Aula18_Atividade_TLC_v2.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Atividade: <p> <center> Uso da Distribuição Amostral da Média via resultado do TLC\n",
    "___\n",
    "    \n",
    "## Aula 18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"indice\"></div>\n",
    "\n",
    "## Índice\n",
    "\n",
    "- [Teoria](#teoria)\n",
    "- [Simulação](#simulacao)\n",
    "    - [Exercício 1](#ex1)\n",
    "    - [Visualizando os dados gerados](#visualizando)\n",
    "    - [Exercício 2](#ex2)\n",
    "    - [Exercício 3](#ex3)\n",
    "    - [Exercício 4](#ex4)\n",
    "    - [Exercício 5](#ex5)\n",
    "    - [Exercício 6](#ex6)\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%reset -f\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"teoria\"></div>\n",
    "\n",
    "# Teoria\n",
    "\n",
    "## Estatísticas e distribuições amostrais\n",
    "\n",
    "Uma **estatística** é qualquer função baseada nas observações de uma amostra aleatória. Usamos estatísticas quando, para fins práticos, não é possível ter acesso a toda uma população.\n",
    "\n",
    "A distribuição de probabilidades de uma estatística é chamada de *distribuição amostral*.\n",
    "\n",
    "\n",
    "## Média Amostral $\\overline{X}$\n",
    "\n",
    "Assuma que $X$ seja uma variável aleatória com média $\\mu$ e variância $\\sigma^2$, ou seja, $E(X)=\\mu$ e $Var(X)=\\sigma^2$.\n",
    "\n",
    "Sejam $X_1, X_2, \\ldots, X_n$ amostras aleatórias de $X$ de tamanho $n$, então:\n",
    "\n",
    "$$\\overline{X} = \\frac{X_1 + X_2 + X_3 + \\cdots + X_n}{n}$$\n",
    "\n",
    "É chamada *Média Amostral*!\n",
    "\n",
    "Pode-se caracterizar a média amostral com os seguintes valores:\n",
    "\n",
    "* Média de $\\overline{X}$:\n",
    "\n",
    "$$\\mu_{\\overline{X}} = E(\\overline{X})=\\frac{\\mu + \\mu + \\cdots + \\mu}{n} = \\mu$$\n",
    "\n",
    "* Variância de $\\overline{X}$:\n",
    "\n",
    "\n",
    "$$\\sigma^2_{\\overline{X}} = Var(\\overline{X})=\\frac{\\sigma^2 + \\sigma^2 + \\cdots + \\sigma^2}{n^2} = \\frac{\\sigma^2}{n}$$\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "## Teorema  Central do Limite (TLC)\n",
    "\n",
    "Assuma que $X$ seja uma variável aleatória com média $\\mu$ e variância $\\sigma^2$, ou seja, $E(X)=\\mu$ e $Var(X)=\\sigma^2$.\n",
    "\n",
    "Dado $X_i$ uma variável aleatória *i.i.d.* (independente e identicamente distribuída) a $X$ com distribuição qualquer, tem-se que:\n",
    "\n",
    "$$\\bar{X}=\\frac{X_1+X_2+...+X_n}{n}=\\frac{\\sum_{i=1}^n X_i}{n} \\sim N\\left(\\mu,\\frac{\\sigma^2}{n}\\right),$$\n",
    "\n",
    "quando $n$ for suficientemente grande.\n",
    "\n",
    "*Obs:* Em alguns casos específicos, pode-se considerar $X_i$ com distribuições diferentes ou com certa dependência entre elas.\n",
    "\n",
    "<br> \n",
    "\n",
    "**Resultado adicional:**\n",
    "\n",
    "Se $X_1, X_2, \\cdots, X_n$ é uma amostra aleatória de tamanho $n$ extraída de uma população com média $\\mu$ e variância $\\sigma^2$ e $\\overline{X}$ é a distribuição da média amostral, a forma limite da distribuição:\n",
    "\n",
    "$$Z = \\frac{\\overline{X} - \\mu}{\\sigma/\\sqrt{n}} \\sim N\\left(0,1\\right).$$\n",
    "\n",
    "É a **distribuição normal padrão**, à medida que $n  \\rightarrow \\infty$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"simulacao\"></div>\n",
    "\n",
