Planejamento_experimentos_aula.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style='color: blue; font-size: 34px; font-weight: bold;'> Planejamento de Experimentos \n",
    "</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red' style='font-size: 30px;'>1.0 Introdução   </font>\n",
    "<hr style='border: 2px solid red;'>\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "<p style='font-size: 18px; line-height: 2; margin: 0px 0px; text-align: justify; text-indent: 0px;'>    \n",
    "<i> “Chamar um especialista em estatística depois que o experimento foi feito pode ser o mesmo que pedir para ele fazer um exame post-mortem. Talvez ele consiga dizer do que foi que o experimento morreu.”  </i>     \n",
    "</p>    \n",
    "\n",
    "<p style='font-size: 18px; line-height: 2; margin: 0px 0px; text-align: right; text-indent: 0px;'>    \n",
    "    <b>Sir Ronald Fisher</b>  \n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red' style='font-size: 30px;'> Introdução à análise de experimentos   </font>\n",
    "<hr style='border: 2px solid red;'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Inserindo o experimento num Data Frame </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando as bibliotecas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    "\n",
    "https://pandas.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy\n",
    "\n",
    "http://www.numpy.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<p style='font-size: 18px; line-height: 2; margin: 0px 0px; text-align: justify; text-indent: 0px;'>\n",
    "    <font color=\"red\"> <b>Ensaios realizados na forma normalizada</b> \n",
    "\n",
    "\n",
    "<img width='800px' src='figuras/Figura_2.png'>\n",
    "\n",
    " \n",
    "   \n",
    "   \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p style='font-size: 18px; line-height: 2; margin: 0px 0px; text-align: justify; text-indent: 0px;'>\n",
    "<font color=\"MidnightBlue\"> Construindo uma matriz representando todos os ensaios realizados:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensaios = np.array([ [-1,-1], [1,-1], [-1,1], [1,1]   ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pyDOE2\n",
    "\n",
    "https://pypi.org/project/pyDOE2/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyDOE2 as doe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Costruindo um planejamento fatorial de 2²\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensaios  = doe.ff2n(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1., -1.],\n",
       "       [ 1., -1.],\n",
       "       [-1.,  1.],\n",
       "       [ 1.,  1.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensaios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incerindo o planejamento em um Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "experimento = pd.DataFrame(ensaios, columns = ['Farinha','Chocolate']    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>Farinha</th>\n",
       "      <th>Chocolate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Farinha  Chocolate\n",
       "0     -1.0       -1.0\n",
       "1      1.0       -1.0\n",
       "2     -1.0        1.0\n",
       "3      1.0        1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experimento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserindo coluna com os resultados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "experimento['Porcoes'] = [19,37,24,49]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>Farinha</th>\n",
       "      <th>Chocolate</th>\n",
       "      <th>Porcoes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Farinha  Chocolate  Porcoes\n",
       "0     -1.0       -1.0       19\n",
       "1      1.0       -1.0       37\n",
       "2     -1.0        1.0       24\n",
       "3      1.0        1.0       49"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experimento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<p style='font-size: 18px; line-height: 2; margin: 0px 0px; text-align: justify; text-indent: 0px;'>\n",
    "    <font color=\"MidnightBlue\"> <b>Conclusão:</b> Temos, por fim, nosso experimento representado por um <i>DataFrame</i> do Pandas. Usaremos este <i>DataFrame</i> para iniciarmos a análise do nosso experimento. \n",
    "    \n",
    "<hr>   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Analisando graficamente o experimento   </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Importando o Seaborn\n",
    "\n",
    "https://seaborn.pydata.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paletas -> Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, icefire, icefire_r, inferno, inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r\n",
    "sns.set_palette('terrain')\n",
    "\n",
    "# estilo -> white, dark, whitegrid, darkgrid, ticks\n",
    "sns.set_style('darkgrid')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Para a farinha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19be8038668>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 413.25x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = sns.lmplot(data = experimento, x = 'Farinha', y = 'Porcoes', ci = None, hue = 'Chocolate')\n",
    "\n",
