAN</font> \n",
    "\n",
    "Esse exercício irá explorar uma modelagem de dados reais.\n",
    "\n",
    "> **Confira alguns itens obrigatórios verificados durante a vistoria do Detran**\n",
    ">\n",
    "> *Todos os veículos, novos ou velhos, precisam passar por uma vistoria todos os anos. (...) O motorista precisa estar atento a alguns itens obrigatórios. Tudo deve funcionar perfeitamente, apresentar bom estado de conservação e estar dentro do prazo de validade.*\n",
    ">\n",
    "> <sup>Fonte: http://extra.globo.com/noticias/brasil/transito-seguro/confira-alguns-itens-obrigatorios-verificados-durante-vistoria-do-detran-10190355.html</sup>\n",
    "\n",
    "Essa matéria lista 14 itens que são inspecionados pelo Detran, dentre os quais têm-se: extintor de incêndio deve estar dentro do prazo de validade; pneus devem estar em bom estado de conservação; buzina deve funcionar perfeitamente; e cintos de segurança para todos os ocupantes do carro. \n",
    "\n",
    "Se, ao final da inspeção, todos os 14 itens estiverem funcionando perfeitamente, o motorista irá feliz para casa assegurado de que seu carro está sem problemas com a vistoria."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"modelo-teorico\"></div>\n",
    "\n",
    "## Modelo teórico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<div id=\"teoria\"></div>\n",
    "\n",
    "## Teoria: Distribuição Binomial\n",
    "\n",
    "A distribuição binomial modela a número de sucessos (o evento de interesse) em uma determinada quantidade de tentativas. Mais formalmente, dizemos que uma variável aleatória $Y$ segue uma distribuição binomial utilizando a seguinte notação: $Y$~$Bin(n,p)$. Essa notação pode ser lida como: $Y$ segue uma distribuição binomial com $n$ tentativas e $p$ como probabilidade de sucesso em cada evento independente.\n",
    "\n",
    "A função de probabilidade (lembrando: que associa uma probabilidade a cada valor possível de $Y$) é dada por:\n",
    "\n",
    "$$P(Y=y)=\\left(\n",
    "\\begin{array}{c}\n",
    "    n \\\\\n",
    "    y\n",
    "\\end{array}\\right) p^y (1-p)^{(n-y)}$$\n",
    "\n",
    "Para que um experimento possa ser modelado por uma distribuição binomial, ele precisa ter as seguintes propriedades:\n",
    "\n",
    "- ser uma contagem de $n$ repetições (ou tentativas, ou ensaios) idênticas;\n",
    "- cada repetição tem apenas 2 resultados possíveis: um é denominado sucesso (o resultado de interesse, que não necessariamente é positivo) e o outro, fracasso;\n",
    "- a probabilidade de sucesso para cada ensaio é denominada $p$ e será constante em cada repetição. Consequentemente, a probabilidade de fracasso $(1-p)$ também não varia de tentativa para tentativa;\n",
    "- as tentativas são independentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"esperanca-variancia\"></div>\n",
    "\n",
    "### Esperança e Variância\n",
    "\n",
    "Se $Y$~$Bin(n,p)$, o valor esperado $E(Y)$ e a variância $Var(Y)$ são dados por:\n",
    "\n",
    "$\\qquad\\qquad E(Y) = np$\n",
    "\n",
    "$\\qquad\\qquad Var(Y) = np(1-p)$\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex1\"></div>\n",
    "\n",
    "### EXERCÍCIO 1\n",
    "\n",
    "Assuma que a variável **Quantidade de itens vistoriados em não conformidade em 14 itens vistoriados** possa ser ajustada pelo modelo binomial com parâmetros $n=14$ e $p=0,10$. Interprete esses parâmetros para o problema em questão e, ainda, discuta se as propriedades da distribuição binomial estão satisfeitas para o problema aqui me questão."