    "# Simulação\n",
    "\n",
    "Vamos tentar entender a intuição do TLC através de uma simulação."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex1\"></div>\n",
    "\n",
    "### EXERCÍCIO 1\n",
    "\n",
    "Faça uma função chamada `amostra_uniforme(n)`, que recebe um número inteiro `n` e que retorna uma lista contendo `n` valores gerados aleatoriamente a partir de uma distribuição uniforme com parâmetros $a=10$, $b=20$. \n",
    "\n",
    "Esses `n` valores representam uma amostra aleatória qualquer extraída de $X\\sim \\text{Uniforme}(10;20)$.\n",
    "\n",
    "**Dica:** você provavelmente vai utilizar o método `stats.uniform.rvs` (pesquise a documentação)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "a = 10\n",
    "b = 20\n",
    "def amostra_uniforme(n):\n",
    "    return stats.uniform.rvs(loc=a, scale=b-a, size=n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"visualizando\"></div>\n",
    "\n",
    "## Visualizando os dados gerados\n",
    "\n",
    "Execute a função `amostra_uniforme(n)` criada no exercício anterior considerando `n=20`.\n",
    "\n",
    "O gráfico a seguir contem um histograma com os valores da amostra gerados por sua função.\n",
    "\n",
    "Execute algumas vezes a célula abaixo para perceber que a amostra pode conter valores diferentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gera uma amostra aleatória de tamanho n\n",
    "#amostra = pd.Series(amostra_uniforme(20))\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# Para construir o histograma com amplitude 1 nas faixas\n",
    "faixa = np.arange(10,21,1)\n",
    "plt.hist(amostra_uniforme(20), bins=faixa, density=True,\n",
    "         color='skyblue', edgecolor='white', \n",
    "         linewidth=1, alpha=0.8)\n",
    "\n",
    "# Para construir a fdp \n",
    "eixo_x = np.linspace(5, 25, 1000)\n",
    "fx_uniform = stats.uniform.pdf(eixo_x, loc=10, scale=20-10)\n",
    "plt.plot(eixo_x, fx_uniform, color='blue')\n",
    "plt.ylabel('densidade')\n",
    "plt.xlabel('valores de x')\n",
    "plt.title('Uma amostra de X ~ Uniforme(10; 20)')\n",
    "plt.ylim(0,0.2)\n",
    "plt.legend(('fdp','amostra'))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex2\"></div>\n",
    "\n",
    "### EXERCÍCIO 2\n",
    "\n",
    "Gere um amostra aleatória de tamanho `n=20` utilizando a sua função `amostra_uniforme`.\n",
    "Calcule a média amostral."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.744959741542516"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "np.mean(amostra_uniforme(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex3\"></div>\n",
    "\n",
    "### EXERCÍCIO 3\n",
    "\n",
    "**a.** Agora repita o [Exercício 2](#ex2) por mil vezes, armazenando as mil médias amostrais em uma lista.\n",
    "\n",
    "**b.** Plote um histograma com essas médias amostrais obtidas. Utilize `bins=20`, por exemplo.\n",
    "\n",
    "**c.** Interprete, descritivamente, a distribuição dessas médias amostrais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,   3.,  11.,  16.,  37.,  53.,  54.,  72., 102., 117., 107.,\n",
       "        109., 100.,  76.,  53.,  45.,  19.,   8.,  12.,   3.]),\n",
       " array([13.04152088, 13.23212581, 13.42273074, 13.61333567, 13.80394061,\n",
       "        13.99454554, 14.18515047, 14.3757554 , 14.56636034, 14.75696527,\n",
       "        14.9475702 , 15.13817513, 15.32878007, 15.519385  , 15.70998993,\n",
