Suicide Rates Analysis - ENG.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing a significance of the correlation between a suicide rate and a number of psychiatrists and psychologists working in the mental health sector."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The subject of the actual analysis is to test if there is a significant correlation between the suicide rate across the world and a number of psychiatrists and psychologists working in the mental health sector. \n",
    "\n",
    "Mental health issues are becoming a serious problem nowadays and there is a lot of discussion, especially in the Europe and USA, on what can be done in terms of a prevention, as well, as a treatment. It makes a logical sense to assume that the number of workers in the mental health sector should be correlated with the suicide rate either because of an availability or, a potential time in which one can start receiving appropriate help.\n",
    "\n",
    "## Research & Hypothesis\n",
    "\n",
    "The questions that the analysis will try to answer: is there a significant relationship between the suicide rate across the world and the number of human resources (psychiatrists and psychologists) working in the mental health sector. Suicide rate is a dependent variable and the number of HR in mental health sector is an independent variable. I can assume that there should be a negative correlation between these coefficients as the availability of mental health specialists should lead to decreasing number of people who decide to commit a suicide. At the same time I am not ruling out that there can be an inverted correlation and the number of the HR in the mental health sector can increase in the countries where there is an increasing demand for mental health care; ergo there are more people with issues related to a mental health.\n",
    "\n",
    "• The analysis will be applied with inferential statistics    \n",
    "• The sample was collected from the data provided by the Global Health Organization   \n",
    "• The sample size is 71 (N=71)    \n",
    "• The suicide rate is an age-standardized rate of committed suicides per 100 000 population    \n",
    "• The HR rate is an average rate of human resources (psychiatrists + psychologists) per 100 000 population    \n",
    "\n",
    "**The null hypothesis states that there is no statistical significance between the suicide rate and the number of HR in the mental health sector.** \n",
    "\n",
    "*H(0): &#x03C1;=0*\n",
    "\n",
    "**The alternative hypothesis states that there is a statistically significant correlation between the suicide rate and the number of HR in the mental health sector.** \n",
    "\n",
    "*H(A): &#x03C1; > 0 or &#x03C1; < 0 or &#x03C1; ≠ 0*\n",
    "\n",
    "**The level of a confidence interval alpha is set at 95%. α = 0.05 for a two-tailed test.**\n",
    "\n",
    "## Experimental design\n",
    "\n",
    "The analysis will use the **correlation coefficient - Pearson's r** to measure if there is a statistically significant relationship between variables and the two-tailed **p-value** to indicate the probability of null hypothesis being true, whereas the analysis is based on the sample data we will take a sampling error into account by calculating a **95% confidence interval** (&#x03C1;). The computation of a confidence interval on the population value of Pearson's correlation (ρ) is complicated by the fact that the sampling distribution of r is not normally distributed, therefore I will use **Fisher's z'** transformation to handle this issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary modules.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from scipy import stats\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating variable for HR dataframe.\n",
    "HR_data = pd.read_csv('hr.csv', header=0, engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Psychiatrists working in mental health sector (per 100 000 population)</th>\n",
       "      <th>Nurses working in mental health sector (per 100 000 population)</th>\n",
       "      <th>Social workers working in mental health sector (per 100 000 population)</th>\n",
       "      <th>Psychologists working in mental health sector (per 100 000 population)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.471</td>\n",
       "      <td>6.876</td>\n",
       "      <td>1.060</td>\n",
       "      <td>1.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.001</td>\n",
       "      <td>7.005</td>\n",
       "      <td>4.003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2016</td>\n",
       "      <td>21.705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>222.572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "               Country  Year  \\\n",
       "0          Afghanistan  2016   \n",