   ]
  },
  {
   "cell_type": "raw",
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   "source": [
    "PREENCHA COM AS SUAS RESPOSTAS:\n",
    "    \n",
    "- n = 14: SUBSTITUA ESTE TEXTO PELA SUA INTERPRETAÇÃO DESTE PARÂMETRO\n",
    "- p = 0,10: SUBSTITUA ESTE TEXTO PELA SUA INTERPRETAÇÃO DESTE PARÂMETRO\n",
    "\n",
    "As propriedades da distribuição binomial [estão satisfeitas/não estão satisfeitas] para o problema em questão?\n",
    "Verifique as propriedades de um ensaio Binomial: \n",
    "[COMPLETE COM A SUA RESPOSTA]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Independente da sua resposta anterior, considere que a distribuição binomial seja adequada para modelar a variável de interesse nos próximos exercícios!**\n",
    "\n",
    "<div id=\"ex2\"></div>\n",
    "\n",
    "### EXERCÍCIO 2\n",
    "\n",
    "<div id=\"pmf-stats\"></div>\n",
    "\n",
    "1. Consulte a [documentação do método `stats.binom.pmf`](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.binom.html). Utilizando esse método, obtenha a probabilidade de cada uma das quantidades de itens em não conformidade, ou seja, de 0 a 14, quando $n=14$ e $p=0,10$. Armazene as probabilidades em uma lista (probabilidades de todas as quantidades possíveis, de 0 a 14) e guarde esta lista em uma variável chamada `probabilidades_teoricas`.\n",
    "1. Calcule o valor esperado e a variância da quantidade de itens em não conformidade utilizando os métodos `stats.binom.mean` e `stats.binom.var` (a documentação está na mesma página do método `stats.binom.pmf`) e compare com a esperança e variância calculados a partir das [fórmulas da distribuição binomial](#esperanca-variancia).\n",
    "\n",
    "*Respostas esperadas para esperança e variância respectivamente: 1.4000000000000001 ; 1.2600000000000002*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4000000000000001 1.2600000000000002 1.4000000000000001 1.2600000000000002\n"
     ]
    }
   ],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "n = 14\n",
    "p = 0.1\n",
    "probabilidades_teoricas = []\n",
    "for i in range(n+1):\n",
    "    probabilidades_teoricas.append(stats.binom.pmf(i, n, p))\n",
    "esp = stats.binom.mean(n, p)\n",
    "var = stats.binom.var(n, p)\n",
    "print(esp, var, n*p, n*p*(1-p))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"resultados-empiricos\"></div>\n",
    "\n",
    "## Resultados empíricos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A base de dados `Carros.txt` apresenta, para cada um dos três mil carros de passeio vistoriados, duas informações:\n",
    "\n",
    "- **Tipo**: tipo de carro (1: Popular e 2: Não Popular)\n",
    "- **Quantidade**: quantidade de itens vistoriados em não conformidade (que pode variar de 0 a 14) em 14 itens vistoriados\n",
    "\n",
    "Vamos começar carregando a base em um `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esperamos trabalhar no diretório: \n",
      "c:\\Users\\ribei\\OneDrive\\Área de Trabalho\\cdados\\cdados\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(f'Esperamos trabalhar no diretório: \\n{os.getcwd()}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parece que o arquivo Carros.txt está na mesma pasta do notebook, yay!\n"
     ]
    }
   ],
   "source": [
    "filename = 'Carros.txt'\n",
    "\n",
    "if filename in os.listdir():\n",
    "    print(f'Parece que o arquivo {filename} está na mesma pasta do notebook, yay!')\n",
    "    \n",
    "else:\n",
    "    print(f'Não encontrei o arquivo {filename}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Quantidade</th>\n",
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       "   Tipo  Quantidade\n",
       "1     1           0\n",
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       "5     1           1"
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   ],
   "source": [
    "carros = pd.read_table('Carros.txt', sep=' ')\n",
    "carros.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex3\"></div>\n",
    "\n",
    "### EXERCÍCIO 3\n",
    "\n",
    "Considerando todos os carros, gere uma tabela de frequências relativas da quantidade de itens vistoriados em não conformidade (variável **Quantidade**). Armazene essa tabela em uma variável chamada `frequencias_relativas`. \n",
    "\n",