       "        15.90059486, 16.0911998 , 16.28180473, 16.47240966, 16.66301459,\n",
       "        16.85361953]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "lista = []\n",
    "for i in range(1000):\n",
    "    lista.append(np.mean(amostra_uniforme(20)))\n",
    "\n",
    "plt.hist(lista, bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex4\"></div>\n",
    "\n",
    "### EXERCÍCIO 4\n",
    "\n",
    "No [Exercício 1](#ex1), a sua função `amostra_uniforme(n)` foi constuída para gerar `n` valores para representar uma amostra aleatória qualquer. Ou seja, de forma teórica $X_1, X_2, X_3, \\ldots, X_n$ é uma amostra aleatória de $X\\sim \\text{Uniforme}(10; 20)$.\n",
    "\n",
    "No [Exercício 2](#ex2), calculamos uma única média amostral a partir dos valores $X_1, X_2, X_3, \\ldots, X_n$. Nos nossos exemplos utilizamos `n=20`.\n",
    "\n",
    "No [Exercício 3a](#ex3), geramos 1000 amostras de tamanho `n=20` e, com isso, foram calculadas 1000 médias amostrais, $\\overline{X}$, todas obtidas de forma independente e da mesma distribuição. \n",
    "\n",
    "O que podemos observar no histograma é que essas 1000 médias amostrais se aproximam de uma distribuição simétrica. Seria uma distribuição normal?\n",
    "\n",
    "Refaça o histograma e adicione a função densidade de probabilidade da distribuição normal considerando como `loc` a média das médias amostrais e como `scale` o desvio padrão da médias amostrais.<br>\n",
    "Avaliar, descritivamente, se as médias amostrais podem ser modeladas por uma distribuição normal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "faixa = np.arange(10,21,0.1)\n",
    "plt.hist(lista, bins=faixa, density=True,\n",
    "         color='skyblue', edgecolor='white', \n",
    "         linewidth=1, alpha=0.8)\n",
    "\n",
    "eixo_x = np.linspace(12, 18, 1000)\n",
    "fx_uniform = stats.norm.pdf(eixo_x, loc=np.mean(lista), scale=np.std(lista))\n",
    "plt.plot(eixo_x, fx_uniform, color='blue')\n",
    "plt.ylabel('densidade')\n",
    "plt.xlabel('valores de x')\n",
    "plt.title('Uma amostra de X ~ Normal(mu; std)')\n",
    "plt.ylim(0,1)\n",
    "plt.legend(('fdp','amostra'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex5\"></div>\n",
    "\n",
    "### EXERCÍCIO 5\n",
    "\n",
    "Pelo TLC, as médias amostrais $\\overline{X}$ devem seguir distribuição normal conforme `n` seja grande. Há estudos que mostram que, com `n>30`, $\\overline{X}$ já apresenta boa aproximação de uma normal não importando o formato de $X$.\n",
    "\n",
    "Ainda, pelo TLC, $\\overline{X}$ tem valor médio igual a $\\mu$ (que é a média da distribuição original $X$, no caso da uniforme) e variância $\\sigma^2/n$. Vamos verificar essa afirmação:\n",
    "\n",
    "**a.** Calcule a média e variância teóricas da distribuição de $\\overline{X}$. Para isso, calcule a média e a variância da $\\text{Uniforme}(10; 20)$. Esses dois últimos valores vão representar $\\mu$ e $\\sigma^2$.\n",
    "\n",
    "**b.** Calcule a média e variância das 1000 médias amostrais geradas no [Exercício 3](#ex3).\n",
    "\n",
    "**c.** Compare os resultados e discuta se eles se aproximam do que era esperado pelo TLC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.0 8.333333333333332\n"
     ]
    }
   ],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "mu = stats.uniform.mean(loc=a, scale=b-a)\n",