       "1              Albania  2016   \n",
       "2               Angola  2016   \n",
       "3  Antigua and Barbuda  2016   \n",
       "4            Argentina  2016   \n",
       "\n",
       "   Psychiatrists working in mental health sector (per 100 000 population)  \\\n",
       "0                                              0.231                        \n",
       "1                                              1.471                        \n",
       "2                                              0.057                        \n",
       "3                                              1.001                        \n",
       "4                                             21.705                        \n",
       "\n",
       "   Nurses working in mental health sector (per 100 000 population)  \\\n",
       "0                                              0.098                 \n",
       "1                                              6.876                 \n",
       "2                                              0.660                 \n",
       "3                                              7.005                 \n",
       "4                                                NaN                 \n",
       "\n",
       "   Social workers working in mental health sector (per 100 000 population)  \\\n",
       "0                                                NaN                         \n",
       "1                                              1.060                         \n",
       "2                                              0.022                         \n",
       "3                                              4.003                         \n",
       "4                                                NaN                         \n",
       "\n",
       "   Psychologists working in mental health sector (per 100 000 population)  \n",
       "0                                              0.296                       \n",
       "1                                              1.231                       \n",
       "2                                              0.179                       \n",
       "3                                                NaN                       \n",
       "4                                            222.572                       "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HR_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    107\n",
       "2017     23\n",
       "2015     21\n",
       "2013      2\n",
       "2014      1\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking how many data landed in each year.\n",
    "HR_data['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing column names.\n",
    "HR_data = HR_data.rename(columns={'Psychiatrists working in mental health sector (per 100 000 population)': 'Psychiatrists', \n",
    "                             'Psychologists working in mental health sector (per 100 000 population)': 'Psychologists'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the data frame with selected data.\n",
    "HR_selected_data = HR_data[['Country', 'Year', 'Psychiatrists', 'Psychologists']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Country</th>\n",
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       "      <th>Psychiatrists</th>\n",
       "      <th>Psychologists</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.471</td>\n",
       "      <td>1.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.179</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.001</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2016</td>\n",
       "      <td>21.705</td>\n",
       "      <td>222.572</td>\n",
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      "text/plain": [
       "               Country  Year  Psychiatrists  Psychologists\n",
       "0          Afghanistan  2016          0.231          0.296\n",
       "1              Albania  2016          1.471          1.231\n",
       "2               Angola  2016          0.057          0.179\n",
       "3  Antigua and Barbuda  2016          1.001            NaN\n",
       "4            Argentina  2016         21.705        222.572"
      ]
     },
     "execution_count": 193,
     "metadata": {},
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    }
   ],
   "source": [
    "HR_selected_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering data with respect to the constraint.\n",
    "HR_2016_data = HR_selected_data[HR_selected_data['Year'] == 2016].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016    107\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking number of observations with regards to constraint.\n",
    "HR_2016_data['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "HR_2016_psylog = HR_2016_data['Psychologists']\n",