    "**Observação:** utilize o método `.sort_index()` no resultado do `.value_counts()` para corrigir a ordenação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "False",
     "output_type": "error",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3652\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 3653\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[0;32m   3654\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx:155\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\index_class_helper.pxi:70\u001b[0m, in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: False",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\ribei\\OneDrive\\Área de Trabalho\\cdados\\cdados\\Aula11_Atividade_ModelosProbabilísticosDiscretos.ipynb Cell 18\u001b[0m line \u001b[0;36m3\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ribei/OneDrive/%C3%81rea%20de%20Trabalho/cdados/cdados/Aula11_Atividade_ModelosProbabil%C3%ADsticosDiscretos.ipynb#X25sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# ESCREVA O CÓDIGO DA SUA RESPOSTA AQUI\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ribei/OneDrive/%C3%81rea%20de%20Trabalho/cdados/cdados/Aula11_Atividade_ModelosProbabil%C3%ADsticosDiscretos.ipynb#X25sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m frequencias_relativas \u001b[39m=\u001b[39m carros\u001b[39m.\u001b[39mvalue_counts(normalize\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\u001b[39m.\u001b[39msort_index()\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/ribei/OneDrive/%C3%81rea%20de%20Trabalho/cdados/cdados/Aula11_Atividade_ModelosProbabil%C3%ADsticosDiscretos.ipynb#X25sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m frequencias_relativas\u001b[39m.\u001b[39;49mloc[\u001b[39m\"\u001b[39;49m\u001b[39mQuantidade\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m==\u001b[39;49m \u001b[39m0\u001b[39;49m, :]\n",
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      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1280\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1278\u001b[0m \u001b[39mwith\u001b[39;00m suppress(IndexingError):\n\u001b[0;32m   1279\u001b[0m     tup \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_expand_ellipsis(tup)\n\u001b[1;32m-> 1280\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_lowerdim(tup)\n\u001b[0;32m   1282\u001b[0m \u001b[39m# no multi-index, so validate all of the indexers\u001b[39;00m\n\u001b[0;32m   1283\u001b[0m tup \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_validate_tuple_indexer(tup)\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:976\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_lowerdim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m    974\u001b[0m \u001b[39m# we may have a nested tuples indexer here\u001b[39;00m\n\u001b[0;32m    975\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_is_nested_tuple_indexer(tup):\n\u001b[1;32m--> 976\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_nested_tuple(tup)\n\u001b[0;32m    978\u001b[0m \u001b[39m# we maybe be using a tuple to represent multiple dimensions here\u001b[39;00m\n\u001b[0;32m    979\u001b[0m ax0 \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39m_get_axis(\u001b[39m0\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1048\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_nested_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mall\u001b[39m(is_hashable(x) \u001b[39mor\u001b[39;00m com\u001b[39m.\u001b[39mis_null_slice(x) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m tup):\n\u001b[0;32m   1044\u001b[0m     \u001b[39m# GH#10521 Series should reduce MultiIndex dimensions instead of\u001b[39;00m\n\u001b[0;32m   1045\u001b[0m     \u001b[39m#  DataFrame, IndexingError is not raised when slice(None,None,None)\u001b[39;00m\n\u001b[0;32m   1046\u001b[0m     \u001b[39m#  with one row.