    "var = stats.uniform.var(loc=a, scale=b-a)\n",
    "\n",
    "print(mu, var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.99973878431935 0.4323158138264886\n"
     ]
    }
   ],
   "source": [
    "mua = np.mean(lista)\n",
    "vara = np.var(lista)\n",
    "\n",
    "print(mua, vara)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex6\"></div>\n",
    "\n",
    "### EXERCÍCIO 6\n",
    "\n",
    "Se os Exercícios de 2 a 5 fossem construídos com um tamanho da amostral menor do que 20, quais seriam as mudanças nos resultados? Assuma, por exemplo, `n=10`. <br>\n",
    "E se esses Exercícios 2 a 5 fossem construídos com um tamanho amostral maior do que 20? Assuma, por exemplo, `n=50`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.0 8.333333333333332\n",
      "15.049574413859503 0.8582267235896004\n"
     ]
    }
   ],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "n=10\n",
    "\n",
    "lista = []\n",
    "for i in range(1000):\n",
    "    lista.append(np.mean(amostra_uniforme(n)))\n",
    "\n",
    "plt.hist(lista, bins=10)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "faixa = np.arange(10,21,0.1)\n",
    "plt.hist(lista, bins=faixa, density=True,\n",
    "         color='skyblue', edgecolor='white', \n",
    "         linewidth=1, alpha=0.8)\n",
    "\n",
    "eixo_x = np.linspace(12, 18, 1000)\n",
    "fx_uniform = stats.norm.pdf(eixo_x, loc=np.mean(lista), scale=np.std(lista))\n",
    "plt.plot(eixo_x, fx_uniform, color='blue')\n",
    "plt.ylabel('densidade')\n",
    "plt.xlabel('valores de x')\n",
    "plt.title('Uma amostra de X ~ Normal(mu; std)')\n",
    "plt.ylim(0,1)\n",
    "plt.legend(('fdp','amostra'))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "mu = stats.uniform.mean(loc=a, scale=b-a)\n",
    "var = stats.uniform.var(loc=a, scale=b-a)\n",
    "\n",
    "print(mu, var)\n",
    "\n",
    "mua = np.mean(lista)\n",
    "vara = np.var(lista)\n",
    "\n",
    "print(mua, vara)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.0 8.333333333333332\n",
      "15.018238514214456 0.166447753511678\n"
     ]
    }
   ],
   "source": [
    "# ESCREVA SEU CÓDIGO AQUI\n",
    "n=50\n",
    "\n",
    "lista = []\n",
    "for i in range(1000):\n",
    "    lista.append(np.mean(amostra_uniforme(n)))\n",
    "\n",
    "plt.hist(lista, bins=10)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "faixa = np.arange(10,21,0.1)\n",
    "plt.hist(lista, bins=faixa, density=True,\n",
    "         color='skyblue', edgecolor='white', \n",
    "         linewidth=1, alpha=0.8)\n",
    "\n",
    "eixo_x = np.linspace(12, 18, 1000)\n",
    "fx_uniform = stats.norm.pdf(eixo_x, loc=np.mean(lista), scale=np.std(lista))\n",
    "plt.plot(eixo_x, fx_uniform, color='blue')\n",
    "plt.ylabel('densidade')\n",
    "plt.xlabel('valores de x')\n",
    "plt.title('Uma amostra de X ~ Normal(mu; std)')\n",
    "plt.ylim(0,1)\n",
    "plt.legend(('fdp','amostra'))\n",
    "\n",
    "plt.show()\n",
    "\n",
    "mu = stats.uniform.mean(loc=a, scale=b-a)\n",
    "var = stats.uniform.var(loc=a, scale=b-a)\n",
    "\n",
    "print(mu, var)\n",
    "\n",
    "mua = np.mean(lista)\n",
    "vara = np.var(lista)\n",
    "\n",
    "print(mua, vara)"
   ]
  }
 ],
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  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
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}