    "HR_2016_psytr = HR_2016_data['Psychiatrists']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking number of nan values in Psychologists column.\n",
    "HR_2016_psylog.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking number of nan values in Psychiatrists column.\n",
    "HR_2016_psytr.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping these nan values.\n",
    "HR_2016_data = HR_2016_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Psychiatrists</th>\n",
       "      <th>Psychologists</th>\n",
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       "      <th>count</th>\n",
       "      <td>82.0</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.259622</td>\n",
       "      <td>10.666988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.732605</td>\n",
       "      <td>31.795299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.211250</td>\n",
       "      <td>0.269750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.161000</td>\n",
       "      <td>1.198000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.031000</td>\n",
       "      <td>5.392750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>48.040000</td>\n",
       "      <td>222.572000</td>\n",
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      ],
      "text/plain": [
       "         Year  Psychiatrists  Psychologists\n",
       "count    82.0      82.000000      82.000000\n",
       "mean   2016.0       4.259622      10.666988\n",
       "std       0.0       7.732605      31.795299\n",
       "min    2016.0       0.014000       0.000000\n",
       "25%    2016.0       0.211250       0.269750\n",
       "50%    2016.0       1.161000       1.198000\n",
       "75%    2016.0       4.031000       5.392750\n",
       "max    2016.0      48.040000     222.572000"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a look at distribution parameters.\n",
    "HR_2016_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new column for HR_rate which is a mean of values in Psychiatrists and Psychologists columns.\n",
    "HR_2016_data['HR_rate'] = (HR_2016_psylog + HR_2016_psytr) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
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       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.259622</td>\n",
       "      <td>10.666988</td>\n",
       "      <td>7.463305</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.732605</td>\n",
       "      <td>31.795299</td>\n",
       "      <td>18.113543</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016500</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.211250</td>\n",
       "      <td>0.269750</td>\n",
       "      <td>0.273875</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.161000</td>\n",
       "      <td>1.198000</td>\n",
       "      <td>1.351000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.031000</td>\n",
       "      <td>5.392750</td>\n",
       "      <td>5.495500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>48.040000</td>\n",
       "      <td>222.572000</td>\n",
       "      <td>122.138500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         Year  Psychiatrists  Psychologists     HR_rate\n",
       "count    82.0      82.000000      82.000000   82.000000\n",
       "mean   2016.0       4.259622      10.666988    7.463305\n",
       "std       0.0       7.732605      31.795299   18.113543\n",
       "min    2016.0       0.014000       0.000000    0.016500\n",
       "25%    2016.0       0.211250       0.269750    0.273875\n",
       "50%    2016.0       1.161000       1.198000    1.351000\n",
       "75%    2016.0       4.031000       5.392750    5.495500\n",
       "max    2016.0      48.040000     222.572000  122.138500"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HR_2016_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      122.1385\n",
       "32      72.9745\n",
       "102     60.7810\n",
       "68      48.9795\n",
       "89      42.4205\n",
       "Name: HR_rate, dtype: float64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for potential outliers.\n",
    "HR_2016_data['HR_rate'].nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>Year</th>\n",
       "      <th>Psychiatrists</th>\n",
       "      <th>Psychologists</th>\n",
       "      <th>HR_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.2635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.471</td>\n",
       "      <td>1.231</td>\n",
       "      <td>1.3510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2016</td>\n",
       "      <td>21.705</td>\n",
       "      <td>222.572</td>\n",
       "      <td>122.1385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.840</td>\n",
       "      <td>0.788</td>\n",
       "      <td>2.3140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Year  Psychiatrists  Psychologists   HR_rate\n",