\u001b[39;00m\n\u001b[0;32m   1047\u001b[0m     \u001b[39mwith\u001b[39;00m suppress(IndexingError):\n\u001b[1;32m-> 1048\u001b[0m         \u001b[39mreturn\u001b[39;00m cast(_LocIndexer, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m_handle_lowerdim_multi_index_axis0(\n\u001b[0;32m   1049\u001b[0m             tup\n\u001b[0;32m   1050\u001b[0m         )\n\u001b[0;32m   1051\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj, ABCSeries) \u001b[39mand\u001b[39;00m \u001b[39many\u001b[39m(\n\u001b[0;32m   1052\u001b[0m     \u001b[39misinstance\u001b[39m(k, \u001b[39mtuple\u001b[39m) \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m tup\n\u001b[0;32m   1053\u001b[0m ):\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1057\u001b[0m     \u001b[39m#  that themselves contain a slice entry\u001b[39;00m\n\u001b[0;32m   1058\u001b[0m     \u001b[39m# See test_loc_series_getitem_too_many_dimensions\u001b[39;00m\n\u001b[0;32m   1059\u001b[0m     \u001b[39mraise\u001b[39;00m IndexingError(\u001b[39m\"\u001b[39m\u001b[39mToo many indexers\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1306\u001b[0m, in \u001b[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1302\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m ek:\n\u001b[0;32m   1303\u001b[0m     \u001b[39m# raise KeyError if number of indexers match\u001b[39;00m\n\u001b[0;32m   1304\u001b[0m     \u001b[39m# else IndexingError will be raised\u001b[39;00m\n\u001b[0;32m   1305\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mndim \u001b[39m<\u001b[39m \u001b[39mlen\u001b[39m(tup) \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39mnlevels:\n\u001b[1;32m-> 1306\u001b[0m         \u001b[39mraise\u001b[39;00m ek\n\u001b[0;32m   1307\u001b[0m     \u001b[39mraise\u001b[39;00m IndexingError(\u001b[39m\"\u001b[39m\u001b[39mNo label returned\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39mek\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1300\u001b[0m, in \u001b[0;36m_LocIndexer._handle_lowerdim_multi_index_axis0\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1297\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39mor\u001b[39;00m \u001b[39m0\u001b[39m\n\u001b[0;32m   1298\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1299\u001b[0m     \u001b[39m# fast path for series or for tup devoid of slices\u001b[39;00m\n\u001b[1;32m-> 1300\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_label(tup, axis\u001b[39m=\u001b[39;49maxis)\n\u001b[0;32m   1302\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m ek:\n\u001b[0;32m   1303\u001b[0m     \u001b[39m# raise KeyError if number of indexers match\u001b[39;00m\n\u001b[0;32m   1304\u001b[0m     \u001b[39m# else IndexingError will be raised\u001b[39;00m\n\u001b[0;32m   1305\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mndim \u001b[39m<\u001b[39m \u001b[39mlen\u001b[39m(tup) \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mobj\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39mnlevels:\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1293\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1291\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_get_label\u001b[39m(\u001b[39mself\u001b[39m, label, axis: AxisInt):\n\u001b[0;32m   1292\u001b[0m     \u001b[39m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[1;32m-> 1293\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mobj\u001b[39m.\u001b[39;49mxs(label, axis\u001b[39m=\u001b[39;49maxis)\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4088\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   4085\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mindex\n\u001b[0;32m   4087\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(index, MultiIndex):\n\u001b[1;32m-> 4088\u001b[0m     loc, new_index \u001b[39m=\u001b[39m index\u001b[39m.\u001b[39;49m_get_loc_level(key, level\u001b[39m=\u001b[39;49m\u001b[39m0\u001b[39;49m)\n\u001b[0;32m   4089\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m drop_level:\n\u001b[0;32m   4090\u001b[0m         \u001b[39mif\u001b[39;00m lib\u001b[39m.\u001b[39mis_integer(loc):\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:3026\u001b[0m, in \u001b[0;36mMultiIndex._get_loc_level\u001b[1;34m(self, key, level)\u001b[0m\n\u001b[0;32m   3024\u001b[0m \u001b[39mfor\u001b[39;00m i, k \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(key):\n\u001b[0;32m   3025\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(k, \u001b[39mslice\u001b[39m):\n\u001b[1;32m-> 3026\u001b[0m         loc_level \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_level_indexer(k, level\u001b[39m=\u001b[39;49mi)\n\u001b[0;32m   3027\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(loc_level, \u001b[39mslice\u001b[39m):\n\u001b[0;32m   3028\u001b[0m             \u001b[39mif\u001b[39;00m com\u001b[39m.