Relationship between Suicide Rate and Happiness Report Factors 2015 - 2016.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the libraries  \n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import suicide rates dataset \n",
    "suicide_rates_GPD = pd.read_csv('Downloads/GDP_per_Capita_and_Suicide_rates.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>GDP_PerCapita_2000 US$</th>\n",
       "      <th>SuicideRate_2000</th>\n",
       "      <th>GDP_PerCapita_2005 US$</th>\n",
       "      <th>SuicideRate_2005</th>\n",
       "      <th>GDP_PerCapita_2010 US$</th>\n",
       "      <th>SuicideRate_2010</th>\n",
       "      <th>GDP_PerCapita_2015 US$</th>\n",
       "      <th>SuicideRate_2015</th>\n",
       "      <th>GDP_PerCapita_2016 US$</th>\n",
       "      <th>SuicideRate_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>242.031379</td>\n",
       "      <td>6.3</td>\n",
       "      <td>543.303042</td>\n",
       "      <td>5.1</td>\n",
       "      <td>578.466353</td>\n",
       "      <td>4.8</td>\n",
       "      <td>547.228110</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>556.836318</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1902.422346</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3587.883798</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4166.979684</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3506.072885</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>1126.683318</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2673.787283</td>\n",
       "      <td>6.7</td>\n",
       "      <td>4094.358816</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3952.830781</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4124.108543</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>33291.419370</td>\n",
       "      <td>3.2</td>\n",
       "      <td>39365.335920</td>\n",
       "      <td>3.1</td>\n",
       "      <td>33893.303510</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38663.383810</td>\n",
       "      <td>2.8</td>\n",
       "      <td>38141.846760</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>7708.100996</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5109.851325</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10385.964430</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13789.060420</td>\n",
       "      <td>8.8</td>\n",
       "      <td>12790.242470</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryName Country Code  GDP_PerCapita_2000 US$  \\\n",
       "0           Afghanistan          AFG                     NaN   \n",
       "1                Angola          AGO              556.836318   \n",
       "2               Albania          ALB             1126.683318   \n",
       "3  United Arab Emirates          ARE            33291.419370   \n",
       "4             Argentina          ARG             7708.100996   \n",
       "\n",
       "   SuicideRate_2000  GDP_PerCapita_2005 US$  SuicideRate_2005  \\\n",
       "0               5.7              242.031379               6.3   \n",
       "1               7.9             1902.422346               7.2   \n",
       "2               5.5             2673.787283               6.7   \n",
       "3               3.2            39365.335920               3.1   \n",
       "4               9.5             5109.851325               9.0   \n",
       "\n",
       "   GDP_PerCapita_2010 US$  SuicideRate_2010  GDP_PerCapita_2015 US$  \\\n",
       "0              543.303042               5.1              578.466353   \n",
       "1             3587.883798               5.7             4166.979684   \n",
       "2             4094.358816               7.8             3952.830781   \n",
       "3            33893.303510               3.0            38663.383810   \n",
       "4            10385.964430               8.7            13789.060420   \n",
       "\n",
       "   SuicideRate_2015  GDP_PerCapita_2016 US$  SuicideRate_2016  \n",
       "0               4.8              547.228110               4.7  \n",
       "1               5.0             3506.072885               4.7  \n",
       "2               6.0             4124.108543               6.3  \n",
       "3               2.8            38141.846760               2.8  \n",
       "4               8.8            12790.242470               9.2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the dataset\n",
    "#check the first 5 rows\n",
    "suicide_rates_GPD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of columns & rows \n",
    "suicide_rates_GPD.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 160 entries, 0 to 159\n",
      "Data columns (total 12 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   CountryName             160 non-null    object \n",
      " 1   Country Code            160 non-null    object \n",
      " 2   GDP_PerCapita_2000 US$  155 non-null    float64\n",
      " 3   SuicideRate_2000        160 non-null    float64\n",
      " 4   GDP_PerCapita_2005 US$  158 non-null    float64\n",
      " 5   SuicideRate_2005        160 non-null    float64\n",
      " 6   GDP_PerCapita_2010 US$  158 non-null    float64\n",
      " 7   SuicideRate_2010        160 non-null    float64\n",
      " 8   GDP_PerCapita_2015 US$  158 non-null    float64\n",
      " 9   SuicideRate_2015        160 non-null    float64\n",
      " 10  GDP_PerCapita_2016 US$  158 non-null    float64\n",
      " 11  SuicideRate_2016        160 non-null    float64\n",
      "dtypes: float64(10), object(2)\n",
      "memory usage: 15.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#check the details about the columns\n",
    "suicide_rates_GPD.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountryName               0\n",
       "Country Code              0\n",
       "GDP_PerCapita_2000 US$    5\n",
       "SuicideRate_2000          0\n",
       "GDP_PerCapita_2005 US$    2\n",
       "SuicideRate_2005          0\n",
       "GDP_PerCapita_2010 US$    2\n",
       "SuicideRate_2010          0\n",
       "GDP_PerCapita_2015 US$    2\n",
       "SuicideRate_2015          0\n",
       "GDP_PerCapita_2016 US$    2\n",
       "SuicideRate_2016          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is any missing value\n",
    "suicide_rates_GPD.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean the data\n",
    "#remove the unnecessary columns \n",
    "suicide_rates_GPD.drop(['Country Code', 'GDP_PerCapita_2000 US$', 'SuicideRate_2000', 'GDP_PerCapita_2005 US$', \n",
    "                        'SuicideRate_2005', 'GDP_PerCapita_2010 US$', 'SuicideRate_2010', 'GDP_PerCapita_2015 US$',\n",
    "                        'GDP_PerCapita_2016 US$'], axis=1, inplace=True)\n",
    "'''I removed the columns that contains GDP for 2015 & 2016, because they have missing values, and the happiness report have\n",
    "this data'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make the country name columns the index so we can join the datasets after\n",
    "suicide_rates_GPD.set_index('CountryName', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SuicideRate_2015</th>\n",
       "      <th>SuicideRate_2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>4.8</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>2.8</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>8.8</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      SuicideRate_2015  SuicideRate_2016\n",
       "CountryName                                             \n",
       "Afghanistan                        4.8               4.7\n",
       "Angola                             5.0               4.7\n",
       "Albania                            6.0               6.3\n",
       "United Arab Emirates               2.8               2.8\n",
       "Argentina                          8.8               9.2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_rates_GPD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SuicideRate_2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                      SuicideRate_2015\n",
       "CountryName                           \n",
       "Afghanistan                        4.8\n",
       "Angola                             5.0\n",
       "Albania                            6.0\n",
       "United Arab Emirates               2.8\n",
       "Argentina                          8.8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we need to split this dataset to two datasets, one for 2015, and the other for 2016\n",
    "suicide_rates_2015 = pd.DataFrame(suicide_rates_GPD.iloc[:, 0:1])\n",
    "suicide_rates_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SuicideRate_2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      SuicideRate_2016\n",
       "CountryName                           \n",
       "Afghanistan                        4.7\n",
       "Angola                             4.7\n",
       "Albania                            6.3\n",
       "United Arab Emirates               2.8\n",
       "Argentina                          9.2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_rates_2016 = pd.DataFrame(suicide_rates_GPD.loc[:, ['SuicideRate_2016']])\n",
    "suicide_rates_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SuicideRate_2015    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make sure these two datasets are clean and there is no missing value\n",
    "suicide_rates_2015.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SuicideRate_2016    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_rates_2016.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e5bcad90>"
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's apply some analysis on suicide rates for 2015 & 2016\n",
    "#apply boxplot to the dataset to explore the min, max, median, & outliers\n",
    "suicide_rates_2015.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SuicideRate_2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Federation</th>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guyana</th>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kazakhstan</th>\n",
       "      <td>24.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SuicideRate_2015\n",
       "CountryName                         \n",
       "Lithuania                       34.9\n",
       "Russian Federation              32.1\n",
       "Guyana                          29.1\n",
       "Belarus                         26.3\n",
       "Kazakhstan                      24.9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''''the boxplot shows that the top 5 countries are outliers, which means that the top 5 countries have suicide rate \n",
    "that differ so much from the rest of the world'''\n",
    "#let's see what are these countries\n",
    "top5_counrties_2015 = suicide_rates_2015.sort_values(by='SuicideRate_2015', ascending=False).head(5)\n",
    "top5_counrties_2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#interesting that 4 of the top 5 countries are in Europe (Kazakhstan locates in Eurasia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e62f33a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's analyze the suicide rates for 2016 as we did with the suicide rates for 2015 data\n",
    "#apply boxplot to the dataset to explore the min, max, median, & outliers\n",
    "suicide_rates_2016.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''we can see there was a slight decline in the number of outliers (from 5 to 4), in the max rates \n",
    "(in 2015 the max rate was almost 35 & in 2016 the max rate was almost 32)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SuicideRate_2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CountryName</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Federation</th>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guyana</th>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suriname</th>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SuicideRate_2016\n",
       "CountryName                         \n",
       "Lithuania                       31.9\n",
       "Russian Federation              31.0\n",
       "Guyana                          29.2\n",
       "Belarus                         26.2\n",
       "Suriname                        22.8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's check the top 5 countries for 2016\n",
    "top5_counrties_2016 = suicide_rates_2016.sort_values(by='SuicideRate_2016', ascending=False).head(5)\n",
    "top5_counrties_2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''besides the slight decline in suicide rates for the top 5 countries in 2016, Kazakhstan was not in the top 5 countries\n",
    "instead Suriname took the 5th place, & as a result, there were 3 counrties from Eurpoe, and 2 from South America'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we finished cleaning and suicide rate datasets, let's move to world happiness report datasets\n",
    "#import world happiness report 2015 dataset\n",
    "World_Happiness_Report_2015= pd.read_csv('Downloads/World_Happiness_Report_2015.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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",
       "\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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the dataset\n",
    "#check the first 5 rows\n",
    "World_Happiness_Report_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158, 12)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of columns & rows \n",
    "World_Happiness_Report_2015.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 12 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        158 non-null    object \n",
      " 1   Region                         158 non-null    object \n",
      " 2   Happiness Rank                 158 non-null    int64  \n",
      " 3   Happiness Score                158 non-null    float64\n",
      " 4   Standard Error                 158 non-null    float64\n",
      " 5   Economy (GDP per Capita)       158 non-null    float64\n",
      " 6   Family                         158 non-null    float64\n",
      " 7   Health (Life Expectancy)       158 non-null    float64\n",
      " 8   Freedom                        158 non-null    float64\n",
      " 9   Trust (Government Corruption)  158 non-null    float64\n",
      " 10  Generosity                     158 non-null    float64\n",
      " 11  Dystopia Residual              158 non-null    float64\n",
      "dtypes: float64(9), int64(1), object(2)\n",
      "memory usage: 14.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#check details about the columns\n",
    "World_Happiness_Report_2015.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          0\n",
       "Region                           0\n",
       "Happiness Rank                   0\n",
       "Happiness Score                  0\n",
       "Standard Error                   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",
       "Dystopia Residual                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is missing value\n",
    "World_Happiness_Report_2015.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean the data\n",
    "#remove the unnecessary columns \n",
    "World_Happiness_Report_2015.drop(['Region','Happiness Rank', 'Standard Error', 'Dystopia Residual'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make the country name columns the index so we can join the datasets after\n",
    "World_Happiness_Report_2015.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .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>Trust (Government Corruption)</th>\n",
       "      <th>Generosity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Switzerland</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>Iceland</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>Denmark</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>Norway</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>Canada</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Score  Economy (GDP per Capita)   Family  \\\n",
       "Country                                                           \n",
       "Switzerland            7.587                   1.39651  1.34951   \n",
       "Iceland                7.561                   1.30232  1.40223   \n",
       "Denmark                7.527                   1.32548  1.36058   \n",
       "Norway                 7.522                   1.45900  1.33095   \n",
       "Canada                 7.427                   1.32629  1.32261   \n",
       "\n",
       "             Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "Country                                                                         \n",
       "Switzerland                   0.94143  0.66557                        0.41978   \n",
       "Iceland                       0.94784  0.62877                        0.14145   \n",
       "Denmark                       0.87464  0.64938                        0.48357   \n",
       "Norway                        0.88521  0.66973                        0.36503   \n",
