main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modules and Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "importing Jupyter notebook from datasetHandler.ipynb\n"
     ]
    }
   ],
   "source": [
    "import import_ipynb\n",
    "from datasetHandler import Dataset\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from IPython.display import Math\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Dataset Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Dystopia Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "      <td>2.51738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "      <td>2.70201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "      <td>2.49204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "      <td>2.46531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "      <td>2.45176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>6</td>\n",
       "      <td>7.406</td>\n",
       "      <td>0.03140</td>\n",
       "      <td>1.29025</td>\n",
       "      <td>1.31826</td>\n",
       "      <td>0.88911</td>\n",
       "      <td>0.64169</td>\n",
       "      <td>0.41372</td>\n",
       "      <td>0.23351</td>\n",
       "      <td>2.61955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>7</td>\n",
       "      <td>7.378</td>\n",
       "      <td>0.02799</td>\n",
       "      <td>1.32944</td>\n",
       "      <td>1.28017</td>\n",
       "      <td>0.89284</td>\n",
       "      <td>0.61576</td>\n",
       "      <td>0.31814</td>\n",
       "      <td>0.47610</td>\n",
       "      <td>2.46570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>8</td>\n",
       "      <td>7.364</td>\n",
       "      <td>0.03157</td>\n",
       "      <td>1.33171</td>\n",
       "      <td>1.28907</td>\n",
       "      <td>0.91087</td>\n",
       "      <td>0.65980</td>\n",
       "      <td>0.43844</td>\n",
       "      <td>0.36262</td>\n",
       "      <td>2.37119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Australia and New Zealand</td>\n",
       "      <td>9</td>\n",
       "      <td>7.286</td>\n",
       "      <td>0.03371</td>\n",
       "      <td>1.25018</td>\n",
       "      <td>1.31967</td>\n",
       "      <td>0.90837</td>\n",
       "      <td>0.63938</td>\n",
       "      <td>0.42922</td>\n",
       "      <td>0.47501</td>\n",
       "      <td>2.26425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia and New Zealand</td>\n",
       "      <td>10</td>\n",
       "      <td>7.284</td>\n",
       "      <td>0.04083</td>\n",
       "      <td>1.33358</td>\n",
       "      <td>1.30923</td>\n",
       "      <td>0.93156</td>\n",
       "      <td>0.65124</td>\n",
       "      <td>0.35637</td>\n",
       "      <td>0.43562</td>\n",
       "      <td>2.26646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country                     Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland             Western Europe               1            7.587   \n",
       "1      Iceland             Western Europe               2            7.561   \n",
       "2      Denmark             Western Europe               3            7.527   \n",
       "3       Norway             Western Europe               4            7.522   \n",
       "4       Canada              North America               5            7.427   \n",
       "5      Finland             Western Europe               6            7.406   \n",
       "6  Netherlands             Western Europe               7            7.378   \n",
       "7       Sweden             Western Europe               8            7.364   \n",
       "8  New Zealand  Australia and New Zealand               9            7.286   \n",
       "9    Australia  Australia and New Zealand              10            7.284   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "3         0.03880                   1.45900  1.33095   \n",
       "4         0.03553                   1.32629  1.32261   \n",
       "5         0.03140                   1.29025  1.31826   \n",
       "6         0.02799                   1.32944  1.28017   \n",
       "7         0.03157                   1.33171  1.28907   \n",
       "8         0.03371                   1.25018  1.31967   \n",
       "9         0.04083                   1.33358  1.30923   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "3                   0.88521  0.66973                        0.36503   \n",
       "4                   0.90563  0.63297                        0.32957   \n",
       "5                   0.88911  0.64169                        0.41372   \n",
       "6                   0.89284  0.61576                        0.31814   \n",
       "7                   0.91087  0.65980                        0.43844   \n",
       "8                   0.90837  0.63938                        0.42922   \n",
       "9                   0.93156  0.65124                        0.35637   \n",
       "\n",
       "   Generosity  Dystopia Residual  \n",
       "0     0.29678            2.51738  \n",
       "1     0.43630            2.70201  \n",
       "2     0.34139            2.49204  \n",
       "3     0.34699            2.46531  \n",
       "4     0.45811            2.45176  \n",
       "5     0.23351            2.61955  \n",
       "6     0.47610            2.46570  \n",
       "7     0.36262            2.37119  \n",
       "8     0.47501            2.26425  \n",
       "9     0.43562            2.26646  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('datasets/whr_2015.csv')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## World Happiness Report from 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mQuantidade de valores faltantes no dataset: \u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Happiness Score                  0\n",
       "Economy (GDP per Capita)         0\n",
       "Family                           0\n",
       "Health (Life Expectancy)         0\n",
       "Freedom                          0\n",
       "Trust (Government Corruption)    0\n",
       "Generosity                       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mLinhas duplicadas: \u001b[0m 0\n",
      "\u001b[1;1mNúmero de linhas e colunas: \u001b[0m (158, 7)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.587</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>0.29678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.561</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>0.43630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.527</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>0.34139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.522</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>0.34699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.427</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>0.45811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.406</td>\n",
       "      <td>1.29025</td>\n",
       "      <td>1.31826</td>\n",
       "      <td>0.88911</td>\n",
       "      <td>0.64169</td>\n",
       "      <td>0.41372</td>\n",
       "      <td>0.23351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.378</td>\n",
       "      <td>1.32944</td>\n",
       "      <td>1.28017</td>\n",
       "      <td>0.89284</td>\n",
       "      <td>0.61576</td>\n",
       "      <td>0.31814</td>\n",
       "      <td>0.47610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.364</td>\n",
       "      <td>1.33171</td>\n",
       "      <td>1.28907</td>\n",
       "      <td>0.91087</td>\n",
       "      <td>0.65980</td>\n",
       "      <td>0.43844</td>\n",
       "      <td>0.36262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.286</td>\n",
       "      <td>1.25018</td>\n",
       "      <td>1.31967</td>\n",
       "      <td>0.90837</td>\n",
       "      <td>0.63938</td>\n",
       "      <td>0.42922</td>\n",
       "      <td>0.47501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.284</td>\n",
       "      <td>1.33358</td>\n",
       "      <td>1.30923</td>\n",
       "      <td>0.93156</td>\n",
       "      <td>0.65124</td>\n",
       "      <td>0.35637</td>\n",
       "      <td>0.43562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Score  Economy (GDP per Capita)   Family  \\\n",
       "0            7.587                   1.39651  1.34951   \n",
       "1            7.561                   1.30232  1.40223   \n",
       "2            7.527                   1.32548  1.36058   \n",
       "3            7.522                   1.45900  1.33095   \n",
       "4            7.427                   1.32629  1.32261   \n",
       "5            7.406                   1.29025  1.31826   \n",
       "6            7.378                   1.32944  1.28017   \n",
       "7            7.364                   1.33171  1.28907   \n",
       "8            7.286                   1.25018  1.31967   \n",
       "9            7.284                   1.33358  1.30923   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.94143  0.66557                        0.41978   \n",
       "1                   0.94784  0.62877                        0.14145   \n",
       "2                   0.87464  0.64938                        0.48357   \n",
       "3                   0.88521  0.66973                        0.36503   \n",
       "4                   0.90563  0.63297                        0.32957   \n",
       "5                   0.88911  0.64169                        0.41372   \n",
       "6                   0.89284  0.61576                        0.31814   \n",
       "7                   0.91087  0.65980                        0.43844   \n",
       "8                   0.90837  0.63938                        0.42922   \n",
       "9                   0.93156  0.65124                        0.35637   \n",
       "\n",
       "   Generosity  \n",
       "0     0.29678  \n",
       "1     0.43630  \n",
       "2     0.34139  \n",
       "3     0.34699  \n",
       "4     0.45811  \n",
       "5     0.23351  \n",
       "6     0.47610  \n",
       "7     0.36262  \n",
       "8     0.47501  \n",
       "9     0.43562  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2015 = Dataset('datasets/whr_2015.csv')\n",
    "data2015.printSample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Relação das variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2015.VariableCorrelations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Regressão linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mCoeficiente de determinação R2:\u001b[0m 0.8004656793512612\n"
     ]
    }
   ],
   "source": [
    "data2015.linearRegression()\n",
    "data2015.printRegressionGraph()\n",
    "data2015.R2coefficient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Equação da Regressão linear múltipla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "| Label | Value |\n",
    "| :- | :-: |\n",
    "| Economy (GDP per Capita) | 1.39651 |\n",