\u001b[39mis_null_slice(loc_level) \u001b[39mor\u001b[39;00m com\u001b[39m.\u001b[39mis_full_slice(\n\u001b[0;32m   3029\u001b[0m                 loc_level, \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m)\n\u001b[0;32m   3030\u001b[0m             ):\n\u001b[0;32m   3031\u001b[0m                 \u001b[39m# everything\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:3160\u001b[0m, in \u001b[0;36mMultiIndex._get_level_indexer\u001b[1;34m(self, key, level, indexer)\u001b[0m\n\u001b[0;32m   3157\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mslice\u001b[39m(i, j, step)\n\u001b[0;32m   3159\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 3160\u001b[0m     idx \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_loc_single_level_index(level_index, key)\n\u001b[0;32m   3162\u001b[0m     \u001b[39mif\u001b[39;00m level \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lexsort_depth \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m   3163\u001b[0m         \u001b[39m# Desired level is not sorted\u001b[39;00m\n\u001b[0;32m   3164\u001b[0m         \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(idx, \u001b[39mslice\u001b[39m):\n\u001b[0;32m   3165\u001b[0m             \u001b[39m# test_get_loc_partial_timestamp_multiindex\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\multi.py:2752\u001b[0m, in \u001b[0;36mMultiIndex._get_loc_single_level_index\u001b[1;34m(self, level_index, key)\u001b[0m\n\u001b[0;32m   2750\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m\n\u001b[0;32m   2751\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 2752\u001b[0m     \u001b[39mreturn\u001b[39;00m level_index\u001b[39m.\u001b[39;49mget_loc(key)\n",
      "File \u001b[1;32mc:\\Users\\ribei\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3653\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3654\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m-> 3655\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m   3656\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[0;32m   3657\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3658\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3659\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3660\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: False"
     ]
    }
   ],
   "source": [
    "# ESCREVA O CÓDIGO DA SUA RESPOSTA AQUI\n",
    "frequencias_relativas = carros.value_counts(normalize=True).sort_index()\n",
    "frequencias_relativas.loc[\"Quantidade\" == 0, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex4\"></div>\n",
    "\n",
    "### EXERCÍCIO 4\n",
    "\n",
    "1. **A partir da tabela de frequências relativas**, calcule a média, variância e desvio padrão da quantidade de itens vistoriados em não conformidade. Ou seja, sem utilizar as funções `.mean()`, `.var(ddof=0)` e `std(ddof=0)`. Para isso, será necessário utilizar as fórmulas vistas em aula:\n",
    "\n",
    "    $\\qquad\\qquad\\overline{y} = \\sum\\limits_y y\\cdot fr_y$\n",
    "\n",
    "    $\\qquad\\qquad var(y) = \\sum\\limits_y (y-\\overline{y})^2\\cdot fr_y$\n",
    "    \n",
    "    $\\qquad\\qquad dp(y) = \\sqrt{var(y)}$\n",
    "\n",
    "sendo $fr_y$ a frequência relativa da quantidade $y$ na amostra.\n",
    "\n",
    "1. Compare os resultados com a média, variância populacional e desvio padrão populacional obtidos a partir das funções `.mean()` `.var(ddof=0)` e `.std(ddof=0)`.\n",
    "\n",
    "1. Explique sucintamente o que você pode concluir a partir desses valores.\n",
    "\n",
    "*Respostas esperadas Média, Variância e Desvio Padrão respectivamente: 1.3713333 ; 1.5794449 ; 1.25675967*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "incomplete input (1279670733.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[67], line 4\u001b[1;36m\u001b[0m\n\u001b[1;33m    \u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m incomplete input\n"
     ]
    }
   ],
   "source": [
    "# ESCREVA O CÓDIGO DA SUA RESPOSTA AQUI\n",
    "media = 0\n",
    "for i in range(len(frequencias_relativas)):\n",
    "    media += *frequencias_relativas[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ESCREVA SUAS CONCLUSÕES AQUI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"comparacao\"></div>\n",
    "\n",