       "Canada                        0.90563  0.63297                        0.32957   \n",
       "\n",
       "             Generosity  \n",
       "Country                  \n",
       "Switzerland     0.29678  \n",
       "Iceland         0.43630  \n",
       "Denmark         0.34139  \n",
       "Norway          0.34699  \n",
       "Canada          0.45811  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check how the dataset look like after cleaning\n",
    "World_Happiness_Report_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(158, 7)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "World_Happiness_Report_2015.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import world happiness report 2016 dataset\n",
    "World_Happiness_Report_2016= pd.read_csv('Downloads/World_Happiness_Report_2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Lower Confidence Interval</th>\n",
       "      <th>Upper Confidence Interval</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>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1</td>\n",
       "      <td>7.526</td>\n",
       "      <td>7.460</td>\n",
       "      <td>7.592</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",
       "      <td>2.73939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>7.509</td>\n",
       "      <td>7.428</td>\n",
       "      <td>7.590</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",
       "      <td>2.69463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3</td>\n",
       "      <td>7.501</td>\n",
       "      <td>7.333</td>\n",
       "      <td>7.669</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",
       "      <td>2.83137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4</td>\n",
       "      <td>7.498</td>\n",
       "      <td>7.421</td>\n",
       "      <td>7.575</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",
       "      <td>2.66465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>5</td>\n",
       "      <td>7.413</td>\n",
       "      <td>7.351</td>\n",
       "      <td>7.475</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",
       "      <td>2.82596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0      Denmark  Western Europe               1            7.526   \n",
       "1  Switzerland  Western Europe               2            7.509   \n",
       "2      Iceland  Western Europe               3            7.501   \n",
       "3       Norway  Western Europe               4            7.498   \n",
       "4      Finland  Western Europe               5            7.413   \n",
       "\n",
       "   Lower Confidence Interval  Upper Confidence Interval  \\\n",
       "0                      7.460                      7.592   \n",
       "1                      7.428                      7.590   \n",
       "2                      7.333                      7.669   \n",
       "3                      7.421                      7.575   \n",
       "4                      7.351                      7.475   \n",
       "\n",
       "   Economy (GDP per Capita)   Family  Health (Life Expectancy)  Freedom  \\\n",
       "0                   1.44178  1.16374                   0.79504  0.57941   \n",
       "1                   1.52733  1.14524                   0.86303  0.58557   \n",
       "2                   1.42666  1.18326                   0.86733  0.56624   \n",
       "3                   1.57744  1.12690                   0.79579  0.59609   \n",
       "4                   1.40598  1.13464                   0.81091  0.57104   \n",
       "\n",
       "   Trust (Government Corruption)  Generosity  Dystopia Residual  \n",
       "0                        0.44453     0.36171            2.73939  \n",
       "1                        0.41203     0.28083            2.69463  \n",
       "2                        0.14975     0.47678            2.83137  \n",
       "3                        0.35776     0.37895            2.66465  \n",
       "4                        0.41004     0.25492            2.82596  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore the dataset\n",
    "#check the first 5 rows\n",
    "World_Happiness_Report_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 13)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of columns & rows \n",
    "World_Happiness_Report_2016.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 157 entries, 0 to 156\n",
      "Data columns (total 13 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   Country                        157 non-null    object \n",
      " 1   Region                         157 non-null    object \n",
      " 2   Happiness Rank                 157 non-null    int64  \n",
      " 3   Happiness Score                157 non-null    float64\n",
      " 4   Lower Confidence Interval      157 non-null    float64\n",
      " 5   Upper Confidence Interval      157 non-null    float64\n",
      " 6   Economy (GDP per Capita)       157 non-null    float64\n",
      " 7   Family                         157 non-null    float64\n",
      " 8   Health (Life Expectancy)       157 non-null    float64\n",
      " 9   Freedom                        157 non-null    float64\n",
      " 10  Trust (Government Corruption)  157 non-null    float64\n",
      " 11  Generosity                     157 non-null    float64\n",
      " 12  Dystopia Residual              157 non-null    float64\n",
      "dtypes: float64(10), int64(1), object(2)\n",
      "memory usage: 16.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#check details about the columns\n",
    "World_Happiness_Report_2016.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          0\n",
       "Region                           0\n",
       "Happiness Rank                   0\n",
       "Happiness Score                  0\n",
       "Lower Confidence Interval        0\n",
       "Upper Confidence Interval        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",
       "Dystopia Residual                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is missing value\n",
    "World_Happiness_Report_2016.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean the data\n",
    "#remove the unnecessary columns \n",
    "World_Happiness_Report_2016.drop(['Region', 'Happiness Rank', 'Lower Confidence Interval','Upper Confidence Interval', 'Dystopia Residual'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make the country name columns the index so we can join the datasets after\n",
    "World_Happiness_Report_2016.set_index('Country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Denmark</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>Switzerland</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>Iceland</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>Norway</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>Finland</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Score  Economy (GDP per Capita)   Family  \\\n",
       "Country                                                           \n",
       "Denmark                7.526                   1.44178  1.16374   \n",
       "Switzerland            7.509                   1.52733  1.14524   \n",
       "Iceland                7.501                   1.42666  1.18326   \n",
       "Norway                 7.498                   1.57744  1.12690   \n",
       "Finland                7.413                   1.40598  1.13464   \n",
       "\n",
       "             Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "Country                                                                         \n",
       "Denmark                       0.79504  0.57941                        0.44453   \n",
       "Switzerland                   0.86303  0.58557                        0.41203   \n",
       "Iceland                       0.86733  0.56624                        0.14975   \n",
       "Norway                        0.79579  0.59609                        0.35776   \n",
       "Finland                       0.81091  0.57104                        0.41004   \n",
       "\n",
       "             Generosity  \n",
       "Country                  \n",
       "Denmark         0.36171  \n",
       "Switzerland     0.28083  \n",
       "Iceland         0.47678  \n",
       "Norway          0.37895  \n",
       "Finland         0.25492  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check how the dataset look like after cleaning\n",
    "World_Happiness_Report_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(157, 7)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "World_Happiness_Report_2016.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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",
       "      <th>SuicideRate_2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Switzerland</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",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</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",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</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",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</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",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</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",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Score  Economy (GDP per Capita)   Family  \\\n",
       "Switzerland            7.587                   1.39651  1.34951   \n",
       "Iceland                7.561                   1.30232  1.40223   \n",
       "Denmark                7.527                   1.32548  1.36058   \n",
       "Norway                 7.522                   1.45900  1.33095   \n",
       "Canada                 7.427                   1.32629  1.32261   \n",
       "\n",
       "             Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "Switzerland                   0.94143  0.66557                        0.41978   \n",
       "Iceland                       0.94784  0.62877                        0.14145   \n",
       "Denmark                       0.87464  0.64938                        0.48357   \n",
       "Norway                        0.88521  0.66973                        0.36503   \n",
       "Canada                        0.90563  0.63297                        0.32957   \n",
       "\n",
       "             Generosity  SuicideRate_2015  \n",
       "Switzerland     0.29678              17.1  \n",
       "Iceland         0.43630              14.0  \n",
       "Denmark         0.34139              11.9  \n",
       "Norway          0.34699              12.4  \n",
       "Canada          0.45811              12.5  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''''now we need to merge the datasets for 2015 in one dataset & the datasets for 2016 in another dataset, so we will have\n",
    "2 datasets, one for suicide rates & happiness report for 2015 dataset & the other one for suicide rates & happiness report \n",
    "for 2016 dataset'''\n",
    "suiciderate_happiness_report_2015 = World_Happiness_Report_2015.join(suicide_rates_2015, how='inner')\n",
    "suiciderate_happiness_report_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>SuicideRate_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Denmark</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",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</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",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</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",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</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",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</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",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Happiness Score  Economy (GDP per Capita)   Family  \\\n",
       "Denmark                7.526                   1.44178  1.16374   \n",
       "Switzerland            7.509                   1.52733  1.14524   \n",
       "Iceland                7.501                   1.42666  1.18326   \n",
       "Norway                 7.498                   1.57744  1.12690   \n",
       "Finland                7.413                   1.40598  1.13464   \n",
       "\n",
       "             Health (Life Expectancy)  Freedom  Trust (Government Corruption)  \\\n",
       "Denmark                       0.79504  0.57941                        0.44453   \n",
       "Switzerland                   0.86303  0.58557                        0.41203   \n",
       "Iceland                       0.86733  0.56624                        0.14975   \n",
       "Norway                        0.79579  0.59609                        0.35776   \n",
       "Finland                       0.81091  0.57104                        0.41004   \n",
       "\n",
       "             Generosity  SuicideRate_2016  \n",
       "Denmark         0.36171              12.8  \n",
       "Switzerland     0.28083              17.2  \n",
       "Iceland         0.47678              14.0  \n",
       "Norway          0.37895              12.2  \n",
       "Finland         0.25492              15.9  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suiciderate_happiness_report_2016 = World_Happiness_Report_2016.join(suicide_rates_2016, how='inner')\n",
    "suiciderate_happiness_report_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we have 2 datasets, suicide rate & happiness report for 2015 datset & suicide rate & happiness report for 2016 datset\n",
    "#now everything is ready for apply some inferential analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>SuicideRate_2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.805143</td>\n",
       "      <td>0.736501</td>\n",
       "      <td>0.752312</td>\n",
       "      <td>0.584813</td>\n",
       "      <td>0.460282</td>\n",
       "      <td>0.244304</td>\n",
       "      <td>0.249692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <td>0.805143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.684385</td>\n",
       "      <td>0.823475</td>\n",
       "      <td>0.392212</td>\n",
       "      <td>0.359190</td>\n",
       "      <td>0.017102</td>\n",
       "      <td>0.290898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.736501</td>\n",
       "      <td>0.684385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572658</td>\n",
       "      <td>0.455587</td>\n",
       "      <td>0.245488</td>\n",
       "      <td>0.145983</td>\n",
       "      <td>0.361473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <td>0.752312</td>\n",
       "      <td>0.823475</td>\n",
       "      <td>0.572658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.384989</td>\n",
       "      <td>0.275358</td>\n",
       "      <td>0.113624</td>\n",
       "      <td>0.236219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.584813</td>\n",
       "      <td>0.392212</td>\n",
       "      <td>0.455587</td>\n",
       "      <td>0.384989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491397</td>\n",
       "      <td>0.415415</td>\n",
       "      <td>0.148356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <td>0.460282</td>\n",
       "      <td>0.359190</td>\n",
       "      <td>0.245488</td>\n",
       "      <td>0.275358</td>\n",
       "      <td>0.491397</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226614</td>\n",
       "      <td>0.003921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.244304</td>\n",
       "      <td>0.017102</td>\n",
       "      <td>0.145983</td>\n",
       "      <td>0.113624</td>\n",
       "      <td>0.415415</td>\n",
       "      <td>0.226614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.050460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SuicideRate_2015</th>\n",
       "      <td>0.249692</td>\n",
       "      <td>0.290898</td>\n",
       "      <td>0.361473</td>\n",
       "      <td>0.236219</td>\n",
       "      <td>0.148356</td>\n",
       "      <td>0.003921</td>\n",
       "      <td>-0.050460</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                  0.805143   \n",
       "Economy (GDP per Capita)              0.805143                  1.000000   \n",
       "Family                                0.736501                  0.684385   \n",
       "Health (Life Expectancy)              0.752312                  0.823475   \n",
       "Freedom                               0.584813                  0.392212   \n",
       "Trust (Government Corruption)         0.460282                  0.359190   \n",
       "Generosity                            0.244304                  0.017102   \n",
       "SuicideRate_2015                      0.249692                  0.290898   \n",
       "\n",
       "                                 Family  Health (Life Expectancy)   Freedom  \\\n",
       "Happiness Score                0.736501                  0.752312  0.584813   \n",