    "| Family | 1.34951 |\n",
    "| Health (Life Expectancy)\t | 0.94143 |\n",
    "| Freedom | 0.66557 |\n",
    "| Trust (Government Corruption) | 0.41978 |\n",
    "| Generosity | 0.29678 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\text{Happiness Score} =  1.959 +  0.908X_1 +  1.183X_2 +  1.018X_3 +  1.214X_4 +  0.712X_5 +  0.824X_6$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Happines Score predito: [7.13359631]\n"
     ]
    }
   ],
   "source": [
    "data2015.linearRegressionEquation(1.39651,1.34951,0.94143,0.66557,0.41978,0.29678)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "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>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.795703</td>\n",
       "      <td>0.770038</td>\n",
       "      <td>0.756960</td>\n",
       "      <td>0.576352</td>\n",
       "      <td>0.302397</td>\n",
       "      <td>0.172463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <td>0.795703</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.681042</td>\n",
       "      <td>0.847096</td>\n",
       "      <td>0.415421</td>\n",
       "      <td>0.227675</td>\n",
       "      <td>0.012482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.770038</td>\n",
       "      <td>0.681042</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.585431</td>\n",
       "      <td>0.528139</td>\n",
       "      <td>0.184599</td>\n",
       "      <td>0.146399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <td>0.756960</td>\n",
       "      <td>0.847096</td>\n",
       "      <td>0.585431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.399800</td>\n",
       "      <td>0.168847</td>\n",
       "      <td>0.110256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.576352</td>\n",
       "      <td>0.415421</td>\n",
       "      <td>0.528139</td>\n",
       "      <td>0.399800</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.463745</td>\n",
       "      <td>0.418458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <td>0.302397</td>\n",
       "      <td>0.227675</td>\n",
       "      <td>0.184599</td>\n",
       "      <td>0.168847</td>\n",
       "      <td>0.463745</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.172463</td>\n",
       "      <td>0.012482</td>\n",
       "      <td>0.146399</td>\n",
       "      <td>0.110256</td>\n",
       "      <td>0.418458</td>\n",
       "      <td>0.213357</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Happiness Score  Economy (GDP per Capita)  \\\n",
       "Happiness Score                       1.000000                  0.795703   \n",
       "Economy (GDP per Capita)              0.795703                  1.000000   \n",
       "Family                                0.770038                  0.681042   \n",
       "Health (Life Expectancy)              0.756960                  0.847096   \n",
       "Freedom                               0.576352                  0.415421   \n",
       "Trust (Government Corruption)         0.302397                  0.227675   \n",
       "Generosity                            0.172463                  0.012482   \n",
       "\n",
       "                                 Family  Health (Life Expectancy)   Freedom  \\\n",
       "Happiness Score                0.770038                  0.756960  0.576352   \n",
       "Economy (GDP per Capita)       0.681042                  0.847096  0.415421   \n",
       "Family                         1.000000                  0.585431  0.528139   \n",
       "Health (Life Expectancy)       0.585431                  1.000000  0.399800   \n",
       "Freedom                        0.528139                  0.399800  1.000000   \n",
       "Trust (Government Corruption)  0.184599                  0.168847  0.463745   \n",
       "Generosity                     0.146399                  0.110256  0.418458   \n",
       "\n",
       "                               Trust (Government Corruption)  Generosity  \n",
       "Happiness Score                                     0.302397    0.172463  \n",
       "Economy (GDP per Capita)                            0.227675    0.012482  \n",
       "Family                                              0.184599    0.146399  \n",
       "Health (Life Expectancy)                            0.168847    0.110256  \n",
       "Freedom                                             0.463745    0.418458  \n",
       "Trust (Government Corruption)                       1.000000    0.213357  \n",
       "Generosity                                          0.213357    1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2015.spearmanCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true,
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.780966</td>\n",
       "      <td>0.740605</td>\n",
       "      <td>0.724200</td>\n",
       "      <td>0.568211</td>\n",
       "      <td>0.395199</td>\n",
       "      <td>0.180319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <td>0.780966</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.645299</td>\n",
       "      <td>0.816478</td>\n",
       "      <td>0.370300</td>\n",
       "      <td>0.307885</td>\n",
       "      <td>-0.010465</td>\n",
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       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.740605</td>\n",
       "      <td>0.645299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.531104</td>\n",
       "      <td>0.441518</td>\n",
       "      <td>0.205605</td>\n",
       "      <td>0.087513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <td>0.724200</td>\n",
       "      <td>0.816478</td>\n",
       "      <td>0.531104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.360477</td>\n",
       "      <td>0.248335</td>\n",
       "      <td>0.108335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.568211</td>\n",
       "      <td>0.370300</td>\n",
       "      <td>0.441518</td>\n",
       "      <td>0.360477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.493524</td>\n",
       "      <td>0.373916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <td>0.395199</td>\n",
       "      <td>0.307885</td>\n",
       "      <td>0.205605</td>\n",
       "      <td>0.248335</td>\n",
       "      <td>0.493524</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.276123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.180319</td>\n",
       "      <td>-0.010465</td>\n",
       "      <td>0.087513</td>\n",
       "      <td>0.108335</td>\n",
       "      <td>0.373916</td>\n",
       "      <td>0.276123</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "                               Happiness Score  Economy (GDP per Capita)  \\\n",
       "Happiness Score                       1.000000                  0.780966   \n",
       "Economy (GDP per Capita)              0.780966                  1.000000   \n",
       "Family                                0.740605                  0.645299   \n",
       "Health (Life Expectancy)              0.724200                  0.816478   \n",
       "Freedom                               0.568211                  0.370300   \n",
       "Trust (Government Corruption)         0.395199                  0.307885   \n",
       "Generosity                            0.180319                 -0.010465   \n",
       "\n",
       "                                 Family  Health (Life Expectancy)   Freedom  \\\n",
       "Happiness Score                0.740605                  0.724200  0.568211   \n",
       "Economy (GDP per Capita)       0.645299                  0.816478  0.370300   \n",
       "Family                         1.000000                  0.531104  0.441518   \n",
       "Health (Life Expectancy)       0.531104                  1.000000  0.360477   \n",
       "Freedom                        0.441518                  0.360477  1.000000   \n",
       "Trust (Government Corruption)  0.205605                  0.248335  0.493524   \n",
       "Generosity                     0.087513                  0.108335  0.373916   \n",
       "\n",
       "                               Trust (Government Corruption)  Generosity  \n",
       "Happiness Score                                     0.395199    0.180319  \n",
       "Economy (GDP per Capita)                            0.307885   -0.010465  \n",
       "Family                                              0.205605    0.087513  \n",
       "Health (Life Expectancy)                            0.248335    0.108335  \n",
       "Freedom                                             0.493524    0.373916  \n",
       "Trust (Government Corruption)                       1.000000    0.276123  \n",
       "Generosity                                          0.276123    1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2015.pearsonCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## World Happiness Report from 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mQuantidade de valores faltantes no dataset: \u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Happiness Score                  0\n",
       "Economy (GDP per Capita)         0\n",
       "Family                           0\n",
       "Health (Life Expectancy)         0\n",
       "Freedom                          0\n",
       "Trust (Government Corruption)    0\n",
       "Generosity                       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mLinhas duplicadas: \u001b[0m 0\n",
      "\u001b[1;1mNúmero de linhas e colunas: \u001b[0m (157, 7)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.526</td>\n",
       "      <td>1.44178</td>\n",
       "      <td>1.16374</td>\n",
       "      <td>0.79504</td>\n",
       "      <td>0.57941</td>\n",
       "      <td>0.44453</td>\n",
       "      <td>0.36171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.509</td>\n",
       "      <td>1.52733</td>\n",
       "      <td>1.14524</td>\n",
       "      <td>0.86303</td>\n",
       "      <td>0.58557</td>\n",
       "      <td>0.41203</td>\n",
       "      <td>0.28083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.501</td>\n",
       "      <td>1.42666</td>\n",
       "      <td>1.18326</td>\n",
       "      <td>0.86733</td>\n",
       "      <td>0.56624</td>\n",
       "      <td>0.14975</td>\n",
       "      <td>0.47678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.498</td>\n",
       "      <td>1.57744</td>\n",
       "      <td>1.12690</td>\n",
       "      <td>0.79579</td>\n",
       "      <td>0.59609</td>\n",
       "      <td>0.35776</td>\n",
       "      <td>0.37895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.413</td>\n",
       "      <td>1.40598</td>\n",
       "      <td>1.13464</td>\n",
       "      <td>0.81091</td>\n",
       "      <td>0.57104</td>\n",
       "      <td>0.41004</td>\n",
       "      <td>0.25492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.404</td>\n",