    "\n",
    "ax1.set(xticks = (-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Para o chocolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x19be93c4cf8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 413.25x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = sns.lmplot(data = experimento, x = 'Chocolate', y = 'Porcoes', ci = None, hue = 'Farinha'     )\n",
    "\n",
    "ax2.set(xticks = (-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Ajustando o modelo estatístico </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "<img width='800px' src='figuras/Figura_3.png'>\n",
    "\n",
    "<p style='margin: 30px 30px;'> \n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas  Stats Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm \n",
    "\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = smf.ols(data = experimento, formula = 'Porcoes ~ Farinha + Chocolate + Farinha:Chocolate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo_ajustado = modelo.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Porcoes   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                    nan\n",
      "Method:                 Least Squares   F-statistic:                     0.000\n",
      "Date:                Thu, 27 Jun 2019   Prob (F-statistic):                nan\n",
      "Time:                        17:38:15   Log-Likelihood:                 126.02\n",
      "No. Observations:                   4   AIC:                            -244.0\n",
      "Df Residuals:                       0   BIC:                            -246.5\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept            32.2500        inf          0        nan         nan         nan\n",
      "Farinha              10.7500        inf          0        nan         nan         nan\n",
      "Chocolate             4.2500        inf          0        nan         nan         nan\n",
      "Farinha:Chocolate     1.7500        inf          0        nan         nan         nan\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.500\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.167\n",
      "Skew:                           0.000   Prob(JB):                        0.920\n",
      "Kurtosis:                       2.000   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\stats\\stattools.py:72: ValueWarning: omni_normtest is not valid with less than 8 observations; 4 samples were given.\n",
      "  \"samples were given.\" % int(n), ValueWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1549: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return 1 - (np.divide(self.nobs - self.k_constant, self.df_resid)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1550: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  * (1 - self.rsquared))\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1558: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return self.ssr/self.df_resid\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1510: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return np.dot(wresid, wresid) / self.df_resid\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:1181: RuntimeWarning: invalid value encountered in multiply\n",
      "  cov_p = self.normalized_cov_params * scale\n"
     ]
    }
   ],
   "source": [
    "print(modelo_ajustado.summary() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Aumentando os Graus de liberdade  </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>\n",
    "\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>     \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "<p style='font-size: 18px; line-height: 2; margin: 0px 0px; text-align: justify; text-indent: 0px;'>\n",
    "    <font color=\"red\"> <b>Replicatas no centro</b>\n",
    "\n",
    "\n",
    "<img width='800px' src='figuras/Figura_5.png'> \n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'> \n",
    "    \n",
    "    \n",
    " \n",
    "    \n",
    "\n",
    "    \n",
    "<hr>    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "centro = np.array([ [0,0,29],\n",
    "                    [0,0,30],\n",
    "                    [0,0,29],\n",
    "                    [0,0,30],             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "centro_dataframe = pd.DataFrame(centro, columns = ['Farinha', 'Chocolate','Porcoes'], index = [4,5,6,7]     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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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>Farinha</th>\n",
       "      <th>Chocolate</th>\n",
       "      <th>Porcoes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Farinha  Chocolate  Porcoes\n",
       "4        0          0       29\n",
       "5        0          0       30\n",
       "6        0          0       29\n",
       "7        0          0       30"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centro_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "experimento = experimento.append(centro_dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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>Farinha</th>\n",
       "      <th>Chocolate</th>\n",