       "Economy (GDP per Capita)       0.684385                  0.823475  0.392212   \n",
       "Family                         1.000000                  0.572658  0.455587   \n",
       "Health (Life Expectancy)       0.572658                  1.000000  0.384989   \n",
       "Freedom                        0.455587                  0.384989  1.000000   \n",
       "Trust (Government Corruption)  0.245488                  0.275358  0.491397   \n",
       "Generosity                     0.145983                  0.113624  0.415415   \n",
       "SuicideRate_2015               0.361473                  0.236219  0.148356   \n",
       "\n",
       "                               Trust (Government Corruption)  Generosity  \\\n",
       "Happiness Score                                     0.460282    0.244304   \n",
       "Economy (GDP per Capita)                            0.359190    0.017102   \n",
       "Family                                              0.245488    0.145983   \n",
       "Health (Life Expectancy)                            0.275358    0.113624   \n",
       "Freedom                                             0.491397    0.415415   \n",
       "Trust (Government Corruption)                       1.000000    0.226614   \n",
       "Generosity                                          0.226614    1.000000   \n",
       "SuicideRate_2015                                    0.003921   -0.050460   \n",
       "\n",
       "                               SuicideRate_2015  \n",
       "Happiness Score                        0.249692  \n",
       "Economy (GDP per Capita)               0.290898  \n",
       "Family                                 0.361473  \n",
       "Health (Life Expectancy)               0.236219  \n",
       "Freedom                                0.148356  \n",
       "Trust (Government Corruption)          0.003921  \n",
       "Generosity                            -0.050460  \n",
       "SuicideRate_2015                       1.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the correlation between the variables in the suicide rate & happiness report for 2015 datset\n",
    "suiciderate_happiness_report_2015.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2548b988a30>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a correlation matrix using heat map\n",
    "corr = suiciderate_happiness_report_2015.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "''''first, create a function that will return: \n",
    "1- a scatter plot with a regression line,\n",
    "2- the Pearson Correlation Coefficient,\n",
    "3- the p-value, \n",
    "4- if there is a correlation & if the correlation is negative or positive and if it is weak, moderate, strong, or very\n",
    "strong, & if it is significant or not, \n",
    "beside this function, residual plot will be drawn to evaulate whether this relationship is or not a linear relationship'''\n",
    "\n",
    "def correlation(x, y, t):\n",
    "    sns.regplot(x, y)\n",
    "    plt.title('Correlation between suicide rate &' + ' ' + t)\n",
    "    pearson_coef, p_value = stats.pearsonr(x, y)\n",
    "    print('The Pearson Correlation Coefficient is', pearson_coef, 'with a P-value of P =', p_value)\n",
    "    if 0 <= pearson_coef <= 0.19:\n",
    "        print('There is NO correlation')\n",
    "    elif 0.20 <= pearson_coef <= 0.40:\n",
    "        print('Weak positive correlation')\n",
    "    elif 0.40 <= pearson_coef <= 0.59:\n",
    "        print('Moderate positive correlation')\n",
    "    elif 0.60 <= pearson_coef <= 0.79:\n",
    "        print('Strong positive correlation')\n",
    "    elif 0.80 <= pearson_coef <= 1:\n",
    "        print('Very stong positive correlation')\n",
    "    elif -0.19 <= pearson_coef <= -0.01:\n",
    "        print('There is NO correlation')\n",
    "    elif -0.39 <= pearson_coef <= -0.20:\n",
    "        print('Weak negative correlation')\n",
    "    elif -0.59 <= pearson_coef <= -0.40:\n",
    "        print('Moderate negative correlation')\n",
    "    elif -0.79 <= pearson_coef <= -0.60:\n",
    "        print('Strong negative correlation')\n",
    "    elif -0.80 >= pearson_coef >= -1:\n",
    "        print('Very stong negative correlation')\n",
    "\n",
    "    if p_value <= 0.05 and 0.20 <= pearson_coef <= 1:\n",
    "        print('This positive correlation is significant')\n",
    "    elif p_value <= 0.05 and -0.20 >= pearson_coef >= -1:\n",
    "        print('This negative correlation is significant')\n",
    "    elif p_value > 0.05 and 0.20 <= pearson_coef <= 1:\n",
    "        print('This positive correlation is NOT significant')\n",
    "    elif p_value > 0.05 and -0.20 >= pearson_coef >= -1:\n",
    "        print('This negative correlation is NOT significant')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#second, make y2015 variable that presents suicide rate for 2015\n",
    "y2015 = suiciderate_happiness_report_2015['SuicideRate_2015']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.24969214561097003 with a P-value of P = 0.0040253334051316095\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now find the correlation between suicide rate for 2015 & each indicator of happiness report for 2015 (7 factors/indicators)\n",
    "#correlation between suicide rate & Happiness Score 2015\n",
    "correlation(suiciderate_happiness_report_2015['Happiness Score'], y2015,'Happiness Score 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e64d9ca0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2015['Happiness Score'], y2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.29089831680774114 with a P-value of P = 0.0007495703026140347\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Economy (GDP per Capita) 2015\n",
    "correlation(suiciderate_happiness_report_2015['Economy (GDP per Capita)'], y2015,'Economy (GDP per Capita) 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e65f2eb0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2015['Economy (GDP per Capita)'], y2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.36147263210601954 with a P-value of P = 2.2160088066931495e-05\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Family (social support) 2015\n",
    "correlation(suiciderate_happiness_report_2015['Family'], y2015,'Family (social support) 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e6702160>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oyqWdJFQRYMWiNsIhCfw+VKsz+yBeoZdSrG+HiPw+TolvAXarasyrwERkARBS1fHU738IXO/V+5nimmGBFOOd9NoJW3YNc8M9uxgaPQrAacuCffWQfWYP0NkaYXI2zoatQzn3/fkj/S7sX8oDQy8WHflXbAlTPxRNEAUmw50pInhYbuME4C5xGiwjwNdV9R6P3su41OgLpPihGYcOH51NcPKSjsxouCBXQN03NsnijpY59+U7s88e6RcW2P7Mizww9AItIThxcUfBaq9BXMLUzRXEH6f+7QNeC2xO3X4dsAXwJEGo6hDwCi9e25igaPRy0bmUekbut1LO7NPblkgqBw7PEEs60wjiCgcOz3Di4vZMv8L8bQ3iFXrRBKGq7wEQkf8EzlbVA6nbK4AveBueaRTNeJbsRr0dLKuhlDPyICjlzD69bU+PHnVGa6WnmSmIwMj4DKf1LMi7rUG7Qi9lRbmV6eSQ8jxwZpXjMQ2oGc+S3fL7YOlH4g5iW3s+6c/n6EyMWEJpjYQ4o6+7aD/CbCJJOCSIOMN5RZyf2UQysNuaSymjmLaIyI9E5K9E5N3AfwE/8ygu00CCOHwvKPwcOuzXuPt6GQ2X/fmsWNRBb3cbna2Rgkk0M5xXhGTSmSkOzoE2mXTuD+K25uM6Qajq+4ENOP0CrwQ2qqqnw1xNfck3VjyIw/eCws+DpV+Ju1YVUCudu1DO55PettN6FpBQiISF3q4WIpEQCYXTehbUVVmRUpqY0iOW6naRIOOdQs1I9dSkUGt+dkz62bzldVt7NZo1Sx29lN1Ud+WbVwHH/l9P71tYl/1uboa5/kJVLxKRcY51uYAzF0JVdaFn0Zm6UaizNYjD94LEr47JU5Z0sveFCY5MxZlNJGkNh1jYEWHlsq6ax1Jt1ej8d3tikzcZXbKaO6IXVG+jfFC0iUlVL0r9262qC7N+ui05mLRCzUiNvqhKvbqwfynD47PMJpKEUh2ow+OzXNi/1O/QKua2WbNQM5Tb5r9G7mNz3cSUqr20U1XHU7e7gNWq+qBXwZn6UexsK2jD9ww8MPQivV2tjE8fu4Lobo/wwNCLrPc7uAq5Ofsv1gzltvnP75FoXiqlD+Jm4Nys25M57jNNypqR6s++sUl6utro7W7P3KeqDXFgc7M/ummGcnNiU04fW73MCyplmKto1upCqpqkxE5u07isGan+NHJ1Xjf7Y7VG15U6Ei2IZb3zKeUAPyQi63GuGgDeC9R/I5upGmtGqi+NftVXbH+s1ui6Ukei1dPs+VISxN8CNwEfwxnN9FPguEV8jDH1IYi1f2qpmgmyUDLKbk7qbouwZ3gcEaE1HKKnq42FHS2B7bMopdz3MPBnHsZijKmxZr7qq0WCnF/ddc/wBPEkhEWJ46zPDc6EuiA27bmZB/G/VfUzIvI55s6DAEBV633AgzGmAbnpCPY6QWY3Jw2NTBAOOaU3EkkFAVF4fnyavu72QDbtubmCeDz176CXgRhjTLUEpUBk9hDYdAG/SEhQIBISZuJJRKXsAR1ej4ZyU+77B6l/v1y1dzXGmAoVOjgGpSM43REeTyiJpBJLOMuttoaF/t4uJmfj9HW3l50cvE6CpUyU+xm5m5heX5VIjDGAv2Pk62V8fvrgOBtPMD4d5+DhaXb8doz3rX0p6994ZmAmr61b08/ff/sRDk3GCAkk1TmIxlUZGZ+mNRIuu2mpFkmwlFFMf5/1ezvwP4B4VaIwJqVeDlBe8bNpJCjNMm5s2DrEbDzBC0dnCeE02yRU+cKWpzjn5MWBKRC5dlUfvV1tTEzHSSi0R5w+iFgyyeRsgk//yTllf7a1SIKljGLaPu+uX4rIfVWLxDSNfEmgng5QXvGzaWTD1iFiiQQvTMwt3ufle5d7QrBvbJLx6TghhFCq4zcsEEskSy4QWe2TkvmvNzIxw+l9XUhqbQhwZqwfnopV9D61SIKlNDFlV/AKAQPA8qpFYppCoSTgd7txEK5evDwrLLZ9e4bHOTwZIxQSwiEhnlRGx2eJJcYrfu988ZR7QnDKkk4OHp4mEso+6EJbOHSsQCTFh7BW+6Qk1+uNT8dpCc/Q03WspEk1DuS1mOhYShPTdo71QcSBvcAVVYvE+KaWB8ZCScDPduOgXL14dVboZvtm40kQMqugiUBS1Ll/3mtVY3+p5IRg3Zp+dvx2jIQq4dSynqrQ3hrm8FSMi27Y7Cq2ap+U5Hq9JZ0tvHg0RmdrZM6B/ML+pVy+cVvZn2Mt5nEUrcUkIq8WkeWqepqq9gPXAbtSP/9dtUiML2pdF6ZQ/Rs/awN5WbK5lJXNvFphzs32tYSdxJBMKqpKMumcD7aGj52lV3N/qaQW0tpVfbxv7UsJiRBLJAkLdLWFOTwdp7M17Dq2aq92mOv1erra6G4Lz6kLddm5J/HtHc+y94UJXpyY4Vd7X2Ddf2znpp88UdL7rV3Vxx3RC/j5la/njugFVT+ZcVOsbwMwCyAia4BPAV8GDgMbqxpNFhF5s4jsFpEnReSjXr1Ps6t1LftCScDP5Te9Wha11AOqV0UP3WzfmScsZNmCViJhp8M3EhaWLWjljBOOLfuSvb+kRw89d2iK9Xc+lHObCiXHSk8I1r/xTDa88zzOX7mMZV1txJNKb1crvd3trvflap+U5Hu9M05YOOdAfvejBzh4eIoDh2eYiiedNatTnexBKtrnJkGEVfXF1O9/irMW9XdU9WrgdC+CEpEw8AXgYuBs4HIROduL92p2tV4vulAS8KMibPoANjI+w5MjExyZimUeq8bVS7nrGlf7rNDNgXDdmn5aI2GWL2rnZSd0090eYWwyxp7h8czBPb2/HJmK8dzhKeIJJRyCo7Px4xJfseRYjROC7M9qYUcLPV1tcx4vti9X+6TEzett2TXMnpEJYgnnCk0VYknnl0RSA7XQkKsEISLpBtE3AJuzHvOq3Pf5wJOqOqSqs8CdwKWF/uCZZ57hBz/4AQDxeJxoNMrdd98NwPT0NNFolHvvvReAiYkJotEomzc7m3Lo0CGi0Shbt24FYHR0lGg0yv333w/AwYMHiUajPPigszbS/v37iUajbN/uDOzau3cv0WiURx55BIAnn3ySaDTKzp07Adi9ezfRaJTdu3cDsHPnTqLRKE8++SQAjzzyCNFolL179wKwfft2otEo+/fvB+DBBx8kGo1y8OBBAO6//36i0Sijo6MAbN26lWg0yqFDhwDYvHkz0WiUiYkJAO69916i0SjT09MA3H333USjUeLxOKcs6eTw7m28cM/nMp/l4cd/Cfd/KXP7W9/6FuvXH6uocscdd/DhD384c/urX/0qH/nIRzK3b7/9dq666qrM7VtvvZWrr74acL7Qr53dQXL7tzJJ4IKpX/HL7/xb5vHXTG7jrfw6c3C88cYbufHGGzOvd8MNN/DZz342c/sTn/gEn//85zO3r7vuOm655ZbM7auvvppbb701c/uqq67i9ttvzxzAnr57A8ue20Y8oTx7aIrhH29g7NGfZb7Y69ev51vf+lbm79/73vdy1113ZW5Ho9G8+95vRw4xuflmpp7eAUBydorJzTfzzKPOvlSrfe+tL3EmaR05MMToPZ9jfHgfsYTypuXTmX1v7ao+3n1Gktktt3DwuWcZm4yxeGIvLQ/cysGDB7lm005aR/cwcs/nGH1hlBBCy8huOh68jfbkNC1h4cYvfzez723YOoTuf4SpzTdDwmmD19/u4OqPrCcej7N2VR9/vPg5ZrfcktkX3tK9j2/edG1Z+94pSzoZe+THjN13bE7v2EP3EHno2P/dLbfcwnXXXZe5/dhPvsnvjPw0c1IiO+/mlS9uySTlUve9+75xM6+d3ZF5veSvv8HvJR7JvN5VV13Fp266hZZQCAW6f3MnHc/80tl3ktD98NfZ+6t7M69Xyb7n9rhXiJsEcQdwn4h8H5gCfg4gIqfjNDN54SRgX9bt/an75hCRqIgMishgLBab/7BxYd2afhJJJaGaOeNJJOHkxR2evefKngWsfVlf5gz51GULPHuvQtJn95GQ0NYS5uQlHU75g1iSrrZIVa5eTl7SSVLnzi9NqrJs3pmu1wZWLuX6S1aztLONRFJZuqCN6y9ZzatesmTO81556mLOWrGQlT1dnLykg0UdrQjQ0RqmJSxIqkPY6dBWkqlxKz1dbXS0hHlhYibzWvvGJmkNzz3EtEZCzGR1eq9asZCzVizM7AsvW95d9jauW9NPMumchaf35WRSOaOv8BrbJy7uyFyFvPWcEzmpwqvGlT0LMq930Rk9nNY79/0PT8U4YWEbAnOmHqfLbyxKDdTYsmuYh/cd4sZ7dxftu/KKqB43Ofr4JznLja4A7lXVo6n7zgS6VHVH1YMSeQfwJlX9m9TtvwTOV9UP5PubgYEBHRy0clHlSI9KabaSzxfdsJnFHS05x6f//MrqFAjIHj2UPYIl6IspFfpsPn7py1l/50McnY3THgnT291Gd3tLpmzEHdELALh847bjRmTNf0611cO+nP5cJqbjDI/PoIDgDBJYvqiD6y9ZDVCz/UZEtqvqQK7HXDURqeq2HPeV1t1emv3AKVm3Twae8/D9mlqzlnyuxUQjv9dcKHdIaqHPZu2qPm76s1fNOYDlamvPN06/0uGdbrYtfX+6PT9I+3f6c+lqj9DeEuL5IzPEkkn6exbw0YvPYu2qPi7fuC0QtaRcXUHUWqrP4wmcPo9ngV8Df66qO/P9jV1BmFLVw9l9JXMOKtk+N3/r5mx9/nMu7F/Kt3c8W/Fnni++9PDRIP+fQvHPLvsK7shUjNGJGWYTSUIibHjneVXdlkJXEIFMEAAi8hbgs0AYuE1VP1Ho+ZYgTDmC3CRx00+e4AtbniKRVNoiIbrbI7RGwq4PdpU28ZT72RRKatVqdsr3OiPjM/R2t9W0WcsL6e2LJ5xFhUIIihISoW9hdUf3VdzE5AdVvRu42+84TGMLavPall3DfGHLUyRViYSEeEJ54egsC1rDrL/zIRZ2tBS9oqh0Zno5n02xGdvVmi2f73WOziY41cWw7SCUVSkk3Qw1PD7tdGYLoMLyRe2EQ1KzpiY3o5iMMTW2YesQ8aSzwIyIU5BOFQ5PxTk6G3c16c6PmenF5n1UK6Z8r7OgNVz09WtdPaAc6TlBqmROEk5c3E53e23Xr7YEYTxXSqmJoPEr9n1jk7SFQ2S3AMeTigLJJDw9epREUgtOuitnElil21ts4mW1Jqble52/uei0oq9f6+oB5Vq7qo9zT13CS5YtoL+3i+5254qplmXLLUEYT9XD2Vo+fsZ+ypJOFnW2ZM4gE8ljcwdawk6l1ecOTRNPJPOeTZY6M70a2+vmCmFBa5j9Y1PsGZ6gJVTecpv5tm39G88sus21rh5QCT/Lz0CA+yBMY/C7hHclSo29mu3a6TboZV1weDLGZNy5lAgB4ZBzXpdEef7IDK86dUne1ymlH8Ht9hbazkIlqLP7J87o62IqlmAylswZixv5tq3YNgdlMSE3/B4mbQnCeCooSz+Wo5TYq10ufM6BITRJYnyGhe1hDk05s4OdGc1KXKt3Nulme4ttZ6EDWlDG9tdiHYVcyj2B8HMghSUI46l6Olubr5TYvbhSyj4wHBvW2cLoxAzT8SSqSkio2mQwN9vrZjvzHdCCcrLgx1l5UNYbKZX1QRhPrVvTz5GpGHueH+fxA4fZ8/w4R6ZiNWtDrUQp7b9et2unY4mEhZ4upz5SSISTFndUrW/EzfZWsp1+rvcxn9frKMxXLx3j81mCMJ5TAMGp6yNz6pMFWimdvF4f/LJjOXhkhkjYSQ4LO1qrdrBxs72VbKffHa5+qqeO8WzWxGQ8tWHrEIs6Wlix6Fh12HrppAb37b+1aNdOx5KrkF61DjbFtreS7fS7w7WaSu1PqNemVksQxlNBaXf2Wi0PfrU42OQ7AFa6nUGduV6KcvoT/OoYr5QlCOOpej1zKketDn5eH2zcjFSq94N8Jdx01OdKsNdfsrrurp4sQRhP1dOZUznDEL2s6ePVWXwx5Y7ICnp9o2Lcxv/E80eYjiWZTSRpDYfo6Wqjuz2SuSrOm2AvWV1XBQPBEoTxWL20O5fTbODl0EU/z+L3jU0SFhgamcg6CLYWbBas17SYTKYAAA+/SURBVGGcaW7j37JrmImZBElVwiKZaqvL4q2c1uOsHFfPk0PnswRhPFcPTRLlfKm9PBBU+7VLObvvbouwZ3iCcEgIh5yyHs8emi64dGe9HxTdxn/DPbtQddb3juPUwgIYm4zx6dRVcSP1u1mCMIbyvtReHgiq+dqlnt1n1ohJj0fWefd7HG8uXjdfuZ1F/sTwBGGB1rAQSyqzCaUtLHS3RzLxNFK/m82DMIbyxvd7Ofehmq9d6iStidkEJy1uJxIWEqqpORftHJ1N5Hx+teOdrxZFE93En/4cBSEcCtEeCdMWCSEinNHXnXlesfke9VTd2BKEMbibxDX/i31h/1LPJn5Vc1JZsUla87erqzVMJByiv7eLVcsX0t/bRSQcKniw93ISXC1mIbudRX5CdxtJlGRSUXV+YsnknOflmnB42bknsWHrEAP/98es+4/tPD06URfVja2JyRiKd6bnaqb59o5nuezck3hg6MWqd8BXs3O/UJNHru06MhXLtC65HXnm5WCEWrTpu/n/PzIVY3I2QTgkoEpCIRwS+pcuOG47s/vdsj/jyZk4SXVWB2yLhFnY0RLovprArkldKluT2nipWmsp+yH7ABVPJHn+yAyxZJIzep1O51hSj9uu1nCIxZ3OyKUFrWFEhPGZuC/DV/3+7NOfXyyRYHR81ln+E1i2oNXVGuHZ8e86eIRwSNAkRMJCf28XqsrhqRg/v/L1nm9LLoXWpLYmJmNcqNdaOnCsyaM1HGL/oWkQOHlxB7Gksmdkgnhi7poMHS1hJmbi3BG9gI9f+nImU2P+izWJeNW27ncNp3QTV09XOyct6aA1HCKpyuRswtViR9n7TmtqlUARmE197kHuwLYEYYwLQapEWo61q/pY3NnKymWdnNHXfazIXyjE8+Mzc56bvV1u2/+97EgudWW8ass+wHe3t9Df28VZyxeyqKPFVQzZ+05vdxuqkFClNRwKfMHCQCYIEblWRJ4VkYdTP2/xOybT3Pw+i81W7pl6rqugExa2Fdwut1dOXnUkp7f1Y99/DICPX/rympTnzlbpyUH2vtPVFmFZVwshETpaQplkBwRyZFMgE0TKv6jqK1M/d/sdjGlufp/FplVypp7rQBcJhzizryvvdrk9OJbaBOcmyQVlPfP0AX50Ypqnhsf57wOH2T82xYX9S139/fx9Z+WyLja88zwGr/7DTB9KELYzFxvFZIxLQZgRnmvG78j4NOvvfIiFHS0FO5Hz1cW6+o9WVVyFtJTJYdmd5mGBh/aNccVXBjmzr4sr33wslqDMzl67qo/L9h/iC1ueIpFU2iNhutsjfHvHs5xz8uKKlw0NynbmEuQriPeLyG9E5DYRyb8quzFNZP6Z+pGpGC8cneXobLzo2Wc5V0Fu/6aUJrj0AdGpYzSNJiEs8PTo0Tmx+z0wIPsq59ZfPM2SzhbOWuHMC+ntbq/aXAy/t7MQ364gROQnwPIcD/0jcDPwcZxJ/h8HbgT+OsdrRIEowKmnnupZrMYExfwz9dEJp4O5PRLOtP0XOvss5yrIzd+UMg8iPa/h6cNHCSGEQoICiaRmDrprV/X5WrJi/vyQA4enmIolMnMXoHoH8SCX5vAtQajqG908T0T+DfjPPK+xEdgIzjyI6kVnTDDNb/KZiScRnNExaX6dfbpNPukD4mwiSTi1Kp6qMwQ0O3Y/S8XPb/Zpj4SZTSQZnZjJJIhqHcSDXBI/kE1MIrIi6+bbgcf8isWYIJnf5NPZGqanu5Xu9mMzjYNy9plPujkqHBKSqiRVUXWSXHbsfg4MmN/s09vdBgoz8WTVR7EFZQBELkHtpP6MiLwSp4lpL7DO33CMCY5cZRyCePaZT7o56tM/fJw9IxO0iLB8URvhkBwXu18DA+Y3+3S3t9DTneDoTILDU7Gqr2sShAEQuVipDWPqXLoUdr62/yCv9FYs9vnPq9U2ZPdBZCfeoJzZV1OhUhuWIIxpYI1woHOzDV4kkPRr7hkeZzaepCUsnHnCwkAl2GoolCCC2sRkjKmCao6xzz4Id9WwgF+xbfBqudP0316zaSeLOpzkVG9LqVYqkJ3UxpjqqNYY++xZzWGBJ0eOOsuSCp4X8Cu2DV6uF1GLtSiCzBKEMQ2sWkUGsw+UoxOzhMVZr3p0YraiAn5uEkixbfByolmQJ7HVgiUIYxpYtYoMZh8oZxNJROaWrC6ngN/8q5KHfjvGFV/5NW/+l/vmJIr0NoyMTzM0MsHjB47MqYVUL0u/1iNLEMY0sGqNsc8+UKbXNEhPboPyCvilE0giqRw4PIPirMXzxPAEV3xlkIs/uzWTKJLJJAePzHB0NkE8tdznV7c9w5Zdw55W2g1SFV8/WCe1MQ2uGmPss2f79nS18uyhaVBYvrAt70GzWAmJTMmN0aOIs4on8dTaRa0hpzbTR779CIpTcwoyi7mRVBibjHHDPbv44YfWVG2501yjoa6/ZLUnS6nWA0sQxpii5tdaOr13ASLCxIyz9Geug2axEhJzSm6EhFiquSokEAo5Vxbj03EQiCVSw/EFUEiqEgkJQ6NHM/FVY1hrztFQl6wO/LKyXrEEYYxxpdSDcLECfukEEhYhmVSSqRwQFsk0X83EE4jIca/txfStIJfd9oslCGOMZwollXQCueGeXTwxPEFInAuEUEgytZkOHnbW0A4BMwnNJAbBaWY6vWduv0clE+bSTV7ZmmnEUi7WSW2M8c3aVX388ENr+NK7BjizrwtSSWJFqjZTd3uErrYIS7taCXGsD6IlLCzubOGjF5+Vea1KV6Br9hFLuViCMMb4bu2qPu758O/zpXe9mleduoSkQl93O//vslfwT5e9gpXLulja1Up3e4SeBS2c95Kl/NNlr5hzdVDppLZmH7GUizUxGWMCI1+TlJtmokqbiEpZ9KhZWIIwxjSEaqzMFtSy236xJiZjTENw20RUjfpQzcKuIIwxDSFfExHA5Ru3sW9sku62CCMTMyzqaKlq5ddGZQnCGNMw5jcRzZ/89uTwBPGksqA1grSKzXUowpqYjDENa/7IpoQqIYHRiZnMc5p9rkMhliCMMQ1rfsHAdHHBdBVasLkOhViCMMY0rPmT33q720gqhENicx1csARhjGlY80c2hUPCks4WVi7trKj8ebOwTmpjTMPKNbLp6j862xKCS74lCBF5B3AtcBZwvqoOZj12FXAFkADWq+qPfAnSGFP3bPJb+fy8gngM+BNgQ/adInI28GfAauBE4CcicqaqJo5/CWOMMV7xrQ9CVR9X1d05HroUuFNVZ1T1aeBJ4PzaRmeMMSaIndQnAfuybu9P3XccEYmKyKCIDI6MjNQkOGOMaRaeNjGJyE+A5Tke+kdV/X6+P8txX871o1R1I7ARYGBgwIM1powxpnl5miBU9Y1l/Nl+4JSs2ycDz1UnImOMMW4FcZjrJuDrIvLPOJ3UZwC/KvZH27dvHxWRZ8p8zx5gtMy/9UM9xVtPsUJ9xVtPsUJ9xdtMsb4k3wN+DnN9O/A5oBf4LxF5WFXfpKo7ReSbwH8DceB9bkYwqWpvBbEMqupAuX9fa/UUbz3FCvUVbz3FCvUVr8Xq8C1BqOpdwF15HvsE8InaRmSMMSZbEEcxGWOMCQBLEI6NfgdQonqKt55ihfqKt55ihfqK12IFRNVGhxpjjDmeXUEYY4zJyRKEMcaYnJoqQYjIm0Vkt4g8KSIfzfG4iMhNqcd/IyLn+hFnKpZisf5FKsbfiMj9IvIKP+LMiqdgvFnPe7WIJETkslrGNy+GorGKyFoReVhEdorIfbWOcV4sxfaFRSLyAxF5JBXve/yIMxXLbSIyLCKP5Xk8SN+xYrEG5jtWLNas51X3+6WqTfEDhIGngH6gFXgEOHvec94C/BCn3McFwIMBjvW1wJLU7xf7FavbeLOetxm4G7gsqLECi3Hm4Zyaut0X5M8W+AfghtTvvcCLQKtP8a4BzgUey/N4IL5jLmMN0nesYKxZ+0pVv1/NdAVxPvCkqg6p6ixwJ07l2GyXAl9RxzZgsYisqHWguIhVVe9X1bHUzW04JUn84uazBfgA8B1guJbBzeMm1j8HvquqvwVQ1aDHq0C3iAjQhZMg4rUNMxWI6tbU++cTlO9Y0ViD9B1z8bmCB9+vZkoQbqrEuq4k67FS47gC56zML0XjFZGTgLcDt9QwrlzcfLZnAktEZIuIbBeRd9UsuuO5iffzOAtvPQc8CnxQVZO1Ca9kQfmOlcrv71hBXn2/gliLyStuqsS6riTrMddxiMjrcHbeizyNqDA38X4WuFJVE86Jrm/cxBoBzgPeAHQAD4jINlV9wuvgcnAT75uAh4HXAy8FfiwiP1fVI14HV4agfMdcC8h3rBhPvl/NlCDcVIkNSiVZV3GIyDnArcDFqvpCjWLLxU28A8CdqZ23B3iLiMRV9Xu1CTHD7X4wqqpHgaMishV4BeBHgnAT73uAT6vTEP2kiDwNrMJFkUsfBOU75kqAvmPFePP98qvTxYdOnggwBJzGsc6+1fOe80fM7UD7VYBjPRVntb3X1sNnO+/5t+NfJ7Wbz/Ys4Kep53biLI/78gDHezNwber3E4BngR4f94eV5O/4DcR3zGWsgfmOFYt13vOq9v1qmisIVY2LyPuBH+H09t+mTuXYv009fgtO7/9bcHaKSZwzs6DGeg2wDPhi6qwhrj5Vn3QZbyC4iVVVHxeRe4DfAEngVlUtOLzQz3iBjwO3i8ijOAfeK1XVl1LVInIHsBboEZH9wP8BWrJiDcR3DFzFGpjvmItYvXnfVMYxxhhj5mimUUzGGGNKYAnCGGNMTpYgjDHG5GQJwhhjTE6WIIwxxuRkCcIYl1JVMh/O+llZ4etdkq7OKiLXisjfVyNOY6qlaeZBGFMFU6r6ymq9mKpuAjZV6/WMqTa7gjCmTCLSJSI/FZEdIvKoiFyaun+liOwSkVtF5DER+ZqIvFFEfikie0Tk/NTz/kpEPj/vNV8qIjuybp8hIttru2XGOCxBGONeR1bz0l3ANPB2VT0XeB1woxyrlHY68K/AOTh1kf4cp9jb3+Os35CTqj4FHBaR9JXKe3BKJxhTc9bEZIx7c5qYRKQF+KSIrMEpyXESTi0kgKdV9dHU83YCP1VVTZXDWFnkfW4F3iMifwf8Kc6aEMbUnF1BGFO+v8BZwe28VOJ4HmhPPTaT9bxk1u0kxU/MvoOzgtlbge0a7CqipoFZgjCmfIuAYVWNpdYMeEk1XlRVp3GK890M/Hs1XtOYcliCMKZ8XwMGRGQQ52piV5VfW4F7q/iaxpTEqrkaE0CpORGLVPVqv2Mxzcs6qY0JmNQIqZfiLCFqjG/sCsIYY0xO1gdhjDEmJ0sQxhhjcrIEYYwxJidLEMYYY3KyBGGMMSan/w+5VuFDIOo9igAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2015['Family'], y2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.23621879711781757 with a P-value of P = 0.006601848024254784\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Health (Life Expectancy) 2015\n",