       "0  Afghanistan  2016          0.231          0.296    0.2635\n",
       "1      Albania  2016          1.471          1.231    1.3510\n",
       "2       Angola  2016          0.057          0.179    0.1180\n",
       "4    Argentina  2016         21.705        222.572  122.1385\n",
       "5      Armenia  2016          3.840          0.788    2.3140"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HR_2016_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>Year</th>\n",
       "      <th>Psychiatrists</th>\n",
       "      <th>Psychologists</th>\n",
       "      <th>HR_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82.0</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.259622</td>\n",
       "      <td>10.666988</td>\n",
       "      <td>7.463305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.732605</td>\n",
       "      <td>31.795299</td>\n",
       "      <td>18.113543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.211250</td>\n",
       "      <td>0.269750</td>\n",
       "      <td>0.273875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.161000</td>\n",
       "      <td>1.198000</td>\n",
       "      <td>1.351000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>4.031000</td>\n",
       "      <td>5.392750</td>\n",
       "      <td>5.495500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>48.040000</td>\n",
       "      <td>222.572000</td>\n",
       "      <td>122.138500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Psychiatrists  Psychologists     HR_rate\n",
       "count    82.0      82.000000      82.000000   82.000000\n",
       "mean   2016.0       4.259622      10.666988    7.463305\n",
       "std       0.0       7.732605      31.795299   18.113543\n",
       "min    2016.0       0.014000       0.000000    0.016500\n",
       "25%    2016.0       0.211250       0.269750    0.273875\n",
       "50%    2016.0       1.161000       1.198000    1.351000\n",
       "75%    2016.0       4.031000       5.392750    5.495500\n",
       "max    2016.0      48.040000     222.572000  122.138500"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HR_2016_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T9u7dyyrQqaioVLqPoYqKCiZMmIAJEyYgIyMDz58/R1xcHN69e4eoqCjcu3ePsT0/Pz/MnDkTDRo0qMpucFJmUCY6WnlFXNMVVQyguAKngoICVhqfz5e4HWXjmtc8JSUFJSUlnDXTXH1mK9NCQ5H+DftQXbiOVb9+/dCsWTOZPi86an1tx+PxYGRkhJkzZ0JPT48VnIeFhSE4OLhSgxrKmw9JuFoYeHp6YtmyZWjVqpWwy9L169cxbNgwheeP67ywt7dHly5dZPq8sroiEUJIVVCwTgip9W7duoUdO3aw0ocMGQITE5Mqr19PTw9ubm5wc3MTpvn4+DAGDsvLy8PLly/Rvn17YRpXrWppaWmV86NI//zzD2JiYlhNWGNjY1kBn4qKCmM5TU1N1voqTnkkcOXKFZn7p3Ids8o0G6+oWbNmMDAwYDQDLygoQFhYGHr37s1YtrCwEKGhoax1KHsQQWnq0j7U9HnfsWNHnDp1ipHWunVrrF+/XupnS0tLFdYaoiaMHDkShw8fRnh4OCN948aNGDBgQI22xuB6YLdnzx40adKEkSZr33B5zzOuVlaqqqoyz35Q2+7dhBACUDN4Qkgt9vXrV2zbtg3Dhw9HSUkJ472GDRtixYoVlVpvSEgI1q9fj6dPn4ptfsnVf1F0BGquYJarqX5NW7x4MaOJZ35+PpYuXcpazsXFhTESfPPmzVnLREVFITIyUvg6MzNTpiBJgOuYvXz5UubPc+HxeJwjSa9cuZIR/PL5fKxevZrVt1tfXx8uLi5VykNV1aV9qOnzvl+/fqxa0N27dyMwMFDs9fzs2TNs3LgRdnZ21ZFFpeK6dmNjY2t8nnCumunnz58zXoeFhXHOg85F3vPMxsaG1WoiMjISa9eu5WwRBJQ/YNi/fz/c3NxYU1kSQkhtQDXrhJBaISsrC8uXLwdQXov98eNHPHz4UOzAQzt27ECrVq0qta3Pnz/D398f/v7+0NPTQ5s2bWBubo5GjRrh69eviI6OZs3nXq9ePdbI8C1btmSte9++fYiPj4eZmRlUVFSgpqYmVzCrDOHh4ejYsSN69+4NHo+H0NBQzmbUs2fPZrzW1dVF27ZtGceipKQEgwcPFja5vXr1KufgaOJwHbN169bh/v37MDQ0BI/Hg7GxMRYsWCDzOgHghx9+wB9//MGYp/zVq1fo1KkTPD090ahRIzx48IA1gCAALFq0SOoc69WhruxDTZ/3TZs2xZQpU7Bv3z5hWnFxMSZPngwfHx+0b98e+vr6+PLlC5KSkhATE1PnBt+TpFevXnB0dERUVBQj/Zdffqny9GdV4eDgwDo3R44cCQ8PDxgYGOD169dyBcQtWrQAj8djPIB58uQJ+vbti3bt2gkfDsydOxdmZmYAgB9//BHjx49nrGfbtm04ceIEnJ2dYWxsjJKSEqSlpeHly5ecg5YSQkhtQsE6IaRWyM3Nxd69e6Uu16hRI+zYsQNDhw5VyHYzMjJw584dqcsNGTKE1ZzT1dUVDRs2ZDxQKCkpYdRwaWpq1miwbmhoiOzsbKSkpCAgIEDscgMHDuTs8zpjxgzWPM55eXk4ffq08HW9evWgpaWFrKwsqfnp06cPqwCen5+PwMBA4WtbW1u5g/VmzZph9+7dmDp1KqNZfXZ2Nv7880+xn/Py8sLMmTPl2pay1JV9qA3n/U8//YSnT5+ymoPHxcUhLi5OadutLX744QdWUBoVFYXQ0FD06tWrRvI0ffp0nDp1inHuFhYWsvqye3h44Nq1a1LXp6WlBRcXF9Z3/ODBA8ZAdqNGjRIG615eXpg/fz5rQNDPnz/XeMsDQgipDGoGTwipE1RVVTFkyBCEhoZWOVCXt1+nu7s7fv31V1a6lpYWVq1aVaW8KFurVq1w5MgRRvN2Ue7u7vj999853xs/fjyGDBki9rNaWlo4fPiwzNPPWVpaKm306qFDh+Ls2bMwNjaWuqyqqirmz5+Pw4cP16pR1+vCPtSG815NTQ2BgYGYPn26zH3QqzIgZW0zcOBA2NjYsNJ/+eWXGshNuQ4dOsDPz0/iiP7Tp0/H999/L/M6169fzzmdnyQ//fQT/P39Jd7zRLVp00Yh458QQoiiUc06IaTW4PF4qF+/PtTV1aGjowNjY2NYWlrC0dERXl5eEucMl8e4ceNgZ2eHW7duISoqCgkJCfj8+TM+f/6MoqIiaGpqwsLCAg4ODhgyZAjc3d3FrmvOnDlo0aIFDh8+jMePHyMzM5PVv76m9evXD+Hh4di5cyeuXr2KpKQkNGjQAG3btsW4ceMwZswYscEej8fDoUOH4O7ujhMnTiA2NhbFxcUwMzODp6cn5s6dC1NTU0aTZGl8fX3RoUMHnDx5Es+fP0d2dnaVB5kTcHd3x5MnT3DmzBlcvXoV0dHRwu9VV1cXVlZWcHV1xfjx42UePby61YV9qA3nvbq6OrZs2YK5c+fi5MmTuHfvHt68eYOsrCyUlpZCS0sL5ubmsLW1haurK/r06SPTQ5C6gMfjYcGCBawWFXfu3MGDBw/g7OxcI/maNm0a7O3tsWPHDty/fx+ZmZlo0qQJ7O3tMXXqVHh6euL27dsyr8/JyQnXr1/H9u3bER4ejtTUVBQWFkr93OTJkzFs2DCcPn0aN2/exLNnz5CZmYn8/HxoamrCxMQErVq1grOzMzw8PGBra1uV3SaEEKXhZWVl1fzkloQQQgghhBBCCBGiZvCEEEIIIYQQQkgtQ8E6IYQQQgghhBBSy1CwTgghhBBCCCGE1DI0wBwhhBBCyL9I586dK/W5R48eKTgnhBBCqoKCdUIIIYSQf5E3b97UdBYIIYQoADWDJ4QQQgghhBBCahmqWSeEEEII+RfJysqq6SwQQghRAKpZJ4QQQgghhBBCahkK1gkhhBBCCCGEkFqGgnVCCCGEEEIIIaSWqZZgPSEhAUZGRtDV1YWuri7Mzc3/0/2pEhMThcdC8DdgwICazladIHrc7OzsajpLVXL79m3WPs2aNaums0XquFmzZrHOq9u3b1dqXf+Vc1SZ92W65xNCCKlJdaX8XFfyKU5WVhbMzc2F+TcyMkJCQkKV1lktA8z9+OOPKCwsFL6eMmUKdHV1q2PThBBCCCGkDkhLS0NERAQiIyMRERGBx48fIycnh7GMq6srQkJC5FrvpUuXEBgYiEePHiE9PR18Ph+Ghobo3LkzhgwZgv79+8u8ruTkZJw4cQJXr15FQkICMjMz0bhxYzRr1gyenp4YN24cTExM5MqfNAUFBTh16hQuXryI2NhYpKenQ0NDA6ampnBzc8OYMWNgb28v8/qePHmC06dPIywsDMnJyfj69SsMDAxgY2ODAQMGYMyYMVBXV6+RvBFSl+nq6mLy5MnYvn07AKCwsBCrV6/G8ePHK71OpQfrUVFRCA4OFr5WV1fHnDlzlL1ZQqpdYmIi6wepMoUKUvdt2rQJvr6+jLRdu3bh22+/raEcEUUSfdhsbm6OZ8+e1VBu/lsCAgJYZYhly5ZhxYoVNZQjokitW7dW6PqSkpIwffp03L17l/VefHw84uPjcebMGXTr1g0HDhyAsbGxxPUdOnQIa9asQV5eHiM9LS0NaWlpePToEbZv344NGzZg0qRJCtmHe/fuYdasWUhMTGSkFxYWIisrCy9evMDvv/+OyZMnw9fXF6qq4ov2xcXFWLZsGY4cOYKysjLGex8/fsTHjx9x7do1bNu2Dfv27YOzs3O15Y38+9jZ2eHDhw+MtP9Cy+o5c+bg999/R0FBAQAgKCgIUVFRcHR0rNT6lH7ViBZYvb29YWhoqOzNkn+pmTNnMl43bty4hnJCCCGEkNoqNTUVAwcOxLt376Que+fOHXh5eeHixYswMDDgXGbHjh1YvXq11HXl5uZiwYIF+Pr1K2bPni13viu6e/cuRowYga9fv0pcrqysDAcPHkRaWhqOHj0KFRV2L9eysjJMmDABly5dkrrdhIQEeHt749y5c3BxcVF63ggR+DeU842MjODt7Y2TJ08K0/z8/HD69OlKrU+pwXpcXByuXLnCSBsxYoQyN0n+5TZv3lzTWSCEEEKIkqmrq8PMzAxv376t1OcXLlzICtR1dHTwzTffgMfj4fLly4xavjdv3mDRokU4duwYa13R0dFYt24dK93FxQW2traIjY3F/fv3Ge+tWbMG3bp1Q/v27SuV/7y8PMyYMYMVDLdo0QLdu3dHeno6rly5gpKSEuF7QUFBOHDgAL777jvW+vbu3csK1FVVVeHp6QkDAwPcvn2bcby+fPmC6dOn4/79+9DU1FRq3ggR+LeU84cPH84I1v/3v/8hLi4OLVu2lHtdSn28dezYMfD5fOFrQ0NDuLm5KXOThBBCCCGkDho5ciR8fX1x48YNfPjwAf7+/pVaz507d1hd0AwMDHDv3j3s27cPe/fuxd27d1m16BcuXGAF3UD52EulpaWMtIULF+Ly5cvw9/fH5cuXsWjRIsb7JSUlWLNmTaXyDwA7d+7Ex48fGWndu3dHeHg4tm3bhpMnT+LMmTPg8XiMZXx8fPDlyxdGWl5eHjZt2sRI4/F4+OOPP3Dy5Els27YN4eHh6N69O2OZDx8+YM+ePUrNGyH/Rj169GDcX/h8fqX7rSs1WD937hzjdb9+/VCvXj3WcseOHWON/nf27FnWckOHDmUss2DBAtYyW7duZa0rLCyMM385OTnYt28fRo0ahXbt2sHU1BRGRkawtbXFkCFD8Ouvv+LTp08S91HcqIWpqalYs2YNnJ2dYWZmBl1dXQQEBEhcl6jY2Fi0bNmStY1Vq1ZxLp+cnAw/Pz8MGjQItra2MDIygrm5OTp27Ii5c+eKPQ4CAQEBrG0Jbu5hYWGYOHEi2rRpA319fVafzdzcXOzduxfDhw+HnZ0dTE1Noa+vDysrKzg7O2Po0KH48ccfceHChSr1V5FllEhJ+xEdHY1Zs2ahXbt2MDQ0RMuWLTFq1CjcunWr0nkSjLzNNYDK3bt3Kz0KdEJCApYuXQoHBwcYGxujefPmGDRoEP7++2+ZPv/161ccPnwY48aNQ/v27WFmZgZjY2O0a9cO3377LU6dOsV46l1Zko53SEgIhg4dihYtWsDMzAxubm7Yt28fiouLhZ8vLCzE3r170aNHD5iZmcHCwgJ9+/bFsWPHWH3quBQXF+PMmTOYNm0aHB0dYWFhAUNDQ9ja2mL48OHYv38/8vPzJa5D3HmVnZ0NX19fdOvWDWZmZmjatCnc3Nywfft2xqCZousR7f4DlPdhEt1OxXtCRkYG/v77b6xfvx5Dhw6Fi4sLbGxsYGRkBGNjY7Rq1QrffPMNVq1ahadPn0o9LtWhNpyjkZGR2LdvH2bOnIlevXrB3t4ezZo1g76+PiwsLODo6IgJEybgxIkTVSokbtq0Sfi9ifrw4UOlR7D99OkT1q9fD2dnZ5iYmMDCwgKenp44evSoTOe/NIWFhTh+/DjGjh0LBwcHmJmZQV9fHy1atEDnzp0xcOBALF26FH/++SfS0tIkrisuLg7r169H37590bp1axgaGsLCwgIuLi5YvHgxoqOjZc5XZmYmdu3ahdGjR6Ndu3bC797Ozg7ffPMNfHx88ODBA+HydnZ20NXV5RzzxtfXV+w9SNT79+/h4+ODfv36wdraGoaGhjAzM0P79u0xYcIEHD9+nPPaFpA0O4Lg98XOzk44A45oX15pJM3kcPbsWQwZMgRWVlYwMjKCvb09Fi9ejPfv38u07posI8jq999/x4wZM+Do6Ij69etXah0AOAvFc+bMQdOmTYWvmzZtytlMXbRmPSEhgdXnXUdHB8uWLWOkLV26FNra2oy0sLAwuc8BoLxgz1VmXLt2LdTU1ISv3d3d4e7uzlgmJyeHdQ8ODAxEbm4uI613797o3bu38LWamhrnw4UTJ04wKt4UnTdZSTr/Hj58iGnTpqFt27YwNDSEtbU1Jk6ciPDwcJnWrcx4IDMzEz4+PujatauwjNO7d2/s27dP7O+bPDOIKHL2l6KiIty4cQO//PILxo0bh27dugnvzwYGBrC0tES3bt0wZ84cXL16lfM3quL3JNpfnes4id4r5P0tTU9Ph7+/P4YOHQpbW1sYGxvD1NQU7dq1w6hRo7B3717WuV+RpGPOdg9TAAAgAElEQVSdkpKCNWvWwMnJSe7f53r16qFfv36MNNG4WGZZWVl8Zfw9fvyYD4Dxt3PnTs5lo6KiWMtOmzaNscznz5/5WlpajGWsra1Z6/Lw8GAs06BBA35ycjJruX379vG1tbVZ2xX909DQ4Pv4+IjdT9Hlzc3N+SEhIfzGjRuz3tu1axc/KyuL/+TJE9Z7rq6ujPVGRkbyjYyMWMt9//33rDxkZGTwly5dym/QoIHU/fH09OQnJCRw7suuXbtYyy9btow/d+5cznUJPnfz5k3OvIr7W7VqVaXPK67jLet+LFmyhK+ioiI2X35+fpXK05gxY2Ted9HvOigoiPX+mDFj+AcOHOBramqKXce8efMk5unYsWP8Jk2aSM2LlZUV/+7du1W61rmO95IlS/jffvut2O326tWLn5yczH/16hXf0dFR7HLDhw/nZ2Zmit32xYsX+ebm5lL309jYmB8UFCTXeXXlyhWJ53WXLl34KSkpEtcj7U9wT8jKyuL7+fnJ9dmJEyfyP336JPM5KWn/Jf3V9nNUT09P5mNmZmbGv3z5Mud6pN2Xly1bJtf3U/HeJG7df//9t8RjMGTIEInnv7S/J0+e8K2srGTO8/jx4znXk5KSwp8yZYrE+6fgb+zYsazrouJfZmYmf8OGDfyGDRvKdQxluc4r/i1btoyx3U+fPvHnzZvHr1evntTPmpiY8P/66y+5roeff/6Zc91Pnjyp8u/Jn3/+yff09BSb30aNGvH/+OMPseus6TJCVf64jrdoeYnrj+u6unXrFmu5sLAw1nJNmjRhLMN1b+7fvz/ndvv168dadsuWLXLv971791jr0dbW5lx248aNrGUHDx7MWGbQoEGsZX7++WfO65OrbBweHq60vMn6J+78W7p0qdh7E4/HY90LRP+UGQ9cu3aNb2hoKHadDg4O/Hfv3rHWJUucIOmewfV7z5U/Wc47SX9dunThx8bGSv2epP3Jm0/B34YNG/jq6upS16+jo8M/cOAA5zrEHeuAgACJ54Usv887duxgfS46Olruc19pNet37txhpXXq1IlzWUGtW0WiT8OePXvGejLy+vVrZGRkCF+XlZXh4cOHjGU6duwIDQ0NRtqOHTswY8YM1nQgXPLz8/Hjjz9i5cqVUpcFykc5HDNmDDIzM2Vansv79+8xePBgpKamMtIXL16M9evXM9L4fD6mT58OPz8/FBUVSV33lStX0K9fP4lPmSo6ceIEdu7cKfb90tJSTJ48mZXX2ubQoUPYsmWLxCdhq1atwqtXr6