       "      <th>Porcoes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Farinha  Chocolate  Porcoes\n",
       "0     -1.0       -1.0       19\n",
       "1      1.0       -1.0       37\n",
       "2     -1.0        1.0       24\n",
       "3      1.0        1.0       49\n",
       "4      0.0        0.0       29\n",
       "5      0.0        0.0       30\n",
       "6      0.0        0.0       29\n",
       "7      0.0        0.0       30"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experimento"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'>  Análise de significância estatística   </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo = smf.ols(data = experimento, formula = 'Porcoes ~ Farinha + Chocolate + Farinha:Chocolate'     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelo_ajustado = modelo.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Porcoes   R-squared:                       0.971\n",
      "Model:                            OLS   Adj. R-squared:                  0.950\n",
      "Method:                 Least Squares   F-statistic:                     45.21\n",
      "Date:                Thu, 27 Jun 2019   Prob (F-statistic):            0.00152\n",
      "Time:                        17:38:15   Log-Likelihood:                -14.155\n",
      "No. Observations:                   8   AIC:                             36.31\n",
      "Df Residuals:                       4   BIC:                             36.63\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept            30.8750      0.710     43.494      0.000      28.904      32.846\n",
      "Farinha              10.7500      1.004     10.708      0.000       7.963      13.537\n",
      "Chocolate             4.2500      1.004      4.233      0.013       1.463       7.037\n",
      "Farinha:Chocolate     1.7500      1.004      1.743      0.156      -1.037       4.537\n",
      "==============================================================================\n",
      "Omnibus:                        4.655   Durbin-Watson:                   0.841\n",
      "Prob(Omnibus):                  0.098   Jarque-Bera (JB):                1.080\n",
      "Skew:                          -0.180   Prob(JB):                        0.583\n",
      "Kurtosis:                       1.237   Cond. No.                         1.41\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=8\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    }
   ],
   "source": [
    "print(modelo_ajustado.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='margin: 200px 200px;'>    \n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "    \n",
    " <img width='400px' src='figuras/Figura_6.png'> \n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>    \n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    " <img width='600px' src='figuras/Figura_7.png'> \n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>        \n",
    "    \n",
    "    \n",
    " <hr>   \n",
    "    \n",
    "\n",
    "  \n",
    " <img width='600px' src='figuras/Figura_10.png'> \n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>       \n",
    "  \n",
    "<hr>        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# <font color = 'purple'> Teste de significância estatística usando o <b>t<b>    </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<img width='600px' src='figuras/Figura_11.png'> \n",
    "\n",
    "\n",
    "<hr>\n",
    "  <p style='margin: 30px 30px;'>     \n",
    "<img width='900px' src='figuras/Figura_8.png'> \n",
    "\n",
    "\n",
    "<hr>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='margin: 150px 150px;'>     \n",
    "<img width='1000px' src='figuras/Figura_20.png'\n",
    "\n",
    "\n",
    "\n",
    "<hr>\n",
    "<p style='margin: 150px 150px;'>    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_valores = modelo_ajustado.tvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            43.494275\n",
       "Farinha              10.708252\n",
       "Chocolate             4.233495\n",
       "Farinha:Chocolate     1.743204\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "nome= t_valores.index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'Farinha', 'Chocolate', 'Farinha:Chocolate']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "distribuicao  = stats.t(df = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7764451051977987"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribuicao.ppf(q = 1 - 0.025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "limite = [distribuicao.ppf(q = 1 -0.025  )]*len(nome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.7764451051977987,\n",
       " 2.7764451051977987,\n",
       " 2.7764451051977987,\n",