    "correlation(suiciderate_happiness_report_2015['Health (Life Expectancy)'], y2015,'Health (Life Expectancy) 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e67146a0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2015['Health (Life Expectancy)'], y2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.14835637530427356 with a P-value of P = 0.09081504980639026\n",
      "There is NO correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Freedom 2015\n",
    "correlation(suiciderate_happiness_report_2015['Freedom'], y2015,'Freedom 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e63eca30>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2015['Freedom'], y2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.003920763559688643 with a P-value of P = 0.9645494785055182\n",
      "There is NO correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Trust (Government Corruption) 2015\n",
    "correlation(suiciderate_happiness_report_2015['Trust (Government Corruption)'], y2015,'Trust (Government Corruption) 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e680dcd0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2015['Trust (Government Corruption)'], y2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.05045986898548821 with a P-value of P = 0.5670718831148069\n",
      "There is NO correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Generosity 2015\n",
    "correlation(suiciderate_happiness_report_2015['Generosity'], y2015,'Generosity 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e67ec670>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8bDT/59P86DgRmfNiMRHZCowAaSClqn1zeb4gqmXJA6+t26CcvGqdiOY6VtHsLWEr71F/vIwJvM/9dw5O+ei/dv/dBHyoQnG8TVVf34gJAGpb8sBrv3gQxg4aoT+52QvdWXmP+uN5sZiI/Bg4XlVfdm8fClxf3fAaQyW6Geb6+rO91lwv4yvRgm+EVnSpLeFaX/lUWq0/66Z0pQwML80mANcu4LgKxKDA/SKiwEpVXVV4BxGJ41Qy5aijjqrAS/ov6LWG5nIZX6mupEboTy5lcHnN5n4uXP04o5Mp0hll9+gkF65+nK+d+bpAf1ZmE/TPupmqlCmia0TkPhH5GxH5KPAT4JcViOHNqnoiTpnqT4rIqYV3UNVVqtqnqn09PT0VeElTaC6X8ZXqSmqEKZqlTEu96t6n2DeWRDMQFkEzsG8syVX3PlWDyE2zKmV20HnuIPFb3UOrVPXOuQagqi+5X/tF5E7gJGDtXJ/XlGYul/GVasFnW9EDIxOMTKSYTGUIh4TTX3fY7A8OEK8t4ef3jBESCIUEABHQjPL8nvq58jH1r5TuoOxMoIpVDRWRDiCkqiPu9/8buKJSz29KU+5lfKVmhKxY3suZ2/dx/Zo/kMpkaAmHmN8eZfXGHbz2iAVWNtqYKvCyveSv3a8jIjKc929ERIbn+PqHAL8WkceB3wI/UdWfzvE561LQ6vuUopIzQh7espcjFrZx/KHzObq3i+7OVk9dS/U4s2hZdwcZhYwqipJRJaPOcWP84mV20Fvcr12VfnFV3QK8rtLPW2/KGVgNUqu3kjNCyu1aqseZRZ9793IuWv04IxMpUukMkVCIhe1RPvfu5bUOzTQRz91Bbq2gTao64t7uBE5Q1UeqFVyzKPUEFpSFXfkqNSOk3K6l6ZLHs7uGOXvVukAky0Irlvfy1TNfZ9MpTU2VMiZwA3Bi3u2xIsdMGUpt/dZjq9ercuv3FEseu0cnGZlMH9RFFKQSDjad0tRaKVNERVU1e0NVM5Q4sGyKK3VqZCOvSi13r4Fi4xKDY0kWdUTruoLqTGNF9TyOZIKjlJP4FhE5H6f1D/AJoH7+mgKs1NZvo9dnKad1XGxcYmg8yeKOlin3q6dkOVO3HxC4LkFTn0pJAn8PfAO4BGeV7y9wV/GauSl1YLVSJY8bTWHyOHvVummTZZAG1qczU7cfQCKVZs9oikQ6Qywcoqs10hBdgsZfpSwW6wfOqmIsTa2U1q/VZ/FmumR5yrJFddGKnmmsaCyRYngiRQghLEIqrezZnyCVnuusbdNsvJSS/qyq/rOIfBN3V7F8qnp+VSIzM2rEAcVKt86nS5b1MrA+U7ffppecTf3yVxtnMkoifdCfqDEz8nIlkC1ksr6agZhg8qvbpFrTXguT5ZrN/Wx8cZB0JkNLJExPVwtdrdFAjhXkX8mk0hl2jUySTCuxcAhVBXehWSaTIaXgTNtIs2Zzf6CSmQk2L4vF7na/frv64Zgg8XM9gh+t8+zvIwIhEVIZ5aV9Exy2AMIhCdzAevZK5pI7n2D70CQALWFh33iCRFrpbA0zNpkmmXG2+ouEnEJ0QezaMsHleYqoiPxSRB4o/FfN4Ext+bnRjB/TXrO/zyFdrU6/pvufnUMTgR5YH9ifJBoWWqMhEGHPaJKOWJixRAYJCbGwEIuECEmIJfO9ldkwJquU2UEX5n3fCvwFkKpsOCZI/Kzv78e01+zvIzGnH3336CSJtKLgaS1CLaxcu4V0RomEBEGcvn+UiWSartYIIxMpMqq0hEO5ri1VDVzXlgmuUmYHbSg49BsRebDC8Zg8tZ7G6Od6BD+mveb/PvPaosxrizKWSNHb1RrIBABO4mqJhEilFXFyFyIwmc7wul6nnFcjrxkx1VdKd9CivH/dIvJuYEkVY2tqs1XF9GO1qJ/7xZa7UrgU9bj/7ZEL2+lqjZBByWQUVXWvDEKce+qyuvydTLCU0h20gQNTRFPAVpzN500VrFy7ZdrFQODPalG/1yNUa9pr/hVVV0sEVWVoPFkX6yuyV0iLO2JTNtr55Iqjc3HbmhEzF5JXDqj4HUTeBGxT1Z3u7Y/ijAdsBS5X1b3VDjJfX1+frl/f+LNV3/il+3OLgUSc6X8ZlPmtEY49ZN5BXQDZbo3b4ifXMOrgyZ/hlN/NFNQxgGKyScxO8mYuRGSDqvYVHvdyJbASeIf7JKcCXwE+BbweWAWcWcE4jSvpLvopthioETZk90u9LAybSSMuDDTB4WVMIJzX2v9LnL2Fv6+qlwLHVC+05haLhHKLgbK7TqHO8UbYkN0vjVxx1ZhK8JQERCR7xfB2IH9tgJWSrpJje7vo7ooRCUluimB3V4xje7uqMhjYqGWJLWEaMzMvSeA24EER+REwDvwKQESOAYaqGFtTO/fUZUTDYZbMb+VVh3S5i4DCuf7gSs6kqcf9eb2y2TPGzGzWgWHIbS15KHC/qu53jx0HdKrqxuqGOFWzDAyDfwOCxUouN9JAc/772BELIyKMTKYCW0LamGqYy8AwqrquyLFnKhGYmZ5fA4JBHmiuxIK57PsYxL2Zjam1UraXNHXGaz9/UPvNK91N5WctJGPqRc0Hdt2Vx/8KhIGbVPWqGofUELy0erOt7Gf7RxiZSLGwPUp3Z8uMJRuqUcpiuues9PTOIF/xGFMrNb0SEJEwcD3wHuB44GwROb6WMTWK2Vq9+a3sJfNaWdQRZXAsyc6h8WkHmqsxgDzTc1Z6emdQr3iMqaVadwedBDynqltUNQHcDpw+0wNeeOEF7r77bgBSqRTxeJx77rkHgImJCeLxOPfffz8Ao6OjxONxHnjAmdW6b98+4vE4a9euBWD37t3E43EeeughAHbu3Ek8HueRRx4BYPv27cTjcTZscGrnbd26lXg8zuOPPw7Ac889RzweZ9OmTQA8/fTTxONxnn76aQA2bdpEPB7nueeeA+Dxxx8nHo+zdetWADZs2EA8Huf7ax/n7FXrOOUfV/LW953FV1Y/7Ny+4Abe+r6zuGvdZgDWrl1LPB5n3759ADzwwAPE43FGR0cBuP/++4nH40xMTLBtcAy2Pcqen34TzTgnPn1hA3+4818AJ0no848wvmYVIkJ3Zys9uzfSvvE/uS1+MiuW93LbbbdxwQUX5N77q765isRD38kllszTa0iu+24usdx0001ceumlufvfeOONfPGLX8zdvu6667jyyitzt6+99lq+9OWv5JLV8O/uJPnYXblk1bLpbvb+9oe5++976HsM/u7u3En7i1/8IjfeeGPu55deeik33XRT7vbFF1/MLbfckrvd+uh/Mbbpl7mZQgM/X8XYU2tzVzznn38+d9xxR+7+n/jEJ7jzzjtzt+PxeMN/9n74G+f5HnroIeLxOLt37wZK++wB3HPPPcTjcVIpp9Dw3XffTTx+YEvyO++8k0984hO523fccQfnn39gk8LCz96tt97KRRddlLt9yy23cPHFF+dul/PZu/rqq3O3r7nmGq655prc7auvvpprr702d/vKK6/kuuuuy90u9bN30UUXceutt+ZuX3DBBdx2222523589qZT6+6gw4Ftebe3A39UeCcRieNuaj9//nx/IquQfWNJPrv6CfrlRbondxIbSx7086///Fla5nfTGQuzL5nmWw9tpbu7h65YmOF0hq/e9zTzFiwqKWMfubCd7dsyAAyPJ9kzNo4OTdCa1lwrOxYOkd8ujoVCjBa0lPMNjE6SSabZvHOYWDjEvGSacEjm1J0yOpmiZ5rW/hsOm8dvXxzJVRZNZTKoTu2m2rp7P2evWse2wTGiz+4m07441730h839bBxqY+nJzk5b3Z0tvOuYJTzd1cr2wTFawiHe8ZolTTsoXPjZG05n+NrPnmHB4h5itQ7O+MbTFNGqvbjIB4F3qerH3dsfBk5S1U9N95h6miLqpW5N4fTMLQOjuYJxy3o6gfKma2ZfO5FKs2d/wjmo0N0VIxoO0x4Nkcyo52mhazb3c+5/biCjSlgkV8tocUeMV3Z3lj2VdLbpqTNNky32/g6NJxFgXlu0bmsF+aXRpwabqeY0RbSKtgNH5t0+AnipRrFUnJeBzcLBykQ6Q0icr1nl9INnK4Cef/ujZFRpzdtPdyyRQkRIpjOe6/evXLuFRR1R9owmUUBCQAYGx5JcNcPCq9kGkmfbR2CmabLF3t8d+8ZBYcn8ttyxudYKqvW+DtViA+X1odqfv1ongd8Bx4rIK4EdwFnAX9U2pMrx8kdWuHFLLBzKXQlklTt4uWJ5L/Paohy1qB3J7kjixjA0nuSDbzyCm379PPsTaTpiYT7+llfm5tMXfui2DY6xuKOFlkiYgZFJEukM0ZDQ3hKZ9gPpZYbSXMpVF3t/027N/XxzObE18tqCzliY5wZGSWeczeu7O1uIhIO313Iz8+PzV9MkoKopETkPuA9niujNqrqpljFVkpeduQpbwvPaIuwaniShaZ56eYhIyNlH4NI/LW/S1HQxdLZEWL1xBz1dLRzltsBXb9wBwOqNOw760HXGnPt0tUbpanVOvNmug+l4neJZ7qK4Yr9bOCSgMuV+c5kB1AhVSItZs7mfPfsTpNJKSCCZzrBj3zgL2qNlf9ZM5fnx+av17CBU9R5VPU5Vj1bVK2d/RP3wUremsA7QgrYYHbEw4XDIab3LgZ18KhmDqhadQnrTr58vetzpPiqtBk+1K3gW+906WyJ0tUYqViuoUauQrly7hXltUY5Y2EY0HEKBSEjo6Wyp6+TWaPz4/NW6O6ihee3qyG8Jn71qHYl05qDBunIz/3QxXPKjJwnL1IHo7s4Y+xNpjiryoRsaT/Kl019TUrdNtfcoLva7ZVuxlaq55Oc+y37KdqWJSO7KLrvjmgkOPz5/lgSqrNSujmoM1hWLoeunEZ7tHyUcEsIhIZVRduyboCUSYjyZLvqh8/K7FG7lmD2pVGvz+OliqlRrdraB63rVqMmt0fjx+bMkEDB+/XGqOl1CiZRO6W7KJNJsHxxnUUeUxR0zl5CAqSf9zliYPfsTzGuLsqAtyngyjQDRkDA0nqSzJUI0pFzyoyc5cm19zLLxe59lvzRqcms0fnz+LAkEjF9/nAOjk0XHGkRgYXuUvfuTJNOa28Sm2IeucObCcwOjpNJKR8uB8QSAhR0tfP49y+p2lk0jbu/YqMmtEVX782dJoEzVmrvr1x9nMq2EQ0IkFGIylSajIDiD0D1drXS0RIouGsr/vYfHk7THwsxvc2YIpTPOTJOBkclcP3O2K6tRZ9nUs0ZMbqZ0lgTKUO25u378ccYiIcYTaTKqZPIuCULueoJi4xCFv/fLQ+PsT6QY3J8gw4EkkL/QLduVZQuTjAmmmk8RrUeNUJc+fw9jwbkKiISFlojzkSg2DlH4e0dDIdIZSGScq4qQQCrjPNfweIJnd42wdc9+BvdP5tYZ5LOBSGNqz64EyuBnq7ZS3U6Fz3PKskXs2DfOkvkRUukMO/ZNoArdnbFp59YX+70BVAGFsITIkEGA7fsmiIaFIxa0kcwow+PJ3BiEDUQaExyWBMow3Qyejlg4V9GyEuMElep2KvY8qzfu4MwTD+fhLXvZPjjGMT0diAijk84q4GKxF/7eaVUiIcio830sHGLJ/DZ2Dk+ydFHblPcHnJIYC9pjNhBpTIBYEihDsRk82eqVyYxWbJygUoOp0z3Pw1v2llQtsvD3DouQSCvRSIiMW69nMuWMBxRb5Tg0nuTez5zq+fXmolGLvhlTaTYmUIbCUg+9Xa30dLYwry06ZZwgmU5z/u2PzrrH73QqtWQ8/3mGx5NsGRjlxb1jbHxxsKSYCn/v7s4YCGT0wIDwwGiCns5YTfv/q7EDmjGNyq4EylQ4g+ctVz8wpb98ZCLJ7pEEChy1qL2sK4NKLRzLPk8qrbw0NE6IA4PBpcZUWOIircrweCpXemJeW4Su1ii7RyfZMThOKpMpuwheua15m45qjHd2JVAhhfvXDoxMgkBLJFT2DCIvBejAOVmevWrdtFcc2efZNTKBgNt6d362Y98Y59/+aFmt5Gx56WU9nSxfMo9lPZ0s7mg5sBBNKLsI3lxa841a9M2YarAkUCGFJ+yJlJMQujtbcvcp9URUrNupcIcsLyfL7POoOl032ULL2W2Y5rYAABFPSURBVMqRY4n0jCfYNZv7ec+1a3nVJffyqkvu5d1ff5A1m/un3bg9kcowvy3Ksb1dLF8yj2N7u5jfFi0pAc5lGq5tKG+Md9YdVCGFK307YhHaY2Hm5XURlXMimm3hWCk1+088aiH9IxPsHJpAcRaGZTLQEpEpJ9jCqaS3rnuBwbEkITd7PDewnwtXP85HTn4FqzfuOKjERXa7x3ylJsC5TMO1ujjGeGdXAhW0Ynkvt8VP5lefO41vnPUGYpFwxeraT+eZXcPsHJpg885htgyMMjyenPZkecqyRWwfHGd/wmmtJ9MZMijdnS20RcM82z9y0FXF9Wv+wNB40q02GnL+uVNJH96yt+iVynGHzJtzS3wurXkvV1DGGIddCVSJHzWA1mzuZ3Qyndv8PTvwuzjlbP5eeN/VG3ewsD1K/8ikM7c/o/R0xpjX5uw77HTjTL2qSGeUVEZpjR7YrUvEeez2wbFpr1Tm2hKfa2s+G1d2cNmPyqU2LdXUI0sCVVTtGkClbP6e7Taa39ZKSyTMS0PjAIxOpuhyr1KKdeO0REKkEmlUnZM/OCuEw6Hp96KtRAKsxHP4uT9wI+9FbBqbJYE69mz/CGOTKdKqaEYJiRALF9/8Pb+PPTtOsXt0kolUJrdCeOXaLQdNSe1qjZBIZ5wN3MWZ45NRWNASzbXKp2sBz/XkN9fnWLl2C8l0mj2jU6ewVmOqqE1LNfXKkkCdWrO5n5GJFBl3r2BVQRXmt0dZurjzoPsXrjmY1xYlEpaDykUXdsHEImE+9bZjuPfJnWzZvR+AY7rb+fx7Xp3rbqlFC9hL18uz/SMMjSUJ5e2etnskQTI9MufnLmRVUk29siRQp1au3cLC9ih79ifQjNNVk0HZuz/JV844uN/cSx/7TF0w57/juGnj8LsF7DXxJNySptny2CKQEXWOz/G5C9l2jaZe1SwJiMjlwN8BA+6hL6jqPbWKJ6ima5VuGxyju7OFlkiY3aOTue6Otmho2j13S9303ktcv926l5aw0Duv9aCNZKrFa+KJhoXxJGQyiohb7RSIhaXY05b03IVsWqqpV7W+Evi6qn6txjEE1kyt0mzLc15blHltUYbHk+wamWBkMs3Zq9aVdILPntCf2TVMMq3EIiHP20q2RkIk0hle2jfBYQugqzVa9Raw166X4w6Zx/O7RxmZODAm0NUaPWjmVDnPXci2azT1qtZJwMxgplZpfsszux8AwOELWkvql8+e0BOpNMMTKQDGE2m27hmd9jny4+rubOGloXEUpX94gnBIqt4C9tr1kn2PlsyPeG6dz6Vbx7ZrNPWo1ovFzhORJ0TkZhFZWK0Xma22TlDNVAMnf0HUzuFJIiHhiIVtzGuLlVRiIXtCH5lIEcLZczgUEobHU9M+R/4Ctd2jkyxoixILh5hMqy8Ls7zWVCpn0ZjX5zamUVT1SkBEfg4sKfKj/wvcAHwJp4TNl4BrgL+d5nniQBzgqKOOKimGep6/PVurNNvyzFYwFTnQ1+21Xz7b/ZFIZwjnDaAm0plp9xkuXKC2bzzJ4o4Yr+3uLGl/gnKV0vVSauvcunVMs6lqElDVd3i5n4j8O/DjGZ5nFbAKoK+vr6SClPU8f9vrYGO5XRhrNvczPJ7k5aFxVEEFd7qpswtY4XN84+fP8K8PPEfaLUGaFmf8YLoFatVcQVvNrpdm6Nax1c0mq2bdQSJyaN7NM4Anq/E69VxW2Gt3RjldGNkrpPZYODeFMpVREuk0mYwyry0y5TkKEwA4i8YSyQzRkNDVOnWBWlA2dqnXrsBqCsr/GxMMtRwY/mcReT1Od9BW4NxqvEi9z9/20iotpwujsIzE7tFJxhNpQHILzvKf46ZfP09InKsFVZw9Atyvhy5oo7ertejz1/IKrJ67AqspCP9vTHDULAmo6of9eJ1mmb9dahdGYRmJeW1RVJWh8SS/+txpB91/fyJNJAQSCpFMZ3K7xGSUou9nEFbQVupk12hdJ0H4f2OCo9azg6rOygoXV2qp5o5YmIxbOC4ckik7hZ154uEHvZ9B2NilEl2Bjdh1EoT/NyY4Gj4JwNQ6/7fFT276BACljyN8/C2vdMYAUmlSeeMCC9sjrN64Y9otLccSKYbHEzzbP8LWPWPsG0sUPYFWo+8+/2Q3MpFky8AoT+0cZmg86fn557LDWVDZNFiTrymSgB/8HICs1Gt1xMJsHxzn2f5RoiGZ8Qrp/Hccx6dPOyZXTzok0NsZ44iFHUVPitkrsGhI2L5vHBSOWNBKIp05qCVdrdZ29mS3e3SCHYPjJNIZQiK0x8JNvV+xXR2bfKJa6hbgtdXX16fr16+vdRhT5A9A5o87nHni4Ty8ZW9F+5Kne61S/ojn8hzF1iQMjyfYOTxJT1fLQb/n2avWHTQwP5ZIEQuHWNAeY9vgGMPjSTpawnR3tk65T2GF03Ks2dzP+bc/ylgiTUskRHdnS24THS/PP138lYjNGD+JyAZV7Ss8blcCFVCsyyCZTnP9mj+U1Lr10sKvRPdEJTdxHx5PsmPfBAJFf89iLelUOsMz/aO592YskWb3SIKRiWTuPpVqba9Y3su8tijLl3SxrKczt5dCKfsVW9eJaWSWBCqg8EQ3MpFk59Akk6kMO4cmGJlIzXqi9dolUonuibk8R+FJcdfIBBl3jODpXSPsHJogkUrnfs9ig5C7hienJKGWSAgEBkYmc/ep5ECl7VdszPQsCVRA4QDkS/smcKfQM5ZI88LeMZ7ZOUwqnZn2ROu1dV6JmR2VPCmm0hkk5G456ZaR2LM/wbO7hoFpWtKZDId0teSes7vT+X4ila5Ka3uurXmbWGAamSWBCsg/yfQPT6DuBMr80ZZEWtmxb4KOWLjoc3htnZdyQpuue6mSJ8X2WISQCKGQIO7X7O+bvW9hS/rYnk4i4QMfvXltURZ3xOiIRarS2rbWvDHTs1LSFZC/YnfrnjFaIyHQNMm8DayyCSF/QDWf15XNXlcHz7ZattQVxtMtmIpFQownnIJyuY1bFKemUF7MxUpKFG5jedWfv9bqARnjM0sCFZI9yWRnk7y4d4xY2KnHo+pMqTx8QSujk6mijy9lZbOXE9psq2VLOSnOlFCO7e1i655RhsfzNnPvKL7PcX78VqnTmGCwJFBh2ZN5WMTZBD4UIoNy2Py23MbuxVT6xFjJ0gBeNrcpZeMWsJa5MUFhSaDCsifzq3+6mWf6R4mG4bCuViLh2XfcquSJsZKF82ZKKNaqN6a+WRKoguzJPNuPvn1wjN6uVl9PjpUsnOd1cxtjTP2xJFBFtTw5VrKF3iyVWI1pRpYEGlilkpB1+RjTuCwJGE+sy8eYxmSLxYwxpolZEjDGmCZmScAYY5qYJQFjjGlilgSMMaaJ1d3OYiIyALxQ5sO7gd0VDKdSghoXBDc2i6s0QY0Lghtbo8X1ClXtKTxYd0lgLkRkfbHt1WotqHFBcGOzuEoT1LgguLE1S1zWHWSMMU3MkoAxxjSxZksCq2odwDSCGhcENzaLqzRBjQuCG1tTxNVUYwLGGGOmarYrAWOMMXksCRhjTBNryCQgIu8WkadF5DkR+XyRn4uIfMP9+RMicmJA4louIg+LyKSIXOhHTB7j+mv3fXpCRB4SkdcFJK7T3ZgeE5H1IvIWP+LyElve/d4kImkROTMIcYnIChEZct+zx0TksiDElRfbYyKySUQeDEJcInJR3nv1pPv/clFAYpsvIneLyOPue/axsl5IVRvqHxAG/gAsA2LA48DxBfd5L3AvIMDJwCMBiasXeBNwJXBhgN6vPwYWut+/J0DvVycHxrVeC2wOynuWd78HgHuAM4MQF7AC+LEf71OJcS0A/gc4yr3dG4S4Cu7/PuCBAL1nXwCudr/vAfYCsVJfqxGvBE4CnlPVLaqaAG4HTi+4z+nAd9SxDlggIofWOi5V7VfV3wHJKsdSalwPqeqge3MdcERA4hpV9y8A6AD8muXg5TMG8Cng+0B/wOLym5e4/gr4gaq+CM7fQkDiync2cJsPcYG32BToEhHBaRDtBVKlvlAjJoHDgW15t7e7x0q9Ty3iqoVS4zoH5yqq2jzFJSJniMhm4CfA3/oQl6fYRORw4AzgRp9i8hSX6xS3C+FeETkhIHEdBywUkTUiskFEPhKQuAAQkXbg3ThJ3Q9eYrsOeDXwEvB74NOqmin1hRpxZzEpcqywhejlPpVWi9f0wnNcIvI2nCTgR9+7p7hU9U7gThE5FfgS8I5qB4a32K4FPqeqaaeh5gsvcW3EqSEzKiLvBX4IHBuAuCLAG4G3A23AwyKyTlWfqXFcWe8DfqOqe6sYTz4vsb0LeAw4DTga+JmI/EpVh0t5oUa8EtgOHJl3+wicTFnqfWoRVy14iktEXgvcBJyuqnuCEleWqq4FjhaR7moHhrfY+oDbRWQrcCbwbyLygVrHparDqjrqfn8PEPXhPfP6N/lTVd2vqruBtUC1JyCU8hk7C/+6gsBbbB/D6UJTVX0OeB5YXvIr+THI4ec/nBbFFuCVHBhQOaHgPn/K1IHh3wYhrrz7Xo5/A8Ne3q+jgOeAPw7Y/8djODAwfCKwI3u71rEV3P8W/BkY9vKeLcl7z04CXqz2e+YxrlcDv3Dv2w48Cbym1nG595uP09/eUe3/hyW+ZzcAl7vfH+J+/rtLfa2G6w5S1ZSInAfchzPCfrOqbhKRv3d/fiPObI334pzYxnAyas3jEpElwHpgHpARkc/gzAgo6fKu0nEBlwGLcVqzACmtcnVFj3H9BfAREUkC48BfqvsXEYDYfOcxrjOBfxCRFM57dla13zMvcanqUyLyU+AJIAPcpKpP1jou965nAPer6v5qxlNGbF8CbhGR3+M0aD+nzlVUSaxshDHGNLFGHBMwxhjjkSUBY4xpYpYEjDGmiVkSMMaYJmZJwBhjmpglAdMUROQQEfkvEdniliV4WETOqHVcWSLy99lSCSLyNyJyWK1jMs2h4dYJGFPILbD1Q+DbqvpX7rFXAO+v4mtGVNVzMa+CtQV/g7NYKggryk2DsysB0wxOAxL5J1pVfUFVvykiYRH5qoj8zt2b4FzI1bZfIyKrRWSziHzXTSaIyBtF5EH3iuK+bAVa9/5fdmvhf1pE3i4ij4rI70XkZhFpce93lYj8j/t6X3OPXS4iF4qz70Af8F23hv2fisid2bhF5J0i8gO/3jjT+OxKwDSDE3AKpxVzDjCkqm9yT9K/EZH73Z+9wX3sS8BvgDeLyCPAN3FqKA2IyF/i7P+QrWC6QFX/RERagWeBt6vqMyLyHZyVut/BWYG6XFVVRBbkB6Oqq92Voheq6no38VwjIj2qOoCzuv0/KvKuGIMlAdOEROR6nEqoCeAF4LVyYOev+ThVNRM4NaW2u495DFgK7ANeg1OxEZwl/S/nPf333K+vAp7XA1Uwvw18Eqf87wRwk4j8BPjxTLG6ieJW4EMi8h/AKYAfZZZNk7AkYJrBJpw6QwCo6ifdypnrcQqofUpV78t/gIisACbzDqVx/l4E2KSqp0zzWtn6MkXrR7s1YU7CKZl8FnAeTnfVTP4DuBsnedxRyliDMbOxMQHTDB4AWkXkH/KOtbtf78PppokCiMhxItIxw3M9DfSIyCnu/aPTbMyyGVgqIse4tz8MPCgincB8dco4fwZ4fZHHjgBd2Ruq+hJOl9QlOBVJjakYuxIwDc/tUvkA8HUR+SwwgNNi/xxwB043z0a3/30AmLbuv6om3K6jb4jIfJy/oWtxrjby7zchzsbfd4hIBPgdzi5ji4AfuWMGAlxQ5GVuAW4UkXHgFFUdB74L9Kjq/5T5NhhTlFURNaYOiMh1wKOq+q1ax2IaiyUBYwJORDbgXLm8U1UnZ7u/MaWwJGCMMU3MBoaNMaaJWRIwxpgmZknAGGOamCUBY4xpYpYEjDGmif1/npL2MzbbiIYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2015['Generosity'], y2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "''''based on this analysis, there are weak positive correlations between suicide rate (as the dependent variable/ target) \n",
    "& Happiness Score, Economy (GDP per Capita), Family and Health (Life Expectancy) for 2015 (as independent variables/ predictors)\n",
    "and these correlations are significant'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to be more sure, let's apply the analysis on the suicide rate & happiness report for 2016 datset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "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",
       "      <th>SuicideRate_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Happiness Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.805731</td>\n",
       "      <td>0.727682</td>\n",
       "      <td>0.789251</td>\n",
       "      <td>0.593817</td>\n",
       "      <td>0.464710</td>\n",
       "      <td>0.215439</td>\n",
       "      <td>0.276766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <td>0.805731</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.712244</td>\n",
       "      <td>0.836156</td>\n",
       "      <td>0.389879</td>\n",
       "      <td>0.342201</td>\n",
       "      <td>0.003712</td>\n",
       "      <td>0.343959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <td>0.727682</td>\n",
       "      <td>0.712244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.622839</td>\n",
       "      <td>0.459686</td>\n",
       "      <td>0.258097</td>\n",
       "      <td>0.153341</td>\n",
       "      <td>0.398522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <td>0.789251</td>\n",
       "      <td>0.836156</td>\n",
       "      <td>0.622839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385867</td>\n",
       "      <td>0.288839</td>\n",
       "      <td>0.082110</td>\n",
       "      <td>0.308110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom</th>\n",
       "      <td>0.593817</td>\n",
       "      <td>0.389879</td>\n",
       "      <td>0.459686</td>\n",
       "      <td>0.385867</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511563</td>\n",
       "      <td>0.396102</td>\n",
       "      <td>0.149709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <td>0.464710</td>\n",
