Aula13_Exemplos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# Exemplo: <p> <center>Distribuição Normal\n",
    "___\n",
    "\n",
    "## Aula 13\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Lista de comandos:\n",
    "\n",
    "### Distribuição Normal\n",
    "\n",
    "Comandos quando $X\\sim N(\\mu, \\sigma^2)$ a partir da biblioteca `from scipy import stats`:\n",
    "\n",
    "* $f(x)$: `stats.norm.pdf(x, loc=mu, scale=sigma)`\n",
    "\n",
    "* $P(X\\leq x)$: `stats.norm.cdf(x, loc=mu, scale=sigma)`\n",
    "\n",
    "* $x$ tal que $p=P(X\\leq x)$: `stats.norm.ppf(p, loc=mu, scale=sigma)`\n",
    "\n",
    "* $E(X)$: `stats.norm.mean(loc=mu, scale=sigma)`\n",
    "\n",
    "* $Var(X)$: `stats.norm.var(loc=mu, scale=sigma)`\n",
    "\n",
    "* $DP(X)$: `stats.norm.std(loc=mu, scale=sigma)`\n",
    "\n",
    "\n",
    "Link: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "# Exemplo 1:\n",
    "\n",
    "Assuma que a variável de interesse $X$ represente notas de alunos, cuja média vale 5 e o desvio padrão, 1,5. Ainda, considere que essa variável seja modelada por uma normal. Ou seja, $X\\sim N(5; 1,5^2)$. \n",
    "\n",
    "**Responda:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item a\n",
    "\n",
    "Qual a probabilidade de encontrar um aluno com nota abaixo de 6,5?\n",
    "\n",
    "*Resposta esperada: 0.8413447460685429*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(X < 6.5) = 0.8413447460685429\n"
     ]
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "\n",
    "# P(X < 6,5) = P (X < mu + 1*sigma) = ?\n",
    "u = 5\n",
    "dp = 1.5\n",
    "item_a = stats.norm.cdf(6.5, loc=u, scale=dp)\n",
    "\n",
    "print(f'P(X < 6.5) = {item_a}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item b\n",
    "\n",
    "Qual a probabilidade de encontrar um aluno nota acima de 8?\n",
    "\n",
    "*Resposta esperada: 0.02275013194817921*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(X > 8) = 0.02275013194817921\n"
     ]
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "\n",
    "# P(X > 8) = P (X > mu + 2*sigma) = ?\n",
    "item_b = 1 - stats.norm.cdf(8, loc=u, scale=dp)\n",
    "\n",
    "print(f'P(X > 8) = {item_b}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item c\n",
    "\n",
    "E da nota ser superior a 9?\n",
    "\n",
    "*Resposta esperada: 0.003830380567589775*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(X > 9) = 0.003830380567589775\n"
     ]
    }
   ],
   "source": [
    "# P(X > 8) = P (X > mu + 2*sigma) = ?\n",
    "item_c =  1 - stats.norm.cdf(9, loc=u, scale=dp)\n",
    "\n",
    "print(f'P(X > 9) = {item_c}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item d\n",
    "\n",
    "Qual a maior nota dos 20% piores alunos? E a pior nota dos 20% melhores alunos?\n",
    "\n",
    "*Resposta esperada: 3.737568149640629 e 6.262431850359372*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.737568149640629"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "stats.norm.ppf(0.2, loc=u, scale=dp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.262431850359372"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.ppf(1-0.2, loc=u, scale=dp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item e\n",
    "\n",
    "Considerando alunos que tiraram acima de 5, qual a probabilidade de ser abaixo de 7?\n",
    "\n",
    "*Resposta esperada: 0.8175775605482642*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8175775605482642"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ESCREVA SUA RESPOSTA AQUI\n",
    "(stats.norm.cdf(7, loc=u, scale=dp) - stats.norm.cdf(5, loc=u, scale=dp))/stats.norm.cdf(5, loc=u, scale=dp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item f\n",
    "\n",
    "Construa o gráfico da distribuição normal que modela as notas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2982cae1c00>]"
      ]
     },
     "execution_count": 75,
     "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 SUA RESPOSTA AQUI\n",
    "plt.plot(np.arange(0,10.1, 1e-3), stats.norm.pdf(np.arange(0,10.1, 1e-3), loc=u, scale=dp))"
   ]
  }
 ],
 "metadata": {
  "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,
 "nbformat_minor": 2
}