       " 2.7764451051977987]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando o gráfico "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19bea0aa048>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hAAAACDgnT55Udna2bty48baXAgBAQCEEAAAAAefBgwfyer1vexkAAAQcQgAAAAAAAAyCEAAAAASU4uJirV69WpK0YcMGmUwmtbS0vHbs7t27ZTKZZLfbe/TdvXtXY8eO1fLly31tra2t+vbbb2WxWBQXF6cJEyZo4cKFstls/VrbhQsX9OWXXyo5OVlxcXHKyMjQjh079PTpU79xZrNZubm5OnjwoFJSUjRhwgRt3LjR13/27FlZrVZNnDhRCQkJys7OVl1dXY96t27d0sqVKzVjxgyNGzdOZrNZJSUlunfvXr/WCwAwHkIAAAAQUCwWi2bOnClJSk1NVUFBgQYNGvTasXPnzlVwcLAcDkePvp9//ller1fz5s2TJLW0tCgrK0tHjhxRQkKC8vPzlZ6erubmZpWUlKiioqLXdTmdTi1evFgul0spKSnKycnRgAED9P3338tqtfYIAq5fv66ysjJZLBbNmTNHCQkJkqQff/xRVqtVV69eVUZGhrKzs9XW1qaVK1dqx44dvuMfPHig/Px8NTQ0aNKkSbJarfr0009ls9mUl5enZ8+e9f+kAgAMI+RtLwAAAOB/YbFY1N7ervr6ek2dOlX5+flvHBsdHa3Jkyfr3Llzun//vj766CNfn8PhUHh4uC9Q2Llzpx4+fKg9e/YoJSXFN27p0qVatGiRampqtHTp0tfW6ezs1DfffKOwsDDt379fn332mSTp+fPnKi4uVnV1tXbt2qWvvvrKd8zDhw+1du1a5ebm+tru3LmjsrIyjR49WpWVlRo8eLAkqbCwUPn5+dq8ebPMZrPGjBkjh8Oh27dvq7y8XFlZWb45ysrKVFlZqTNnzmj69On9P7EAAENgJwAAAHinZWZmyuv16tixY7621tZWNTU1aebMmQoPD/eNW79+vV8AIEnjx49XWFiY2tra3ljD6XTK4/EoLy/PFwBIUkhIiC8cOHjwYI/jZs+e7ff56NGjevr0qVasWOELACQpLCxMK1askNfr1eHDhyXJ92LEpqYmvXjxwje2sLBQp0+fJgAAALwWOwEAAMA7bdasWSotLZXD4dCSJUskSbW1tZJeXfh3S0xMVGJioh49eiS3262//vpLf/75p86fP68nT574XWj/t99//12SlJSU1KNvyJAhGjVqlNxutzo6OhQZGSlJCg0NVXR0tN/YS5cuSXr1ToDr16/79T1+/Niv1uzZs7Vt2zbZ7XYdP35cqampSktL07Rp0/Txxx/3/wQBAAyFEAAAAAS8vXv3qqOjw69t0qRJSk5O1sCBA2U2m+VwOHT37l3FxMTI4XBo6NChfnf9PR6PNmzYoJqaGj179kxBQUEaNmyYJk+erCtXrvRav7OzU5IUERHx2v7o6Gi53W79888/vhAgLCysx7ju7/DDDz+8sZbH45EkxcTE6KefftL27dtVX1+v6upqVVdXKzQ0VAsXLtSaNWv0/vvv97puAIDxEAIAAICAt3//frW2tvq1FRQUKDk5WdKrO/61tbWqq6tTWlqa3G63cnNzFRLyn59CRUVFamhoUE5OjubPn68xY8b4Luqrq6t7rT9w4EBJ0t9///3a/vb2dknShx9+2Os83Y8mOJ1ODR8+vNexkjR8+HCVl5frxYsXunTpklwulw4dOiS73a7IyEgVFRX1OQcAwFgIAQAAQMAJCgry+3zixIlex6empmrIkCE6ceKEb1t9978CSK8u0hsaGjRu3DiVlpb6HdvS0qInT57o5cuXb5x/7NixkqTGxkZZLBa/vs7OTrndbo0YMULvvfder+s0mUxyOp26ePFijxDg5s2bstvtSkpKktlsVn19vVwul77++mtFREQoPj5e8fHxysrK0vTp09XY2NhrLQCAMfFiQAAAEHC67+D392/wQkJClJGRocbGRtXU1GjEiBGKj4/39YeGhio4OFjt7e1+f+XX1dWldevW9VnLYrEoMjJSBw4c0OXLl33tz58/1/r169XV1aX58+f3uc7MzEwNGDBAmzZt0r179/zmWbdunXbv3q1Hjx5Jkv744w/ZbDbZbDa/Obp3RHzyyR8DN4QAAAHjSURBVCd91gMAGA87AQAAQMCJiYmRJNlsNnk8HuXm5vra3iQzM1MVFRW6ceOGCgoK/Po++OADpaen69ixY1q0aJGmTJmix48f65dfftH9+/cVFRWljo4Oeb1eBQf3vIcSERGh8vJyFRYWKicnR+np6Ro6dKjOnTuna9euKTExUV988UWf32vkyJEqKirSxo0bNXfuXJnNZkVFRenUqVNqbm7WjBkzfC8z/Pzzz1VVVaXvvvtOv/32m0wmk9ra2lRXV6fw8HAtX768v6cTAGAg7AQAAAABJykpSUuWLJHH41FlZaWam5v7PCY+Pl4jR46U5P8oQLfy8nItW7ZMHR0dqqiokMvlUlxcnGw2mxYsWKCuri79+uuvb5x/1qxZOnDggKZMmSKXy6WqqipJ0qpVq7R3794+HwXoZrVatXPnTsXGxur48eOy2+0KCQlRcXGxtmzZ4tsFERUVpYqKCi1evFg3b97Uvn37dPLkSaWlpamqqkqxsbH9qgcAMJagl7094AYAAAAAAN4Z7AQAAAAAAMAgCAEAAAAAADAIQgAAAAAAAAyCEAAAAAAAAIMgBAAAAAAAwCAIAQAAAAAAMAhCAAAAAAAADIIQAAAAAAAAgyAEAAAAAADAIAgBAAAAAAAwiH8BzC95qGH5QRUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pareto = sns.barplot(x = t_valores, y = nome )\n",
    "\n",
    "pareto.figure.set_size_inches(15,6)\n",
    "\n",
    "pareto.tick_params(labelsize = 20)\n",
    "\n",
    "pareto.set_xlabel('t-valores', fontsize = 20)\n",
    "\n",
    "\n",
    "pareto.plot(limite, nome, 'r')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Propondo um novo modelo   </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "<img width='800px' src='figuras/Figura_3.png'>\n",
    "\n",
    "<p style='margin: 30px 30px;'> \n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style='margin: 200px 200px;'>\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "<img width='600px' src='figuras/Figura_9.png'> \n",
    "\n",
    "<p style='font-size: 18px; line-height: 2; margin: 0px 0px; text-align: justify; text-indent: 0px;'>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "\n",
    "    \n",
    "<hr>    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Gráfico Padronizado de Pareto do novo modelo    </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando o gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red' style='font-size: 30px;'> Preditos por observados  </font>\n",
    "<hr style='border: 2px solid red;'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Explorando o modelo   </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definindo a função"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p style='margin: 150px 150px;'>\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "<img width='700px' src='figuras/Figura_22.png'> \n",
    "\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'purple'> Mapa de cores   </font>\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>\n",
    "    \n",
    "<hr style = 'border: 1px solid purple;'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p style='margin: 150px 150px;'>\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "<img width='700px' src='figuras/Figura_23.jpg'> \n",
    "\n",
    "#### Fonte: National Centers for Environmental Prediction\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "\n",
    "<p style='margin: 50px 50px;'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<p style='margin: 150px 150px;'>\n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "<img width='600px' src='figuras/Figura_24.png'> \n",
    "\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n",
    "\n",
    "<p style='margin: 30px 30px;'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construindo a superfície de resposta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://matplotlib.org/users/colormaps.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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