       "      <td>1.44015</td>\n",
       "      <td>1.09610</td>\n",
       "      <td>0.82760</td>\n",
       "      <td>0.57370</td>\n",
       "      <td>0.31329</td>\n",
       "      <td>0.44834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.339</td>\n",
       "      <td>1.46468</td>\n",
       "      <td>1.02912</td>\n",
       "      <td>0.81231</td>\n",
       "      <td>0.55211</td>\n",
       "      <td>0.29927</td>\n",
       "      <td>0.47416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.334</td>\n",
       "      <td>1.36066</td>\n",
       "      <td>1.17278</td>\n",
       "      <td>0.83096</td>\n",
       "      <td>0.58147</td>\n",
       "      <td>0.41904</td>\n",
       "      <td>0.49401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.313</td>\n",
       "      <td>1.44443</td>\n",
       "      <td>1.10476</td>\n",
       "      <td>0.85120</td>\n",
       "      <td>0.56837</td>\n",
       "      <td>0.32331</td>\n",
       "      <td>0.47407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.291</td>\n",
       "      <td>1.45181</td>\n",
       "      <td>1.08764</td>\n",
       "      <td>0.83121</td>\n",
       "      <td>0.58218</td>\n",
       "      <td>0.40867</td>\n",
       "      <td>0.38254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness Score  Economy (GDP per Capita)   Family  \\\n",
       "0            7.526                   1.44178  1.16374   \n",
       "1            7.509                   1.52733  1.14524   \n",
       "2            7.501                   1.42666  1.18326   \n",
       "3            7.498                   1.57744  1.12690   \n",
       "4            7.413                   1.40598  1.13464   \n",
       "5            7.404                   1.44015  1.09610   \n",
       "6            7.339                   1.46468  1.02912   \n",
       "7            7.334                   1.36066  1.17278   \n",
       "8            7.313                   1.44443  1.10476   \n",
       "9            7.291                   1.45181  1.08764   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "0                   0.79504  0.57941                        0.44453   \n",
       "1                   0.86303  0.58557                        0.41203   \n",
       "2                   0.86733  0.56624                        0.14975   \n",
       "3                   0.79579  0.59609                        0.35776   \n",
       "4                   0.81091  0.57104                        0.41004   \n",
       "5                   0.82760  0.57370                        0.31329   \n",
       "6                   0.81231  0.55211                        0.29927   \n",
       "7                   0.83096  0.58147                        0.41904   \n",
       "8                   0.85120  0.56837                        0.32331   \n",
       "9                   0.83121  0.58218                        0.40867   \n",
       "\n",
       "   Generosity  \n",
       "0     0.36171  \n",
       "1     0.28083  \n",
       "2     0.47678  \n",
       "3     0.37895  \n",
       "4     0.25492  \n",
       "5     0.44834  \n",
       "6     0.47416  \n",
       "7     0.49401  \n",
       "8     0.47407  \n",
       "9     0.38254  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2016 = Dataset('datasets/whr_2016.csv')\n",
    "data2016.printSample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Relação das variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2016.VariableCorrelations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Regressão linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mCoeficiente de determinação R2:\u001b[0m 0.7206939125540606\n"
     ]
    }
   ],
   "source": [
    "data2016.linearRegression()\n",
    "data2016.printRegressionGraph()\n",
    "data2016.R2coefficient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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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>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.808486</td>\n",
       "      <td>0.762077</td>\n",
       "      <td>0.770846</td>\n",
       "      <td>0.562503</td>\n",
       "      <td>0.311914</td>\n",
       "      <td>0.149173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <td>0.808486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.698863</td>\n",
       "      <td>0.856341</td>\n",
       "      <td>0.397277</td>\n",
       "      <td>0.223493</td>\n",
       "      <td>0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.762077</td>\n",
       "      <td>0.698863</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.618060</td>\n",
       "      <td>0.508152</td>\n",
       "      <td>0.177294</td>\n",
       "      <td>0.120647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <td>0.770846</td>\n",
       "      <td>0.856341</td>\n",
       "      <td>0.618060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.352924</td>\n",
       "      <td>0.168427</td>\n",
       "      <td>0.080010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.562503</td>\n",
       "      <td>0.397277</td>\n",
       "      <td>0.508152</td>\n",
       "      <td>0.352924</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.466232</td>\n",
       "      <td>0.400576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <td>0.311914</td>\n",
       "      <td>0.223493</td>\n",
       "      <td>0.177294</td>\n",
       "      <td>0.168427</td>\n",
       "      <td>0.466232</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.252983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.149173</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.120647</td>\n",
       "      <td>0.080010</td>\n",
       "      <td>0.400576</td>\n",
       "      <td>0.252983</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Happiness Score  Economy (GDP per Capita)  \\\n",
       "Happiness Score                       1.000000                  0.808486   \n",
       "Economy (GDP per Capita)              0.808486                  1.000000   \n",
       "Family                                0.762077                  0.698863   \n",
       "Health (Life Expectancy)              0.770846                  0.856341   \n",
       "Freedom                               0.562503                  0.397277   \n",
       "Trust (Government Corruption)         0.311914                  0.223493   \n",
       "Generosity                            0.149173                  0.000406   \n",
       "\n",
       "                                 Family  Health (Life Expectancy)   Freedom  \\\n",
       "Happiness Score                0.762077                  0.770846  0.562503   \n",
       "Economy (GDP per Capita)       0.698863                  0.856341  0.397277   \n",
       "Family                         1.000000                  0.618060  0.508152   \n",
       "Health (Life Expectancy)       0.618060                  1.000000  0.352924   \n",
       "Freedom                        0.508152                  0.352924  1.000000   \n",
       "Trust (Government Corruption)  0.177294                  0.168427  0.466232   \n",
       "Generosity                     0.120647                  0.080010  0.400576   \n",
       "\n",
       "                               Trust (Government Corruption)  Generosity  \n",
       "Happiness Score                                     0.311914    0.149173  \n",
       "Economy (GDP per Capita)                            0.223493    0.000406  \n",
       "Family                                              0.177294    0.120647  \n",
       "Health (Life Expectancy)                            0.168427    0.080010  \n",
       "Freedom                                             0.466232    0.400576  \n",
       "Trust (Government Corruption)                       1.000000    0.252983  \n",
       "Generosity                                          0.252983    1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2016.spearmanCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.790322</td>\n",
       "      <td>0.739252</td>\n",
       "      <td>0.765384</td>\n",
       "      <td>0.566827</td>\n",
       "      <td>0.402032</td>\n",
       "      <td>0.156848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <td>0.790322</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.669540</td>\n",
       "      <td>0.837067</td>\n",
       "      <td>0.362283</td>\n",
       "      <td>0.294185</td>\n",
       "      <td>-0.025531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.739252</td>\n",
       "      <td>0.669540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588377</td>\n",
       "      <td>0.450208</td>\n",
       "      <td>0.213561</td>\n",
       "      <td>0.089629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <td>0.765384</td>\n",
       "      <td>0.837067</td>\n",
       "      <td>0.588377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341199</td>\n",
       "      <td>0.249583</td>\n",
       "      <td>0.075987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.566827</td>\n",
       "      <td>0.362283</td>\n",
       "      <td>0.450208</td>\n",
       "      <td>0.341199</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502054</td>\n",
       "      <td>0.361751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <td>0.402032</td>\n",
       "      <td>0.294185</td>\n",
       "      <td>0.213561</td>\n",
       "      <td>0.249583</td>\n",
       "      <td>0.502054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.305930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.156848</td>\n",
       "      <td>-0.025531</td>\n",
       "      <td>0.089629</td>\n",
       "      <td>0.075987</td>\n",
       "      <td>0.361751</td>\n",
       "      <td>0.305930</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Happiness Score  Economy (GDP per Capita)  \\\n",
       "Happiness Score                       1.000000                  0.790322   \n",
       "Economy (GDP per Capita)              0.790322                  1.000000   \n",
       "Family                                0.739252                  0.669540   \n",
       "Health (Life Expectancy)              0.765384                  0.837067   \n",
       "Freedom                               0.566827                  0.362283   \n",
       "Trust (Government Corruption)         0.402032                  0.294185   \n",
       "Generosity                            0.156848                 -0.025531   \n",
       "\n",
       "                                 Family  Health (Life Expectancy)   Freedom  \\\n",
       "Happiness Score                0.739252                  0.765384  0.566827   \n",
       "Economy (GDP per Capita)       0.669540                  0.837067  0.362283   \n",
       "Family                         1.000000                  0.588377  0.450208   \n",