oxV9zu3LmD7777TmIt4I4dO3Dz5k3O9w4ePIgJEybg8+fPUrf19u1b9O3bF8+fP69sdsXmQVJLktDQUGzcuBHffvstoqKixC539uxZseu5fPkyvLy8OJ/cikpJSYG3tzdu3LghPfMor+UeOnSoxPM6PDwcW7dulWl9ynD06FGxrWyUra6eox8/fsSoUaOqPOepIsTFxWH06NESj8Fff/1VpeleZs+eXen+vgJFRUXw9vbGoUOHZKrpP3nyJEaOHCm2xmju3LlYvXq11EGpFKmsrAzjx4/Hjh07WE2ZuSQnJ8Pb2xtBQUEyrf/WrVtYuXKlTOuujB9++IE1/k9FeXl5mDBhAmfLhpouI9SE9+/fc15XXCPNW1tbs9I+f/7MaOLNdVy5Picu/cmTJxLzy0XR25R1fTwej/M4VVxfTRwPcQICAuDn5yf23sTn8+Hr64v9+/dzvq/seGDYsGESWyw9fvwYI0eOVNq9Q9nCw8MxevRohbTSlNeSJUuwevVq4YjrkmRnZ2PatGnYu3evTOt+8eIFJkyYIPG8kOX3mSvu5YqPpVHaAHOPHj1ivFZXV5c4JUfXrl1x5swZ4evY2FhkZ2dDR0cHADj7EPH5fNy/f184P+azZ89YB1a0/83Dhw+xdu1a1rqMjIzg7u6O+vXr4+bNm6xmZbt374arq6vUJswVf+CcnJzQtm1b5ObmIiIiQuLnBJKTkzFo0CBWX6ClS5dy3iD27t3L2azC2dkZ1tbWyM3NRWhoKKPpeWxsLJYsWSLTSfvPP/8AABo1aoSePXvC0NAQ//zzj7CJzcOHDxEfH8/4jJaWFnr06AFjY2MUFhYiOTkZL168QFJSkvQDoCTp6ekAyh8Mubm5ITU1FZcvX2Y07SouLsahQ4c4mzBL4u7uDh0dHeTm5rICS1NTUwwaNIiR1qJFC4nrEwSfJiYm6N27N758+YLg4GBG03EA2LdvH3r27MlIi46OZjXNAwArKyt07NgRKioquH//PuM7ExT07t+/jwYNGkjdX1kIHqK5uLjA2toa4eHheP36NWMZQQGPx+PBw8MDRkZGuH79OpKTkxnL7d27F+PGjWOkJSUlYdq0aawfuKZNm6JLly7Q0NBAZGQkXrx4IXyvuLgYU6dORUREBJo0aSIx/4IgVFNTE56enmjUqBEuXrzIKgAeOnQIy5cvF05HIxjFNCIignXN9+rVi1VwsbGxYW1bV1cXbdq0gZ6eHvT09KCmpobs7Gw8f/6csT8AcODAAcyaNQvNmzeXuD+KVtvOUR6PB1tbW5iamkJPTw86OjooLi5GUlISwsPDGffl7OxsbN26Ve4Ao3PnzsLvV/TeqaWlxZqWT9oItikpKcLlPD09wePxEBwczJoSat++fZgwYYJceQXK792iTXfV1NSE3U1KS0uRmpqK2NhYic10165di3v37jHSVFRU0L17d1haWuLTp0+4fv06o6vJrVu34Ofnx/rNOnDgAOfDtwYNGqBbt26wtLREQUEBnj9/zirQjxs3DpmZmXj16hVCQ0MZ73Xq1IlVKOrcubPw/9u3b+ccAdvW1hYuLi7Izc3F1atXkZ2dLXyvrKwMM2fORMeOHWFqairu8AD4/9/JBg0aoGfPnjAzM0NaWlqlCmVcPn78CB6Phx49eqBly5aIj49HaGgo4/ersLAQ8+fPZ3XpqukyQk0QLZMAgLa2Nue84erq6tDW1maVHd+9eyesROJan7gR47nSZRmNXpbPyLPNxMRElJaWol69eigpKeHsKlHZfVBk3qpKUFZu06YNnJ2dkZmZiatXr7IeJK9duxZeXl6MqfmqKx7Q1dWFp6cnNDQ0WIP4AeXlhf3797NGQa9JampqsLOzg76+PvT09KCpqYn8/HzExcXh0aNHjOD8yZMnOHv2LEaPHg2gvFwj2JeAgADWgz9F7Of58+c5H8A0a9YMbm5uKCkpwfXr11kPSlatWgUXFxd06NBB4voFFa5V/X22traGmpoao2vVo0ePWGVaaZQWrAsKIgJ6enoSp2vo3r07I1gX1JL36dMHAHewLkgXBOtcfVO6devGeL1582bWEzjBg4JGjRoBKK9JmDJlCmN+eAD4+eefZepvrKamhiNHjrD6KkjrL5meno7Bgwezan1WrVqFJUuWsJbPz89n1eypq6vj5MmTjH5CWVlZGDBgAGJiYoRpZ86cwaJFi9CqVSup+2Nvb48zZ87AyMhImCY4YQU/1BVduXIFtra2rPT4+HhcvHhRasFHWYYPH449e/YI+8Ht3r2bVZjkmotVmhEjRmDEiBFITExkFUQtLS0rNbJlt27d8McffwhHYA0ODmZd3KIFaKC8X63oE85Vq1Zh8eLFwoFeSkpK8P333zPy+u7dO5w+fbpSQYE4S5YsEdb8fvnyBc7OzqyHUED59zBmzBgA5eeTg4MDowbo+fPnyMrKYvRr8vf3Z900J0+eDD8/P+H3y+fzsWnTJvj5+QmXyczMxJ49e/Djjz9Kzb+RkREuX74MS0tLAOWDCbm7uzNazWRkZODFixfC0X4F3/WmTZtYwfrw4cMlzrPevXt3XL9+HQ4ODmLvlbt27WLUppeWluLSpUvCPrPVqbacozt37oSTkxP09fU58/np0yf06dOHUeAODg7Gjh07WIMfSeLh4QEPDw8A7GBdV1e3Ute5tbU1QkJChHmPioqCp6cn4/jExMSwzn9ZcN2bjx07hr59+7LSk5KScOXKFdbDr6SkJBw8eJCRpqenh8DAQMYYHR8+fEDfvn0ZD2R3796N2bNnC/NdUFDAeYzat2+P48ePo1mzZoz0N2/e4OjRo8LXggc8AQEBrGC9d+/eYudZ//LlC3777TdW+ty5c7FhwwbhOZCSkoL+/fuzRsPetm2bTA9wLSwscP78eVhZWQnTCgsLFTJNFY/Hw7Fjx+Dl5SVMu3TpEsaOHcsI2J8+fYrbt28LKylqQxmhJlR86CIgOpp5RRoaGqxgveJredbXsGFDVpostbayfEaebfL5fOTm5kJXV1dsK4nK7oMi86YIs2fPxsaNG4XXcnx8PPr27csI1ATjo1S8T1RHPGBlZYVLly4JH1qUlJRgxowZrAdoe/bswYwZM+T6TVIGExMTnDt3Dl27dmW1ShaIjo6Gu7s749gFBQUJg/WOHTuiY8eOAMrHLRI9/xQx2jvXmCTe3t7Yt2+fsAyYm5sLb29vRuVxaWkpfH19cerUKanbUMTvs4qKCvT09BiVUKLxsSyU1gxeUJMpIK2WQbQGHABjcJmK/68452PFIF40oFdTU4OTk5PwdXZ2NucAKr/99pvwwgTKn477+/uzBjeJiYmRqfnkggULWIE6IPnHIjs7G0OGDGHVPq5du5YzUAfKm1KI1vSNHTuWNaAH12B8ZWVluHDhgsT9AMoHSDh8+DDjRxiA8HhVPG4CojWjApaWlpgzZw6GDh0qdbuKpqamhk2bNjG+06lTp7Ke7Mo6UI+ybdq0iXG+DBw4kPWQIzs7m1Ebkpubi+vXrzOWad26NZYsWcL4AVBVVeUMVgMDAxWVfRgaGmLp0qXC15qamujatStrOQcHB2GgDpTXjFe8ZgVEvxfRc1dHRwebN29mfL88Hg9Lly5l/eDIup8LFiwQBupA+fkreHgoKW+VZWtrK/yBi4iIwPHjx7F582asWbMGy5cvx/LlyxmFaQFFNimUR205R/v37w99fX2kpaUhJCQEO3bsgI+PD1auXInly5dj69atrHt5VlZWpQZ9UrQ1a9YwHjI4OjpyNpurzDkmz73Z1NQUkyZNwtSpUxnply5dYjWdnj9/PmswTXNzc9Zn8/LycPXqVeHrO3fusAZo0tDQwKlTp1iBOgC0atUKPj4+nPmVx507d1iDmjZt2hTr1q1jnHPGxsasbmYAWCOKi7Nr1y5GoA6U/+5UZZA0gQEDBjACdaB8wF7RNACsY17TZYSawDWgqKRaXK7vqGLlijzrE7SyErcuWcmzTXHnmGC74rqcVHYfFJm3qjIyMmJdy5aWlli8eDFr2YrXRnXFAz/99BOjdYGqqiq2bNnCaiGWmJhYK7phNm7cGL1794aGhgbevHmDs2fP4pdffsH69euxYsUKLF++HKdPn2Y9hKnOckhsbCzevHnDSNPQ0MCvv/7K+J60tLQ4uyreuHFD6qDDgOJ+n0XjX9H4WBZKq1kXfRKppaUlcfnmzZvD3Nyc0f9UUFOekJDAKGR8//33wsA8OjoaBQUFUFdXZwXrnTp1YjR7evLkCavmwNLSkrN5voGBATp27MhaZ1RUlNQmp5Jqz8Th6o+5YcMGzJs3T+xnuJrWHzp0CIcOHZJpm6L9+7l06dJFYrNtZ2dnqKurM/qMeHt7w8LCAjY2NmjZsiVat24NBwcH2NnZKaTZU2X07NmT1RxLTU0Nurq6jMKMrP30lKlNmzaco18aGRmxuhJUfDodHR3NqrF8/fq1zE+vRbuuVIWbmxvrx61i8zMB0UKjuOUqfi+JiYmsvuTZ2dmswqI4cXFxyMjIgJ6ensTlRo0axUrj2oaizpnS0lJs374du3btkjrqbEUVx+2oLrXpHH3+/DnWrFmDmzdvyjV6ekZGRrV3H6hIW1ub86Guos4xGxsbGBsbM57iL1iwAL6+vrCxsYGVlRVatWoFe3t7ODg4cHYv4PqNWbduHed801wePnyIESNGiF1X3759GaNzK0NkZCQrzd3dnTMg6dOnD+rVq8coJ3z8+BFpaWkwNDQUuw0LCwvOCgdF8fT05Ez38PBgBdTPnj0T/r82lBFqAleNoKQ+taLddwBm5Yo86+NKl1RRI4482+TKf8XtctVuS1qftH1QZN6qqkePHpz