Aula 12_Exemplos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Exemplos: <p> <center> Distribuições Uniforme e Exponencial\n",
    "\n",
    "___\n",
    "## Aula 12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Lista de comandos:\n",
    "\n",
    "### Distribuição Uniforme\n",
    "\n",
    "Comandos quando $X\\sim Uniforme(a, b)$.\n",
    "\n",
    "* $f(x)$: `stats.uniform.pdf(x, loc=a, scale=b-a)`\n",
    "\n",
    "* $P(X\\leq x)$: `stats.uniform.cdf(x, loc=a, scale=b-a)`\n",
    "\n",
    "* $x$ tal que $p=P(X\\leq x)$: `stats.uniform.ppf(p, loc=a, scale=b-a)`\n",
    "\n",
    "* $E(X)$: `stats.uniform.mean(loc=a, scale=b-a)`\n",
    "\n",
    "* $Var(X)$: `stats.uniform.var(loc=a, scale=b-a)`\n",
    "\n",
    "* $DP(X)$: `stats.uniform.std(loc=a, scale=b-a)`\n",
    "\n",
    "Link: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.uniform.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "## Exemplo 1:\n",
    "\n",
    "O torque suportado por parafusos de uma linha de montagem varia segundo uma distribuição uniforme entre 100 Nm e 200Nm. (sendo Nm: Newton metros)\n",
    "\n",
    "### Item a\n",
    "\n",
    "Qual a probabilidade de o torque de um parafuso qualquer estar entre 150Nm e 175Nm?\n",
    "\n",
    "*Resposta esperada: 0.25*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "stats.uniform.cdf(175, scale=200-100, loc=100)-stats.uniform.cdf(150, scale=200-100, loc=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item b\n",
    "\n",
    "Um parafuso sobreviveu a um teste de torque que garante que ele suporta pelo menos 150Nm. Qual a probabilidade que suporte até 175Nm?\n",
    "\n",
    "*Resposta esperada: 0.5*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "stats.uniform.cdf(175, scale=200-150, loc=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item c\n",
    "\n",
    "Construa um gráfico que mostre o formato da função densidade de probabilidade de uma Uniforme com a=100 e b=200.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1cfd5af4250>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "b = 200\n",
    "a = 100\n",
    "x = np.arange(a,b,1e-3)\n",
    "f = [1/(b-a) for i in x]\n",
    "plt.plot(x, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Lista de comandos:\n",
    "\n",
    "### Distribuição Exponencial\n",
    "\n",
    "Comandos quando $X\\sim Exp(\\lambda)$. Lembrando que nesse caso $E(X) = \\mu = 1/\\lambda$.\n",
    "\n",
    "* $f(x)$: `stats.expon.pdf(x, loc=0, scale=mu)`\n",
    "\n",
    "* $P(X\\leq x)$: `stats.expon.cdf(x, loc=0, scale=mu)`\n",
    "\n",
    "* $x$ tal que $p=P(X\\leq x)$: `stats.expon.ppf(p, loc=0, scale=mu)`\n",
    "\n",
    "* $E(X)$: `stats.expon.mean(loc=0, scale=mu)`\n",
    "\n",
    "* $Var(X)$: `stats.expon.var(loc=0, scale=mu)`\n",
    "\n",
    "* $DP(X)$: `stats.expon.std(loc=0, scale=mu)`\n",
    "\n",
    "Link: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.expon.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "## Exemplo 2:\n",
    "\n",
    "Admita que o tempo até que uma venda seja realizada em uma loja siga um modelo (distribuição) exponencial com média de 0,2 horas (12 minutos).\n",
    "\n",
    "### Item a\n",
    "\n",
    "Qual é a probabilidade de uma venda demorar mais de meia hora para ser feita?\n",
    "\n",
    "*Resposta esperada: 0.08208499862389884*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08208499862389884"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "1-stats.expon.cdf(0.5, loc=0, scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item b\n",
    "\n",
    "Dado que você sabe que a próxima venda vai ser feita em menos de meia hora, qual a probabilidade de que seja em menos de 5 minutos?\n",
    "\n",
    "*Resposta esperada: 0.37123194335935633*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37123194335935633"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "stats.expon.cdf(1/12, loc=0, scale=0.2)/stats.expon.cdf(0.5, loc=0, scale=0.2)"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
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