       "Health (Life Expectancy)       0.588377                  1.000000  0.341199   \n",
       "Freedom                        0.450208                  0.341199  1.000000   \n",
       "Trust (Government Corruption)  0.213561                  0.249583  0.502054   \n",
       "Generosity                     0.089629                  0.075987  0.361751   \n",
       "\n",
       "                               Trust (Government Corruption)  Generosity  \n",
       "Happiness Score                                     0.402032    0.156848  \n",
       "Economy (GDP per Capita)                            0.294185   -0.025531  \n",
       "Family                                              0.213561    0.089629  \n",
       "Health (Life Expectancy)                            0.249583    0.075987  \n",
       "Freedom                                             0.502054    0.361751  \n",
       "Trust (Government Corruption)                       1.000000    0.305930  \n",
       "Generosity                                          0.305930    1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2016.pearsonCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## World Happiness Report from 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mQuantidade de valores faltantes no dataset: \u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Happiness.Score                  0\n",
       "Economy..GDP.per.Capita.         0\n",
       "Family                           0\n",
       "Health..Life.Expectancy.         0\n",
       "Freedom                          0\n",
       "Generosity                       0\n",
       "Trust..Government.Corruption.    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mLinhas duplicadas: \u001b[0m 0\n",
      "\u001b[1;1mNúmero de linhas e colunas: \u001b[0m (155, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Happiness.Score</th>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.537</td>\n",
       "      <td>1.616463</td>\n",
       "      <td>1.533524</td>\n",
       "      <td>0.796667</td>\n",
       "      <td>0.635423</td>\n",
       "      <td>0.362012</td>\n",
       "      <td>0.315964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.522</td>\n",
       "      <td>1.482383</td>\n",
       "      <td>1.551122</td>\n",
       "      <td>0.792566</td>\n",
       "      <td>0.626007</td>\n",
       "      <td>0.355280</td>\n",
       "      <td>0.400770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.504</td>\n",
       "      <td>1.480633</td>\n",
       "      <td>1.610574</td>\n",
       "      <td>0.833552</td>\n",
       "      <td>0.627163</td>\n",
       "      <td>0.475540</td>\n",
       "      <td>0.153527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.494</td>\n",
       "      <td>1.564980</td>\n",
       "      <td>1.516912</td>\n",
       "      <td>0.858131</td>\n",
       "      <td>0.620071</td>\n",
       "      <td>0.290549</td>\n",
       "      <td>0.367007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.469</td>\n",
       "      <td>1.443572</td>\n",
       "      <td>1.540247</td>\n",
       "      <td>0.809158</td>\n",
       "      <td>0.617951</td>\n",
       "      <td>0.245483</td>\n",
       "      <td>0.382612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.377</td>\n",
       "      <td>1.503945</td>\n",
       "      <td>1.428939</td>\n",
       "      <td>0.810696</td>\n",
       "      <td>0.585384</td>\n",
       "      <td>0.470490</td>\n",
       "      <td>0.282662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.316</td>\n",
       "      <td>1.479204</td>\n",
       "      <td>1.481349</td>\n",
       "      <td>0.834558</td>\n",
       "      <td>0.611101</td>\n",
       "      <td>0.435540</td>\n",
       "      <td>0.287372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.314</td>\n",
       "      <td>1.405706</td>\n",
       "      <td>1.548195</td>\n",
       "      <td>0.816760</td>\n",
       "      <td>0.614062</td>\n",
       "      <td>0.500005</td>\n",
       "      <td>0.382817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.284</td>\n",
       "      <td>1.494387</td>\n",
       "      <td>1.478162</td>\n",
       "      <td>0.830875</td>\n",
       "      <td>0.612924</td>\n",
       "      <td>0.385399</td>\n",
       "      <td>0.384399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.284</td>\n",
       "      <td>1.484415</td>\n",
       "      <td>1.510042</td>\n",
       "      <td>0.843887</td>\n",
       "      <td>0.601607</td>\n",
       "      <td>0.477699</td>\n",
       "      <td>0.301184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Happiness.Score  Economy..GDP.per.Capita.    Family  \\\n",
       "0            7.537                  1.616463  1.533524   \n",
       "1            7.522                  1.482383  1.551122   \n",
       "2            7.504                  1.480633  1.610574   \n",
       "3            7.494                  1.564980  1.516912   \n",
       "4            7.469                  1.443572  1.540247   \n",
       "5            7.377                  1.503945  1.428939   \n",
       "6            7.316                  1.479204  1.481349   \n",
       "7            7.314                  1.405706  1.548195   \n",
       "8            7.284                  1.494387  1.478162   \n",
       "9            7.284                  1.484415  1.510042   \n",
       "\n",
       "   Health..Life.Expectancy.   Freedom  Generosity  \\\n",
       "0                  0.796667  0.635423    0.362012   \n",
       "1                  0.792566  0.626007    0.355280   \n",
       "2                  0.833552  0.627163    0.475540   \n",
       "3                  0.858131  0.620071    0.290549   \n",
       "4                  0.809158  0.617951    0.245483   \n",
       "5                  0.810696  0.585384    0.470490   \n",
       "6                  0.834558  0.611101    0.435540   \n",
       "7                  0.816760  0.614062    0.500005   \n",
       "8                  0.830875  0.612924    0.385399   \n",
       "9                  0.843887  0.601607    0.477699   \n",
       "\n",
       "   Trust..Government.Corruption.  \n",
       "0                       0.315964  \n",
       "1                       0.400770  \n",
       "2                       0.153527  \n",
       "3                       0.367007  \n",
       "4                       0.382612  \n",
       "5                       0.282662  \n",
       "6                       0.287372  \n",
       "7                       0.382817  \n",
       "8                       0.384399  \n",
       "9                       0.301184  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2017 = Dataset('datasets/whr_2017.csv')\n",
    "data2017.printSample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Relação das variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2017.VariableCorrelations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Regressão linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TD1CRH8XBt/rT8ZqMU+7Kdff6atdtfJeQqirGjXmUd7+Yzm3xJ6+zOb+9qMAixhi719CgtLQ0k5GRYfcylGqWqXc6WfxVDgkXnEivHH6/NxJaRcLIb+t9XfEnvRk/OBljYFlWDjGu9MwF2V+zOy6R7xK7E3e0mApHKHnrBjJ+sNXmoT7Z2TBzlpPFS6j928s0vXkbSEQk0xiT5u45vZGrlB/MmB5C2abaN4dLtnYlpt/OBl9XvQms+uZydDbMfPNRXlzxILevWwlYDdIK8jp7tJnK3+MZVcuj6R2l/CAlBZ6dE8qtdwwhdlAOMf13UlUW7vEmsJSehk/uWMHp/7yTOI7w2KBf88zIq6nIj9L0jGoUDfpK+cnNNwOEcsfU7hSvT0ZC6s/TVzteRrloCQP+cQNH+53Fo33n8s93zqRoffNuLqvgpEFfKT+6+WYYOTKUmbOc/Pt5Q1FWEu1GuS+jFFNF+8wwrpoEjBsHR4/S5pZbuC8khPuOH6XVNqpx9EauUjbJzrbaKtTtqQ+QnL+HR15/htMPHMa5YQs9z4y2aZWqNdIbuUq5tKQZtu5aN0hFFTd/tIo1835H6v5sCu/8f/RMjfL/4lTA0it9FTRqTsOK6Jt7fCNU+ZYkyjefPA3LX6rLKN9adIhlBZdyNl+zqcelxC56lu7ndvX/glSr19CVvgZ9FRQaSqWAtSO1+E3fjSf0SFUVjB8PV18N110HIjYtRLV2mt5RQc+u8YSnsuuVr/ihy3B6J+TiCBPi31/K1M+uJftHDfjKN/we9EUkXkRWiMh2EdkmIsP8vQYVfOwYT9ig0lJ+vOYeTrtmGFGHfuBnI988dX98pbzAjpLNWcDbxpixIhIO6F0q5XMNNSyr5s3xhA3673+puOFWeu78kQU/v5JHx0ygKCIawbjtu6P198qb/HqlLyJxwAjgeQBjzDFjjPvft5XyosZOw/Jplc+LL5Jf6ODy1CXcf/WtFEWcXI5pV7pJBT5/p3d6AnnAiyKSJSLzROSk/+JFZIqIZIhIRl5enp+XqAJRY6ZhNXosoSfefBM2bbL+/uST9Hd8Q+Y5pzX4Er+mm1TQ8HfQDwUGAc8YYwYCJcC9dQ8yxsw1xqQZY9ISExP9vETlT/6qm/d0GtbVV4Y0fixhQw4csCpyLr8c/vUv67G2bdl/JKbRw9eV8gZ//xe1C9hljFnn+n4F1oeACkI+uaKuR30zbMt2tGP/onPY//JQSgpDSL/MSWVMEY6oY25/jsdpF2NgyRLo0wdefRUeegjmzTv+IUdI44evK+UNfq/TF5FPgFuNMd+KyANAtDHmf+s7Xuv0A5NddfM1+8kX5jsg1EnbwTuI6ndis1bxxm4Ub+hGhzEbiEw5Ob1YkR9FyYoRFBxqoO/NggVw440wdCg8/zz06VNrc9jRI2GExBzzqJd+Q/3xlXKnpdXp3wksEZGNwADgERvWoGxmV918dT/5zK9CiI5z0um6dbQdWTuNkzDyWzpek8HBt/pTkX9ycVm9aZeqKms2LVibq+bNg08/hT59yM6mVtqo7TnZFG/o1ujh60o1l9+DvjFmvStf388Yc6UxJt/fa1D2s7tu3pMPnZj+Oyn6Jvmk59ymXb7/HkaNgvPOg5ISaNMGbrkFQkLcni8soZQOYzZwYGUa+R+dcTzdVJEfRfEnvSl+c4j2x1c+4XHQF5FLRUTvKimvKCxw2Hoj05MPnZh+OynZ1uWkx6urfACorLRu0PbrB+vXw/33Q9TJvx24O19kSh6dr/8cU+lg35Jh5D52CXtfHMH4wcmsz7SnD5AKfI3ZnPU6cEBEFgLzjTHbfLQmFQQaGvRd7cQVtfdTHJ5u1qoqDa/12PG0y8IQOHgQ0tMhIwOuvBKeegq6nPwh0dD5whJKaTd6G+1Gb8M4hV2PpzNntrZgUL7TmMuoFGAuMA7YLCJfiMhtrg1XSjVKY+rmfcHTzVqOyIr60y7t2rnKgpbDK6/UG/Abcz6t1lG+5nHQN8bkGGPuN8b0AC4CfgBmAntFZJGIXOCrRarA42ndvK9uZHryoVO6IZlQQtj1eDolK6y0y7Z/f036Q+fA7t3gcMDLL8O1156yI2ZjP+RaUt9/FVialDA1xnxgjLke6AVkApOAtSLyk4jcJSI6hlE1qL66eX/dyPTkQ6dyexJbN4bidAoFuUeZ47ib06871wr4u3d7/XzVH3L+3L+ggk+T6vRFZCRwE3ANUAEsAV4DLgbuAFYZYyZ6Y4Fapx/YatbNFxXUGPQ9zfeDvo/XzffNJSJ1B6FxZVQWRlrBeUvSiaEqa9fCbbdBTg5MnQp/+xvExvrkfL16tYK+/6rF88oQFRFJBm5wfXUHPgTmAa8YY8prHHcVsNgY45Whnhr0lS959KFz443wxRdW3f155/n0fFPvdLIsK4eY4e6HpYNu2lKn5q2g7wT2APOBF4wxP9VzXC/gOWOMV3L8GvSVv2RnW/X0S16CC/Lf4EBsdwb8uh9331JCzzPCIDLS7bGFBQ7i4quYNNFK4zTnCjy+vZPosR83WNXk0Y5gFdS8tSP3MiDZGPOn+gI+gDHmO28FfKX8pTqP/sG6DBbFj+ZVczXTOv+BZVk59D8/ijUfRp50rC9y7nbvX1CBz+MbrsaY1b5ciFJ2yc6GceMruCn1Qf7yzSwiK47yzxG/Zu7ZVxMTUnugCZxop1Az5+6t4Sd2719QgU8vF1TQe/wJJ7/u9ASzP/8rP7Tvxq9uepKnh42jMsQK8jX7APm6Z5Dd+xdU4PN7l83G0py+8pmqKti5k/hBp5Nw1VrG7lnDytRRVDlOvoKuzqMDPs2529V9VAWWltZlUyn7ffstjBwJ552HM7+UqnZO/tPvIrcBH07k0X2dc7d7/4IKfLqJSgWXigp47DF44AGrMdrMmYTcFXlSHr0iP4qib5Ip2dqVqrJwHG2OEdrGSUS4+Dznnp4O6zNDmTkrmcVLkmuXdi70/f4FFdj0Sl+1Gs1uTXDwIAwZAn/4A1x6KWzdCjfcwKRJUiuPXpadyL5F5yChVXSe/DlJ96yh8/WfE9VvB0fLDUc3+T7nXt33v+BQiLUj+FAIc2ZrwFfNp1f6qlWoOXUqemwubV1TrpZlJbFwcPKJ3bNuZP9geHxWFUuWxPNUfm/eifkDVVFXw//BqlVOa4JWSHfKDrUhZkAuB9/qT8drMk6qzmk7cjuhnQo4tLo/4Sn76s25H+/CqVQLpDdyVYvX2Jub1RunFi2GMwu+4HFmcI3jP+wO7Ux4QikVBTGY8lAkopKon+8lbuiPiMNQvD6Jwqwk4gbtIOH8+scYHn5jEEdzEokbWLudQmlGCiXbuhAeJpQWe2/DllKN1dCNXL3SVy2eJ2WSFX1zmTkrmTHpIYybUEnCGVuZ2fUBbip4jd0xHfl5ciZ7vr2ckPgyOlyxvtY83P1LhtFhzAYSLthO8aZuxPTf2eB6Ys/bjtnTkfGDT+Tc20RVUeE0xA7cQZvUXNo34jcRpfxJc/qqRXGXt5/3gmejFRcuslJAYwY/yefbL+KmLa+xYPClXHzbHH66NIJO49dRntseoN55uFVHwzyqziktchzPuX/3neAIMbS/Zh2x59WetxszfDsxl61j3IRKbYusWgQN+qrFqK+9wbEyz8oki484iEjdwdgDb3E0NJxrJ/2DBy+8ndJwq4VCfXNvaz7uiDzW6GEndg15V6opNOirFiE7+0R7g5jhta+WPQnEv1z/Nf1Csojom8sDF97OmJtmk3l6n5OOq2/ubfXj0X12U7yxW4PnqludY/eQd6UaQ4O+ahEaulpuKBAnFh/mmVcf4d9rH2CaczahbcsojoiiPDTc7fHu5t7WfDx20A6K1yc1aqKXNklTrYn+V6hahIaulmMH7aB4Q7fagdgYrt34Hmvn/YYLfsjgz20e4a7Y5