    "## Comparação: resultados empíricos *vs* modelo teórico\n",
    "\n",
    "Vamos comparar as probabilidades teóricas ([exercício 2](#ex2)) com as frequências relativas observadas nos dados ([exercício 4](#ex4)). Para isso, temos, por exemplo, as duas opções gráficas a seguir para verificar se os resultados empíricos se encaixam no modelo teórico proposto. Procure entender pelo menos a ideia do código que gera os gráficos abaixo (pode ser necessário procurar alguma documentação, mas não se preocupe em entender todos os detalhes), eles serão úteis na última questão.\n",
    "\n",
    "<div id=\"opcao1\"></div>\n",
    "\n",
    "### Opção 1: Frequências relativas *vs* Probabilidades teóricas\n",
    "\n",
    "A primeira opção gráfica contrasta a frequência relativa e a probabilidade teórica para uma determinada quantidade de itens em não conformidade. Essa opção contrasta a frequência relativa $fr$ para um valor de $y$ com a probabilidade pontual $P(Y=y)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fr = frequencias_relativas   #Calculadas no Exercício 4 - Amostra\n",
    "pr = probabilidades_teoricas #Calculadas no Exercício 1 - Modelo teórico\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(len(fr)), fr, 'o', alpha=0.8)\n",
    "plt.plot(range(len(pr)), pr, 'D', alpha=0.8)\n",
    "plt.legend(('Freq.relativa','P(Y=y)'), loc='upper right')\n",
    "plt.title('Frequência relativa VS Probabilidade teórica')\n",
    "plt.xlim(-1,15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"opcao2\"></div>\n",
    "\n",
    "### Opção 2: Frequência relativa acumulada *vs* Probabilidade acumulada\n",
    "\n",
    "A segunda opção gráfica contrasta a frequência relativa acumulada com a probabilidade acumulada até determinada quantidade de itens em não conformidade. Ou seja, compara a frequência relativa acumulada $fra$ até $y$ com a probabilidade acumulada $P(Y\\leq y)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fra = fr.cumsum() #fra contem as frequencias relativas acumuladas calculadas no Exercício 1 - Amostra\n",
    "n = 14\n",
    "p = 0.1\n",
    "fda = stats.binom.cdf(range(n), n=n, p=p) #fda contem as probabilidades acumuladas pelo modelo teorico\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(len(fra)), fra, 'o', alpha=0.8)\n",
    "plt.plot(range(n), fda, 'D', alpha=0.8)\n",
    "plt.legend(('Freq.rel.acumulada','P(Y<=y)'), loc='lower right')\n",
    "plt.title('Frequência relativa acumulada VS Probabilidade teórica acumulada')\n",
    "plt.xlim(-1,15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex5\"></div>\n",
    "\n",
    "### EXERCÍCIO 5\n",
    "\n",
    "Baseado nos gráficos acima, refine sua conclusão quanto ao uso do modelo teórico para ajustar a variável em questão ([exercício 1](#ex1))."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ESCREVA SUA RESPOSTA AQUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex6\"></div>\n",
    "\n",
    "### EXERCÍCIO 6\n",
    "\n",
    "Na prática, qual a necessidade de um modelo probabilístico já que temos os dados observados?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ESCREVA SUA RESPOSTA AQUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex7\"></div>\n",
    "\n",
    "### EXERCÍCIO 7\n",
    "\n",
    "Considerando apenas os carros populares, o modelo binomial com parâmetros $n=14$ e $p=0,10$ é adequado para ajustar a variável **Quantidade**? Se sim, justifique; caso não, sugira novos valores para os parâmetros da distribuição para que ela se ajuste aos dados. **Dica**: utilize a [fórmula de $E(Y)$](#esperanca-variancia).\n",
    "\n",
    "*Resposta esperada para probabilide de sucesso estimada a partir dos carros populares: 0.13103174603174603*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"comandos\"></div>\n",
    "\n",
    "___\n",
    "\n",
    "## Lista de comandos utilizados neste notebook\n",
    "\n",
    "Os seguintes comandos foram utilizados neste jupyter notebook. Para facilitar sua consulta, escreva um resumo do que cada um deles faz:\n",
    "\n",
    "- [`stats.binom.pmf`](#pmf-stats): ESCREVA AQUI O RESUMO\n",
    "- [`stats.binom.mean`](#pmf-stats): ESCREVA AQUI O RESUMO\n",
    "- [`stats.binom.var`](#pmf-stats): ESCREVA AQUI O RESUMO\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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