       "      <td>0.342201</td>\n",
       "      <td>0.258097</td>\n",
       "      <td>0.288839</td>\n",
       "      <td>0.511563</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.267328</td>\n",
       "      <td>0.057626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Generosity</th>\n",
       "      <td>0.215439</td>\n",
       "      <td>0.003712</td>\n",
       "      <td>0.153341</td>\n",
       "      <td>0.082110</td>\n",
       "      <td>0.396102</td>\n",
       "      <td>0.267328</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SuicideRate_2016</th>\n",
       "      <td>0.276766</td>\n",
       "      <td>0.343959</td>\n",
       "      <td>0.398522</td>\n",
       "      <td>0.308110</td>\n",
       "      <td>0.149709</td>\n",
       "      <td>0.057626</td>\n",
       "      <td>-0.012547</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                  0.805731   \n",
       "Economy (GDP per Capita)              0.805731                  1.000000   \n",
       "Family                                0.727682                  0.712244   \n",
       "Health (Life Expectancy)              0.789251                  0.836156   \n",
       "Freedom                               0.593817                  0.389879   \n",
       "Trust (Government Corruption)         0.464710                  0.342201   \n",
       "Generosity                            0.215439                  0.003712   \n",
       "SuicideRate_2016                      0.276766                  0.343959   \n",
       "\n",
       "                                 Family  Health (Life Expectancy)   Freedom  \\\n",
       "Happiness Score                0.727682                  0.789251  0.593817   \n",
       "Economy (GDP per Capita)       0.712244                  0.836156  0.389879   \n",
       "Family                         1.000000                  0.622839  0.459686   \n",
       "Health (Life Expectancy)       0.622839                  1.000000  0.385867   \n",
       "Freedom                        0.459686                  0.385867  1.000000   \n",
       "Trust (Government Corruption)  0.258097                  0.288839  0.511563   \n",
       "Generosity                     0.153341                  0.082110  0.396102   \n",
       "SuicideRate_2016               0.398522                  0.308110  0.149709   \n",
       "\n",
       "                               Trust (Government Corruption)  Generosity  \\\n",
       "Happiness Score                                     0.464710    0.215439   \n",
       "Economy (GDP per Capita)                            0.342201    0.003712   \n",
       "Family                                              0.258097    0.153341   \n",
       "Health (Life Expectancy)                            0.288839    0.082110   \n",
       "Freedom                                             0.511563    0.396102   \n",
       "Trust (Government Corruption)                       1.000000    0.267328   \n",
       "Generosity                                          0.267328    1.000000   \n",
       "SuicideRate_2016                                    0.057626   -0.012547   \n",
       "\n",
       "                               SuicideRate_2016  \n",
       "Happiness Score                        0.276766  \n",
       "Economy (GDP per Capita)               0.343959  \n",
       "Family                                 0.398522  \n",
       "Health (Life Expectancy)               0.308110  \n",
       "Freedom                                0.149709  \n",
       "Trust (Government Corruption)          0.057626  \n",
       "Generosity                            -0.012547  \n",
       "SuicideRate_2016                       1.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see the correlation between the variables in the suicide rate & happiness report for 2016 datset\n",
    "suiciderate_happiness_report_2016.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e68be7f0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a correlation matrix using heat map\n",
    "corr = suiciderate_happiness_report_2016.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make y2016 variable that presents suicide rate for 2016\n",
    "y2016 = suiciderate_happiness_report_2016['SuicideRate_2016']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.27676597926752955 with a P-value of P = 0.001434358771619454\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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retrz7GHMZ8wwGMY84PL1a4gnldHJBKruO55ULl+fPeOrYZhhMIx5wYa1vVx9wUn0dsUYGIvT2xXj6gtOsqgkIycWrmoY84QNa3vNEBhFYS0GwzAMIwMzDIZhGEYGZhgMwzCMDMwwGIZhGBmYYTAMwzAyqKhhEJGYiGwWkQdF5GER+We/fJGI/ExEHvPfPZWUwzAMwyieSrcYJoBzVPUU4FTgPBF5DvAB4A5VPR64w/83DMMw6oCKGgZ1DPu/Uf9R4ELg637514GXV1IOwzAMo3gq3scgImEReQDoA36mqvcCy1R1N4D/zjnqRkQuE5EtIrJl3759lRbVMAzDoAqGQVWTqnoqsAI4U0ROLmHf61V1naquW7p0aeWENAzDMKaoWlSSqh4CNgLnAXtF5AgA/22zhRiGYdQJlY5KWioiC/3vNuBFwFbgVuBSv9mlwA8qKYdhGIZRPJVOoncE8HURCeOM0HdV9Yci8mvguyLyZuAJ4OIKy2EYhmEUSUUNg6r+Djgtx/IDwLmVLNswDMOYHTby2TAMw8jADINhGIaRgRkGwzAMIwMzDIZhGEYGZhgMwzCMDMwwGIZhGBmYYTAMwzAyMMNgGIZhZGCGwTAMw8jADINhGIaRgRkGwzAMIwMzDIZhGEYGZhgMwzCMDMwwGIZhGBmYYTAMwzAyMMNgGIZhZFDpGdwMw6gDNm7t47pN29jZP8rKnnYuX7+GDWt7ay2WUadYi8EwmpyNW/u48taH6RsaZ2FblL6hca689WE2bu2rtWhGnWKGwTCanOs2bSMaFtpbIoi472hYuG7TtlqLZtQpZhgMo8nZ2T9KWzScsawtGmZX/2iNJDLqHTMMhtHkrOxpZyyezFg2Fk+yoqe9RhIZ9Y4ZBsNoci5fv4Z4UhmdTKDqvuNJ5fL1a2otmlGnmGEwjCZnw9perr7gJHq7YgyMxentinH1BSdZVJKRFwtXNYx5wIa1vWYIjKKpaItBRFaKyM9F5BEReVhErvDLrxKRJ0XkAf95SSXlMAzDMIqn0i2GBPAeVb1fRLqA+0TkZ37d51T10xUu3zAMwyiRihoGVd0N7Pa/h0TkEeCoSpZpGIZhzI2qdT6LyGrgNOBev+gdIvI7EfmaiPTk2ecyEdkiIlv27dtXJUkNwzDmN1UxDCLSCXwP+FtVHQS+BBwLnIprUXwm136qer2qrlPVdUuXLq2GqIZhGPOeihsGEYnijMJ/qur3AVR1r6omVTUFfBk4s9JyGIZhGMVR6agkAb4KPKKqnw0sPyKw2SuAhyoph2EYhlE8lY5Kej7weuD3IvKAX/YPwCUiciqgwHbg8grLYRiGYRRJpaOS7gYkx6ofV7JcwzAMY/ZYSgzDMAwjAzMMhmEYRgZmGAzDMIwMzDAYhmEYGZhhMAzDMDIww2AYhmFkMKNhEJF3iMgS//s4EdkkIodE5F4ReUblRTQMo1nYuLWPS66/h7OuuZNLrr+HjVv7ai2SkYNiWgx/rar7/e9rcemyFwLvB/69YpIZhtFUbNzax5W3Pkzf0DgL26L0DY1z5a0Pm3GoQ4oZ4BbcpldVbwFQ1Y1+jgVjnrJxax/XbdrGzv5RVva0c/n6NTZLWINSjXt53aZtRMNCe4tTKe0tEUYnE1y3aZs9N3VGMS2Gm0XkBhFZA9wiIn8rIqtE5I3AExWWz6hTrPbXPFTrXu7sH6UtGs5Y1hYNs6t/tKzlGHNnRsOgqh8CNgI3Au8GPgLcBhwPvLaSwhn1S7D2J+K+o2Hhuk3bai2aUSLVupcre9oZiyczlo3Fk6zoaS9rOcbcKSoqSVVvUNVnq+oSVe1S1aer6j+o6kClBTTqE6v9NQ/VupeXr19DPKmMTiZQdd/xpHL5+jVlLceYO3MKVxWRF5dLEKOxsNpf81Cte7lhbS9XX3ASvV0xBsbi9HbFuPqCk6x/oQ6Za3bVrwKryiGI0Vhcvn4NV976MKOTCdqiYcbiSav9NSjVvJcb1vaaIWgAZjQMInJrvlXA4vKKYzQKG9b2cjXOP72rf5QVFpXUsNi9NLIRVS28gUg/8DpgOHsV8B1VXVYh2TJYt26dbtmypRpFGYZhNA0icp+qritln2JcSfcAo6p6V44CHy2lMMMwSsPGihi1oJhw1fNV9ed51q0vv0iGYYCNFTFqR0lRSSKySER6KiWMYRjT2FgRo1YUk0RvlYjcJCL7gHuB34hIn1+2utICGsZ8xcaKGLWimD6G7wCfB16rqkkAEQkDFwM3Ac+pnHiGMX9Z2dNO39D4VG4hsLEitWQ+9fcU40paoqrfSRsFAFVNqupNWLiqYVQMGylcP8y3/p5iDMN9IvJvIvJsETnSf54tIv8G/LbSAhrGfMVGCtcP862/pxhX0v8B3gz8M3AUbvzCTuB/cCOfDcOoEDZSuD7Y2T/KwrZoxrJm7u+Z0TCo6iTwJf8pCRFZCXwDWA6kgOtV9VoRWYTru1gNbAdepar9pR7fMAyjGsy3/p6iwlVF5C9F5M0icnTW8jfNsGsCeI+qnojrpH67iDwd+ABwh6oeD9zh/xuGYdQl862/p5hw1Y8BHwKeAdwpIu8MrH5HoX1Vdbeq3u9/DwGP4NxRFwJf95t9HXh56aIbhmFUh/nW31NMrqTfA6epakJEFgLfBh5V1b8Tkd+q6mlFFeTGPGwCTgae8PNGp9f1q+phA+dE5DLgMoBVq1Y9a8eOHcWdlWEYhgHMLldSMa6kiKomAFT1EPAyoFtE/gtoKVKwTuB7wN+q6mCxwqnq9aq6TlXXLV26tNjdDMMwjDlQjGH4s4i8MP3Hj2F4M/AocOJMO4tIFGcU/lNVv+8X7xWRI/z6I4DmDAY2DMNoQIoJV70410JV/bCITEUqichJqvpwcBsREVxI6yOq+tnAqluBS4FP+O8flCq4YVST+TTq1TCKya46pqpjedY9Gfj7zRybPB94PXCOiDzgPy/BGYQXi8hjwIv9f8OoS+bbqFfDmOvUnkEke4Gq3p1ruefcMpZtGBUjOOoVoL0lwuhkgus2bbNWg9GUlJR2ewYKhzcZRoNiWU6N+UY5WwyG0ZQ06qjXUvpFrA/FCFLOFsNkGY9lGHVDI456LaVfpJH7UDZu7eOS6+/hrGvu5JLr72kImRuBog2DOF4nIlf6/6tE5Mz0elW1eRmMpqQRR72Wkg20UTOHNrJBq3dKcSX9Gy4R3jnA1cAQbnzCGRWQyzDqikbLclpKNtBGzRxqQQGVoxRX0rNV9e3AOIDPhlrUyGfDMKrLyp52xuLJjGX5+kVK2baesKCAylGKYYj7KT0VQESW4loQhmGUkXL4zUvpF2nEPhRoXIPWCJTiSvoX4BagV0T+L3AR8I8Vkcow5ilpv3k0LBl+86uhJPfIhrW9XI1zt+zqH2VFgUijUratJdmRU89ds4ib73+S0ckEbdEwY/FkQxi0RmDG7KoZG4usxQ1ME9x8Co9USrBs1q1bp1u2bKlWcYZREy65/p7DQmNHJxP0dsW48bL5G98RNJhBI3DR6Ufx620H69qg1ZrZZFctusUgIt9U1dcDW3MsMwyjDDRqR3ClydfR/OttB+e1wawUpbiSTgr+8f0NzyqvOIYxv2nUwXSVxgxm8WS73EKtnd2lHqOYGdw+KCJDwDNFZFBEhvz/PiwrqmGUlUbtCK401tFcHLnGdoS7l6wq9TjFZFf9uKp2AZ9S1W5V7fKfxar6wVlJbxhGThpxMF01MINZHLkGK1JKR7KnaFeSqn5QRHqA44FYYPmmUgs1DCM/jTaYrho0SuRUrcnlcgMteVhBKZ3PbwGuAFYADwDPAX6NGwltGIZRUcxgzkyuPiqQknPilbLDFbj0FztU9WzgNGBfqQUahpEbSwhnzJVcLjfcTJolUUpU0riqjosIItKqqltF5GmlFmg0N5a+eXaUa2CbMb/J5XJLDu5/otTjlGIYdonIQuC/gZ+JSD/wVKkFGs2LKbfZU62EcGa4m59sl9tNlw8PlnqMol1JqvoKVT2kqlfhUmF8Fbiw1AKN5qVR0zfXA9VICGdpqo1imdVEPap6Fy7L6o/LK47RyFi2y9lTjTh9M9zNQ6X7o4oZ4HaOiPxRRIZF5Fsi8nQR2QJ8HPhSWaUxGhobhDR7qhGnb4a7OahGy6+YFsNngMuAxcDNwD3AN1X1War6/bJJYjQ882EQUqVqatUY2GaGuzaU+5mpRsuvmM5nVdWN/vd/i8g+Vb22bBIYTUOzD0KqdOd6peP0L1+/hitvfdjSVFeRSjwz1cgbVYxhWCgi/yvwX4L/rdVgBGnmQUiNPpVksxvueqQSz0w1Ei0WYxjuAl6W578CZhiMeUEzZPhsZsNdj1TimalGy29Gw6Cqb5ztwUXka8BLgT5VPdkvuwp4K9Ojpv9BVS26yah7LCW2USqVeGaq0fIrJVfSMuBjwJGqer6IPB14rqp+tcBuNwBfAL6RtfxzqvrpUoU1jFpiPnqjVGb7zMw0ELHSLb9SxjHcANwOHOn//xH420I7+MyrB2clmdGwNGvOH0uJbZTKbJ6ZehiIWPSczyLyG1U9Q0R+q6qn+WUPqOqpM+y3GvhhlivpDcAgsAV4j6r259n3MlyoLKtWrXrWjh07ipLVqB355uY1BWoYxVHueb9nM+dzKS2GERFZjOtwRkSeAwyUUpjnS8CxwKnAbtw4iZyo6vWquk5V1y1dunQWRRnVxkbXGpWgWVuhuaiHgYilGIZ3A7cCx4rIL3H9Bu8stUBV3auqSVVNAV8Gziz1GEb9Ug8PtdFc1INrpZrUw0DEUmZwu19EXgg8DRDgUVWNl1qgiByhqrv931cAD5V6DKN+acbIHctIWlsaffxIqcw1yCH7eQ21dnaXKsOMhiFrcFuQE0Sk4AA3EbkR2AAsEZFdwD8BG0TkVJxLajtwealCG/VLs0XuWCrx2tMM40dKYS7hqLme13D3klWlylBMiyE9mK0XeB5wp/9/NrCRAgPcVPWSHIsLhbcaDU6zja6db7XVYqh2C6oZW6EzMdtw1Os2bSMSglg0jCrEImEoNsIoQNED3ETkh8DT024gETkC+GKpBRrNTzONrp1vtdWZmKkFVQmjUc+t0Fq6GVWVeFKZTKaIJ1LEkyke3z9MVyxCPJECIBQSQFOlHruUGdxWB/oGAPYCJ5RaoGE0EvOxtlqIQi0ooCJut1q1QmdS+tV0M056xR9PpphMpJwxSOphjYHl3W0cGJnICgCRkufdKcUwbBSR24Ebcf0DrwF+XmqBhtFI1HNttRYUakFV0u1W7VZoMUq/EuebrfydQTjcAOTjNWes5No7H2MsniQWDTE2mQQRKVWOUqKS3uE7ol/gF12vqreUWqBhNBLN1mcyVwq1oJrJ7VaM0p/L+SaSvtafUCaSrrIRT6RIBQzA5m0Huek3O9k9OMYR3W285oyVnLlmUcHjnrlmEVdwPDf9Zid7Bsc4amE7ycH9T5R6/qW0GNIRSJZN1Wgo5uoHbqY+k7lSqAV13aZtTeN2K0bpF+NmTKY0q/bvvlMztAA2bzvItXc+RiQkdMciHBiZ4No7H+MKji/KOKS36WyN8P23Dw8WfeKeYqb2vNt/D4nIYOAzJCIlF2gY1WS+DY6qNIVy/zTTDH7FDDILnm8qlWJkIs5EIsUlZ65kz8A4TxwYZceBEXYPjHFgeIKh8Tjj8eSMRgHgpt/sJBJyaWUE9x0JCTf9ZmfR57B520H++lv3E126+hnFn7mjmKiks/x3V6kHN4xaY+Gm5SdfC6qZ3G75WkZvfcExjMeTTCZTPGPFAv7u3OP5xj072D0wxnLv7jn5qAWMTibmVP7uwTG6Y5nqORYNsWdwrKj90y2OlrCApkoWppS0288BHlbVIf+/EzhJVe8ttVDDqBbN5PduBJrF7bb+hKV8+CUn8uW7t/Fk/xjLF8R4zRkrWb2kg6cOTSvnU1Yt5DOrFpa9/CMC0UUjkwkOjkwykUjRFg2zeZtLWF2o/2GqxdFSUm/BFKXs9SXg9MD/0RzLDKOusHBTIx+plB8DkEyRSCrxlPtOJJVEKsVxyzq55pXPrIls6eii/tEJ+kfiIBASaIuGuOb2rYDrP8jX/5CrxVEKpewpGoiZUtWUiMy+ZGNeUu0BQRZuOjeaIU9UMmjUqbwAACAASURBVKXEkykmAmMB4gmn/OuVdHTRR370BxRoCYfoaW+hszXC9gMjILC0sxVg6rm+6Tc7OXPNIjZvO8jweIL9QxO0REIgoXDh0g6nFMW+TUTehWslAPwNYLmUjaKpRd6hZvJ7V5tGyxMVjABqFANQiDPXLKIzFmFBW4T+0Th9Q+P0j4aIJ5VwVthQuv9h06P7+MLGPxEOgSqMx1NIKNxSatmlGIa3Af8CfBg3wO0O/CQ6hlEMteoIbha/d7Wp1477tOsnaAAmEymSqZJTAtU9HdEwOw6OEhJBgHgihQKpFPSPTrrxD35MRDKlXPXDP5Sl3FIGuPXhRjsbxqywjuDGopb3K+FTPmT0ASRTJFLFjwJuNFKq9I9MsmdwnL2DE+wdHGffyCRJhWTWOaeAfcOTeY8lQCQstIRDaDJeXChTgGLSbr9PVT8pIv+Kn70tiKq+q9RCjepSSz9xsOzBsTiJZIqlXbGp9dYRXL9Uo+M+3QE8kQjkACpiAFgjkkwp+4adwt87OMHegXFvBNz/vqFx4snizzsWCaFAV2uEM1Yv4oxjerhx805GJhJ0tIQREUIh4bEK5Up6xH9vKfXgRu2Zi594rgYlu+xkKkXfkKvlLOlstY7gOqfcHfeJLAOQHgncCBSTnmIykWLfkFP8QYWf/r1vaIJivV3dsQi93TH2DU6QVBemGgmHiIaEREpZ2tnK515z6mH7tUcjXHvnY4wnUpXNlaSq/+O/v17qwY3aM1s/cTk6HrPLXtLpWgojE0kGxuIN3RHcDNE6MzHbjntVnVL8QSPQqH0A6cFiIYHWSIhdh0b52E8e4bSVPYRCTBmAAyP5XTvZLOpoYVl3K8u6YizrbmX5ghjLutOf1ql3JpgaIxYNMR53fQyXnJl77p1y5UqSYv11IvJzcruSzim10Nmwbt063bLFGi2lctY1d7KwLZpRaVBVBsbi/OL9+W/dJdffc5gbYXQyQW9XjBsve05Fy653gkYzWJNOp4aYL0xF/qT9//67UVoB2QyPJwK1/bTLZ5zfbO9nIpEsurYfEtciTiv5tMJfHvjdEineu5NurewZnB5dPVO+pDSdrRGWLWi7T1XXFV0gpUUlvTfwOwa8EpjbuG+j4szWT1yOjsdmHVxWr9E6lSA4CGwqAibReJ3A6QpJ0LWzZ8AbgCH3f2QiOfOBPNGwixI6Z+2yrBp/K0s7W4lkx5POgWBSvGpRSlTSfVmLfikid5VZHqPMzNZPXA6l3qyDy5otuiqVcqN+40mdigZK1/wbxf2TUuXA8OR0x+6Uj3/6/3iiuJZMSyTEsq5pZX/f9n4mkkk6WiJEQkIkJIwnUizuaOV95z2twmdWG0rJlRQ0WSFgHbC87BIZZWU2fuKNW/s4NDrJ9gOjRMPCsi5XAypVqTfr4LJGawmlff7pdA9TqR/8dyNEACVTOtWxm+3u2eM7douN6OloCbOsO0ZvdyvL026eBdNun2z3Z9rPDy4EdDzuWkyvOWNlRc61Hiilj+FxpvsYEsB24GpVvbsyomVifQzVIeg/TyRT7B2cIJ5KcfzSTj5w/okNr9TLQb32MUwN/PIdvolU44z8nUyk6BvyUTwD4969438PjrN/uLSInmxlv9xH+Nz5aB/7hieKnvgmzVz8/LWkYn0MInIGsFNVj/H/L8X1L2wHyjPMzqgbsv3n3W0tjE4m6OloNaPgqUZLKF/UU8IP8kqkdGoC+HRroJ5r/mPxZKZfP8vVM5uInuWBKJ7pDt4YbS2HpwbavO0g/3X/rllNfAO18fPXkmJcSdcBLwIQkfXAx4F3AqcC1wMXVUw6o+o0m/+8nFQrRPXnj+zlylsfJhISOlvCPHVolH+45fe865zilFgtyIzoyYzf3zs4wcBYvKjj5IroWZ6l/EuJ6EkTnPgGDk88Z2RSjGEIq+pB//vVuLmevwd8T0QeqJxoRi1oNP95tSh3Qrl0xs/JZIpk0rUA0suuveNPAETDIVIKrZEwKa2dEktH9GT49YMRPQPjjEwWF9ETCQlL0x27WTH8y7tjLOlsKWtET5q5TnxTb4RDQkiEcEgO+x0OCWERQiGIhGZ3LYsyDCISUdUEcC6ZifMK7i8iXwNeCvSp6sl+2SLgO8BqnDvqVaraX7roRiVo1kiiuVJqiKqqEk86ZZ/u5M3O95+PaiuxzIiezE7d9O+JIiN6WiOhgrX9RR0thEMlD8SdM8GJb9KMx1Ms726ruiy5yFbsU/9FCIenFX3YL5/FYOaSKMYw3AjcJSL7gTHgFwAichwwMMO+NwBfAL4RWPYB4A5V/YSIfMD/f3+Jctcd5XIz1HpEbbNGEs2Vnf2jhAW27RtmMpkiGhKWdLbwxMERBsfjXuGniKfc91zCPMutxBLJlM/RM5Gh/IOpGoqO6GkNBxR+UOnnjuipF9IT34zFk8SiIQ6NTnJoLMHQeJx3f+fBinQmp5V7xCv2cEiIhEI1UfSlUlRUkp/W8wjgp6o64pedAHSq6v0z7Lsa+GGgxfAosEFVd4vIEcBGVZ0xGLieo5LKFaVSr9Eu1abWxhGma/yJlOvs/V9f/CWP7x8hLALict2nVDl6UTuXrT92xjw6pZArDUIipVyRp49hMpHKqfDT/0uJ6FnQFs1Z208bgs45zApWa9KRRTsOjjA8kWBBLEJPR8uM1xcK1+hDoWnXTnp5SGaVoqgiiEjJUUlFh6vOlhyG4ZCqLgys71fVnjz7XoZ3Xa1atepZO3bsqKisaUpVTOVIH1HO4zQy5TSOM93HtE8/OLVjepBXdo3/LV/fwo4DI4RCgqQNQ0pZ2tWKiBStxItl87aDXP8LJ7uqsrwrxouevowF7dHDQjoPlhDRs7ijJdPVsyDT1RNspTQjIsK7v/OAb5FFSOvusXiSpZ2tfPXSM6YU/ZQrpw5r9KUwG8NQ1+ZfVa/HRT6xbt26qsTizaaTsVyRPBYRVL50Exu39vGPP3iIaFjoao2we2CMD/33Q7z3xSew7phFJFOlhXeOTCZY1t1K/2iceDJFNByip7OFfcOTLkRyltEuqsrwRCKjM3fPwDhbdw/xxMFRUqqkFHYNjHPDrwtXjIIRPcsXZObmWdbdSm/X7CJ66p1iXTYhn4Z63/DEYS6vSEjYOzjOgvZogZLmD7UwDHtF5IiAK6mvBjLkZTaKqVyRPOU6Tj24YmZLKcZRNTOax30798+1d/iRqqEQyZTSEg6RTCX5+q93cMqqhYcdaybSfv+VgXsxFneROLFoprINdhSrKv2j8cNSNewZHKfPu31Gi4zoAZeu4eQjuw/v2F0QY2lna006dsuJSKZ7JuyV+dR3qHj/fL73wCLvZqYWhuFW4FLgE/77B8XstHXPEJdcf0/Fldxsau3liuQpx3EabZ7ebIIvraqiOHfa8gUxDo5MTg/wmiGy56mB8kb2ZHdepl1GKxbEGJ5IEhGZckWNx5OERLj0a5vpG5ooOqInFojo+f2TA7S3hImGQ+4TcspweCLJpy8+ZVbnUEsyQim9cg/W6oPrykGh98Ai72amooZBRG4ENgBLRGQX8E84g/BdEXkz8ARwcTHHioSkKkpuNrWJckXylOM4jZL5M1jbT6SUpPfxv+pZK/jUTx8lnkzRGplWwK88bQWHRov3pZcrsicd0dMSDfH8Y5dw95/2s394jHAoREskxN7Bybwdu0MTmcmHO1rDgWiew109CwLujXd/58HD5B+LJ+smvBJcaywdK5/+Tiv3SFZsfbUp9B5cvn4NHS1htu0fAeCYxe384189va7ej1pTUcOgqpfkWXXubI5XDSU329pEuSacTx8n3Qz+8A8eYuWm4g1EvfRTpGv2aRdPsJaf9AYhF6esWsi7zjm+pLw0uWbXylfDz058NhFPsndo4jBXjxvENcGBkXwRPSmYyFwiArFImGOXdnDiEd1TCn82ET3Fyl9ugq6coKJPK/vgdz13yOZ7Dx7rG5pqSRzf28lYPMlovP5zSVWbuu58zkWllVw9xPHPxR1UDf9petBW2qcfHMSVNgKlRLvlUuyffXVx7pJgaGdGDpxzjueKc47nP+99gqcGRumOtXD6qgU8sOsQtz28Z1YRPSFxLdeWSAgRl4//laevYP0JS+gtc0RP9kxcc03clo6cCmUp+Eg4NPU/7cevBeXuF8v3HkwmUixoq/8Wda2peLhquVi4aq2+4O+/Mi/CN+cStjqXcM9USklqpnsnqdNGIK34y5msrdSY/SCqyhU3PcC+oQnCIZmaTGYikQSEaFgYHC9uLqmQ4FI15EnM9qnbH6V/dPIw187ijtaijVi5yfbbB2v4ISEr1r5+a/eVGL+T75gjE3GOWNDWdLMKFqLpwlWzGZ1MzItOorm4g3K1eN561jE897jFjE4mnGJPMaXw08q+3Aq/WAolNzvjmJ6MiJ7sgVt7BsanIoNyEczbFg2LU/RdAb9+IIZ/poievUPjVU1Tke6YjQaUfiTcOK6cUqhEv1i+lv91m7ZZRFIRNIxhSKaU3q5YQ4VezpaZ3EEpr8STOq3kk959k0wpTz+ym8+86pQMt86T/bNXYLlcPeVIH5BMKbsOjRKLhBgcj0/PIJZSnhoY5/x/uZvJIiN6RCDqlacIdLdGecsLjpmq/fd0tBCagyItV2d22qWT6bN3RiAScO/UmmqGPFeqXyxfv59FJM1MwxiGpy3vamr3UTBK59LnHs1Hf/QIyVScWCQ09fC+4rQjeXz/SNnn2i2k+PP68IvIYx9PpqZm3dqTIwd/39BEwZxCwXWdrZGcOfj3D03yX/ftdC6DlnDZRh5nM1NncNptEw2H/Pd0/H06XULax1/vVDvkuZrjCuqhD7ERaBjD0Gika+/ZtfrU1DLvv8/htz9heRfvOPu4wzoe161eVBGjUEjxF3L1nLJyQSCiJ3PGrXSOnmKlDYlLM532jZ+ztpczj1k0lZK5szX/o7qip62is2tFQiHWP20psWiIb937BLsPjXFUTztvOesYzj6xl0gTuXWg+iHP1R5XUK4Iwplo5IGmDdP5XMskekEffPo7lSLgznFpC6YU/hz89ZVy2+QjX7z84o5WPvLyk7j0PzbTGgn5cQaaMVF8sYnZBFjc2ZI3fn/ngTG+/9snazJt4lRtPpzZgRsNu9/RcP0q/UopnrOuufOwlBGV7qBNn0uz1OLrKSFm03c+z5VctfhUQJknVVHNNAQppey19HzMxW1TKqrK4HiCJ/pHaI2E6B9NTin9RErZ1T/Gy77wy6KOFRLo7XJTKu4ZGCMaDhGLhkhftivOPZ7nHbck7/5HL+7grBPyr58taYWfVvaR0PT/iPfp16vSn4lKuntqkTKiWrX4atEoA03z0dCGIajop5R5ChdeGVT+NYy6KYVyTj+YmaNn/LAkbXsHJwpG9GQTSfvNxSnTl5y8nOcet9jPuuUiet79nQdJJFOHtT5uvu/JgoZhtqQVf9TH4kemvoVoKNQQ/vzZUknFk3bt7B8eZ2A0zkQyRSQU4sJTjiyH6LOmkVwz9TLQdLY0jGGIJ5WnDo1luHHqXdGXSikzdyVTygE/+UqueXb7hiaKj+jBuVTcwC2ncF/6jCN4/nFLWNbdymN7h/null0zunrKPfNYOoInEnDrpJV/upO30SiXcquk4tmwtpeLdh3iixv/TDKlxCJhumIRbr7/SZ65YmFNlHGj5QBr9ER9DWMYUuqSkzUzwZBIVefTH5lIEIuGueFX20uK6AnSFYtMza+7bEEsI1XDkwfH+Na9O9h1aIx4MsXKhW1ctv7YDMW/uLOV5xy7uCT502SHdGb3oVxy5kqef/wSWsIhp/R9bT/dEmgmyqncKq14fvz73YgwNV9BayRMJCw1c4Vct2kbk4kkB4YTTCZTtIRDdMUideuaafREfQ1jGJqNiXhyejJ17+oBZc/guI9Yytz+GwVy8S9si04P1uqannxleXeM3gIRPZu3HeQrv3ycSEhYvbid8XiKsSJbGbnIF9L52mevoq0lzOZtB/nCz/9ENCws7mhhcHySL278M8u6Y3X5cpebcrp/Kql4Nm7t47F9wy7UVoREUnlqYIwjF8Rq5gr5495BBscThJiW6cDIJInkYE3kmYlGD4s1w1AhRiYSOSdW//O+YfYMjJMosrYvpCdfyZxbd/mCGMu6YvR2txIL1NBLiWoqZ59GOqSzrSXMt+7ZwVMDY6zoaedt69dw9onLpsprjYamFGNHa6ihOuTmSjndP5VUPNdt2kY0FEJx7jwRIAV7hyY4bWXOyRYrTnpO6nS/kYgb6DlZ5FzVtaCRO9TNMMwCVWVwLOE6cgfH2TswbQC27R+hb3CCZAn9H4vao6xa3B4I4ZwO6Vza1Vq0S6XUqKZS+gTCvnM3mu7k9S6fdGhnOrrnwtOO4sLTjsopX6N3yM2VSrl/yq0ad/aPsqy7ld0DE6RQN5UpSiJJzVwhLZEQY5NJUqpTU6uiNOWMdPWAGYYcpFTpH5nM27G7d3Cc8RJS9S7pbGH14g4e3z/ionZawlN+9HgyxZLOGJ991dwTsZXaApjqE2gJ43KFwkTCKaolXa0+RcPMsfzFdqg2eofcXCmn+6ca4apHLoyxb2iCyWSKsAjHLu2oaA240HN0fG8X2w8MMzg23cfQ3RFl9eLOiskzGzmbhXlpGJIpZf/wxJR7Z+9UrX98aiRvvMgmals0POXT/9PeYeKpFO3RsKtdh4XJhFP8n7zomVzy5XtY1NE6pYQBImEpWyK2YloA0bCbYKYlHOLyF67hYz9+hEQyRXtLhLF4ElXhHWcfR3esuLlvS1FQjd4hN1fK6f6pRrhqNCwcs6Rj6j69/7y1czpuIWZ6jtIyLV8Qqemz02jRUbOlKQ1DPJmib2hiWtln1fz7hsaLHrXbFYtk+vazcvV0xyJTtelLvnwPPVmKP9wyrfg7omF2HBwllVI3mXx7C+GQlG1WrnQLoL0l4iJKgLF4ilWLOjiqp42WcCij5n/+M46gLRqu2oxxpSrGZqyZlcvvXOlw1Wp3nM70HNVLZ26jD1wrloY0DOPxZM6O3fTvg8OTRftde9qjOVM1pFsBQbfHTBQK19y87SD9Y3GSSSUkboazvYPjdLdFefuG40q8Am6sQUvEzQfc4n3+bz/7WK7+4R9IpFJTtaqUwt9sOJbWSO5JZOaqqEpVUMWWV0rNrBkNyExU2i1X7Y7TYp6jeujMnS/9ZA1jGJ46NMZff+t+9g6OcyiYaL8A2RE9yxdkTsCyrKuV1jLOulUoA+dNv9lJZ2uE9pYwB0cmiSdThENCT3tLwQigdMbOoAsoPdArmxeftJxoOFS2WlUxCrdSCuoTP3mEvqFxkimlJRxiSWcr0Rxx9POlaZ9No7jlmq3/qVHknCsNYxiGJxI8uncoY1k4JPR2tWYq+1lG9JSDQtMxfu6OPzq3E0KHf6gUZWg8kZGyORoWolMGoPTRveWqVRWrcCuhoEqJo58vTfts6sW1Uohm7H9qFDnnSsMYhgVtUd581mpf03e1/0UdLXWXFuHMNYvyRgAdHPX+f1x8+Fg8werFHaxe0lF9QWegWIVbCQVVShz9fGna56IeXCuFqGT/U61oFDnnSsMYhmXdMV777KNrLUZRRELe7eM/0bBwxbnH8U//8wfiyWn/fzIFb3vhsbUWNyelKNxyK6hS4ujnS9O+Vsyl/6ZS/U+1plHknAsNYxjqjXSCt3QHcNTn9mkJ587qefaJyxCRstc0KtXxmkvhHhiZYGQiyVnX3FnRTt5S4uibuWlf6071ufbfmNEunVrf8zQNM1HPM049XX/ws01VLzckzucfDcuU3z9tCGqdy7+Sk4FkH/vAyAR9Q5Ms7WxhSWdrRSceKfW8mm2SF8h/DS46/Sh+ve1gVRTHJdffc5hiH51M0NsVK2qa3XqarKYRqNT1ms1EPTUzDCKyHRgCkkBiJsErbRgioRDRSCACqED0T70w1xd3JoIKd2AsTntLmKVdsYqUVajsaij7eqmppcl1b/cPj3NwJM6KnraqKNpyzORW70a7nu57pd7nRpzB7WxV3V/NAtNhn8W4f+qdSne8Bn2paSVRqbIKlV1p6jHkNde9HRiNk0zpjJ255VJ25XAF1bM/vt7uez0FUtTaMFSMoAFoCbiCZuP+qadaRVCefUMT7B+eYFlXjG7/QM304s72XJrZX1zLkNd89yPX9Z5IpohlDVTMVhzlUHZpmR7rG2JoPEFPezTDfdgM/TdQf6HO9fSO1dJPosBPReQ+Ebks1wYicpmIbBGRLQcP5G5YRMMujfPC9haWdrVyVE8bxyzpYOUil610UUcLna0RWiPhWRuFK299mL6h8YwXbePWvpKPVQ42bu3jvTc/yG939pNIKRPxFDsPjjI4NsnoZKLgizuXc7l8/RriSWV0MoGqzlhWI7GzfzRjtDpUp6ZW6H7kut6RkJucJki24ggqOxH3nR4YWKpMy7tjLOqI0j8aZ8/AGL1dsabqH6jVfc9HPb1jtTQMz1fV04HzgbeLyPrsDVT1elVdp6rrFi9eQkerMwC93bEMA5Ae09AVi87aAORjri9aufnETx7h0GgcTUE05AbGpYAnD8384pZyLhu39nHJ9fdw1jV3csn19wBw9QUn0dsVY2As3lRKYmVP+2HzX1ejplbofmxY23vY9X77hmNpiYQLKo65KrtsmZZ0xljR08bxy7q58bLnNMX9TlOr+56PXPe8Vu9YzVxJqvqU/+4TkVuAM4G8vcstkRDLumP5VleMevL7ATx+YJSQTE9YEg0LIUkhIjN2UBV7LnndERecVJGO5lpTq5DXme5HLv/8M1csLNiZO1d3RL097+Uk22333DWLuPn+J+sq1Lle+mRqYhhEpAMIqeqQ//0XwNW1kGUm6snvN1eKPZd6871WmlqNZp3NszWT4pirkWum5z1IrsrOzfc/ORX+W69RU7WiVi2GZcAt3uUTAb6tqrfVSJaC1NsAqjVLOnisbxgJzGSVUjh+6cxpNYo9l2auNeajFjW1SjxbczVy9fa8l4t8lZ1fbzvYlK3guVITw6Cq24C5T1lWBapRmywlUuj9563l729+kKHxBIlkikgoRE97tKhJVIo9l2atNdYblXq25mLkmjUX0Hys7MyFhhn5vG7dOt2yZUutxSg7pYx2DIYRTiZStISF45d1lz21RldrhH3DEyxoi9qIVaMpqPRg0HpmNgPc6ndY7zyh2Eih7DDCpV2ttLdGy2YUgmGTk8kUgot6qnV0hGGUg3oKBW0EmnaAW6NQbBO3kh3CuY4N0NPRym1/19y1qSDlHMhYb4Mi5zvN6iKrFGYYakyx/vxK+kjneuxmUIKFRgwDRZ/fxq19XHPbVv7YN0w0LCzraq15qoVGINtNGg0LJ5TJTZqmXkJBGwFzJdWYYpu4lRyMM5dj19vI8NmSz6V3zW1biz6/9LV4fP8IYQFNwVMD4ySSWtNBkfVO+rptPzDMwGicsXiSwfEEj+8fbshnqRkww1Bjih3tWEkf6VyOXY6R4dmjrMuhCEo9Zr4Rw9v2jxBPJtkzMM7WPUM8vn+EXf2jvOum3x52zPS1SPqAjngqRTyp7OwfJZFMWQRMHtLXbXAsQSgkREIhQghD4wkzqDXCXEl1QDFN3Er6SOdy7HK4ocqd4XI2x8zn0kupsn9oEgSSKafwFTcHefYx09ciLMJ4wnXggxtn8uShcY4rYqzJfCR93SaTqampekVg0s92WMyz1AzuzHrCDEMDUUkf6WyPPdcxD5XoVJ/NMfMN7IqEhERKp4wCgrMMkJHXCKavRbrFkA4ETxuIWk/sVI9s3NrH4FicPQPjpFRRVaLhMKrQEg4V9SzVW/rsZsBcScacmKuLqxIZLos9ZtDddN2mbVx0+lGHufTaW9xxUuoUfXDYT/YxL1+/hsGxOPFk5tggBXraIwxPJGZ9Ts1IWqF3tIYRXCshkYKJRJIUSlcsUtSzVG+JLpsBazEYc2KuLq5KjLIu5pj5cudk9++csKybx/cP0zc0QUpxCQyBVt+qCB5zw9peFne0cGBkcmrbsAghEYYmkhzX2z3rc2pk8rl50gp9QVuM1kiYfUMTjMeTIMKCWIRjlnQW9SzZqObyY4bBmDNzcXFVIjdPMccs1t2UPtay7tapvgYgb212eDLJyp42dg9MIOJqwamUkkhS9Dk1k7+8kJsnqNC7YlG6YtGSpw4FS+FSCcyVZNSUSuSgL+aYxbqb0sdavbiTBe0uRUi6NptLzpU97UTCIY5cGCMSEpIpJSTCCb2dRZ1Ts4T/pink5ilXCLaNai4/1mIwak4lOtVnOmYptcxS5Eu3MKJh4ZglHVOtlWKSHELzpTwv5Ob5yIUnl6W1aKOay48ZBmNeUqn00nNVUs3mLy9kgMup0G1Uc3kxw2DMSyo9LmS2x2k2f/lMBtgUen1ihsGYt9SjUmq2iXLMzdOYmGEwjDqiGRVpPRpgozBmGAyjzjBFatQaC1c1DMMwMjDDYBiGYWRghsEwDMPIwAyDYRiGkYEZBsMwDCMDUdWZt6oDRGQfsCPP6iXA/iqK02jY9ZkZu0aFseszM/V6jY5W1aWl7NAwhqEQIrJFVdfVWo56xa7PzNg1Koxdn5lppmtkriTDMAwjAzMMhmEYRgbNYhiur7UAdY5dn5mxa1QYuz4z0zTXqCn6GAzDMIzy0SwtBsMwDKNMmGEwDMMwMmhYwyAiMRHZLCIPisjDIvLPtZapHhGRsIj8VkR+WGtZ6hER2S4ivxeRB0RkS63lqUdEZKGI3CwiW0XkERF5bq1lqhdE5Gn+2Ul/BkXkb2st11xp5LTbE8A5qjosIlHgbhH5iareU2vB6owrgEeA7loLUsecrar1ODCpXrgWuE1VLxKRFqAxp5OrAKr6KHAquEoY8CRwS02FKgMN22JQx7D/G/Uf60kPICIrgL8CvlJrWYzGRES6gfXAVwFUdVJVD9VWqrrlXODPqpovQ0PDOQvMkgAABUpJREFU0LCGAabcJA8AfcDPVPXeWstUZ3weeB+QqrUgdYwCPxWR+0TksloLU4esAfYB/+Fdkl8RkY5aC1WnvAa4sdZClIOGNgyqmlTVU4EVwJkicnKtZaoXROSlQJ+q3ldrWeqc56vq6cD5wNtFZH2tBaozIsDpwJdU9TRgBPhAbUWqP7yL7QLgv2otSzloaMOQxjdtNwLn1ViUeuL5wAUish24CThHRL5VW5HqD1V9yn/34XzDZ9ZWorpjF7Ar0Bq/GWcojEzOB+5X1b21FqQcNKxhEJGlIrLQ/24DXgRsra1U9YOqflBVV6jqalwT905VfV2NxaorRKRDRLrSv4G/AB6qrVT1haruAXaKyNP8onOBP9RQpHrlEprEjQSNHZV0BPB1HwkQAr6rqhaSaZTCMuAWEQH3LnxbVW+rrUh1yTuB//Tukm3AG2ssT10hIu3Ai4HLay1LubCUGIZhGEYGDetKMgzDMCqDGQbDMAwjAzMMhmEYRgZmGAzDMIwMzDAYhmEYGZhhMOoeERnO+v8GEflCBcr5cXpsTLUQkTf57K6/E5GHROTCapZvGLlo5HEMhlFWVPUl1SzPJzn8EHC6qg6ISCewdI7HDKtqsiwCGvMWazEYDY2IvExE7vUJ3v6fiCzzy68SkW+KyJ0i8piIvNUv3yAim0TkFhH5g4j8u4iE/LrtIrJERFb7eQe+7Of6+KkfXY+IHCsit/mke78QkbV++cW+xv+giGzyy07yc4Y84FsEx2eJ3wsMAcMAqjqsqo/7fY/z5/OgiNzvyxUR+ZQv5/ci8urAOf1cRL4N/N4nl/yUiPzGl9s0A6+MKqGq9rFPXX+AJPBA4PME8AW/rofpgZpvAT7jf18FPAi0AUuAncCRwAZgHJc1NAz8DLjI77Pdb7saSACn+uXfBV7nf98BHO9/PxuXagTg98BR/vdC//2vwGv97xagLeu8wsDt/nz+A3hZYN29wCv87xhuDoRXennDuFHbT+AyAGzAJbc7xm9/GfBh/7sV2JJeZx/7FPMxV5LRCIypy6ILuD4GYJ3/uwL4jogcgVO+jwf2+4GqjgFjIvJzXIK8Q8BmVd3mj3UjcBYuOVyQx1X1Af/7PmC1d/U8D/gvn0YDnOIF+CVwg4h8F/i+X/Zr4EPeZfR9VX0sWICqJkXkPOAMXA6iz4nIs4DP4IzMLX67cS/rWcCN6lxFe0XkLr/voD+n9Ln/BfBMEbnI/18AHJ91bQwjL+ZKMhqdf8W1Hp6By1UTC6zLzveiMywPMhH4ncT1x4WAQ6p6auBzIoCqvg34MLASeEBEFqvqt3GpmMeA20XknOxC1LFZVT+OS3b4SkCyt/PkWw6uxRDc7p0BGY9R1Z8W2NcwMjDDYDQ6C3DTKQJcmrXuQnFzgy/GuVt+45efKSLH+L6FVwN3F1OQqg4Cj4vIxQDe53+K/32sqt6rqlcC+4GVIrIG2Kaq/wLcCjwzeDwROVJEgimsTwV2+HJ2icjL/XatPlHbJuDVvg9hKW5mtc05RL0d+GtxU94iIifY5DpGKZhhMBqdq3CunV/gFHKQzcCPgHuAj6ifewHn4vkELsX245Q2R+9rgTeLyIPAw0A6vPRTvkP4IZwCfxBndB4SN8vgWuAbWceKAp8Wka1+m1fj5ugGeD3wLhH5HfArYLmX83f+2HcC71OXFjubr+BSY9/v5bkOi0A0SsCyqxpNiYhcBQyr6qezlm8A3quqL62FXIbRCFiLwTAMw8jAWgyGYRhGBtZiMAzDMDIww2AYhmFkYIbBMAzDyMAMg2EYhpGBGQbDMAwjg/8PJKGTflb34VMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''we will use the correlation function we've created to find the correlation between suicide rate for 2015 & each indicator\n",
    "of happiness report for 2016 (7 factors/indicators)'''\n",
    "#correlation between suicide rate & Happiness Score 2015\n",
    "correlation(suiciderate_happiness_report_2016['Happiness Score'], y2016,'Happiness Score 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e7995190>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2016['Happiness Score'], y2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.34395868691788917 with a P-value of P = 6.154306342692557e-05\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Economy (GDP per Capita) 2016\n",
    "correlation(suiciderate_happiness_report_2016['Economy (GDP per Capita)'], y2016,'Economy (GDP per Capita) 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e79d7a30>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2016['Economy (GDP per Capita)'], y2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.39852179778191205 with a P-value of P = 2.6530309008460027e-06\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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+ARehFy8g55dwjfLdIvIRb9nbcQ2bfHwe19h8QPaOm/Jvfz5wEq7+/LIn/4BvfSZqDVyPxHhmhar+AHfd78Nd+8lM+fIhpVnmhmHsb3g9AEep6jtm2K4VZ0W/2utxaCpE5EKcwz/n4HERWYULQigq20AZ5+/F9WC8JIe/ui6xwYuGYeTF64K7iOmWSU48Z/IxgQu1n6Kq/bguuYahaXKRGYZRWUTkb3Fdy7eq6upay2M0HtZFZhiGYQSCWTCGYRhGIDSMD6a7u1uXLFlSazEMwzAaigcffHCHqvbMvGXlaRgFs2TJEtasWVNrMQzDMBoKEck5ZqYaWBeZYRiGEQimYAzDMIxAMAVjGIZhBIIpGMMwDCMQTMEYhmEYgdAwUWSGYRiNwKp1/Vy9uo9Ng2Msmt/OJWcsZcXynNNWNT1mwRiGYVSIVev6ufyWtfQPTzCvLUr/8ASX37KWVev6ay1aTTAFYxiGUSGuXt1HNCy0t0QQcd/RsHD16r6Zd25CTMEYhmFUiE2DY7RFw9OWtUXDbB4cq5FEtcUUjGEYRoVYNL+d8URq2rLxRIpD57fn2aO5MQVjGIZRIS45YymJlDIWT6LqvhMp5ZIzsmc+3j8wBWMYhlEhVizv5YpzjqW3K8bQeILerhhXnHPsfhtFZmHKhmEYFWTF8t79VqFkYxaMYRiGEQimYAzDMIxAMAVjGIZhBIIpGMMwDCMQAlUwIhITkftF5FERWSsi/+otXyAivxeRp73v+UHKYRiGYVSfoC2YSeBMVT0OOB54nYicBnwcuF1VjwRu9/4bhmEYTUSgCkYdI97fqPdR4FzgR97yHwFvDFIOwzAMo/oE7oMRkbCIPAL0A79X1fuAA1R1G4D3nTNoXEQuFpE1IrJmYGAgaFENwzCMChK4glHVlKoeDxwKnCIiLyxh35WqepKqntTT0xOckIZhGEbFqVoUmaruBlYBrwO2i8hBAN73/jlZgmEYRhMTdBRZj4jM8363Aa8B1gG3AO/2Nns3cHOQchiGYRjVJ+hcZAcBPxKRME6Z/VxVfy0i9wA/F5GLgI3AWwKWwzAMw6gygSoYVX0MeEmO5TuBVwd5bsMwDKO22Eh+wzAMIxBMwRiGYRiBYArGMAzDCARTMIZhGEYgmIIxDMMwAsEUjGEYhhEIpmAMwzCMQDAFYxiGYQSCKRjDMAwjEEzBGIZhGIFgCsYwDMMIBFMwhmEYRiCYgjEMwzACwRSMYRiGEQimYAzDMIxACHrCMcMwjKZm1bp+rl7dx6bBMRbNb+eSM5ayYnlvrcWqC8yCMQzDKJNV6/q5/Ja19A9PMK8tSv/wBJffspZV6/prLVpdYArGMAyjTK5e3Uc0LLS3RBBx39GwcPXqvlqLVheYgjEMwyiTTYNjtEXD05a1RcNsHhyrkUT1hSkYwzCMMlk0v53xRGrasvFEikPnt9dIovrCFIxhGEaZXHLGUhIpZSyeRNV9J1LKJWcsrbVodYEpGMMwjDJZsbyXK845lt6uGEPjCXq7YlxxzrEWReZhYcqGYRizYMXyXlMoeQjUghGRRSLyBxF5SkTWishl3vLPisgWEXnE+5wdpByGYRhG9QnagkkCH1bVh0SkC3hQRH7vrfuKqv5HwOc3DMMwakSgCkZVtwHbvN/DIvIUcEiQ5zQMwzDqg6o5+UVkCfAS4D5v0QdE5DER+YGIzM+zz8UiskZE1gwMDFRJUsMwDKMSVEXBiEgncCPwj6q6B/g2sAw4Hmfh/Geu/VR1paqepKon9fT0VENUwzAMo0IErmBEJIpTLj9V1V8AqOp2VU2pahr4LnBK0HIYhmEY1SXoKDIBvg88par/5Vt+kG+zNwFPBCmHYRiGUX2CjiJ7OfBO4HERecRb9i/ABSJyPKDABuCSgOUwDMMwqkzQUWR3AZJj1W+CPK9hGIZReyxVjGEYhhEIpmAMwzCMQDAFYxiGYQSCKRjDMAwjEEzBGIZhGIFg6foNw5hi1bp+rl7dx6bBMRbNb+eSM5ZaKnqjbEzBGIYBOOVy+S1riYaFeW1R+ocnuPyWtVwBDadkTFHWB6ZgDMMA4OrVfUTDQnuLqxbaWyKMxZNcvbqvoSrnSilKU1KzZ0YfjIh8QES6vd9HiMhqEdktIveJyIuCF9EwjGqwaXCMtmh42rK2aJjNg2M1kqg8/IpSxH1Hw8LVq/uKPkZGSfUPT0xTUqvW9QcoefNRjJP/71R1h/f7KtxEYfOAjwHfCUwywzCqyqL57YwnUtOWjSdSHDq/vUYSlUclFGUllJRRnILxd6P1quovAVR1FdAVhFCGYVSfS85YSiKljMWTqLrvREq55IyltRatJCqhKJvFmqs1xSiYG0TkhyKyFPiliPyjiCwWkfcAGwOWzzCMKrFieS9XnHMsvV0xhsYT9HbFuOKcYxvO71AJRdks1lytEVWdeSORC4G/w00S1gpsAm4CrlTVoSAFzHDSSSfpmjVrqnEqwzAanIyDfvPgGIeW4aD3Bwq0RcOMJ1IkUtqQCldEHlTVk2py7mIUTD1gCsYwjGoyWyVVL9RSwcwqTFlEXquqv6+UMIZhGPXCiuW9DalQ6onZjoP5PrC4EoIYhmFUAhu/Uj/MqGBE5JZ8q4CFlRXHMAyjfJopG0EzUIwF8wrgHcBI1nIBTqm4RIZhGGXSLNkImoViFMy9wJiq3pm9QkT+XHmRDMMwymPT4Bjz2qLTllVj/Ip1y+VmxnEwqnqWqv4hz7ozKi+SYRhGedRi/IqllclPSfPBiMgCEZkflDCGYRizoRbZCCytTH6KSXa5WESuE5EB4D7gARHp95YtCVpAwzCMYqlFNgJLK5OfYnww1wNfBd6uqikAEQkDbwGuA07Lt6OILAKuAQ4E0sBKVb1KRBZ4x10CbAD+RlUHyy+GYRiGo9rjVxbNb6d/eGIqsAAsrUyGYrrIulX1+oxyAVDVlKpex8xhykngw6p6NE4R/YOIHAN8HLhdVY8Ebvf+G4ZhNBzNkiQ0CIpRMA+KyLdE5FQROdj7nCoi3wIeLrSjqm5T1Ye838PAU8AhwLnAj7zNfgS8sfwiGIZh1I5mSRIaBDPmIhORFuAinFI4BDf+ZRPwK+D7qjpZ1Imcv2Y18EJgozenTGbdoKruEzwgIhcDFwMsXrz4xOeee66YUxmGYRgeTZ/sUkQ6gTuBL6jqL0RkdzEKxo8luzQMwyidWiqYosKUReSvROQiETksa/l7i9g3CtwI/FRVf+Et3i4iB3nrDwIsYNwwDKPJKCZM+YvAJ4EXAXeIyAd9qz8ww76CS4j5lKr+l2/VLcC7vd/vBm4uRWjDMIxV6/q5YOW9nH7lHVyw8l4b2FiHFGPBvAE4U1X/ETgROEtEvuKtkxn2fTnwTuBMEXnE+5wNfBl4rYg8DbzW+28YhlEUNnq+MShmHExEVZMAqrpbRN4ArBSR/wFaCu2oqneRXwm9uiRJDcMwPCypZWNQjIJZLyKvzCS79MbDXCQinwfeHKh0hmEYOahVUstCWMLLfSlGwbwl10JV/ZSIfDvzX0SOVdW1FZPMMAwjD7UaPZ9PiWS67BKpFENjCbYNjfPQxkH+YcUyLn3NUYHKVM8Uk015XFXH86zb4vv744pJZRiGUYBajJ4v5Pe5enUfiVSKnSMJUgrRcIi0Kt9ctX6/9guVlE15BmZy+BuGYVSEWoyeL5Q1edPgGENjCUQgJIIghEVIpXW/zqpcTBdZsQQ/YtMwDMOj2kktC/l9Fs1vZ9vQONHw3ja7KrRGQvt1VuVKWjCGYRhNS77JzDpawuwei5NKw2QiTTKVIp1W0ihdsch+nVW5kgomXsFjGYZhVJ1Cgzdz+X2GxhPsHI0TT6Xp7WxBgUQaQFnY0UJLJLxfZ1UuWsGI4x0icrn3f7GInJJZr6p554UxDMOod2YavJnL79PT2cqctijtLREOmNvGkoXtxCIhEOHw7s79PqtyKT6Yb+EmDTsTuAIYxuUYOzkAuQzDMKpKMYM3s/0+p195xzS/TFcsSmdrhKHxBNdebG3uUhTMqap6gog8DKCqg14qf8MwjIannMGb1R6P02iDOUvxwSS8qZIVQER6cBaNYRhGw5PPiV9IWVRzPE4j5l8rRcF8Dfgl0CsiXwDuAr4UiFSGYRhVphxlUc3xOIXG4dQrRXeRqepPReRBXJJKAd6oqk8FJplhGEYVWbG8lytwFfnmwTEOLbILqlrjceox/9pMFK1gROTHqvpOYF2OZYZh0Hh95MZ0qj14sxRqlX9tNpTSRXas/4/njzmxsuIYQWITNAVLI/aRG41DLfKvzZZiZrT8hIgMAy8WkT0iMuz978dmomwYrPILnkbsIzdmRzUbbbXIvzZbZuwiU9UvAV8SkS+p6ieqIJMRADZBU/A0Yh+5UT6ZRls0LNMabVdAYO9UPXfh5aIUJ/8nRGQ+cCQQ8y1fHYRgRmWxyi94GrGP3CifajXaGtmvV0qqmPcBq4HfAv/qfX82GLGMSlNOjL9RGo3YR26Uz6bBMdqi4WnLKt1oa/Su7VKc/Jfh0sI8p6qvAl4CDAQilVFxrPILnkbsIy8XCxgpvdFWzjVrdL9eKaliJlR1QkQQkVZVXSciLwhMMqOilBvjb5RGo/WRl0MtfA/1yCVnLOXyW9YyFk/SFg0znkjlbbSVe80avWu7FAWzWUTmATcBvxeRQWBrMGIZQbA/VH5G8FjAiKOURlu516zR/XqlOPnf5P38rIj8AZgL3BqIVIZh1C2N3qquJMU22sq9ZqVYSfVIWROOqeqdwATwm0LbicgPRKRfRJ7wLfusiGwRkUe8z9nlyGAYRm2wgJHSKfeaNbpfb0YLRkTOBL4DHIzrHvsicA0uH9kXZtj9h8A3vO39fEVV/6NUYQ3DqD2N3qquBZlrtmNkgqGxBJOpNJFQiHOPO3jGfRu5a7sYC+Y/gYuBhcANwL3Aj1X1RFX9RaEdvTEyu2YtpWEYdUMprWqLNnOsWN7LeSccwq7RBPGUEouEmd8e5YaHtjT1NSnGB6Oqusr7fZOIDKjqVbM87wdE5F3AGuDDqjqYayMRuRin3Fi8ePEsT2kYRqUoplXd7NFmpQ6AvKdvF4fOb5vmsG/24IhiLJh5IvLXmQ8gWf9L5dvAMuB4YBvOQsqJqq5U1ZNU9aSenp4yTmUYRq1o9DEchShnAGQ1BmbWG8VYMHcCb8jzX4GC3WTZqOr2zG8R+S7w61L2NwyjMWjmaLNywo4bPeS4HIpJdvmeSp5QRA5S1W3e3zcBTxTa3jCMxqSZ56svR3lWOzjijqe2O0XYs+RFgZygCErJRXaAiHxfRG71/h8jIhfNsM+1wD3AC0Rks7f9v4nI4yLyGPAq4J9mIb9hGHVKM89XX07YcdAhx+m0u8Y7Rya5cc0mPnnTEzw/NA6aTlbkBGVQykj+HwL/DXzS+/8X4Hrg+/l2UNULcizOu71hGPVPsZZCNdMTldNlNRuLp1xrJBMckTn3p25+gkWry7suqspEIs14IsV4IkU8mUZVAfjxvRuJhIS2llKq+MpTytm7VfXnIvIJAFVNikhqpp0Mw2geSo0Mq9f56isR4dYeDfHsTnf8pd0dfPr1y4vat9xzqyqTyTTjcadQJn0KJZtte8aZE6utcoHSRvKPishCnGMfETkNGApEKsMw6pJ6jQwrtctqNuXIKIhEWjmyt5ND57cxGi++rV3suZ2FkmL3WJxtQ+Ns2DnG1t3jDI7FmUik8ioXgIPmtDGRSBctU1CUomA+BNwCLBORu3Gj8z8YiFSGYdQl9RpqW6q/ZzblmK2S9Z97eCJB38AIz+0c5aGNg/zuiecZGkvw/NAEz3kKZddonPF4YYWSzfknLyKZVsbjNXO/ACUoGFV9CHgl8DLgEuBYVX0sKMEMw6g/6jUPWakO9NmUYzbKadW6fvaMJ1j3/DBPbx9m484xEqk0AijKZ361llsf38ZYPEm6BIWSzSlLF3DZmUfS3RkDCdWsr6yYXGT5BlMeJSLMlC7GMIzmoZ7zkJXi75lNOcoNv161rp9P3/wEbS0hxiaTTCSdAkmlFAlBT0cr4ZBw3QObOGXpgqLKUYhTli7gzKN7+cU/bHh81gcrk2I0W2ZQZS/OernD+/8qYBUlDrQ0DKNxaZaJ66B3pawAACAASURBVGZTjlKUUzK1N8rrqtufBmBurIVIKMSW3RMApIGDumJ0tkZQlOf3jJdUlvv7dnHdA5vYtmecg+a0cf7JiyqioCpB0QMtReTXwDGZQZIichDwzWDFMwyj3mjk7L5+Si2HP6y5syWMiDA0npimnNJpZSKZYjyeYiyeIpHa62jfOjQ9sst1i7mPiFs2kUhz4Jy2omW6v28XV93xNJGQMCcWYefoJFfd8TSXcWRdKJlS+uaW+EbgA2wHjqqwPIZhGHVHdmixs1rSfO7cF3LasoWMx1Ns3T1eMHT4oDlt7BydJK1K/55JQgIpb9PtQxPM74gSCYc5/+RFRct13QOb3HgXzyfUFg0zGk/y/bueZdueCTbtqm3wRSkKZpWI/Ba4Fqd0zwf+EIhUhtFEVDOFSTNQj9crEznWFg2jCi3hEMlUkqtuf5rFC4sLcDj/5EVcdcfT7ByZBJSQCIoSFkgrjMXTfPr1y6csj5m6vtKqbNo9RktY2DGSJJ5KM5lIk0g7rZXpkqslpUyZ/AHP4f8Kb9FKVf1lMGIZRnPQ7CnrK029Xa9UWplMptiwc5Su1gjx5N4ur5ZIiG1DxftLTlm6gMs4kk/f8gQKRMNCT3vrlO9leCI5Tbn4u74GRib499/9mVcc2U1KlfX9ozy7Y3SfSDg/0bBwVG8Xz5Vd+tlTUviaFzFmTn3DKJJyUpjsz9T6eiVSaSYSKSYS7jvjQzmgK8bO0clp4cml+kvAKZljD5qb91hpVZ4fmuA7q9czOulClf1WyU2PbM153GhYiIaE8YQLee6d00I0HGb3eIJQa+ecEi9DxSgmTPkuVT1dRIbxRvFnVuEmI6uZ8EZp1GPXQ7PTzCnrg6Da18sf5TURT5NM5x79nuneGk+kiEVDTCTSJNNakr8k+1ij8SQiMDrp0r6ERDjnG3czViArQEjglMMXsKynk2U9HSzt7mTr7nF+vmYza7cNOauoq5UOT0FPJlOEOuYdWLKQFaKYKLLTve+u4MUxgqLeuh72F/bHOUBmQ6WvV3aj6qKXL+GUZQuZzLJQZiLTvXXdA5t4fs84B5YQDqyqPL9ngvX9o6wfGKFvxyjxZJqdo/Fp2z3nc8gLEPF8Pq2REK2RMGlVertifPFN07PvL17YzmnLFnLBd+9lTiyCIFPrYtEwEo60FlXIACi6i8zLPbZWVYe9/5240fz3BSWcUTlq3fWwv1LPAxPrkVzXa2g8QUs4xOlX3jFleQMzWuN3PLmdy3+1lkhI6GgJs2X3GJ++ZS2XnVleCO8pSxfMuN94PEXfjhH6BkZZPzBKn6dQClklPZ2tLOvtYFlPJ0u73ffW3eN8/Q/PEAnJlMWkSk6LKRMMsGs0zuBYnO4O59cBXM6yVHKy5MJWiFJ8MN8GTvD9H8uxzKhTrKumNjTLwMRqkX29OlrCCBBPpacs74/c8CgCzGmLTrPGL0+nOXXpQiaTzjr52h3PILiIL1WIRcKopioyUt5vlfTtGPGUyShbd4+TL8FLSyTE4Qs7WNrT4bq3PIUyJ+u9BGeVhERmtJj8wQA9nS1s3zPJ9j0TMKeVcChEWiE9uvv5WRV2FpSiYER9Ad6qmhaR2ueDNorCumpqR7MMTKwW/ut1wcp7SaR1muW9Zfc4mlZ658RIqRINh0ikknzjjvUs7emcOk6ulPWxaKjkkfLj8RTP7vC6twb2dnPNZJVkFMmynk6W9nRw6Px2wiHJu082xVhM08fBhJG5wsDwJAMjcY49aC4Xvuww3vSJkT1Fn7TClKIg+kTkUpzVAvD3QG1zdBtFY101RiOSsbxVlbQ6yyGZct1FCV/IcGtkX8WRGdhYbORXxirJKJFirJJoWDi8e68SWVbAKgmCbCXa0RKhfWGY4Ykk//XW46a6ympFKWd/P/A14FO4aLLbgYuDEMqoPNZVYzQKmYm1JpNpDuiKMTAyQSyyV0mEQkJ2jZ9LcRSK/BpPpHh2oPpWSaUpVYlWm1IGWvbjRu8bDYp11TQvjRyCHk+mmUy6sSeTSWdZZ3rjX3zIHH5y/25SaaUlLHTFIlMhuDOFDJ+ydAGX6hFcc+9zbBsaJxaN0N3Zwtf/8MyMVsmShU6JLOvtYGm385fMrZJVUgqVDJ8OgmLGwfyzqv6biHydfdoNoKqXBiKZYRhF0Ugh6MlUeiqlyaSnWFLp3FX9/X27uO3J7cyNRRiNuznnd48neccpi3nBgXP2cYC/aNFcntq2x9e95ayTqdkmx5M8v2di2jm6O1umdW8tqwOrpBQy4dPXr3HX4uC57Vz4ssN45fJeIiGhJVzKnJKVpxgL5inve02QghiGUR65QtB3jExw6XUPM6ctyqL57bx06QLu6dtVVQvHTfnrlMhk0imVfAMZc5FxYHd1tLKgwy0bT6R4aONuXnvsgbz5xEOmure+seoZtgw2h1WSi0goRCQs7uP9joZChEPC4ad0cP6pi2stYk6KGWj5K+/7R8GLYxhGqWSHoA9PJNgxHEeBxQvaeXbHCPdv2EVvVwsLO1oDs3CSqTQTyTSTiRQTyTTxApmFi2HbnnE6W11ASsYnM5lMsXlwnLd9L//wO79VsrTbKZRFdWyVhENCOCREw05hRLz/U0olJIjUp+wzUcpAyz+Qu4vszAL7/AD4v0C/qr7QW7YAuB5YAmwA/kZVB0uS2jCMKbJD0AeGJ0GgNRxCRBieSBIS2DOepLszVpFBtolUmoTX1ZXp8irFOslGVdm+Z3LK2b5+YITdYwm278k/RjBjlfi7t+rRKgnJXgWSsTwy1kg0FHJBC01KKVFkH/H9jgFvBpIz7PND4BvANb5lHwduV9Uvi8jHvf8fK0GOwGlkh6mx/5Edgj6RTBESobvTZQiJp9KExH1nKGWQrd8ymfQsk9nMF3/XX3bw43uf4/nhCVrDITpjEQZGJhmdzB/BlSEk8Iojunn3y5dUzCqZ7YyQIfGURThEJCREwiGiYU+pNLkCmYlSosgezFp0t4jcOcM+q0VkSdbic4EV3u8f4aZdrhsF00gOU6NxCLLRsu/o9wjtLeGpsRgt4RDxVHqawzffINt0Wol7GYUnk2nu/HM/P7uvvMpXVdk+PEmf53RfPzDCk1v2sMOXg2sYpv3Ptkr6+kf433XbSStTUWR/6R+hf2iSJQs7yrtgPoqZETK7Cyua5Q+p1663eqCULjL/UxUCTgLKydJ5QGZmTFXdJiJ53zIRuRhvrE3rvF4uWHlv4NaE5ewyKk2uRstHb3iUhR0tjMRTFVE4/hD0zPkyFk1XLMLASJw5bRFUdWqQ7d+efjjjXnTWZCrl0sL7rJxSpuOdSGRGu+8dW9K3Y6SgVRIJCS0RV0EvaG/hM+ccw6Hz2oj4FOGHrn+Ug+a2TRvnMZ6oTLoXcIEEbiKxCCLQ2RpiPJHiFw9v4a9PPJRouDr+j2btNSmli+xB9vpgkjj/yUWVFsiPqq4