zybext1rN7HwxJKieh2iP0vDyH/A892xOr8W9WaaNBXLUJDV8thCaV0GLOBAyvTOOwKxGM3vM+/1sxic1gf+ksWj4X+nm49Qji6ueGNU8UbuxHde0+9j5fvjqd8Z3s6XJ2B82go+xYMJ/exdI68bM3IXZ95chWONklTrYmWbKoW4VQthSNT8uh42dfEvtGZPd+O4MnCYRwMPZOV3UcROzSHeMd+dmX05MjXyYT3rH/jVPGGbnS+/vOTH1+fRNQZeziwMo0Ol64nsschwuLLcERUUrLldEoKw1i4qApjnCfV3c+YHsLCwcmE9dANW6rl06CvWoRJE2FZVhJh9YwJTDm4k0fW/JkURx592YRj8mY+7NqJ9mw5fkzbizYjbYvZv2wIbQfnEtXvxMap4g1JFGUm06ZnHgDGKdbjWckUZVlX6RJWRefrPycsoZSy7EQOvtWfmP476Xz9Z8fr+t3V3Vc3SRs3YQgVDcy/1Q1aqiXw+45cEckBigAnUFnfrrFquiM3ONS36zbUWcnt61byu89epsTEsuycx/mTOZeY8+rfMXvkvTPpVtWFnByh+IgDQqugypp2mPIzQ06OUFJ4oonZgoUQd92JdskV+VHsW3TOSa0YqtXX3tjOIe9K1eSVGbleXEwOkGaMOejJ8Rr0g8fx/jquq+WOoftYvOxP9D2Uzcqwa4mf/yTX3NnB6/3sHSGGbnevQRzWv4XD7/dGQqtIGFn/B4sOJ1ctmfbTV61CdUvh8YOSKFkxgo3PXMfmoqHM/dVKBmxbzuiJnXxSHlm3+qZka1di+jXcikHr7lVrZUfQN8C7IpIpIlPcHSAiU0QkQ0Qy8vLy/Lw8ZaeU3R8z58thFGzYi7PKwXXli5ny1tXH0yO+KI+sW31TVRaudfcqYNnxX+25xphBQDowVURG1D3AGDPXGJNmjElLTEz0/wqV/xUWwtSp1jSrQ4dg3z63h/miPHLG9BDrhqtrH0BTWjEo1Vr4PegbY/a4/jwAvAqc7e81qBZmzRpITYVnnoHp02HTJkhzf3+/boCuy92O2VOpO6Iwsuf+RrdiUKq18GvQF5FoEYmt/jvwS2CzP9egWqD//AdiY+Hzz2HmTIiOrvdQX82QPX4/YXAysrMrRRndvfrBolRL4dfqHRHpiXV1D9YegZeMMX9t6DVavROAjLECfa9eMGAAFBVBeDhERHj8I3xdHlm3kshd3b32x1ctVYsq2WwsDfoBZs8eK3f/2mtw003wwgt2r6heWnevWisN+sp+xlgB/u67obwcHnrIyt+H6qZwpbxNxyUq+82fD7fealXnzJsHP/uZ3StSKihp0Fe+43TCjh3QsydMnAhhYdafDq1vV8ou+q9PecTd7Nqpdzrrn/u6ZQuce651ZV9SYt2knTxZA75SNtMrfXVKxytZUncQPTaXtg10nOTYMfjHP6ycfVwczJ4NUVG2rl8pdYIGfdWgmrNra3acDEsoJWz4dsJ67GPcBFfHybg8GD3a2lw1frwV8HVHtVItiv6urRrU0OxagIiuBUT02cHMWU7o0AEGDoTXX4elSzXgK9UCadBXDWpodi3A0NyNfPB9Ov9duBNEYMECuPxyP65QKdUYGvSDmCc3Z+trZRxbXsJf35nDy0vvo21FEVGFHo1H8Pr6lFKNozn9IOXpzVl3s2svyP6aR96eQ8eSfOaedRX/OPM2Dr020Jb1KaUaR4N+EGrMzVl3s2vTv/2MI21iuOOq+9jQ5QyKPznDqx0nG3XzWNshKNUomt4JQh7dnO2by8xZTquV8aYkLvksg777rbzKg6OncNmNT7Chyxk+6TjZmPUppRpHg34QOtXNWTgxDjAlYhc/9L6GZz99gAmrP7VaGIdGU1rYtlmtjL21PqVU42h6JwgVFjhoe4pxgGGxJYzPnwd9f0+nigoO/uEx1hX+lpKlIbU7Ti70fsdJT9an4wqVahr9VxOEPJkze/XXH/OsucOaYLVpEx0emcH0u8KZNBFi46soLHCwYCFceU0lcQnera7xxRxcpZRFg34Qqm/OrKPKSXL+HgCWHruZFy9eAmvXQkoKa9bAgMGVLMvKIXrsx3S4KoPScie5bXKIHfcx3e5eQ/TYj1mWlcOAwZWsWeP99dWk4wqVahoN+kHI3ZzZM/JyeGXxPbz80h9w7IigeNvPGPHURBCpVU0T46riObS6Hx3Hfk3C+d8SllCKOCYtHXoAABI9SURBVAxhCaXEDN9OzGXrGDehsslX/L6Yg6uUsmjQD0I158we+6gnv3tvOavmT+P0/Dz+1OXP5K0eWevm7ONPOAnve6KapuibZGL67/RZdY2v5uAqpTToB630dNj07mEyvruWGd8sZHnVeM5us502F09l/TdhtTY+LVgIbVJPVNOUbO1KTL+dDf785lbX1BxUXrJiBLseT6dkxQjGD05mfaZuzFKqqbR6JxgZAyJ0PysRLj8Lxv6DSWPG4C5Fnp0NJYUO2teopqkqC3fbmqEmb1TXpKTAnNkhzJld/Yimc5RqLr3SDzYffACDBsFOV4O0F1+EMWPqPfzxJ5w4IiprVdM4Io9pdY1SrZQG/WBRUAC33Wb1uy8uhoOeNUhb8hJE/nwvxRu7HX8sus/uWt+7483qGm28ppT3aNAPBm+8AX37wgsvwO9/Dxs3Wn3vPVBY4CBu6I8Ub+h2vJomdtCOWt/X5c3qmrqlot4sDVUqGGnQDwZvvAEdOrBr5Tqmlj5C/OnhHl8xx8VXIQ5DhzEbOLAyjfyPzgCgffpGDqw4i/z//sJn1TV1S0W9XRqqVDASY4zda2hQWlqaycjIsHsZrYsx8NJL0Lu3lb8vLubtD8K59tcOq5FZ31xCXa2Ky7ckUb65/lbFU+90siwrh5jh26nIj6Lom2RKtnWhqjQcaVNBaNtSKvNj4FgocQmu1gzTvNOaoea561P8SW/GD05mzmy9yatUNRHJNMakuX1Og36A2bkT7rgDVq+GW26BefPIzrZSJHVbFVcr3x1P8ZvuWxU357XNFd/eSfTYj2v18q+rIj+KkhUjKDikQV+pag0FfVvSOyISIiJZIrLKjvMHpKoqeOYZK3f/4YfwxBPw3HNA81oV27lRqr6pXTVp4zWlGseufy3TgG02nTswzZ8P//M/MGQIbN4M06ZBiHX129xWxXZtlNLGa0p5n9+DvoicDowB5vn73AGnshK+/976++TJsGwZvPsu9OhR6zBvXDFXb5QqOBSC0ykUHAphzmzvt1WuSRuvKeV9dlzpPwH8Hqj38kxEpohIhohk5OXl+W9lrcmGDTB0KFxwAZSUQHg4jBtnbbiqw9Mr5siYlnXFrI3XlPI+vwZ9EbkUOGCMyWzoOGPMXGNMmjEmLTEx0U+rayXKy+FPf7L63O/caeXuo6IafMmkiXB0c8NXzMUbunHsmGlR5Y/aeE0p7/P3lf65wOUikgO8DIwSkcV+XkPrdeCAtanq4Ydh4kTYuhXGjnV7dV/TjOkhFGU1fMVcvLEbUX32cOU1lS1q56s2XlPKu2wr2RSR84F7jDGXNnSclmxyvEEaxlitFMaOhUsuadSPiI51crTSScyAXGL67SQ0rozKwkiKN3ajeEM3OozZQGi7EvYtGE7nGz71uI5fKdXyNFSyqV02W7r33oO774ZVqyApCeY17f53WamDzjd/QvH6JPYtGUZVaTiOqGNE995D5+s/JyyhFOMUqo6FHK+LD0soJWz4dsJ67GPcBN/U4iul/Mu2oG+M+RD40K7zt3j5+XDPPVa/nF694PBhK+g3UXU7hXajt9FutPtq2crCSBxRx056PKJrARV9c5k5S3e+KtXa6a6WlujVV6FPH1iwAO6916rUGTCgWT/Sk/LH4o3diO69x+1zzR2KopRqGTTo+1iT2gK/9RZ07gxffQV/+xu0adPsdXhS/li8oRuxg3a4fV53vioVGDSn70Nr1lhdIiNSdxA9Npe2rpujy7KSWDi4xs1RY2DRIkhNtRqkzZpl1d2HhXltLdXlj+MmDKGiby4RqTtO3MzNSqZ40+l0uHR9vX1uTux81fSOUq2ZBn0fqdkWuGbPm7o3Rze/sZvkR26Hd96xKnPmzoXoaJ+sqbr8ceasZBYvSaaowEFsfBVJ3Qy7+uwmMqX+jXC681WpwKC/r/vIqZqctelymN+0+yudLkyFTz+F2bPh2Wd9vi537RReWxlKZXZX3fmqVBDQoO8jp2pydu3Gtfzzpwf43AyFzZvJ/tWdTJ1mbNkYpTtflQoemt7xkcICB23rNDkLdVbS7ch+fmrXlVdTL6AoLIq5q/7Kqm0Oz3L/PlRf6mfyJLhroW8bqyml/EeHqPhI3QEgffdn8481s+lQks8Ft82lLLwNFflRFC4bARhbhpQopQJTixuiEgyq6+IjKo/xvx8t4PUFd9Gp+BD3X3gHZeFWCWb55mS69zBNHnCilFKNpVf6PpKdDRcO3MN7MoyfFeay/MwLeXjUrRS2iQFOXMGLCLHjdCSgUsp7tPeOvxlDSorw9Mun8cmVF3Fvn/P4YlgSoWFlVOZHWpuktiSxfGkoYy41JOhIQKWUn2jQ97Z33rF65qxaRfqvksneNo/MWU5KluD25mj1gJOGrvR1Y5RSylv08tFbDh+GG2+0Wh5XVkKBlaM/1ZhBHQmolPInDfresHKl1SBt8WL44x8hKwv69/fopToSUCnlT5re8YZ33oEuXeDttxvdDbOhnjg1c/9arqmU8gat3mkKY2D+fDjzTGtWbUkJRERAaNM/Q7OzYeYsJ4vr5v6n6cYopVTjNFS9o0G/sXJyYMoUa6LVlCnw3HN2r0gppWrRzVne4HRaTdFSU+GLL+Cpp+CZZ+xelVJKNYrm9D01fz5Mm2Y1qXn22WaNLlRKKbto0G9IRYWVbP/FL+DXv4Z27eDKK0HE7pUppVSTaHqnPt98A2edBaNGWTdqw8Lgqqs04CulWjUN+nWVlVnDyM8+Gw4cgKef9tkkK6WU8jdN79S0fz+cdx58/z3ccgs8+ijEu980pZRSrZEGfYCqKnA4oGNHuOACqypn9Gi7V6WUUl6n6Z3Vq60yzJwcK1//3HMa8JVSAcuvQV9E2ojIVyKyQUS2iMiD/jx/LQcPwvXXw5gxVrAvLLRtKUop5S/+vtIvB0YZY/oDA4BLRGSon9cAy5dbDdJefhn+/GerUqdfP78vQyml/M2vQd9Yil3fhrm+/NoHIjsbPv3LWr45nEw/Zybxc/7M1HtCyc725yqUUsoefr+RKyIhQCbwM+ApY8w6n5/UGHjhBT4v7sfF9w8kvvfvkJv2IQm7iD5yiGVZSSwcnMzypaGkp/t8NUopZRu/B31jjBMYICLxwKsikmqM2VzzGBGZAkwBSGpuu4Mff4TbboMPPmBb+G3EjLuBkBpDyMMSSgkbvp2wHvsYN2EI6zO1jbFSKnDZVr1jjCkAPgQucfPcXGNMmjEmLTExsWkncDph5kyr/fHXX7P0/Ke5N+1/iagR8GuK6FpARN9cZs5yNu18SinVCvi7eifRdYWPiEQCFwLbfXKyF1+EGTOsuvutW/nNximEp+5q8CURqTtYvMQnq1FKqRbB3+md04AFrry+A1hujFnlkzPdcAMkJsLll4MIhQWGtm3LGnxJaFwZRQW6dUEpFbj8GvSNMRuBgX45WVgYXHHF8W/j4quoPBJJWEJpvS+pLIwkNr4K0Hm0SqnAFDSXtZMmQvmWhm8Kl29OZvIkPy1IKaVsEDRBf8b0EGvQ+G73DdTKd8dTviWJu6bpVb5SKnAFTdBPSYHlS0MpfnMIxZ/0piI/CuMUKvKjKP6kN8VvDmH5Ui3XVEoFtqDqspmeDuszQ5k5K5nFS5IpKnAQG1/F5Elw18IQDfhKqYAnxvi1C0KjpaWlmYyMDLuXoZRSrYaIZBpj0tw9FzTpHaWUUhr0lVIqqGjQV0qpINLic/oikgfs8OKP7AAc9OLPawkC8T1BYL6vQHxPEJjvqzW/p2RjjNvGZS0+6HubiGTUd4OjtQrE9wSB+b4C8T1BYL6vQHxPoOkdpZQKKhr0lVIqiARj0J9r9wJ8IBDfEwTm+wrE9wSB+b4C8T0FX05fKaWCWTBe6SulVNDSoK+UUkEkKIK+iLQRka9EZIOIbBGRB+1ekzeJSIiIZImIb6aQ+ZmI5IjIJhFZLyIB03hJROJFZIWIbBeRbSIyzO41NYeInOH6/6j6q1BEptu9Lm8QkbtcsWKziCwVkTZ2r8lbgiKnLyICRBtjikUkDPgUmGaM+dLmpXmFiMwA0oA4Y8yldq+nuUQkB0gzxrTWjTFuicgC4BNjzDwRCQeijDEFdq/LG1wjUHcDQ4wx3txM6Xci0hUrRvQxxpSJyHJgtTFmvr0r846guNI3lmLXt2Gur4D4tBOR04ExwDy716LqJyJxwAjgeQBjzLFACfguo4Hs1h7wawgFIkUkFIgC9ti8Hq8JiqAPx1Mg64EDwHvGmHV2r8lLngB+D1TZvRAvMsC7IpIpIlPsXoyX9ATygBddqbh5IhJt96K8aDyw1O5FeIMxZjfwKJAL7AWOGGPetXdV3hM0Qd8Y4zTGDABOB84WkVS719RcInIpcMAYk2n3WrzsXGPMICAdmCoiI+xekBeEAoOAZ4wxA4ES4F57l+QdrlTV5cB/7F6LN4hIAnAF0APoAkSLyGR7V+U9QRP0q7l+pf4QuMTmpXjDucDlrhz4y8AoEVls75Kazxizx/XnAeBV4Gx7V+QVu4BdNX7DXIH1IRAI0oFvjDH77V6Il1wI/GSMyTPGVACvAOfYvCavCYqgLyKJIhLv+nsk1v+p2+1dVfMZY/5gjDndGNMd69frD4wxrfqKRESiRSS2+u/AL4HN9q6q+Ywx+4CdInKG66HRwFYbl+RNEwiQ1I5LLjBURKJcRSCjgW02r8lrgmVG7mnAAleFgQNYbowJiPLGANQJeNX6t0Yo8JIx5m17l+Q1dwJLXOmQH4GbbF5Ps4lIFHARcLvda/EWY8w6EVkBfANUAlkEUEuGoCjZVEopZQmK9I5SSimLBn2llAoiGvSVUiqIaNBXSqkgokFfKaWCiAZ91WqIyAMiYmp87RGRlSKS4sNzrhCRD+usweNGcCIS7nrNgDqPd3e9h1bfIE+1Lhr0VWtzBBjm+roHGAC878c+NvOAixtxfDhwP9Y6a9qL9R4+9dK6lPJIsGzOUoGjskZL7C9FJBf4BPgVdXq/uDbjhRhjjnnr5MaYXVgtFZr7c8qBgGjtrVoXvdJXrV11s7nuIjJfRDJE5EoR2QIcBYYAiEiSiLwsIodFpFRE3qnREgHXMd1EZLWIlLkGudxa92Tu0jsi0l5EnhORvSJyVES+rTFMpMj154s10lLd3aV3XJ1gHxCRXBEpdw3xmFjnXNXv8SIR2SgiJSLyqYj0bdb/iipo6JW+au26u/7cB/R1ff9P4C/AfuAnEWmHlUY5BNwBlGJ1uFwrIr1cgzIEeB3oANyC9YHxINAO+L6+k7t6OX0IdHQdvx34mesLYBTwAfAw8Jbrsb1YrUHq+gtWm+wHga+Ba7DaNhhjTM3eNknAv4C/AmVYbYCXi0iq0S326hQ06KtWxzXYAqwe9U9jXU2vxWqM1R640BizvsbxDwHRwABjzGHXY58BOcDNwFNYnSIHAkOrO2GKSCaQTQNBH/g11ofNoBrn/KDG81+7/syuOanN1Vuo5ntqB0wHHjbGPOx6+B3XkJwHqN3QrB1W++nvXa91YHUjPYMAaCSofEvTO6q1aQ9UuL6+xQr81xlj9rqe310z4LtcCLwHFIpIqOtDowgrNZTmOuZsYH/N4TquKVCnmlUwCshyc87GSsWa0FS3J/0yoJeIdKzxWE51wHep7tZ5ejPXoIKAXumr1uYIVhA3WCmdPXVSGu56uncAhgLXuXnufdefnbGmqtV1AIhtYD3tsdI1zVWd7qm7/urvEzixvrpjFqtvVAfM8G7lOxr0VWtTaYzJaOB5dzntw8AbwENunqu+0boPKy9fV0esvHl9DnEif98c1R8cHV0/s1on15+HvXAOpTS9o4LC+1h59y3GmIw6X9+6jvka6CQiQ6pfJCJJnHq61fvAQBHpV8/znl6Fb8a6wXxtncfHAd8ZY/JO8XqlPKJX+ioYPA5MBj4QkSeB3VhX0COBT12VMauBDcB/ROT/sKp3/oL7lE9NC4GpWIPcH8C6z9AD6GWMudcYc0xEfgLGichm18/dWPeHGGMOi8gTwP8TkUogA7gaa//BhGa9e6Vq0KCvAp4x5qCIDMUqcZwJxGOlUz7FFYCNMUZELseakPQCVrB/BGsqVIcGfvZRERkF/B3rQyIOqyro6RqH3YFVVrkWiMD6UHDnz1iTmn6D9aH0AzDZGPNyo9+0UvXQyVlKKRVENKevlFJBRIO+UkoFEQ36SikVRDToK6VUENGgr5RSQUSDvlJKBREN+kopFUQ06CulVBD5/1nF6wPhNjg6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mCoeficiente de determinação R2:\u001b[0m 0.8335247266900785\n"
     ]
    }
   ],
   "source": [
    "data2017.linearRegression()\n",
    "data2017.printRegressionGraph()\n",
    "data2017.R2coefficient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "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>Happiness.Score</th>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness.Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.824992</td>\n",
       "      <td>0.773619</td>\n",
       "      <td>0.787647</td>\n",
       "      <td>0.556133</td>\n",
       "      <td>0.136443</td>\n",
       "      <td>0.300966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <td>0.824992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.711866</td>\n",
       "      <td>0.856650</td>\n",
       "      <td>0.406784</td>\n",
       "      <td>0.014544</td>\n",
       "      <td>0.245625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.773619</td>\n",
       "      <td>0.711866</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.631133</td>\n",
       "      <td>0.477790</td>\n",
       "      <td>0.081003</td>\n",
       "      <td>0.202904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <td>0.787647</td>\n",
       "      <td>0.856650</td>\n",
       "      <td>0.631133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.356579</td>\n",
       "      <td>0.075827</td>\n",
       "      <td>0.175660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.556133</td>\n",
       "      <td>0.406784</td>\n",
       "      <td>0.477790</td>\n",
       "      <td>0.356579</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349625</td>\n",
       "      <td>0.473072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.136443</td>\n",
       "      <td>0.014544</td>\n",
       "      <td>0.081003</td>\n",
       "      <td>0.075827</td>\n",
       "      <td>0.349625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.230818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <td>0.300966</td>\n",
       "      <td>0.245625</td>\n",
       "      <td>0.202904</td>\n",
       "      <td>0.175660</td>\n",
       "      <td>0.473072</td>\n",
       "      <td>0.230818</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Happiness.Score  Economy..GDP.per.Capita.  \\\n",
       "Happiness.Score                       1.000000                  0.824992   \n",
       "Economy..GDP.per.Capita.              0.824992                  1.000000   \n",
       "Family                                0.773619                  0.711866   \n",
       "Health..Life.Expectancy.              0.787647                  0.856650   \n",
       "Freedom                               0.556133                  0.406784   \n",
       "Generosity                            0.136443                  0.014544   \n",
       "Trust..Government.Corruption.         0.300966                  0.245625   \n",
       "\n",
       "                                 Family  Health..Life.Expectancy.   Freedom  \\\n",
       "Happiness.Score                0.773619                  0.787647  0.556133   \n",
       "Economy..GDP.per.Capita.       0.711866                  0.856650  0.406784   \n",
       "Family                         1.000000                  0.631133  0.477790   \n",
       "Health..Life.Expectancy.       0.631133                  1.000000  0.356579   \n",
       "Freedom                        0.477790                  0.356579  1.000000   \n",
       "Generosity                     0.081003                  0.075827  0.349625   \n",
       "Trust..Government.Corruption.  0.202904                  0.175660  0.473072   \n",
       "\n",
       "                               Generosity  Trust..Government.Corruption.  \n",
       "Happiness.Score                  0.136443                       0.300966  \n",
       "Economy..GDP.per.Capita.         0.014544                       0.245625  \n",
       "Family                           0.081003                       0.202904  \n",
       "Health..Life.Expectancy.         0.075827                       0.175660  \n",
       "Freedom                          0.349625                       0.473072  \n",
       "Generosity                       1.000000                       0.230818  \n",
       "Trust..Government.Corruption.    0.230818                       1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2017.spearmanCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true,
    "scrolled": true
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Happiness.Score</th>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness.Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.812469</td>\n",
       "      <td>0.752737</td>\n",
       "      <td>0.781951</td>\n",
       "      <td>0.570137</td>\n",
       "      <td>0.155256</td>\n",
       "      <td>0.429080</td>\n",
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       "    <tr>\n",
       "      <th>Economy..GDP.per.Capita.</th>\n",
       "      <td>0.812469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.688296</td>\n",
       "      <td>0.843077</td>\n",
       "      <td>0.369873</td>\n",
       "      <td>-0.019011</td>\n",
       "      <td>0.350944</td>\n",
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       "      <th>Family</th>\n",
       "      <td>0.752737</td>\n",
       "      <td>0.688296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.612080</td>\n",
       "      <td>0.424966</td>\n",
       "      <td>0.051693</td>\n",
       "      <td>0.231841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health..Life.Expectancy.</th>\n",
       "      <td>0.781951</td>\n",
       "      <td>0.843077</td>\n",
       "      <td>0.612080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349827</td>\n",
       "      <td>0.063191</td>\n",
       "      <td>0.279752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.570137</td>\n",
       "      <td>0.369873</td>\n",
       "      <td>0.424966</td>\n",
       "      <td>0.349827</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.316083</td>\n",
       "      <td>0.499183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.155256</td>\n",
       "      <td>-0.019011</td>\n",
       "      <td>0.051693</td>\n",
       "      <td>0.063191</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
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      "text/plain": [
       "                               Happiness.Score  Economy..GDP.per.Capita.  \\\n",
       "Happiness.Score                       1.000000                  0.812469   \n",
       "Economy..GDP.per.Capita.              0.812469                  1.000000   \n",
       "Family                                0.752737                  0.688296   \n",
       "Health..Life.Expectancy.              0.781951                  0.843077   \n",
       "Freedom                               0.570137                  0.369873   \n",
       "Generosity                            0.155256                 -0.019011   \n",
       "Trust..Government.Corruption.         0.429080                  0.350944   \n",
       "\n",
       "                                 Family  Health..Life.Expectancy.   Freedom  \\\n",
       "Happiness.Score                0.752737                  0.781951  0.570137   \n",
       "Economy..GDP.per.Capita.       0.688296                  0.843077  0.369873   \n",
       "Family                         1.000000                  0.612080  0.424966   \n",
       "Health..Life.Expectancy.       0.612080                  1.000000  0.349827   \n",
       "Freedom                        0.424966                  0.349827  1.000000   \n",
       "Generosity                     0.051693                  0.063191  0.316083   \n",
       "Trust..Government.Corruption.  0.231841                  0.279752  0.499183   \n",
       "\n",
       "                               Generosity  Trust..Government.Corruption.  \n",
       "Happiness.Score                  0.155256                       0.429080  \n",
       "Economy..GDP.per.Capita.        -0.019011                       0.350944  \n",
       "Family                           0.051693                       0.231841  \n",
       "Health..Life.Expectancy.         0.063191                       0.279752  \n",
       "Freedom                          0.316083                       0.499183  \n",
       "Generosity                       1.000000                       0.294159  \n",
       "Trust..Government.Corruption.    0.294159                       1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2017.pearsonCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## World Happiness Report from 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mQuantidade de valores faltantes no dataset: \u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Score                           0\n",
       "GDP per capita                  0\n",
       "Social support                  0\n",
       "Healthy life expectancy         0\n",
       "Freedom to make life choices    0\n",
       "Generosity                      0\n",
       "Perceptions of corruption       1\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mLinhas duplicadas: \u001b[0m 0\n",
      "\u001b[1;1mNúmero de linhas e colunas: \u001b[0m (156, 7)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.632</td>\n",
       "      <td>1.305</td>\n",
       "      <td>1.592</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.202</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.594</td>\n",
       "      <td>1.456</td>\n",
       "      <td>1.582</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.555</td>\n",
       "      <td>1.351</td>\n",
       "      <td>1.590</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.495</td>\n",
       "      <td>1.343</td>\n",
       "      <td>1.644</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.487</td>\n",
       "      <td>1.420</td>\n",
       "      <td>1.549</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.441</td>\n",
       "      <td>1.361</td>\n",
       "      <td>1.488</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.638</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.328</td>\n",
       "      <td>1.330</td>\n",
       "      <td>1.532</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.324</td>\n",
       "      <td>1.268</td>\n",
       "      <td>1.601</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.314</td>\n",
       "      <td>1.355</td>\n",
       "      <td>1.501</td>\n",
       "      <td>0.913</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.272</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.647</td>\n",
       "      <td>0.361</td>\n",
       "      <td>0.302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score  GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0  7.632           1.305           1.592                    0.874   \n",
       "1  7.594           1.456           1.582                    0.861   \n",
       "2  7.555           1.351           1.590                    0.868   \n",
       "3  7.495           1.343           1.644                    0.914   \n",
       "4  7.487           1.420           1.549                    0.927   \n",
       "5  7.441           1.361           1.488                    0.878   \n",
       "6  7.328           1.330           1.532                    0.896   \n",
       "7  7.324           1.268           1.601                    0.876   \n",
       "8  7.314           1.355           1.501                    0.913   \n",
       "9  7.272           1.340           1.573                    0.910   \n",
       "\n",
       "   Freedom to make life choices  Generosity  Perceptions of corruption  \n",
       "0                         0.681       0.202                      0.393  \n",
       "1                         0.686       0.286                      0.340  \n",
       "2                         0.683       0.284                      0.408  \n",
       "3                         0.677       0.353                      0.138  \n",
       "4                         0.660       0.256                      0.357  \n",
       "5                         0.638       0.333                      0.295  \n",
       "6                         0.653       0.321                      0.291  \n",
       "7                         0.669       0.365                      0.389  \n",
       "8                         0.659       0.285                      0.383  \n",
       "9                         0.647       0.361                      0.302  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2018 = Dataset('datasets/whr_2018.csv')\n",
    "data2018.printSample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Relação das variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2018.VariableCorrelations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Regressão linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mCoeficiente de determinação R2:\u001b[0m 0.7008240719570977\n"
     ]
    }
   ],
   "source": [
    "data2018.linearRegression()\n",
    "data2018.printRegressionGraph()\n",
    "data2018.R2coefficient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.822604</td>\n",
       "      <td>0.775751</td>\n",
       "      <td>0.793352</td>\n",
       "      <td>0.534761</td>\n",