EVgLMW7xcq2FNWM4uo9JkN1pSaWVwLMHwZJIjejorbiVnWzSHd3fy1pPmc0/fLjYPjnHg3Bjnn7yIJT0dBSfMyjUd71g8yTX3PEcinZ7ylfQNjM4YwXXYgg6W9XZw9zM7mNsWJRYJT7X8M5Nt5bJIKpHuRcQ5yrN9IOGQMDAywfz2lmlKpDMkbBsapyVSnRDfZu41KWYczMnAJlU93Pv/bpz/ZQPwZBnn3C4iB3nWy0FAf7E7VsOasJxdRqXJbrQMDHvzsacVEanoc53p4nrJYfP52gUvIZ7aG8117ksOKelYW4fGaI2EGBpPTEVwTSbTbN49wadvXptzn4UdLVPO9sz3ovl7rZIPXf8oO0cnp3Ur+UeeZ/tDOloiTCTSM45Uz6VEohHXhVXIClm8oCOQ970Ui6SZe02KsWCuBl4DICJnAF8CPggcj7MuzivxnLcA7wa+7H3fXMrOQVsT9Zqzq1lN6P2B7EZLPJWeyvKboZznOjNoMe6F8MaT6aLnN/GjqvQPT/rSprhBiv3D8bz7RMPCYQv3ZgVe1u38JvPaWwqeq9DI81xdciOTe+OIYtEQk8k0KVXe+/IlLOxspcVzqEfKHFAYxPteqkXSzL0mxSiYsKru8n6/FVipqjcCN4rII4V2FJFrcQ79bhHZDHwGp1h+LiIXARuBt5QicNDWRD3m7GpmE3p/ILsSC4uQTOtUlBcUfq5TaSXhUyaJVJpEUssKC55IuPnlXZr5vV1c/oo8m5C4iavCXrjthS9bwtkvOrCsSr3QxF0fuv7Rffwh4ZDQEhbmd7Sydfd4xd/HIN73fBbJlbety9lIbOZeE5lpIJSIPAEcr6pJEVkHXKyqqzPrMuNbgmbe4uV64mXfIZFSrjjn2P2qYr1g5b37PIBj8SS9XTGuvfi0GkpmFMuqdf18+daneHbnGOm0klYlk7MxEgrRFYvwpb9+ES87optE0nVzJdNOoWSnUsl0I23YOUIipbREQhy2oGNahJeqMjA8Oc3pvn5ghC27x8mTmWXKKnGTXrkort1jCX792LaSZ3EsRMTrvoqGQ9Miss78j1X7+ENUlaHxBH/8WN7hdnXH6Vfewby26LRy7BmPs3n3BEsWtk+zlK4451iAqQakf915JxxSkewLIvKgqp5UsQKWQDEWzLXAnSKyAxgH/gggIkcAQwHKNo1U2k0XWmtrohY0swm9PzGWSHPIvBjJVJrNgxMk1E2NGw4pybSyYzjO80MTBY+R6UZKJFNTVsdEPMUGHeZLtz3FsQfNZSSenNEqWdjRss8ARb+vxM+ZR5dVqdEScd1XreHw1DiRWvhDqk0ui2T7nsm8fpZrLz5tHyvqpUsXcMNDWxq+16KYVDFfEJHbgYOA3/kmHQvhfDFV4QUHdu23rfVmNqGbEVUlkXLdWsmUkkin+dodTwNKJBRm29AE4bAQVjfv+qL57UWF3qqqi+Dykj5m3C0K7B534cB/6ts5bZ9ISDhsYfuUIsmkUJnJV1IsGed6ayRENBzylEqorAisevV/lkrOcqTTHDpvemCCv5GYnen8gpX3NoXjv6gwZVW9N8eyv1RenP2HUpz2zfLiNRMZv0gyrSQyfpG0mwwrV3bgLbv3htsmUump0d0Zp3x26O1kIsWGnWPTMwPvGGV4Ir9VEg4JYYE3veQQlvV2sqyAVVIqIkI0LJ5Tfa8iqeQ4kXr0fxYi3zucqxzRkJDIei4KNRKbpdfCpjyuAaU67RvtxWsWVJ0vJJHylEja/c6nRArhnxgqGg6RTCuoszASqTTDk0mioRCf+/WTrB8YZfPgWF5fCbiuNfAm30KJhoSerhgLO1q55JXLyi4zOEXVEgnRGgnTEgnRUsAiqXR0Y6PMWTTTO5xdjuzBrzM1Epul12JGJ3+9cNJJJ+maNc0xY4A57euHjBJJppRkaq9zvdworXzc37eLr97+F1RhIpliz3gSBUSg0Cs4vz26t3urt5OxyRTXPbCRZCrN7vGEVwiY3xElEg5z2Zn7jrLPRyb9ScYiKTXk11/JZjuuG0FJFEsuJXr16r6S3+HMcYppJFby2ta7k9+oMM1i/jYKqs6JnlEgmUzAlVYi/vPtGIlPi95aPzDK9j2T+4x2zyiXsAhLutunBihmfCXzc/hKDpwTY+Xq9eweS5BUiISgqzXKxWcsy6lcMlFbLeEQUU+R3P30Dr5317OzsjyaeYBghnyWylg8yYFzYtO2nekdLsU6a5ZeC1MwNaBZzN96w684MookmQpGiWSYTKR4btcY6/s9X8kO913IVxIJCYd3d3DC4nlTCmXxgvaSfCXjyTQHzWubGqw4nkyD4CyRSIjWcJjWqFMm2dl8V63r519//eSsI5T2h4ZSPiUaT6YZT6QCfYcbpbuwEKZgaoA57csjY4lkHOwZf0jCc7TPJoV8MefOtkr6BkbZVMBXkongmhOL8szACLGIS02fTCkjk0lesmh+WWNKrluzaarSE4GucJiJRJKbH9nK+afMPLNhpSyP/aGhlE+JtoSFREpn9Q7vD9k5TMHUgGYxf4MgmYnMyigQz7GeCtgS8ZPLKukbGGVPAavE7yvJWCWLFrQTDYf40PWPMq8tOpVPKxoqLiPwVOSWZ5FkBifuGJncZyBfe0ukaMuhUpZHJRpK9V7J5lOiRx4wZ8oXU847XOnsHPV6HU3B1IhmMH+zKfYh948PSST3KpRkSgO1QrIp1ypZ7I0ryYx4X9rTyYKO/ONKis0IHA2HaI266K3WSIjWSChnCPBMlsNM96FSlkepDaVsuRphMOElZyzlIzc8ypbd46TSSjgkdLZG+PTrj5nVO1xJ/1U9p5IyBWNUhOyHfPuecT518xN8YnI5py1buHfgYVpnNU97uUxZJf75SgZGZrRKlmYUSe9eX0m0xHEl/hDlKXmSaQ6Z1053l0vYmE+Z5KKQ5VBMZVPJLtpiK9lccn1z1XoWdESZ2+ac5fUaJCAA6hokqFCJUT+V9F/Vc7CFKRijLFJp12WVCe/9xh+eQbyR6vFUmkgoRCKV4rt/fJajD55TNbkyVsmtj2/jt2u3s2vMZQSOJ9N55ysJh4TDFrTvkzqlkFWSi3xT777jtMV85X+fJp5K094SZiKRAoQPnnkEc2Klzx9fyHIoZgR4Lbpoc1WCqbQyNJagu3NvNFa9BQlcvbqPOW1RDpy7dxR+JSrvSvqv6jnYwhSMsQ/+sN5kOu051XWaUsnuyto0OMacWGSadVLqxFClEk+mXWbgcq0ST6EsXli6VZJNJkdYpoW+dWiMy295gq5YhKMOmMNbTzp0asKvSlTo+SyHYiubanfR5pKrNRJiIjl9xst6CxLIdz2f3r6HC1beW7bPo5JWZD0HW5iC2Q/xpznJOM+TaZ32v1RydQPlmhiqHDJWSd+OEdb3e8pkxyibdhUe7Z7penLdT9DbFePrb3vJrOXJEA2HiEVdOPAvH9lCe0uY9pYIe8YTDI65QZDj8RQbdo7w0MZBOlvDHOU5h/e3CfNyydUVi5AcU3aMTDA0lmDSs3zPPe7gGko6nVxy7xiZZGgiycMbB0mm0+wYnuSjNzzKv593XE3GudRzVKopmCYkM6th0suNlYnGchbJdB9Ivm6dUik0kVQp+K2SPl8erkJWyby26LTorZV/7KM1LOweTzKeSJJMh5jfHmXn6GTJ5coQEnHKJBKa+vaPL9mye3yqpbtjZJIQgoQgnlJ2jiRQlIlEOnAHbL1WNrnkaomEOfuFC/jNE9tJpZVYJExXLMIND23hxYfOq7n/IJ/cO0fjpNNKyMt6oAqDYwmuvG1dSTJXyoqs56hUUzANij/FSWYyqszvYvNk5ZpB8Ko7nuYyik83kqHQRFL55N856iK4/JNfFbJK/L4S/4j3bF/Jz9ds5rmdo4RCQijkJvfavmeSw3LM+Z6PSChELBoi1hIm5uXkKoS/pRtPpQmLoAppdZFHIcTzwQTrgK3XyiafXFev7uPQ+W37pFypBwc15JZ7484xd0+9oAwRUFH6dozWVM56uF7ZmIKpYzLKYu/3XkuknKlxs7nugU1EQjLVrZVpoc00PiMfpyxdkHO/eDLNcxmrxBvpvr5/Zqsk2+m+eEF7cWngMxZaRlFp1vIc+BVKJiFlKfhbui3hkDctsss0LAKadl12wxMJ+vdMsGHnGBesvDeQyr/cyibosRS55PrUzU/UrYM6Q7bcL/jUrTWJhGxETMHUiMycIVP+D29Uut+5XohKdG0VOz6jWPxWiX9u940zWCWLF7RP6+Ja1tPJ/PZo2WngRxMpDpjTyuBYgkQqTTQcYn57C2MJ51DOTIYVi4Ro9bq7Zuvk97d0h8biJNPKgo4oQ2OJqTTtHS0Rtu6eQFFikVBdjVeo1ViKSvqMqjXY8PCF7TwzMIqkdSpZaVrhiO7aO9XrDVMwAZI98VSl8mNVqmtrNo75cqySuVO+koxlUoJVUgKZci3KVFLixsH0dMU4eF5bSWNOSsHf0s1Udk+nhhmeSDK/PcqeceeLAejubK2r8Qq1GktRKZ9RNRXkx886mo/c8Cgjk8mpwZfzWqN8/KyjK3qeZsAUzCzJhO6m0koi6UanZ7qwKtGNlYtKdW0V45jPWCV9WXO7F22VeIMUl3Z3sKCjJZCKPZu3nbqIq25/ZmrcyWQyjSJ84FVHEPMp0yDJpWzu37CLWCREd2crc7xuoXrpDqrVWIoVy3s5b/NuvnfXs4zGU3S0hHnf6YfXdWbnFct7+Y/zjqs7P1c9YgpmBtLpvUojk97E70yvZmqTDJXq2sp2zPd2xVhxVA+D43G+vWr9VJr5ocy8IznIWCWZ9PJBWSWFyIQLr9mwix/9aQObvdkjVZXhiWTNK4CMssk1D1A9hBBD7cKbV63r54aHttDT1cpir6FUThTZbBRkOV1r9epUrzf2ewWTPSLdPyaknJkLq8Fsx5yoKrtG4/TtcN1bCztbGJpIsHbbHh7bMpRzn4xVcrhvgOKynupZJX5ao2FiXrhwLBomHBJWrevnS7eum+oiyXS1fO7cFxasCKqZJDCIEOJKyV+r8OYgMjvvGU+wY2SSyaSzYFet6y9qYq96y+PVDNRMwYjIBmAYSAHJoGZcy4wDSaTTpDLfniO9VhbIbCllzEk8mWbjrrF9EjruLsEqWdrdwWELO6pqlWQQkamxJ7FoiFgkvM/8JlBeRVXtyqXSIcR++cMCD28c5KJrHuDInk4+ftbRJY/JqEV4c6UzOw8MT7Bz1KUHEqCjNVzwntZzHq9moNYWzKtUdUe5O2e6r1LpfVOb5BpU2CzkGnPy1pMOZVlvBw9s2MX6/hFvXMkoG3eN5bXCQoLnK9nbvbW0p4OFNbBK9soke5WJF+FVjCzlVFS1qFwq2bWSkT+VVrYNTSLiZsbcsGusYKWaz+opRbZKWU6Vzux86XUPk1Y3aLOnq5WuWLTgPa3nPF7NQK0VTNGk0srA8ORUd1ajWh+VIJ5Ms6Czhde98ICpPFxX/vbPBa2SObHIVEbgTKr5WlklGTLznbRGwl6aepeqvhzKqagavXLJyP/sjlFEnHJW3LsSDUvOSrUSVttsjpEvZX+lMjvPaYuyeEH7tEZJoXtar6l1moVaKhgFficiClytqisLbZxMK8MT+SvQIKhUGpVyUVUGxxLeaPfqWyWVLn+51kkxlONDqHTlUolWfb5j5FqekT+eShP2ug1V3YDOfJVqJay2co+RSzHd8NAWzjvhkIolAi31ntZrap1moZYK5uWqulVEeoHfi8g6VV3t30BELgYuBjj40NJyWs2WSqZRKYZEKs3GnWNTkVuZPFzFWCUuM7BTJEsqZJVUovzZCiXIEOFyfAiVrFyCtAzO27w758Rc551wCDc8tIWwCOm0IkAapbszlrdSrYTVVswxcinEfIrpnr5dXHvxaUWfvxCl3tN6Ta3TLNRMwajqVu+7X0R+CZwCrM7aZiWwEuBFx59Q1f6wSqdR8bNrdN9ZFJ+bwSpZ5Fkl/oGKQfpKyil/NRVKLkr1b1SycgnSMvjeXc/S09Was2K+4pxjufK2dfylf4RoGA7uihHx5ovPValWwmorZkbNXIpyLJ7kwDmxaceqdJfkbGfZNOVSWWqiYESkAwip6rD3+/8AV9RClnxUYqxJIpWJ4HKj3Pu8NPOZVO65mBOLTKVMyXxXyiophWLKH2SXV7WolNM9SMtgNJ5icZayzhw7I3+motw8OEZvVyxvRVkJq22mY+RTlPFkmvFEKnB/x2xm2bQQ5cpSKwvmAOCXXmUUAX6mqrfVSJaclDrWJGOVZLq3irVKMt1by3o7WNrdSXdn7SK4/OSb5vfgeW0s6GhpWIUSFEFaBh0t4Rkr5mIr1UpYbTMdI5+ibPEsq3rxd1iIcvDURMGoah9wXC3OXSz5xpqcd+IhrO8fYf2O0SnHe9/ASENYJaWQKf9kMkVbS4TJZApVuPTMI5nXXtpUwpWg3rsygrQM3nf64RWLtILKWG2FjpFPUR7pTbZWL/6ORo8ibASkUcaJvOj4E/Tm36+eecMKcvuT27n2gU0MDE8QCbsQ2h0jkyQLjO6PRUK84MAuTl6yYMpX4rdKah2ZVggRIRZ1EUixaJh7ntnByj8+W/PKwN+V4a9grzjn2LpSMv5uqnKvV75jVOLY1aJR7leu1D1j8SS9XbGKBR3UAyLyYFAD2Wc8tymY6b4S/yyKhaySrliEZT0ddLREeGLrELFImM5YmHjSDfC87Mx9o638kVl+qyjXttUgo1BikTBtLfXb5bW/VATNRCMoxEZRhLOllgqmYQZaVorBsbjr4hrYO4vixp1jea2SkDiTf2lWmvmMVfKh6x9lTizqi7Yib7RVkJFpxVDPTvlCXWDN1pVR7919laAUR3utroWFKAdP0yqYjFWSnWa+GKtkabc34r23k8MWtNNaINy2lGizSk/wNRPhkKdQvJHy1Q4bLpaZonmaabR1vUUuVbqCzzVS/56+XTmPn7kW8WSK4Ykkzw9N8NDGQf5hxTIufc1RlSpiQSwrcrA0hYIZHNs7X0mme+u5GaySQ+e375NmvpwIrlKizWabBXkmwp511Bp10/7WcxCBn5mieep1tHU5lXM9RS5VWtllH2/DzhHu37CLns4Wujtb9zn+1av7iCdT7ByNE0KIhISUKt9ctb7kdP1GfdJQCibpH1dSglXiH+m+rKeTJQsLWyWlUEpm41K2LYYpCyUa5v6+nfzg7g110e1SasU7UxdYPXZllFs510t336p1/Vx63cOMxpNFJ4aciWzluWc8SUhgeCJJT1eM9pYIA8MTXHrdw7REQuwcjaPqsh5Hw0JIXFboRCptocJNQsMomOd2jnL21+4qySpZ2t1BT1droH6GXJmN80WGlbJtLjI+lLZomFjL3sSQq9b187n/91RddLuUU/EW0wVWb10Z5Voi9dDdl7lHY/EUkZCQTCtbd09w8DzobI2UreyylWc8lSYk7htgz3iCnaNx0mlldNLlTQOXlDCeStNCCBFoDYcaVwtq7QAACRdJREFU1r9mTKdhFMxkMj2lXDpbI/t0bx22sL1mPoZTli4oWkmUsm2xqVfqqdulHFnqtQusEOVaIvVQ1qtX95FIpUirkkwCuDxmzw9NcMj8trKVXbbybAmHnOIIu67aHSOTAKTZq1z8JFJpouEQczuiDelfM/alYRRMd2cLX3jjC1nWE7xVUiuiYS9lvRfhVazCrJdul3JlqccusJko1xKph7I+3T/M4Ggcf2eAAhPJNHvGE3z69ceUddxs5TmnLUL/cJwub/rqyWQagaluMWS6olFgYWeUaDhc140Lo3gaRsEs6GjlpcsW1lqMitLim/Y3FgkRCZfnlK+HbpfZylJvXWAzMRtLpNZljSfTpLxKPtuQWNjRUrZs2cpzycJOLjh5wVQq/vaWMB2tYZ4fmsx7jHltLXzsdcsren32h7DweqVhFEyjIyJOoWTNJV8J6qHbpR5lCZJ6sETKJRr25o7JWh4SGI2nZnXsXMrzUu/b75+Lp3QfAVrCMuvzZ1NvYeH7G6ZgAiIccjM1VmNQYz1VdvUkS9DU2hIpl6MOmMOaDTtJKqC42TCBSCQUqNWbeTauvG0df94+PNVFJ7j35eB5bYRDuWfiLJd68k/uj5iCqRD+kOFYtPypf8ulniq7epLF2JdLzljKM/3DDI4lCHm9sml1wTNBW5r+6QUu+cmDpNJpWn1h0qpaUd9hPfkn90dMwZRJJBRyiqRGCsUwymXF8l7+/bzjuPK2dfTtGAXgiO52Pn7W0VVN03LC4vmB+w7ryT+5P2IKpkgyCiXW4lKvNMooecPIRT1YmdXw1+0vPsF6xRRMHiKhELGW0NTAxmiZEV6GYeSmGv66/cknWI9Yun6PaDhEq28uFFMohmE0A5auvwZEw6Eph3xbNFz2GBTDMAwjN/uNgskolLaW2Q1qNAzDMIqjaRVMNBxyymSWo+QNwzCM8mgaBeNPu9JWwVHyhmEYRnk0pIIJMu2KYRiGURkaRsGEBOa1t7gZGyMhQqZQDMMw6pqaOSZE5HUi8mcReUZEPj7T9tFwiAUdLbS1hE25GIZhNAA1UTAiEga+CZwFHANcICLlTUJhGIZh1CW1smBOAZ5R1T5VjQPXAefWSBbDMAwjAGqlYA4BNvn+b/aWTUNELhaRNSKyZmBgoGrCGYZhGLOnVgomlxNln5w1qrpSVU9S1ZN6enqqIJZhGIZRKWqlYDYDi3z/DwW21kgWwzAMIwBqpWAeAI4UkcNFpAU4H7ilRrIYhmEYAVCTcTCqmhSRDwC/BcLAD1R1bS1kMQzDMIKhZgMtVfU3wG9qdX7DMAwjWCwDpGEYhhEIDTPhmIgMA3+utRwB0g3sqLUQAWNlbHyavXzQfGU8TFVrEobbMLnIgD/Xala2aiAia5q5fGBlbAaavXywf5SxWlgXmWEYhhEIpmAMwzCMQGgkBbOy1gIETLOXD6yMzUCzlw/2jzJWhYZx8huGYRiNRSNZMIZhGEYDYQrGMAzDCIS6UjAzzXIpjq956x8TkRNqIedsKKKMb/fK9piI/ElEjquFnLOh2NlKReRkEUmJyHnVlG+2FFM+EVkhIo+IyFoRubPaMs6WIp7TuSLyKxF51Cvje2ohZ7mIyA9EpF9EnsizvuHrmrpAVevig8tJth5YCrQAjwLHZG1zNnArLt3/acB9tZY7gDK+DJjv/T6rGcvo2+4OXLqg82otd4Xv4TzgSWCx97+31nIHUMZ/Aa70fvcAu4CWWsteQhnPAE4AnsizvqHrmnr51JMFU8wsl+cC16jjXmCeiBxUbUFnwYxlVNU/qeqg9/de3FQGjUSxs5V+ELgR6K+mcBWgmPK9DfiFqm4EUNVmLKMCXSIiQCdOwSSrK2b5qOpqnMz5aPS6pi6oJwVTzCyXRc2EWceUKv9FuFZUIzFjGUXkEOBNwHeqKFelKOYeHgXMF5FVIvKgiLyratJVhmLK+A3gaNw8To8Dl6lqujriVYVGr2vqgnpKFVPMLJdFzYRZxxQtv4i8CqdgTg9UospTTBm/CnxMVVOuAdxQFFO+CHAi8GqgDbhHRO5V1b8ELVyFKKaMfwU8ApwJLAN+LyJ/VNU9QQtXJRq9rqkL6knBFDPLZaPPhFmU/CLyYuB7wFmqurNKslWKYsp4EnCdp1y6gbNFJKmqN1VHxFlR7HO6Q1VHgVERWQ0cBzSKgimmjO8BvqzOYfGMiDwLLAfur46IgdPodU1dUE9dZMXMcnkL8C4vwuM0YEhVt1Vb0FkwYxlFZDHwC+CdDdTi9TNjGVX1cFVdoqpLgBuAv28Q5QLFPac3A68QkYiItAOnAk9VWc7ZUEwZN+IsNETkAOAFQF9VpQyWRq9r6oK6sWA0zyyXIvJ+b/13cBFHZwPPAGO4VlTDUGQZLwcWAt/yWvhJbaDMrkWWsWEppnyq+pSI3AY8BqSB76lqznDYeqTIe/g54Ici8jiuO+ljqtowKe5F5FpgBdAtIpuBzwBRaI66pl6wVDGGYRhGINRTF5lhGIbRRJiCMQzDMALBFIxhGIYRCKZgDMMwjEAwBWMYhmEEgikYwwC8rM6P+D5LZnm8czJZiEXksyLykUrIaRiNRN2MgzGMGjOuqsdX6mCqegv7Dk40jP0Ks2AMIwci0ikit4vIQyLyuIic6y1fIiLrROR7IvKEiPxURF4jIneLyNMicoq33YUi8o2sYy4TkYd8/48UkQerWzLDqB6mYAzD0ebrHvslMAG8SVVPAF4F/Kfszcx5BHAV8GJc/q234ZKSfgQ3T0pOVHU9MCQiGUvpPcAPgyiMYdQD1kVmGI5pXWQiEgW+KCJn4NK9HAIc4K1+VlUf97ZbC9yuquqlTVkyw3m+B7xHRD4EvBU394phNCVmwRhGbt6Om6nxRE/xbAdi3rpJ33Zp3/80MzfabsTNVPp/gQcbMFu2YRSNKRjDyM1coF9VE97cPIdV4qCqOoFLIvlt4L8rcUzDqFdMwRhGbn4KnCQia3DWzLoKH1uB31XwmIZRd1g2ZcOoMt6YmLmq+ulay2IYQWJOfsOoIl6E2jLcVMOG0dSYBWMYhmEEgvlgDMMwjEAwBWMYhmEEgikYwzAMIxBMwRiGYRiBYArGMAzDCIT/DyelZjw1x2NKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Family (social support) 2016\n",
    "correlation(suiciderate_happiness_report_2016['Family'], y2016,'Family (social support) 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e6938c70>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2016['Family'], y2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.3081100473814231 with a P-value of P = 0.00036215339299702434\n",
      "Weak positive correlation\n",
      "This positive correlation is significant\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Health (Life Expectancy) 2016\n",
    "correlation(suiciderate_happiness_report_2016['Health (Life Expectancy)'], y2016,'Health (Life Expectancy) 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e66c7250>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2016['Health (Life Expectancy)'], y2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.14970854983834767 with a P-value of P = 0.08912171442898578\n",
      "There is NO correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Freedom 2016\n",
    "correlation(suiciderate_happiness_report_2016['Freedom'], y2016,'Freedom 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e64ce250>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2016['Freedom'], y2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.05762605922817429 with a P-value of P = 0.5148955423356858\n",
      "There is NO correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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NuOkOn8C9+x24ht2FuAoG3Jj8Clz5+AFuLPsXoXvcgOvyfFRVxy2B6eRruSlDOboCpxgOiMg7Ctx/FOehuQU3/ncIp3AX4RrFY7hpAM/1aX8OePkMhhLe4p/jAK6+yZ9W8wiuDt2Fa5C8NpTGEfVOHjOuy0VkMW6ctNg0n+fjHN9eKbnzXJcDqOpPgf8CbsHVBQ/h6taA9+F6tt6FK+PDfh++4XcBzgnnIM4ImDJvZfq9MoZhNAoi8mycE9hkQxtGHeCtyW+o6tIqpP1x4K+q+rlKpz1TTPkZhmE0ANVUfvVIzcf2NAzDMIxyY5afYRiG0XSY5WcYhmE0HTUdLLUcLFq0SFesWFFtMQzDMOqKzZs371XVnmrLERUNr/xWrFjBpk2bqi2GYRhGXSEi+dGMGgrr9jQMwzCaDlN+hmEYRtNhys8wDMNoOkz5GYZhGE2HKT/DMAyj6Wh4b89S2bClj6s2bmV7/xDL5newfu1K1q3qrbZYhmEYRgSY5YdTfJfdcC99AyPMa0/SNzDCZTfcy4YtfdUWzTAMw4gAU37AVRu3kowLHS0JRNzfZFy4auPWaotmGIZhRIApP2B7/xDtyXjOvvZknB39Q1WSyDAMw4gSU37AsvkdDKcyOfuGUxmWzu+okkSGYRhGlJjyA9avXUkqowyNpVF1f1MZZf3aldUWzTAMw4gAU37AulW9XH7+anq72jg4nKK3q43Lz19t3p6GYRgNik118Kxb1WvKzjAMo0kwy88wDMNoOkz5GYZhGE2HKT/DMAyj6TDlZxiGYTQdpvwMwzCMpqMmlZ+ItInI7SJyt4jcKyIf8PsXiMgvROR+/3d+tWU1DMMw6o+aVH7AKPB0VT0VOA04V0TOAd4F3KSqJwI3+W3DMAzDmBY1qfzUMeg3k/6/AhcAX/X7vwo8rwriGYZhGHVOTSo/ABGJi8hdQB/wC1X9PXCUqu4G8H8LzkoXkUtFZJOIbNqzZ0/lhDYMwzDqgppVfqqaUdXTgKXA2SLyuGlce7WqrlHVNT09PdEJaRiGYdQlNav8AlT1ALABOBd4VEQWA/i/ttqsYRiGMW1qUvmJSI+IzPO/24FnAluAG4BX+NNeAfyoOhIahmEY9UytBrZeDHxVROI4Bf0dVf2xiPwO+I6IvBp4GHhRNYU0DMMw6pOaVH6q+kfg9AL79wHPqLxEhmEYRiNRk92ehmEYhhElpvwMwzCMpsOUn2EYhtF0mPIzDMMwmg5TfoZhGEbTYcrPMAzDaDpM+RmGYRhNhyk/wzAMo+kw5WcYhmE0Hab8DMMwjKbDlJ9hGIbRdJjyMwzDMJoOU36GYRhG02HKzzAMw2g6TPkZhmEYTYcpP8MwDKPpqMnFbA0jnw1b+rhq41a29w+xbH4H69euZN2q3mqLZRhGnWKWn1HzbNjSx2U33EvfwAjz2pP0DYxw2Q33smFLX7VFMwyjTjHlZ9Q8V23cSjIudLQkEHF/k3Hhqo1bqy2aYRh1iik/o+bZ3j9EezKes689GWdH/1CVJDIMo94x5WfUPMvmdzCcyuTsG05lWDq/o0oSGYZR75jyM2qe9WtXksooQ2NpVN3fVEZZv3ZltUUzDKNOMeVn1DzrVvVy+fmr6e1q4+Bwit6uNi4/f7V5exqGMWNsqoNRF6xb1WvKzjCMslGTlp+ILBORW0TkPhG5V0Te4ve/X0R2ishd/v951ZbVMAzDqD9q1fJLA29X1TtFpAvYLCK/8Mc+qaofq6JshmEYRp1Tk8pPVXcDu/3vARG5DzimulIZhmEYjUJNdnuGEZEVwOnA7/2uN4rIH0XkKyIyv8g1l4rIJhHZtGfPngpJahiGYdQLNa38RKQT+B7wVlU9BHweOB44DWcZfrzQdap6taquUdU1PT09FZPXMAzDqA9qVvmJSBKn+L6pqt8HUNVHVTWjqlngi8DZ1ZTRMAzDqE9qUvmJiABfBu5T1U+E9i8OnfZ84E+Vls0wDMOof2rS4QV4EvAy4B4Rucvvew9wkYicBiiwDVhfHfEMwzCMeqYmlZ+q/hqQAod+UmlZDMMwjMajJrs9DcMwDCNKTPkZhmEYTYcpP8MwDKPpMOVnGIZhNB2m/AzDMIymw5SfYRiG0XREpvxE5I0issj/PkFENorIARH5vYicElW6RnRs2NLHRVffxpM/cjMXXX0bG7b0VVskwzCMGRGl5fc6Vd3rf1+JW4poHvBO4AsRpmtEwIYtfVx2w730DYwwrz1J38AIl91wrylAwzDqkignuYfv3auqPwBQ1Q1+jb6aY8OWPq7auJXt/UMsm9/B+rUrG3718FKf+aqNW0nGhY4W91o7WhIMjaW5auPWhs8jwzAajygtv+tF5BoRWQn8QETeKiLLReSVwMMRpjsjmtGymc4zb+8foj0Zz9nXnoyzo3+oUuIahmGUjciUn6q+F9gAXAv8M/AfwE+BE4GLo0p3poQtGxH3NxkXrtq4tdqiRcZ0nnnZ/A6GU5mcfcOpDEvnd1RKXMMwjLIRqbenql6jqk9Q1UWq2qWqJ6vqe1T1YJTpzoRmtGym88zr164klVGGxtKour+pjLJ+7cpKiWsYhlE2qjLVQUSeVY10J6MZLZvpPPO6Vb1cfv5qervaODicorerjcvPX23jfYZh1CXVWtXhy8DyKqVdkPVrV3LZDfcyNJamPRlnOJVpeMtmus+8blWvKTvDMBqCyJSfiNxQ7BCwMKp0Z8q6Vb1cjhsH29E/xNIm8PZsxmc2DMMAEFWN5sYi/cBLgcH8Q8C3VfWoSBLOY82aNbpp06ZKJGUYhtEwiMhmVV1TbTmiIspuz9uAIVW9Nf+AiPw5wnSbjmacn2gYhjEbopzq8FxVvaXIsbVRpdtsNOP8RMMwjNlSEW9PEVkgIvMrkVaz0YzzEw3DMGZLlIGtl4vIdSKyB/g9cIeI9Pl9K6JKt9loxvmJhmEYsyXKMb9vA58CLlbVDICIxIEXAdcB50SYdtOwbH4HfQMj4zE3ofHnJzYyNn5rGJUhym7PRar67UDxAahqRlWvowanOtQrFnmlcbDxW8OoHFEqv80i8jkReYKILPH/nyAinwP+EGG6TYVFXmkcbPzWMCpHlN2eLwdeDXwAOAY3v2878D+4CC9GmbDIK43B9v4h5rUnc/bZ+K1hRENkyk9Vx4DP+//TQkSWAV8DjgaywNWqeqWILMCNJa4AtgEvVtX+cslsGNXExm8No3JEOtVBRJ4jIq8WkWPz9r9qikvTwNtV9bE4x5g3iMjJwLuAm1T1ROAmv20YDYGN3xpG5YhyqsN/Au8FTgFuFpE3hQ6/cbJrVXW3qt7pfw8A9+G6Ti8AvupP+yrwvHLLbRjVwsZvDaNyRBnb8x7gdFVNi8g84FvAn1X1bSLyB1U9vcT7rAA2Ao8DHlbVeaFj/ap6xOR5EbkUuBRg+fLlZz700EOzfh7DMIxmotFje0bZ7ZlQ1TSAqh4A/h7oFpHvAi2l3EBEOoHvAW9V1UOlJqyqV6vqGlVd09PTMwPRDcMwjEYmSuX3VxF5arDh5/i9Gvgz8NipLhaRJE7xfVNVv+93Pyoii/3xxYBNgDIMwzCmTZRTHV5UaKeqvk9Exj1ARWS1qt4bPkdEBDcd4j5V/UTo0A3AK4AP+78/KrvUs8QidBiGYdQ+Ua7qMKyqw0WO7Qxtfr3AKU8CXgY8XUTu8v/Pwym9Z4nI/cCz/HbNYBE6DMMw6oMoLb9SkfwdqvrrQvs9z4hWnJkTjtAB0NGSYGgszVUbt5r1ZxiGUUNUZEmjKYjG3bQK2AoLhmEY9UEtWH51S/74XldrguFUxiJ0VBEbczUMoxRqQfmNVVuAmRCM7yXjMj6+d3A4Nd5X256MM5zKWISOClLonVx2w71cDiUrQFOehtEcRN7tKY6Xishlfnu5iJwdHFfVulzXr1AE/rntSRbOabEIHVVitqsimMOSYTQPlbD8PocLTv104HJgADd/76wKpB0ZxSLwHxxO8dO31aU+r3tmuyqCOSwZRvNQCYeXJ6jqG4ARAL8KQ0kRXmqZZfM7GE5lcvbZ+F51me07MYclw2geKqH8UiISx3t1ikgPzhKsaywCf+2wYUsfF119G/f3DbCjf5g9AyMzeifWoDGM5qES3Z6fBn4A9IrIh4AXAv9WgXQjZd2qXi7HdZXt6B9iqTlHVIR8h5QnrlzA9XfuJBkXju5uIxkfZf/hFOlMlhOP6p7WO1m/diWX3XAvQ2Npc1gyjAYnslUdchIRWYWbnC649fjuizxRz5o1a3TTpk2VSs6IkLA3Z6CcdvQPs2BOkkWdbePnDY2l6e1q49pLpz/2GihXa9AYzU6jr+oQueUnIl9X1ZcBWwrsM4ySKeSQkskqB4dSOcpvNuN061b1mrIzjCagEt2eq8MbfvzvzAqkazQYhbw5WxMxRtI2TmeUH5vz2dhEuZL7u0VkAHi8iBwSkQG/3UcNrsZg1D6FHFK62hIkYjFzPDLKis35bHyiXNXhClXtAj6qqt2q2uX/L1TVd0eVrtG4FPKwbUnEecO64y2wgFFWZhswwah9Iu/2VNV3i8h84ESgLbR/Y9RpG43FZB62b662cEZDMduACUbtUwmHl9cAbwGWAncB5wC/w0V8MYxpYQ4pRiVYNr+DvoERC1LfwFRikvtbcKHMHlLVpwGnA3sqkG7FCCZZP/kjN3PR1bfZuIBh1DkWxKLxqYS354iqjogIItKqqltE5KQKpFsRyrGSwGzTN480wygvFsSi8amE8tshIvOAHwK/EJF+YFcF0q0I0wmGXG5FVW3FaxiNjHWxNzaRd3uq6vNV9YCqvh8X1uzLwAVRp1spSg2GHIXrtHmkGYZhzIxKjPmNo6q34lZ3+Ekl042SUoMhR6GobBUCw4gGG8dvfKKc5P50EfmLiAyKyDdE5GQR2QRcAXw+qnQrTakD41EoKluFwDDKj01wbw6itPw+DlwKLASuB24Dvq6qZ6rq9yNMt6KsW9XL5eevprerjUcODrNnYHR8zC/8sUShqMwjrfxYi9+w4YTmIErlp6q6QVVHVfWHwB5VvTLC9KrGulW9rF+7ko7WJD1drRzd3XZEazEKRRVWvBbdZPZYi98AG05oFqL09pwnIv8Q2pbwdiNZfzC112dUrtPmkVY+puO5azQuNsG9OYhS+d0K/H2RbQUaSvmVEg7JFFVtYyGtDLBFjZuFyJSfqr5yNteLyFeAvwP6VPVxft/7gX9iIkLMe1S1JjxHrbVY/9g7NMAmuDcLkU91EJGjROTLInKj3z5ZRF5dwqXXAOcW2P9JVT3N/68JxQfmfNII2Ds0IDcYhSm+xqUS8/yuAX4GLPHbfwHeOtVFftWH/dGJVV4q4XxinojRYg5Ehjk9NQ+iqtEmIHKHqp4lIn9Q1dP9vrtU9bQSrl0B/Div2/MS4BCwCXi7qvYXuO5S3DQLli9ffuZDDz1UnoepIuFQZuFxCKucDaN8XHT1bUd0fQ+NpentauPaS8+pomSVR0Q2q+qaassRFZWw/A6LyEKckwsicg5wcIb3+jxwPHAasBs3l/AIVPVqVV2jqmt6enpmmNQEtWBx2dwjw4gem+bQPFRC+f0zcANwvIj8Bvga8KaZ3EhVH1XVjKpmgS8CZ5dPzMLUSjeIfZSGET0WNal5qMRK7neKyFOBkwAB/qyqqZncS0QWq+puv/l84E/lkHGy1RZqZe5XvXsi2tJLRj1g0xyah8iUX94E9zCPEZEpJ7mLyLXAOmCRiOwA/h1YJyKn4bpQtwHrZyvnVMsC1crcr3r+KG3pJaNesGkOzUOUll8wob0X+BvgZr/9NGADU0xyV9WLCuz+crmEC5jKsqsVi6ucH2WlrbBasZ4NoxQsGEVzEPkkdxH5MXBy0F0pIouBz0aV7nTYsKWPOx/uJ5PN0pqI09PVSldbMseyqyWLqxwfZTWssFqxnpsB6142jNKohMPLitA4HcCjwGMqkO6kBEpABGIipLPKrgMjDIykciy7Rpv7VQ2vUXMiqAy14pw1XWrBm9poPiJ3eAE2iMjPgGtxY3UvAW6pQLqTEiiBo7ra2HVwGFEA5ZGDI/R2t+VYdo3UDVINK6yWrOdGph67l2082KgWlfD2fKN3fnmK33W1qv4g6nSnIlAC0ljUhRsAACAASURBVCIA7B0cZSyjKNS1ZTcV1RjDNCeCylCphk05u1brUWEbjUElLL/As7OmVnEIK4Hu9iTd7cnxSA6TfXT1PqZSLSuskaznWqUSDZtyW2o2HmxUi8jG/ETk1/7vgIgcCv0fEJFDUaVbKjMJYlyvYyphGm0M05igEoG5yz1m3IzjwdUe48xkldF0hqGxNIdGUvQfHmPv4CiPHhph54Fhtu8fYtvewxWVqRpE6e35ZP+3K6o0ZsNMuuIapYvGrLDGpBLdy+W21JptPDjKMc5MVsf/p7NZ/1cn/maUjCpRx3OuFyLv9vSxPO9V1QG/3QmsVtXfR532VExXCVgXjVHrlFKmS+m6L3ZOubtWm208eKYN6HQmSzqkxAopN1Nq06MSY36fB84IbQ8V2FcX1MqEd8OYKaVYHpOdE4Wl1kw9EeEGdKCsWhMxHt5/mEMjKbIhhZYxxRYplVB+oqE3p6pZEamIo025KfeHX+/OM+XE8qIylGJ5THbOtZeeE6ml1gjlQFVzrLKMBtaa0tvVyt7BUdqScb/OjWtA93S2sXdgtLqCNxmVUEJbReTNOGsP4PVAXa7DU+4QYza/yWF5UTlK6bqf6pyoLLVaLwfZrHLzfY9y9a8eZMeBIZbMbefl5xzLE45fOG6lZb3iK8aLz1zGlTffj2qGtmSMkZTrznzJWcsq+CQGVEb5vRb4NPA+XFvnJvxCs/VIuT78RnGeKQeWF5WjlK77anXvV7McHOEkklFSwXbGKbTb/rqPK2++n0RMmNMS59FDI1zx0y285ekncvbKBSWlc/bKBbyFE7nuju08cmiYo7vbeclZy0q+3igflZjk3oeL6mKEMOeZCSwvKkcpXffV8sCMohxkg27HMjiLXHfHdhIxGV9XM8ib6+7YPi3ldfbKBabsaoAolzT6V1X9LxH5DOO92xOo6pujSrsemG3rutbHRqYjnzkSVY5Suu6r5YE5VTkIxtIyqmSzuC5GVafgQvvD+7JldBTZfWiY7rbcKrMtGeORQ8NlS8OoHFFafvf5v5siTGNa1JLCmE3rutbHRqYrX7PN9aompX4DlfDAVM31bHzZOcv50E+2kM6maEvEGPHl4B9OP4Ztew+XVZHNhMXd7ew7PDpu+QGMpLIc3d1eRamMmSKN7kK7Zs0a3bRpU06FHK5gqxndJKiIptu6vujq245oIQeh2a699JwoRS6Jmcj36V/+hS/9+kEOj2WY0xLnNU8+jjc/s+qLfzQUlfwGgnlpqYzrYkxlwtZZcavs9q37a3Y87Pat+8fH/MLOKtMZ86snju/t2qyqa6otR1RUYpL7LRTu9nx61GmHqUWnipm2rmt9jGy68m3Y0sf1d+6kp6uV5b5Svv7OnTx+6byasGQbhel8A8FYWVYVVXL/AqrOcgv2BYotnZndvLRaHg8zZ5XGohLenu8I/W4DXgCkK5BuDrWuMKZDrY+RTVe+WmyYNCLBN6BegaHQEo/x0L7D7BkYJZ3Njns2VruLsVapZeVsTI9KeHtuztv1GxG5Nep086l1hTEdan2MbLryNVLDpFKoKlklZ25ZvvMHCtmQZdbT6SZYh8eshlMZervaGBhJVfFpDKPyVKLbM9xMigFrgKOjTjefWlcY0yHwxvvwjfdxf98gACsXzamuUCGm6y3YSA2TmRKEtcqGnEDC+8KTqLPKjLoVL1zjJlgPp2yCtWFUottzMxNjfmlgG/DqCqSbQyMG0B1KZVk6v31cmc/G47PcnrDTGc9spIYJ5M4tC5RVNsfRg3ElFyi4Sjie2ZiVYUwQ5Ty/s4Dtqnqc334FbrxvG/B/UaVbCo0wmlHOcbJqT52o5YZJviJzFhluorSGlFnWOX/U+pIxNmZlGI4oLb+rgGcCiMha4ArgTcBpwNXACyNM+wiqXcGXm3KOk5WqSKOcJxnVvLJsoLTyPBODrsPw76xOTJCuB0VmGGGy3tt2LJMllckylnZ/Uxkd3w6OpdLqj7l9Y6Ht4JpGJ0rlF1fV/f73hcDVqvo94HsicleE6eaQySq7Dw7zmZsfQERJxuNkskpLPEYmm+G/b3mA04+dT0xARBBABGIigPsbbAfHxB+rJuUcJytFkUbReAhc5TX4DTlu9cqEYho/FnLsCLoPFbcff074OsOIiqDxlAopnECxhBXQWCZXCaXSWcZCCilQQGGlNBbsy1NKY6Hj+YotPUlAbeNIIlV+IpJQ1TTwDHKDWU+Zroh8Bfg7oE9VH+f3LQC+DazAdZ++WFX7J7uPAsNjGXYcGKK7LTFeSQIk48KO/iH2Dc5sKZGwshTEK8aJ/YHiDI7jzwWQIvdAyOmX1SKdtBefvZwrfrqFTBANI+0+hIufsJyDQymQXCWekyfeyglufXR32xFegEOpDEd1t7HHL7MSbjyks0oyHiOdSfPpm+/npMVd/r4hRebvnQ0puHD6hjETxq2bHKUSKBEtqkgCxZGrpHKvOeIeBY9nJ5RdOluXQyjJuNASj5H0/1sSMZJxCW273w9VW9CIiVL5XQvcKiJ7gWHgVwAicgJwsITrrwH+G/haaN+7gJtU9cMi8i6//c5ShIkiNFF4vtRsRhKDqBbb9g2SyigtiRjHLpgzqTPCycd086annXCE88LJS7rZd3h6yvyFZyzlypvvJ5PVHC/AF56xlJv+71Guu2M7f9x5gNZEjPkdSmerKzYtiRi7DgwzPJaZ8bMbtU2+dTOhAIoonCMsGvWWTq5lVMy6GSt4fOK+9WjdxMTNp0wmYrSMK5s8hRMXkomQQorHSHol1JK3nYzH2Ll/mE0P9XNwZIwFHa2sO6mHU5bOzVNi/rq4jKeZiEnJPVfXvy7ijKkykSk/Vf2QiNwELAZ+HlrQNoYb+5vq+o0isiJv9wXAOv/7q8AGSlR+Lzlr5m7egXLafWiYxWX2kAtCJqXSGQZH3dz/0VSGnQcOc+XN9/MWjgydlC/PW5/xmFnJU8wLEBgP59QSF1KZLH0DI0Abna0Ji2sYEVnV8Qq/6PhNfjdYWCllQl1lBe4zltcFVyid8XPq2boZr/xzlU2gCFryto88R8aVVr5SailwfljBhZVQPFbeYZLbt+7n+s07SMSE3q5WRlJZfnrvI5x0VBenLTNnplKp6dieXvn9ONTteUBV54WO96vq/ALXXYrvZl26bNmZt252zqUziRsYjueXyWbZe3iMdEZZsXAOlz5l5ayV4D9/+272HR5l7+Ao6YwSiwlZVRIxobM1ztBYls62xLjSBSKLL5ivVA8Nj5HKKu3JOIfH0vQdGkVxXZ6LOlsbJq5hEGD5iPGaKbrG8i2g3G64I62ZYtZPvhVVj9ZNPCY5yiJfOYStm5aQZZKjQPKsnWSeMplQWv6avO2ZWDf1SFBn5AcrWDinlU9ceGrZ0rHYnnWIql6N8yjltDPOHK9JZuLmHazhlckqfQOjCEJcYEf/UFHLbDoEy6SkMtnx8TkRGEtn6fet7iXz2th3eJQrb76f9mS8LGuK5RNW8t1tCfYdHmXngWGO7m4F4sxpSdDbDfsGRxlNZ1k4p3VWFnC+dTOhaPy+kAVScOA/k2/xaJ71k+9gUKhLrn6tGwFf+U81fnPkOYUUSa71k2vdHKGUKmDdGMWxpZXKQ70pv0dFZLGq7haRxUBf1AkGBW1H/zCCEIvJuHdhIiazVjrBWKRzIFFEAo9Hxn8/uPcwyXiMztY42wdHWbEw16OzlIKfb93c/uB+fnjXTvoGRlg4p5XB0TTpTJYYMQ6PZlB1FeyjA84iDbwtWxJxutvjHN87h99u3cet9+8pahVN5kBQr9ZN2OoopBwKd40V6B4LKY6prJtwOoEyize4dWMUx5ZWKg/1pvxuAF4BfNj//VHUCQYFLWyZqUIyHptS6WSyYWtlwkMs7I125rHz+f4fXP/9aDpLJuNcIwNlIzhFOJrOMpLKArDzwAhxkXE3/4xXxOu/vplUJsvASJpDIykyXsHERCZVNvsOTxLXUaFvcCxn16GRNN+7c2dJ+TcbClk34a6y3PGY4hZQ/hhPvnVT0AnBrBujRpmN/4IxQc2O+YnItTjnlkXAo8C/Az8EvgMsBx4GXhSaS1iQkx9/un7m2z/NUUJHjNPkd5eFxmd29g9z946DjKYzhLMqGffzAGNCb1dbjnUTdKdl6tC6gWDahf8vzsrIZJ0X6lFdbSzobDnCuilo/RToTmvJH9MpYkW1eGVj1k0uUTpfGfVDJdY9bPQxv5pVfuWidfGJuvgVn6q2GEUJrJuwlZHNKnsPj+WcE5CIC6ccM5eH9g0xnMrQ2ZLgccd0c+yiOSTjMf73j7sZGkvTEo+NzzlMZbLMbUvylmeeSDIe470/vIeu1sTEPESEobEUjxwaY+n89qZYqLMeabbFVI3q0ujKr966PctCIiZHWB0teZZJoS62/YfH+OuewwyNpelqS3La0rkc19vpr5ecLritfYf54d27XEWVEMYyLjLJa560krOPW0BLIsZdDx/gO5u288jAyBHenC1xIZ1Rsrgu0GQM5rYnWTp/Dh97UXGPrh/dtZNFnS3jk+rBTZQfGEkzNJrhuju2MTCSZnAkTU9XK3N8hJhEPM5xCzvobm+xoMc1SuB8VW5nJ8NoRhpe+bUmYqxc1MFoKsvCTucKXCjqSbm5ftNO5rTExyuqtqRzR77xT4/wrNVHcfvW/fz3hgdyvCvD3pw9Xa30HRolLoAqEhOSifiU/frFBsM7WhLjVkNPZwuPHhrlkYMjHNXdSjwWI51V3rDuBKtEaxjz8jOM8hGrtgBRIwiJWIyO1jh9AyMVUXzgKqq2ZG72hiuqcCtekHGlt71/iLZkzE8taCUREx+nkpK6t15y1jLSWWU4lUFxf9P+BkF6na1JjupuIx4X9gyOsXBOq3Wd1QGLu9vHnZ4CzMvPMGZGw1t+ATOpJGbjXDCVO3KxVnxwXnvSza2b05IYn8BaStrForV88qa/5KTX2ZpgTmucgZF0WSfG5hMTyY1xGop7GouFjvvzg3NiQs6YZHAsuGfglOP2++N5aee3c8LOM/lj3cFmoSDb4ePunCNXgyB03Xg8U82VIxx0O1g5wq33V9q4ezW9/MzRxmg0mkD5TVg/06kkCk36ns6k9qkqqmLKcdm8dobT2VlVcPmT+W/fup/BkTR7B0ZpScSY39FyRHiyyYJ0B4oo5n8HXpjx2MQxOHIFjFgNTw3I9yLN3ay83MVWuFAmFOn5py9hXkeSr/xmGzsPDLFkXjuv/JsVPOnEHj89JryyxYRiDu4RXgV+XNmH06Bw0PHZfguGUYs0vPLLKDOKRjJb54KpVs0uphzfsO6E8fRLcTzJUThhJeWtqtse2Md/3/IAnW1xRlMZRtNZdh0cAaAlLrzozKWs7OksOV+MaBD//vxW0fPOPWUx556yODI5wmscBlbp9/6wg9ZEjPaWOCjMaY0xPJbmu5t38NRVPXlW8YRVm78iieQ9l62XaFSThld+J/R2zqhbrxzOBYEFFnQZffKmv7D4jglllqMc57bzj2cv40kn9hATePpje4nFhETMWViJWDDvjXELrJQ5cN+8/WFakzHmt7SACn1+iaKYwII5LVx/504ev3TepGvyRbmIbTVotOcpJyJCIp5brnYfHGFeezKnvCXa3JqOi+fObrwxULZujUa/mHD+eo2BIg0Ff4AjLdZ869YwJqPhld9MKUcIIRHhjgf38emb7ydYBLZ/eIzP3PIA72lbxfmnL+H5Zx4zrtiiILxQ7eBompZEDMFVMj1dbQVXbA8TxSK21aTRnqcSlHPh5HwCZVvuikg1d0w2UKKBtRmsZ1loweRC3c7hcdzpjNMatYspvyIU65a8+AnL6WhJEIsxrrSCca/AMovLROzF93z/Htpb4uMVR0siztBYmm/8/mH+9tQlkT9HuOIay2SJxwTNuvXF4MgV2/O5auNWknEZl7+jJTGlwoyKclhstfQ85SRKa3b92pVcdsO9DI2lx7v/Uxll/dqVZbl/FIi4APRRjt+67mEdV4wB4S7f8S7kwLrVXGUaVqrWDVxZml755SitmBAXp7Se87ij6W5PcM1vH2LXgSGWzu/gtU89ftoVStjyCphK4ZSTcMWVjAkp75q4qLMNmLoFf3/fAEOjaVJZpSUeo6erlc7WRMXkDyiXxVbt9xEFUVuz61b1cjmu4bCj330L1lXs64syK9dJlSSg2ZDH8LjinPAyDjs05VxnSvUIGl75CTCnNeHWG4vFiMfdOFowljbZuNl5j1/CeY+fnXUWWF6ZrLJnYNRZXyIct2jOrO5bjEIWwOXnr+aqjVs5OJxiYCTN/I4kXW3O4pmsBb9hSx8DI2myqsRjLjj2rgMjLOxMsmJhZZxkgue58+F+ROCorjakxUW/6RsYYf03NnPG8vklV8ZRduFVi0pYs+tW9Ta9sqsEsZgQi8Ba1Twlmt+NO74v2zzdug0/yT0eE47qbmNRZytzO5J0tiZoS8ZJxGMVCZq8fu1KDg2n2NE/TCqTRYB0VtkzOMqGLeVdkSmwAPoGRnIsAIBrLz2HTe97Fle99EyOW9TJweEUvV1tXH7+6qKV2lUbtzK/w1lJmnUNCUXZfzhVkS6v8PNkslmyWWXXwWEeOTjMroPDrpWczY4/Zyn5uX7tSlIZZWgsjapO2QCoB7b3D+WMTUP9W7NGeRFxy7ElfOjG1kSctmSc9pY4c1oTdLYm6G5LMrcjyfw5LSzsbK22yJHT8JZfNQmslgNDqfEuifZknEWdrSTiUvZxplIsgOm04Lf3D7Gos5XWRJy9g85qbYnHaE/GKmIFhJ+nNREnnVVEYd/hMZKxGAi0+nUQS7UCG7ELrxGtWcOIGlN+EREeh1FcaDEFFnW20t2eRFXL3jIv93hWUKl2tyfp9vcdGkvT29U2a1lLIfw8PV2t7DowAkxM3EaFOS1xdh0c9useaknjXY3WhReM6+4ZGGFgJM1o2jk2XVABhyqj8Qga7cmeFadUW5Yoafhuz6jYsKWPi66+jSd/5GYuuvq2I7rc8q0WEdeXv3fQzbOLomW+bH4Hw6lMzr7ZpDOTLsKp8mU6hJ+nqy3JknltOZP5l8xr4/BYhhhu7LY14bxqk96qbhbWrerlhWccQ/9QipF0hpa4sGBOkuvv3Fn2rnWj9pnNNxgeakCz6QjFrDqm/GZAsbG1cCELj8P0dLWOuz+PZbKRjTOVezxr3apeLj9/Nb1dbSWNEZaSL7N5nnhM6O1u423POJHe7jbiMWEskx2fi9XT5cYpmnG863db97N0fjsnL57L8b1dLOpsa7pGgDH7bzB/6KSRafwnjIBSxtbC4zBdbUnmd2ToGxhFgT0Do7zmyceVveutXONZM50zVm6vw8me5/FL5/n9rsvz6LltdLW5LtJmHO9qxCkcxvSZ7TdYqBw1Kqb8ZkApFU14fl06k2Xf4THiMeGYeW0k4rGSworNhJmOZwUK7y+PHmJwNMOCOUkWzmmd1pyxKCrgYs8T7A9auvGYoKp1MQE7CszpxYDZf4OFylGjYt2eM6CUsbVwl+Ejh0ZJxIVj5rXT3d5Sc+NS4a6SwVEX/Hr3wVEe6Bskk1WSceEjP90y5ThCucccS2G6XbO1SDnGSRtxCocxfWb7DYbLUaNjym8GlFrRrFvVy7WXnkNPVysn9HSOe0xCbXVJBV0l6YwymnaLpQq4FSAOjDAwnOIvfYNTjiNUqwIO8vlX73w61156Tt0pvnKMkzZCI8CYPbP9BsPlCIk1tPnX0A8XFdMdW6v1Lqmgq+TBg4f9RHaCGe2IwJ7BMVqTsSnHERpxDl3UlHOctNGmcBjTpxzfYFCOZP22e6KTtPqY8psh06loKhUYeKaOKoFyHstkScQglXVhj4SJuIFHdeVGfChmuVoFPD3MUcUoN/YNloYpv2kyUwXTkYzx4D5Xoa1cNId/+9tVs3JMCdJ/4soF/G7r/lk5qgTKOR4TsllIxJRM1scZjAntPixSmFqyXOuZWu8VMIxGxcb8psFMxmeCa1JZ5cTeTpbOb+fwWKbo+dNJf9u+Qa68+QEe3DvISCpLVpV9gykGR9MFnWqKOVYE/fwrFnSQUSURi7F8QTsrFnbQ29XG6556vDlTRIQ5qhhGdahLy09EtgEDQAZIq+qaSqQ72fhMcDzfIiznmE7+vQ4Np4kJDIykx1eLCOYRdrUlc7rPplr2Jjx1IBgv6O1qKzCvrrbG8up9VXYbJzWM6lCXys/zNFXdW8kEi43P3N83UFSxbO8fIi6wdc/geGDoRZ0tMxrTyU9/LJMlJozfN51RJOa2Ibf7rFQlPNW8ulqiUVZlr8W8NYxGp56VX8UpNj4zls4yt72wYulqTXB/36Bb+NKvibfzwAgn9k6+Hl4hiyac/qHhFJmsklKICXS0xzkwnIIsJGNyRPdZvuIcGEnRd2iEbfuGuOjq22ZlbVTL+mrUVdmrTb1b04ZRCvU65qfAz0Vks4hcmn9QRC4VkU0ismnPnj1lS7TY+EwyLkXXUxtfQVlD/5l8ZeViY4tPXLmAVEbZMzDiVjLwyxEKcGA4xZyWODEROloTR8zzCk9+HRhJsevACKms0paIzSoGZ7njeRa6f7EJ4LaOXfmJ+n0aRq1Qr8rvSap6BvBc4A0isjZ8UFWvVtU1qrqmp6enbIkWm0j8mKO6i0ZVGBzL+JBm4pxJ4i7E2WROL2GLRkTGnVd+t3U/l5+/mqGxDFlV2hJxjupqpS0ZH18v8KqXnsmm9z3riMneYcXdd2jELQmEW2JpNhFnislajug1xSriT//yL1x09W3sGRjlgT2DHBpOjV9jnpKzI8r3aRi1RF12e6rqLv+3T0R+AJwNbKxE2sXGZ4rN47tq41b6BkZY2TPRzTnVmniTzf1at6qX7vYk8zuS7B0cY//QGC3xGEvntZPVydewCxwrtu0boi0RG19bMHz/6RLlPLVC3Zp7B0f47Ia/snR+O0d3t7LzwAg7DwwDSiIeM0/JWWLzDo1moe4sPxGZIyJdwW/g2cCfqinTZKGlZuLKPlV8vq7WBDsPjJDOas44Ymfr5G2ZIAzY2SsWcPTctpxwazO1mKKM51moW/PgkBvr7GhJ0N3ewtL57SRiwiOHRi2kVxmoRnxWw6gGdaf8gKOAX4vI3cDtwP+q6k+rLFPR+JIzibk4lcKcyTjidO4/HaKcp1aoIh7NZGlNTBTbrrYkJ/R20tvVWndxPWsRm3doNAt11+2pqluBU6stx3SYrit7uIvy/r4BxtLZnHGXYBxx7+DY+DSHoztbS548X865ZVHOUysUFi4Ri9HVlltszTIpHzbv0GgWpFRroV5Zs2aNbtq0qdpizIjx6DCZDAeHUoxmsiRiMXrmJGltSeRMuQjGEa+99JyKyFUpV/jwpPulPpzb9XfuHPewDcZXrbvTMMqLiGyuVACRalB3ll8zcdXGraQyGfYNphCBZDxGJqs8OjjG3HbXaIkyUHYhKj2xvJDVXKvRZgzDqB+aQvnV66Td7f1DHBxyii/mJ/XFBVKZLENjGQZG3IKTxy3s4N/+9uSmmVhuEVEMw5gtDa/8BkbSZbNUolai+ffvak2w++AwydCKCumsm883ls6y6uguhlMZhlLZyGTIf8YoXOHrtXFSr1h+G0Z9entOiz0Do2WZtFuJSCb5998zOEpMhExWUVWyWSWTVUSgNRGr2KTy8DOW2xXeIopUFstvw3A0vPIby2TLEgIr6sgXwf0zWeXBvYd5eP8QgyNpulsTxMTN5UvEfddnTJjTEmfrnkG2PHKI3QeGub9voGwyTPaMYVf4Q8Nj3P/oAA/uPczmbfs58T0/4aT33ci5n7y15MrUIopUFstvw3A0vPJricfKYqlEHUdye/8Q6UyWXaHJ61lVDoykecO64zlrxQIWzGmhqy1BZ2ucA8Pp8fNSWWVgJD3r1nspzxjMW2yJx9hxYISsKqowllVSWSWTzfLAnsO84/q7S5LH4nNWllrN78liuBpGFDS88uvpai3LpN2oI18sm9/Bo4dGx51bgn9BTM9gAv2nX3I6Q2NZFEWAdEZJZ5SxdJY3X/eHWVUapT7julW9zOtoYcXCjvHxSPH/swpxEQZH0yVZEzPJV6soZ04tRnCxrlijGjS88utqS0w7wkohoo58sX7tSlLZLKoT43tZlKO6Wo+wvDpb47TEY97SUuIxaEkIQ2OZWVUa03nGwIIYSWdzAs1kFUQgk9WSrInp5qtVlLOjFiO4WFesUQ0a3tsTyuMaH3Xki3Wrejmxp5Nt+4fIZNUveutWg8gPgv2Yo7rpGxjhkYOuizQmQjartCZkvNKIOlrLsvkdPLh3kGz2yCAJwfhkKdbEdPO1FqZaFKMevChrMYKLBdM2qkFTKL9yMZUSnW3l967nPnZ8WsZkk9eDsF8j6QyJmJDNQhZlUWfbrCuNUhsK69euZP03NiMC+UGC0lllXkeyZGtiOo2TWq0o62lV+VqbJ1lskWgLWWdEScN3e1aKcnTHlRoEOzhvTkuCTBYScWHJ3Ha625MVqzTWreqlqy1BWyJGTNxq8uDG/RIx+NgLT42kgq3FMSuwrrvZUItdsUbjY5ZfmShXd1yprfJ1q3r59EtOz7EUK11pnNjbdUSLPYgxGpVlUSjYdS1UlLVqkdYDtdgVazQ+pvzKRDUqv2pXGtVQRNV+5mJY193sqLWuWKPxMeVXJqpV+VWz0qiWIqrFirJWLVLDMApjyq9MNGvlV4uKqBrUqkVqGEZhTPmVCav8DGsIGEb9YMqvjFjlZxiGUR/YVAfDMAyj6TDlZxiGYTQdpvwMwzCMpsOUn2EYhtF0mPIzDMMwmg7R/KjEDYaIDAB/rrYcNcIiYG+1hagBLB8clg8TWF44wvlwrKr2VFOYKGmGqQ5/VtU11RaiFhCRTZYXlg8Blg8TWF44mikfrNvTMAzDaDpM+RmGYRhNRzMov6urLUANYXnhsHxwWD5MYHnhckbr+AAACUJJREFUaJp8aHiHF8MwDMPIpxksP8MwDMPIwZSfYRiG0XQ0jPITkXNF5M8i8oCIvKvAcRGRT/vjfxSRM6ohZ9SUkA+rROR3IjIqIu+ohoyVooS8uNiXhT+KyG9F5NRqyBk1JeTDBT4P7hKRTSLy5GrIGTVT5UPovLNEJCMiL6ykfJWihPKwTkQO+vJwl4hcVg05I0dV6/4/EAf+CqwEWoC7gZPzzjkPuBEQ4Bzg99WWu0r50AucBXwIeEe1Za5yXvwNMN//fm4Tl4lOJsb/Hw9sqbbc1ciH0Hk3Az8BXlhtuatUHtYBP662rFH/bxTL72zgAVXdqqpjwHXABXnnXAB8TR23AfNEZHGlBY2YKfNBVftU9Q4gVQ0BK0gpefFbVe33m7cBSyssYyUoJR8G1dd6wBygEb3gSqkjAN4EfA/oq6RwFaTUfGh4GkX5HQNsD23v8Pume0690wzPWCrTzYtX43oGGo2S8kFEni8iW4D/BV5VIdkqyZT5ICLHAM8HvlBBuSpNqd/FE0XkbhG5UURWV0a0ytIoyk8K7MtvvZZyTr3TDM9YKiXnhYg8Daf83hmpRNWhpHxQ1R+o6irgecB/RC5V5SklHz4FvFNVMxWQp1qUkg934uJ6ngp8Bvhh5FJVgUZRfjuAZaHtpcCuGZxT7zTDM5ZKSXkhIo8HvgRcoKr7KiRbJZlWmVDVjcDxIrIoasEqTCn5sAa4TkS2AS8EPiciz6uMeBVjynxQ1UOqOuh//wRINmB5aBjldwdwoogcJyItwEuAG/LOuQF4uff6PAc4qKq7Ky1oxJSSD83ClHkhIsuB7wMvU9W/VEHGSlBKPpwgIuJ/n4FzhGi0hsCU+aCqx6nqClVdAVwPvF5VG83qKaU8HB0qD2fj9ESjlYfGWNVBVdMi8kbgZzhvpq+o6r0i8lp//As4763zgAeAIeCV1ZI3KkrJBxE5GtgEdANZEXkrztvrUNUEj4ASy8RlwEJcCx8grQ0W0b7EfHgBrmGYAoaBC0MOMA1BifnQ8JSYDy8EXiciaVx5eEmjlQew8GaGYRhGE9Io3Z6GYRiGUTKm/AzDMIymw5SfYRiG0XSY8jMMwzCaDlN+hmEYRtNhys8oGyKyMBQJ/hER2RnabpnhPd8qIh2THL9eRFb6350i8nkR+auI/EFENovIP830eWoVEblERJZMcvwdIrJFRP7kQ1S9vMLyzROR14e2l4jI9bO43y9FZH55pDMMhyk/o2yo6j5VPU1VT8PFR/xksK2qYyIyk3mlbwUKKj8fczCuqlv9ri8B/cCJqno6cC6wYAZploSIxKO69xRcAhRUfn6+1rOAs1X1ccBaCoe0Kkj+M83wGecB48pPVXep6myWB/p6+H6GUQ5M+RmRIiLXiMgnROQW4CMi8n4JrSPorZMVIjJHRP7XWyp/EpELReTNuEr+Fn99PhcDP/L3OR4Xsf59qpoFUNU9qvoRf1xE5KP+3veIyIV+/7dF5Lw8eV8gInF//h3i1rpb74+vE5FbRORbwD1+e4O3QLeIyDdD0TG2ich/ils/cZOInCEiP/OW6WtDaf5LKJ0P+H0rROQ+EfmiiNwrIj8XkXZxa8ytAb7pLer2vDx5Dy4yySGfBwdV9av+ns/wFvE9IvIVEWkNyXmZiPwaeFGB7Q0issafu0hc+K/AAv2RiPxU3Ppw/+5l+DAuRNpdPg9XiMif/DVtIvL/vAx/EBdXNbjX9/297heR/wo90w3ARUULmWHMAFN+RiV4DPBMVX37JOecC+xS1VO9xfJTVf00Lu7g01T1aQWueRKw2f9eDdwdKL4C/ANwGnAq8Ezgo+KWtLoOCBRhC/AMXDSgV+NC4J2FW//wn0TkOH+vs4H3qurJfvt0nIV6Mm6dtCeF0t2uqk8EfgVcg4uecQ5wuU/z2cCJ/p6nAWeKyFp/7YnAZ1V1NXAAeIGqXo+L0HOxt6iHg4REpAvoUtW/5j+8iLT59C9U1VNw0Z1eFzplRFWfrKrXFdkuxtm4RshpOEW5BngX8Fcv37/knf8GAC/DRcBXvWz4e1wInAJcKCLL/Ln9QKuILJxCFsMoGVN+RiX4bgmR8u8BnikiHxGRp6jqwRLuuxjYU+iAiLzXWx5B0N4nA9eqakZVHwVuxSm1G4GneyvoucBGr1CejQv5dRfwe1wYtBP9vW5X1QdDyd2uqju84r0LWBE6FsRNvAe3WO6Aqu4BRkRknk/n2cAfcNH0V4XSeVBV7/K/N+fdt+BjU3wVj5P8/YIYpl/FdYkGfDvv/PztYvzCd3cP4+KkTrUK/JNx3Zio6hbgIVzjCOAmb6mOAP8HHBu6ro8iXb2GMRNM+RmV4HDod5rcctcG4CvlM3FK4goRuayE+w4H1+Mqy1NFJObv9yE/9tjtjxcc9/IV7QbgOTir47rQ+W8KjVkep6o/L/A8AKOh3xlyY+YGx7J552X9eQJcEUrnBFX9cgn3LfQsh4DD4h2A8phq3C//mYq9s7a88/KV7VTxEieTY7LnbcO9b8MoC6b8jEqzDTgDxlcQOM7/XgIMqeo3gI8F5wADQFeRe90HnACgqg/gugM/GDhp+O60oLLdiOtKi4tID87qud0fuw4X6PwpuIC/+L+vE5Gkv9djRGTOrJ68MD8DXiUinT6dY0Skd4prJsuTK4DPiki3v1+3iFwKbAFWiMgJ/ryX4azfUtiGa5iA67YN8ywRWeDHHp8H/GYK+TbiukkRkccAy4E/T5a4H0M92sthGGWhIVZ1MOqK7zHRnXgHEHTDnYIbh8sCKSbGo64GbhSR3QXG/f4XWAf80m+/Bvgo8ICI7MdZCsECtT8AngjcjbNO/lVVH/HHfg58DbhBVcf8vi/huhnv9JXvHlzlXlZU9eci8ljgd95PZhB4Kc7yKcY1wBdEZBh4YnjcD/g80AncIW6VhhTwcVUdEZFXAt8V53V7B6WvWP4x4Dsi8jLg5rxjv8Z1Y54AfEtVNwGIyG+8k8uNwGdD53/Oy34PzqK8RFVH/bMX40zgNlVNlyivYUyJrepg1C3e2rgFeFKDr75dk4jIJcAaVX1jxOlciWuY3BRlOkZzYd2eRt3iLZ5/B46ptixGpPzJFJ9RbszyMwzDMJoOs/wMwzCM/99eHQgAAAAACPK3HuSSaEd+AOzID4Ad+QGwIz8AdgLBJrhmNlAPKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Trust (Government Corruption) 2016\n",
    "correlation(suiciderate_happiness_report_2016['Trust (Government Corruption)'], y2016,'Trust (Government Corruption) 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e670e760>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2016['Trust (Government Corruption)'], y2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is -0.012546607198814303 with a P-value of P = 0.8873349329392579\n",
      "There is NO correlation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation between suicide rate & Generosity 2016\n",
    "correlation(suiciderate_happiness_report_2016['Generosity'], y2016,'Generosity 2016')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e7a7e4c0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.residplot(suiciderate_happiness_report_2016['Generosity'], y2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''' as in the previous analysis, there are weak positive correlations between suicide rate (as the dependent variable/ target) \n",
    "& Happiness Score, Economy (GDP per Capita), Family and Health (Life Expectancy) for 2016 (ad independent variables/ \n",
    "predictors) and these correlations are significant.'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Results\n",
    "'''A country having high happiness Score, high economy (GDP per Capita), high family and Health (Life Expectancy), \n",
    "tends to have a higher suicide rate.\n",
    "In Addition, we can state that the developed countries tend to have higher suicide rates than the developing countries'''\n",
    "'''to summarize, happier countries tends to have a higher suicide rate according to analysis applied to suicide rate in 2015 &\n",
    "2016 and happiness report 2015 & 2016 databases'''"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}