       "      <td>0.112308</td>\n",
       "      <td>0.234759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.822604</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716055</td>\n",
       "      <td>0.862616</td>\n",
       "      <td>0.368499</td>\n",
       "      <td>-0.006363</td>\n",
       "      <td>0.219857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.775751</td>\n",
       "      <td>0.716055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675091</td>\n",
       "      <td>0.451799</td>\n",
       "      <td>0.044919</td>\n",
       "      <td>0.160472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.793352</td>\n",
       "      <td>0.862616</td>\n",
       "      <td>0.675091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.370033</td>\n",
       "      <td>0.020466</td>\n",
       "      <td>0.213665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.534761</td>\n",
       "      <td>0.368499</td>\n",
       "      <td>0.451799</td>\n",
       "      <td>0.370033</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.348274</td>\n",
       "      <td>0.465174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.112308</td>\n",
       "      <td>-0.006363</td>\n",
       "      <td>0.044919</td>\n",
       "      <td>0.020466</td>\n",
       "      <td>0.348274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.287153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>0.234759</td>\n",
       "      <td>0.219857</td>\n",
       "      <td>0.160472</td>\n",
       "      <td>0.213665</td>\n",
       "      <td>0.465174</td>\n",
       "      <td>0.287153</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Score  GDP per capita  Social support  \\\n",
       "Score                         1.000000        0.822604        0.775751   \n",
       "GDP per capita                0.822604        1.000000        0.716055   \n",
       "Social support                0.775751        0.716055        1.000000   \n",
       "Healthy life expectancy       0.793352        0.862616        0.675091   \n",
       "Freedom to make life choices  0.534761        0.368499        0.451799   \n",
       "Generosity                    0.112308       -0.006363        0.044919   \n",
       "Perceptions of corruption     0.234759        0.219857        0.160472   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "Score                                        0.793352   \n",
       "GDP per capita                               0.862616   \n",
       "Social support                               0.675091   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.370033   \n",
       "Generosity                                   0.020466   \n",
       "Perceptions of corruption                    0.213665   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "Score                                             0.534761    0.112308   \n",
       "GDP per capita                                    0.368499   -0.006363   \n",
       "Social support                                    0.451799    0.044919   \n",
       "Healthy life expectancy                           0.370033    0.020466   \n",
       "Freedom to make life choices                      1.000000    0.348274   \n",
       "Generosity                                        0.348274    1.000000   \n",
       "Perceptions of corruption                         0.465174    0.287153   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "Score                                          0.234759  \n",
       "GDP per capita                                 0.219857  \n",
       "Social support                                 0.160472  \n",
       "Healthy life expectancy                        0.213665  \n",
       "Freedom to make life choices                   0.465174  \n",
       "Generosity                                     0.287153  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2018.spearmanCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.802124</td>\n",
       "      <td>0.745760</td>\n",
       "      <td>0.775814</td>\n",
       "      <td>0.544280</td>\n",
       "      <td>0.135825</td>\n",
       "      <td>0.392151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.802124</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.672080</td>\n",
       "      <td>0.844273</td>\n",
       "      <td>0.332275</td>\n",
       "      <td>-0.011241</td>\n",
       "      <td>0.285298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.745760</td>\n",
       "      <td>0.672080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.667288</td>\n",
       "      <td>0.411087</td>\n",
       "      <td>0.018226</td>\n",
       "      <td>0.226757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.775814</td>\n",
       "      <td>0.844273</td>\n",
       "      <td>0.667288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.355475</td>\n",
       "      <td>0.020751</td>\n",
       "      <td>0.311923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.544280</td>\n",
       "      <td>0.332275</td>\n",
       "      <td>0.411087</td>\n",
       "      <td>0.355475</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297988</td>\n",
       "      <td>0.466651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.135825</td>\n",
       "      <td>-0.011241</td>\n",
       "      <td>0.018226</td>\n",
       "      <td>0.020751</td>\n",
       "      <td>0.297988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.360302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>0.392151</td>\n",
       "      <td>0.285298</td>\n",
       "      <td>0.226757</td>\n",
       "      <td>0.311923</td>\n",
       "      <td>0.466651</td>\n",
       "      <td>0.360302</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Score  GDP per capita  Social support  \\\n",
       "Score                         1.000000        0.802124        0.745760   \n",
       "GDP per capita                0.802124        1.000000        0.672080   \n",
       "Social support                0.745760        0.672080        1.000000   \n",
       "Healthy life expectancy       0.775814        0.844273        0.667288   \n",
       "Freedom to make life choices  0.544280        0.332275        0.411087   \n",
       "Generosity                    0.135825       -0.011241        0.018226   \n",
       "Perceptions of corruption     0.392151        0.285298        0.226757   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "Score                                        0.775814   \n",
       "GDP per capita                               0.844273   \n",
       "Social support                               0.667288   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.355475   \n",
       "Generosity                                   0.020751   \n",
       "Perceptions of corruption                    0.311923   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "Score                                             0.544280    0.135825   \n",
       "GDP per capita                                    0.332275   -0.011241   \n",
       "Social support                                    0.411087    0.018226   \n",
       "Healthy life expectancy                           0.355475    0.020751   \n",
       "Freedom to make life choices                      1.000000    0.297988   \n",
       "Generosity                                        0.297988    1.000000   \n",
       "Perceptions of corruption                         0.466651    0.360302   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "Score                                          0.392151  \n",
       "GDP per capita                                 0.285298  \n",
       "Social support                                 0.226757  \n",
       "Healthy life expectancy                        0.311923  \n",
       "Freedom to make life choices                   0.466651  \n",
       "Generosity                                     0.360302  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2018.pearsonCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## World Happiness Report from 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mQuantidade de valores faltantes no dataset: \u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Score                           0\n",
       "GDP per capita                  0\n",
       "Social support                  0\n",
       "Healthy life expectancy         0\n",
       "Freedom to make life choices    0\n",
       "Generosity                      0\n",
       "Perceptions of corruption       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mLinhas duplicadas: \u001b[0m 0\n",
      "\u001b[1;1mNúmero de linhas e colunas: \u001b[0m (156, 7)\n"
     ]
    },
    {
     "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>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.769</td>\n",
       "      <td>1.340</td>\n",
       "      <td>1.587</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.596</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.600</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.573</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.592</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.554</td>\n",
       "      <td>1.488</td>\n",
       "      <td>1.582</td>\n",
       "      <td>1.028</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.494</td>\n",
       "      <td>1.380</td>\n",
       "      <td>1.624</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.354</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.488</td>\n",
       "      <td>1.396</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.480</td>\n",
       "      <td>1.452</td>\n",
       "      <td>1.526</td>\n",
       "      <td>1.052</td>\n",
       "      <td>0.572</td>\n",
       "      <td>0.263</td>\n",
       "      <td>0.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.343</td>\n",
       "      <td>1.387</td>\n",
       "      <td>1.487</td>\n",
       "      <td>1.009</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.267</td>\n",
       "      <td>0.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.307</td>\n",
       "      <td>1.303</td>\n",
       "      <td>1.557</td>\n",
       "      <td>1.026</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.278</td>\n",
       "      <td>1.365</td>\n",
       "      <td>1.505</td>\n",
       "      <td>1.039</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.246</td>\n",
       "      <td>1.376</td>\n",
       "      <td>1.475</td>\n",
       "      <td>1.016</td>\n",
       "      <td>0.532</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score  GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0  7.769           1.340           1.587                    0.986   \n",
       "1  7.600           1.383           1.573                    0.996   \n",
       "2  7.554           1.488           1.582                    1.028   \n",
       "3  7.494           1.380           1.624                    1.026   \n",
       "4  7.488           1.396           1.522                    0.999   \n",
       "5  7.480           1.452           1.526                    1.052   \n",
       "6  7.343           1.387           1.487                    1.009   \n",
       "7  7.307           1.303           1.557                    1.026   \n",
       "8  7.278           1.365           1.505                    1.039   \n",
       "9  7.246           1.376           1.475                    1.016   \n",
       "\n",
       "   Freedom to make life choices  Generosity  Perceptions of corruption  \n",
       "0                         0.596       0.153                      0.393  \n",
       "1                         0.592       0.252                      0.410  \n",
       "2                         0.603       0.271                      0.341  \n",
       "3                         0.591       0.354                      0.118  \n",
       "4                         0.557       0.322                      0.298  \n",
       "5                         0.572       0.263                      0.343  \n",
       "6                         0.574       0.267                      0.373  \n",
       "7                         0.585       0.330                      0.380  \n",
       "8                         0.584       0.285                      0.308  \n",
       "9                         0.532       0.244                      0.226  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2019 = Dataset('datasets/whr_2019.csv')\n",
    "data2019.printSample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Relação das variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2019.VariableCorrelations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Regressão linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;1mCoeficiente de determinação R2:\u001b[0m 0.5831750841034311\n"
     ]
    }
   ],
   "source": [
    "data2019.linearRegression()\n",
    "data2019.printRegressionGraph()\n",
    "data2019.R2coefficient()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Coeficiente de Spearman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.814483</td>\n",
       "      <td>0.816181</td>\n",
       "      <td>0.807275</td>\n",
       "      <td>0.551974</td>\n",
       "      <td>0.070483</td>\n",
       "      <td>0.217348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.814483</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.768539</td>\n",
       "      <td>0.851513</td>\n",
       "      <td>0.393426</td>\n",
       "      <td>-0.041304</td>\n",
       "      <td>0.223203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.816181</td>\n",
       "      <td>0.768539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.725997</td>\n",
       "      <td>0.449491</td>\n",
       "      <td>0.007695</td>\n",
       "      <td>0.114017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.807275</td>\n",
       "      <td>0.851513</td>\n",
       "      <td>0.725997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.412906</td>\n",
       "      <td>-0.006773</td>\n",
       "      <td>0.165309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.551974</td>\n",
       "      <td>0.393426</td>\n",
       "      <td>0.449491</td>\n",
       "      <td>0.412906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.324146</td>\n",
       "      <td>0.403586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.070483</td>\n",
       "      <td>-0.041304</td>\n",
       "      <td>0.007695</td>\n",
       "      <td>-0.006773</td>\n",
       "      <td>0.324146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.286171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>0.217348</td>\n",
       "      <td>0.223203</td>\n",
       "      <td>0.114017</td>\n",
       "      <td>0.165309</td>\n",
       "      <td>0.403586</td>\n",
       "      <td>0.286171</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Score  GDP per capita  Social support  \\\n",
       "Score                         1.000000        0.814483        0.816181   \n",
       "GDP per capita                0.814483        1.000000        0.768539   \n",
       "Social support                0.816181        0.768539        1.000000   \n",
       "Healthy life expectancy       0.807275        0.851513        0.725997   \n",
       "Freedom to make life choices  0.551974        0.393426        0.449491   \n",
       "Generosity                    0.070483       -0.041304        0.007695   \n",
       "Perceptions of corruption     0.217348        0.223203        0.114017   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "Score                                        0.807275   \n",
       "GDP per capita                               0.851513   \n",
       "Social support                               0.725997   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.412906   \n",
       "Generosity                                  -0.006773   \n",
       "Perceptions of corruption                    0.165309   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "Score                                             0.551974    0.070483   \n",
       "GDP per capita                                    0.393426   -0.041304   \n",
       "Social support                                    0.449491    0.007695   \n",
       "Healthy life expectancy                           0.412906   -0.006773   \n",
       "Freedom to make life choices                      1.000000    0.324146   \n",
       "Generosity                                        0.324146    1.000000   \n",
       "Perceptions of corruption                         0.403586    0.286171   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "Score                                          0.217348  \n",
       "GDP per capita                                 0.223203  \n",
       "Social support                                 0.114017  \n",
       "Healthy life expectancy                        0.165309  \n",
       "Freedom to make life choices                   0.403586  \n",
       "Generosity                                     0.286171  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2019.spearmanCorrelation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Coeficiente de Pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Generosity</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.793883</td>\n",
       "      <td>0.777058</td>\n",
       "      <td>0.779883</td>\n",
       "      <td>0.566742</td>\n",
       "      <td>0.075824</td>\n",
       "      <td>0.385613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.793883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.754906</td>\n",
       "      <td>0.835462</td>\n",
       "      <td>0.379079</td>\n",
       "      <td>-0.079662</td>\n",
       "      <td>0.298920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.777058</td>\n",
       "      <td>0.754906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.719009</td>\n",
       "      <td>0.447333</td>\n",
       "      <td>-0.048126</td>\n",
       "      <td>0.181899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.779883</td>\n",
       "      <td>0.835462</td>\n",
       "      <td>0.719009</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.390395</td>\n",
       "      <td>-0.029511</td>\n",
       "      <td>0.295283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.566742</td>\n",
       "      <td>0.379079</td>\n",
       "      <td>0.447333</td>\n",
       "      <td>0.390395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269742</td>\n",
       "      <td>0.438843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.075824</td>\n",
       "      <td>-0.079662</td>\n",
       "      <td>-0.048126</td>\n",
       "      <td>-0.029511</td>\n",
       "      <td>0.269742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perceptions of corruption</th>\n",
       "      <td>0.385613</td>\n",
       "      <td>0.298920</td>\n",
       "      <td>0.181899</td>\n",
       "      <td>0.295283</td>\n",
       "      <td>0.438843</td>\n",
       "      <td>0.326538</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Score  GDP per capita  Social support  \\\n",
       "Score                         1.000000        0.793883        0.777058   \n",
       "GDP per capita                0.793883        1.000000        0.754906   \n",
       "Social support                0.777058        0.754906        1.000000   \n",
       "Healthy life expectancy       0.779883        0.835462        0.719009   \n",
       "Freedom to make life choices  0.566742        0.379079        0.447333   \n",
       "Generosity                    0.075824       -0.079662       -0.048126   \n",
       "Perceptions of corruption     0.385613        0.298920        0.181899   \n",
       "\n",
       "                              Healthy life expectancy  \\\n",
       "Score                                        0.779883   \n",
       "GDP per capita                               0.835462   \n",
       "Social support                               0.719009   \n",
       "Healthy life expectancy                      1.000000   \n",
       "Freedom to make life choices                 0.390395   \n",
       "Generosity                                  -0.029511   \n",
       "Perceptions of corruption                    0.295283   \n",
       "\n",
       "                              Freedom to make life choices  Generosity  \\\n",
       "Score                                             0.566742    0.075824   \n",
       "GDP per capita                                    0.379079   -0.079662   \n",
       "Social support                                    0.447333   -0.048126   \n",
       "Healthy life expectancy                           0.390395   -0.029511   \n",
       "Freedom to make life choices                      1.000000    0.269742   \n",
       "Generosity                                        0.269742    1.000000   \n",
       "Perceptions of corruption                         0.438843    0.326538   \n",
       "\n",
       "                              Perceptions of corruption  \n",
       "Score                                          0.385613  \n",
       "GDP per capita                                 0.298920  \n",
       "Social support                                 0.181899  \n",
       "Healthy life expectancy                        0.295283  \n",
       "Freedom to make life choices                   0.438843  \n",
       "Generosity                                     0.326538  \n",
       "Perceptions of corruption                      1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2019.pearsonCorrelation()"
   ]
  }
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