3Lq4H68+fPwefzwePx6uWeEBVVZUx2r6Anp4eOnbsyDk2F1fXuOoWFBSEjRs34uXLlzJ/pjrLIVz3dCcnJ85yhL29PUxNTRnlksLCQsTExIgdRw1Q7O+z6AwRXC11pFFasC7oay4gSzMgV1dXnD59Wvg6KioKxcXFjAvEysoKHh4eaNiwIb5+/YqioiJERkbCxMSE1bRA9MeTa8AR0YHtKuIqREh7IqKtrQ0LCwuJy8jC1tZWar8OeQr0XKRN0wMAbdu2lfh+48aNsW7dOixfvpyR/v79e9bTJgMDA0yaNAkLFy4U27xGWcR9J6J92Co2Kawp5ubmnOlc/e0qBidVPR/y8vLw5csXhfyIcl1XXPnnusa4nsQrcj+B8qkVJQXrjRo14ny/4rQ0XHmriqlTp+Kvv/6S+3PVOVCXQG05RyMjIzFo0KBK1dLUxHGryMzMjLOJtKLOMRUVFWzZsgWTJk1iFEqTk5ORnJzMaEqura2NMWPGYMWKFYwCjyJ/Y7h+OyWNY6Mo8vzuq6urQ19fn/UwMD09XWKwLu13sqrE5Zfr/lnxO6sNZYSaIFr+BCRf71zvVSxgc61P3D2HK120sC4Lrs/Is00ejyesJBNXWVbZfVBk3qpK3LXB1d2yqKgIOTk50NHRqZZ4QF9fn/N+Li5/iijbVNX+/fvFtuaVpDp/Tyvz3YlWIkj77hT5+ywa/3LdT6RRWjN40QGcRJuhcRENrvPz8xEdHc0I1l1cXFC/fn04OjoK0+7fv8/ZN1K0vzpXIKbo/iGVuSlziY2NxYwZMxQWCHCRpYAry0k1c+ZM/P333+jTp4/YGxNQfnFs2bIFEydOlCufiiAuMFNEf0JFq8m8KqppGtd5wHWtSTpflEnaforrtqOsliFXrlypVKAO1MwDptpyji5ZsqTS52xNP5irjnPMy8sL169fx5AhQyQ+hBPMMezl5SW2JqwypH031dE/szp+9ytT+JKHPPlV5L4pqoxQ3Sp2XxLIycnhbPqan5/PWTNWsbUA1/rEPcjgCgIq0/KA6zPybLNZs2bCe4mqqipnhUVl90GReasqcee7tPTquC/Iuz5py4uLBxRVq52RkYE1a9YoZF3KVB3fnSJ/n0VnBxM3vo4kSqtZF23KmpGRgbKyMqmDzIm6f/8+K1gX/CsYafX+/fusp97q6uqM0WAB7gMkadonrgF6xI14KVDZE6ZZs2as/p3nz5+Hjo4O/P39OT/DtT/dunVDu3btZNqmpJoCAVn3p0ePHujRowfy8/MRExODN2/eID4+HjExMaz+IVeuXMHDhw85+yaTyuM6H1q0aCG2CSWX6m7xUBlc+2lgYIBhw4bJvA5Zzv3qxNUvtmPHjli3bh3s7OyEtZ1v3rxh3dfqEkWeo8nJyaxp/+rVq4cff/wR3t7eMDExETaPnDp1KueI2P8FHTp0wJEjR1BcXCy8NyckJODly5e4evUq46n/s2fP8Pfff2P48OEAuL+vfv36cfYx51Jx9gOudVVHH015fvcLCgo4a7eU9bsvK3H55SqjVHyQVpvKCNXJwsICTZo0YdXAvX79mjXeAtc5qK+vz6ip69ChA06ePMlaFxeu9UkbeZoL12fevHkjbMYt7zY7dOjAau34+vVrVnNxPp/P6g8suj5F560q5Lk26tevL6zRr454ID09HYWFhZyVElyzI1W8drliJXFTlMkyfa0sRGf1AMr3cfPmzXBzc4Oenp4wWLW2tq6xKZur47tTlLKyMtbDFK6untIoLVjv3LkzTpw4IXxdUFCAN2/eiJ2LESi/wVpYWDBuKBcvXmRc7F26dGH8C5QPPidak9+5c2fWBWJvb8/qjxYfH4/Xr1+zmuOlp6dz9ouoWKOvSGZmZti7dy+GDh3KuCAPHz4MHR0dzj6CgsGoKhJcWNLw+Xyl1CxpaGiwptG5efMmhgwZwlguIiLiXxescz1xU2bLCFH29vZQVVVl9B3Lz8/Hxo0bZXoaqKipVJTNwsIChoaGjCf52dnZWL58uUx9n6tjP7nWL2keVa5R8rdu3QoHBwdGmiLHFagJijxHuY6Zh4cHfvjhB9ZnRIP6qlJRUWFc29V5nVdW/fr10aFDB0ZBOS4ujvU7EhERIQzWO3bsyBo1t3Xr1pwDsYkSvc64HjL973//Q1JSklwzhMh7bXH9ToaGhqKkpITVb/3atWusdZmZmdX4w72rV69yztQhOlgjAOHMFEDtLCNUlz59+jC6VQLl37tosC46swAA1owJnp6eWLZsGeN43L17lxWIFRYWslp58ng8zv7T0tja2rLKwzk5OYiMjGT1tZV1H0THNwgNDcWcOXMYaREREaxmu82bN2eU3RWdt6oICwtDUVERq9/6tWvXWMva2dkJHyZURzxQUlKCGzdusPo+Z2Zmcv4mVbx2ZR0g9MWLF6wpXSuL6zd1/vz5rIqQ9+/fyxyoi7tfV6UMxnVfe/jwIeeo7E+fPmU9GFFTU6u27jsvX75kTNsGcP8WSqO0NotcIz/LUmASbboeHh4uvEEaGBigZcuWAMoHExB82Tk5Oax5MLlq6XV0dNCjRw9W+oIFCxjNvYqKirBw4UJWc8C2bdsqdUCiLl26YP/+/awnar/99hu2bdvGWr5bt26s5qiBgYHYs2eP2MLL27dvsW3bNnTq1EkhT+M+fPiAWbNmISwsjHVCCnBd1KIjDP8bcN1c3717p9BmpZJoa2ujV69ejLTk5GTMmjVL7JgRnz59QkBAAPr3748//vijOrJZZTwejzUKclFRESZOnCi2z1dOTg4CAwMxatQo/PLLL0rPI1eTY0m1iFz99CsOFCV4XReaqEmiyHOU65jFxcUx+s4VFRVh+fLlnPMkV4XotZ6WlsZq6lbTSktLMWHCBFy6dElsc2aupqsV7839+vVjHefdu3cjMDBQbCD37NkzbNy4kTUAYbdu3Vg1Ivn5+Rg9ejTnaLqJiYmccyDLe21169aNVYD7+PEj1q9fz9iH1NRUzutLlumZlC04OBhBQUGMtCtXrrDSAAinFwRqRxmhpowfP56Vtnv3bkYt28ePH7F7926pn23evDmrbJqTk8Oa0s/X15d1H+vRo4fMLVEq4vF4nIMVr1+/nlHWunbtGm7cuMFYRltbG4MGDWKkDRkyhNVP/Pr164wHPoWFhZwP4saNG8eoMVd03qoiNTUV69atY1zLCQkJnKOAV7w2qiseWLNmDaMrQElJCZYtW8YqL1tYWDAeiOjq6rK6mKSkpDAeuOTn57OmH64KWcohnz59wowZM2ReJ9f9Wp6B67jY2tqyppTMz8/HokWLGN9Tbm4u56wA7u7u1daKlGuQT9F7iSyUVrPeqlUrmJiYMJ4ERUREMKZp4tK9e3dWcyOBilO2aWlpoU2bNqwTSUDcwVi+fDlr1OB79+7B0dERvXv3hqqqKsLCwjin9VHkRSGOl5cX/Pz8WCfY2rVroaury+jv3bBhQyxatIgx7zIArFixAjt37kTHjh1haGiIgoICpKSk4MWLF5xNb6qiuLgYp06dwqlTp9CwYUPY2tqiRYsW0NbWRmlpKeLi4jjHExA8dPk30dXVZTW9S01NRa9evRgtPb799lvOkbQVYeXKlbhx4wajIHbmzBlcvHgRrq6uMDExAVA+QMerV6/w9u1b4bVQmVkMasrChQtx+vRpxo9qWFgY2rZti65du8LMzAz169dHRkYGXr9+jdevXwtrcxXZBE8crvN73759iI+PFw5coqamJiwYOTg44PLly4zlf/jhB1y4cAHm5uZITExEWFiYxBrEukJR56iNjQ00NDQYzfbevn0LJycn9OjRQzg4qTKmY2zRogWio6OFr4uLi9G7d29069ZNOPJy//794ebmpvBty4rP5+PChQu4cOECGjRoIJydQ9AX78OHD7h16xbrcxXP3aZNm2LKlCnYt2+fMK24uBiTJ0+Gj48P2rdvD319fXz58gVJSUmIiYkRO3CPuro6li5dypjSESiv+ejUqRO6d+8OS0tLFBQU4NWrV4iMjETTpk1ZwQPXtRUcHIyhQ4fCyspK+BB/9erV0NTUhKamJhYsWMBqnbZjxw5cv34dzs7OyMvLw5UrV1ij9GpqauL777/n3J/qxOfzMWHCBPTs2RMtW7ZEfHw8bty4wXpg0q5dO8Y5VxvKCPLy9fVlPPjiyk98fDxrUNsRI0YwatxcXV3Rv39/XLx4UZiWlpYGV1dXfPPNNwCAy5cvs8ZTGjRoEKOsKbBhwwb07t2bcd/69ddfce/ePbRp0wYvXrxgjRauqqqKn376SZbd5jR79mwcO3aM8YDh9u3b6NKlC9zc3JCWloYrV66wzoMff/yRFSQ1atQIK1asYJRj+Xw+Ro0aBU9PTxgaGiIsLIz1YNPc3ByzZs1Sat6qavfu3QgLC4OzszOysrJw5coV5OXlMZZp2LAhJk+ezEirjnhA0HXN09MTDRs2xJ07d/D27VvWcjNnzmQ9EOnatSsuXbrEWG7q1Knw8vKCpqYmbty4wdnEu7JEW/IB5b/Nr1+/RocOHfD582eEhYXJNGC4QMuWLVlTzg4bNgy9e/cWPjzq0qULBg8eLFdeV6xYgSlTpjDSzp07h8jISLi5uaGkpATXr19nVRbWq1cPy5Ytk2tbVSEarJuZmXGOgSGN0oJ1ABg6dCjjqWVISAi2bNkid791AdEbqIuLC2ewLmiKzcXJyQnr16/H6tWrGempqaliHxIA5Tem6nrCPm3aNCQnJ7NqAH/44Qdoa2sz5imfPXs2IiMjcf78ecay//zzj0IvYll8/foVkZGRnM2FKmrWrFmlmoXVBR4eHk6X1LcAACAASURBVKwa6ufPnzOm5uvSpYvSgnUHBwf4+vqyHvbk5eXhf//7n1K2WROaNm2K/fv3Y/z48YzCU2FhIWezu+rm6uoqnLFCoKSkhFFw1NTUFAYi48ePx7Zt2xjLl5aWspryeXh4cDbvq0sUdY6qq6tj4sSJ2Lt3LyP948ePCAgIEL7W1taGjY2NTNNQyapPnz6MYB0ob0Xz7t074eumTZvWaLBeUVFREZ4+fYqnT59KXE5HR0c41ZrATz/9hKdPn7KmGYqLi0NcXJxc+fjuu+8QGRnJukcWFRVxNunmYmNjw5rmlc/nIzQ0lHHtL1myRBgUzJ8/Hw8ePGAVfCU1IVVRUcHevXvlaqKvLLa2toiNjWXtY0UNGjTAjh07WOm1qYwgixMnTkit0U9KSmJd93Z2dqzmsf7+/nj58iXjuszKymI1jxewsrIS2/KqQ4cOWLduHavsKDquUkU//fQTo2mzvLS0tPD7779jxIgRjN8G0XtNRQMHDsS0adM435sxYwZu377NuA5Ef5cq0tTUxP79+zmDa0XnrbJsbGzw8uVLxMTEsILCitasWSN8ECyg7HhAV1cX6urqSElJwZkzZ8Qu5+joiO+++46VPmPGDNY9q7i4mHUtc43PUBldu3ZF+/btWb8T0dHRjN+7du3aIS0tTabZIvr06cPqfpGSksL4jS4tLZU7WPf29kZ4eDj279/PSE9ISEBCQoLYz23cuLFaKmyA8v0S/f7kGVupIqUO3Ttx4kTGk6KUlBThoHDimJmZiW1aUrGfOtdrAScnJ855FwXmzZuHffv2yTRyu7q6Onx8fPDzzz9LXVaRVq9ejbFjxzLSysrK8N133zEKNTweD/v378eKFSvkGlnbyclJISPXyzu4TPPmzXHq1CnOKZ7+DWTtN61M06ZNw4kTJ+QaQMPS0rLOtXbo378/goOD5eqaYmxsLPPgSlWhpaXFqs2SxNTUFL///rvE62LgwIEy9TWtCxR1jq5du1biA14dHR0cPXpU4ef27NmzFTJFpzLJe2/W19dHQEAAq3+2mpoaAgMDMX36dJn7GaqoqKBnz56c7+3Zswfr1q2rdDNEFRUV+Pj4yDXzgIqKCo4fP4558+bJtA8mJiY4f/48q7tNTfn5558ZD+lFNWzYEEePHuWsGavpMkJNMjIyQnBwMGe3TFGurq4ICgqSeE+aN28efvnlF84ubxU1atQI/v7+mD17ttx55srX2bNnpd5veDwepkyZgsOHD4u9NurVq4djx45h0qRJUu8PzZo1w7lz5zhbGSgjb5U1ePBg+Pj4iL2ueTweFi9eLHYqZGXGA1paWjh//rzEacXat2+PM2fOsMbPAICePXti3rx5Yj/boEED+Pn5yTVAqyQ8Hg9HjhyRmF9ra2ucOnVK5nvJqFGjlDbWl5+fHzZs2CBTPKGjo4P9+/dLnRJbkcLCwhgtzXg8HsaNG1epdSm1Zt3a2hoeHh64evWqMO3s2bNSaxu6d+/OejLSsGFD1hNKccG6LP0BRo0ahX79+uHkyZMIDQ1FTEwMPn/+jLKyMjRu3BjW1tbo0aMHxo8fX22jBoravn070tPTGcevuLgY48ePx/nz54U3UUGzjilTpuDkyZO4ffs2YmNjkZmZiaKiIjRq1AimpqawsbFB165d4eHhobC+95aWloiOjsbNmzcRERGB169f49OnT/j06RO+fPmChg0bwsjICG3atEHfvn0xYsQIiQ9S6jpLS0uEhYVh+/btuHnzJpKSkjini1G2gQMHwsPDA+fOncP169fx+PFjfP78WfidGBgYoFWrVujcuTN69erFOWBHXdClSxdERkYiODgYly5dQlRUFFJTU5GXlwcNDQ3o6enBysoKjo6O6NmzJ7p06VJtg+jNmTMHLVq0wOHDh/H48WNkZmYyBlYTNXDgQISFhcHf3x+3bt1Ceno6dHR00KZNG4wbNw4jR47kbI5XVyniHNXQ0EBgYCCOHDmC06dPIzY2FmVlZTA1NYWHhwfmzJkDCwsLibUaldG4cWPcuHED27dvx7Vr15CYmKiwaQ8VpV69enj58iVCQ0Px6NEjxMbGIj09HZ8/f0ZOTo5wTnEbGxt4eHhg9OjRYuc+VldXx5YtWzB37lycPHkS9+7dw5s3b5CVlYXS0lJoaWnB3Nwctra2cHV1RZ8+fcSOeKuiooIFCxZgwoQJwt+rmJgYZGRkoLS0FAYGBjAzM4Orq6vYQujgwYMREhKCPXv2ICIiAunp6VLHBlFVVcWGDRswbdo0HD9+HHfv3kVcXByysrJQv359NGnSBPb29vD09MTIkSNrbFpJLqqqqjh8+DAGDx6MY8eO4enTp8jJyYGxsTH69OmD+fPnS+wXXZNlhJpmamqKixcv4uLFiwgMDMSjR4+ENYIGBgZwcnLC0KFD0b9/f5nWN3XqVPTv3x/Hjx/HlStXkJiYiKysLOjo6KB58+bo06cPJkyYwKrFrYquXbvi4cOHOHXqFEJCQvDixQt8/vwZ6urqMDExgZubG8aOHStTjWH9+vXx22+/YdKkSTh16hRu3bolLKfo6+vD1tYWAwYMwNixY2UKghSZt8qaO3cuXF1dsWfPHty9exfp6enQ1dWFs7MzZs2aJfVhjTLjgTZt2uDu3bvYtWsXgoODhd2yWrdujREjRmDatGmcfcUFNmzYgE6dOuHgwYN48uQJCgoKYGxsjJ49e2LOnDlo3bo1ZzeFymrRogVu3bqFHTt2ICQkBImJiVBTU4OlpSWGDBmCGTNmCLt6yaJBgwYICgrCrl27EBISgnfv3iEvL08hg1fyeDzMmzcPo0ePxvHjx3Hr1i28evUKmZmZ4PF40NPTQ7t27dCrVy+MHTu22h8+nj17lvHa09OT1ddeVrysrCylDvcZERHBGNRBQ0MDT58+rbEAmBBCCCGkNpo1axZrBP6goCCJLUgI+S8ICAhgjV6/bNkyrFixooZyxCTaqtLc3FzsuFrk3y0tLQ3t27dnzO51/fr1SleMKbUZPAB06tSJ0bcjPz+fc/RNQgghhBBCCCGkrtq5cycjUB84cGCVWrAqPVgHyjv0V2xSdvDgQdYInIQQQgghhBBCSF2UlZWFw4cPC1+rqanBx8enSutUap91gebNm3POtU0IIYQQQgghhNR1urq6Ume0kFe11KwTQgghhBBCCCFEdhSsE0IIIYQQQgghtYzSR4MnhBBCCCGEEEKIfKhmnRBCCCGEEEIIqWUoWCeEEEIIIYQQQmoZCtYJIYQQQgghhJBahoJ1QojSFRQU4N27dygoKKjprNQ6dGwko+MjGR0f8ejYSEbHRzI6PoSQ2oCCdUJItSgtLa3pLNRadGwko+MjGR0f8ejYSEbHRzI6PoSQmkbBOiGEEEIIIYQQUstQsE4IIYQQQgghhNQyFKwTQgghhBBCCCG1DAXrhBBCCCGEEEJILaNa0xkghPy3RKUX4a+EfBhqqGCitSa06tMzQ0IIIYQQQkRRsE4IqTZ/JRZi5r084eujr7/ihpcBBeyEEEIIIYSIoBIyIaRalPCB1VFfGGlvskvwV3x+DeWIEEIIIYSQ2ouCdUJItYjOVkF6AZ+V/ii9qAZyQwghhBBCSO1GwTohpFrc/FyPM/2vBKpZJ4QQQgghRBQF64QQpePz+biZwR2st9KmoTMIIYQQQggRRcE6IUTpnmaWIrWQ+3aTWVhWzbkhhBBCCCGk9qNgnRCidJc+iu+Xnl5AwTohhBBCCCGiKFgnhCjdxQ/ig/XcYj7yS9gDzxFCCCGEEPJfRsE6IUSp3mYX43VOqcRl0gskv08IIYQQQsh/DQXrhBClCnlfIHWZ9HxqCk8IIYQQQkhFFKwTQpQqOFH61Gxp+VSzTgghhBBCSEUUrBNClCb5aykepRdLXY4GmSOEEEIIIYSJgnVCiNJcfC+9Vh2gZvCEEEIIIYSIomCdEKI0N5MKZVqOmsETQgghhBDCRME6IURpUr7KFoR/ombwhBBCCCGEMFCwTghRmpwi2eZPp5p1QgghhBBCmChYJ4QoTW6xbDXmNMAcIYQQQgghTBSsE0KURtaadRpgjhBCCCGEECYK1gkhSlFaxkdeiWzBekZhGUrKZFuWEEIIIYSQ/wIK1gkhSpFbzB18q/DYaXzQIHOEEEIIIYRURME6IUQpcsT0V2+prcqZTv3WCSGEEEII+X8UrBNClCJXTH91scE6jQhPCCGEEEKIEAXr5F8tKioKI0aMQLNmzWBqagp3d3f8+eefMn8+Pj4emzZtwujRo2FrawtdXV3Y2dmJXf7p06fw8fGBh4cHrKysYGhoCHt7eyxatAhJSUmcnxkwYAB0dXU5/4YNGyb3PtcW8tasp9Egc4QQQgghhAhxl5oJ+Re4ffs2hg0bhgYNGsDb2xva2toICgrC9OnT8f79eyxatEjqOu7duwdfX1/Uq1cP1tbWSE1Nlbj8woULERkZCUdHR3h7e0NNTQ0RERE4ePAg/vrrL1y6dAmtW7fm/OyyZctYaS1atJBtZ2shcTXrVmKbwVPNOiGEEEIIIQIUrJN/pZKSEsyfPx+8/2PvzuOjLM/9j39nySQhmRASIZSyJCwCCqUlLlUUS7T0VFSQEapA61a0WtEqLlWwyNHfSZFq0R9oTqv8WsuiAqaF4ymlAh4EjgtQC7gAhrBoTJCQZbJMMpOZ3x9pYsI8M5kkk2Qy+bxfrxbyPM88c88FDnPNdd33bTLpzTff1Lhx4yTVJ8STJ09Wdna2pk2bpmHDhgW9z4QJE/T3v/9dY8aMUXx8vNLS0oJeP3PmTP3+979XRkZGs+PLli3TE088oYULF+r11183fOyjjz7ailcY+QJV1ocmWQyPs30bAAAA8DXa4BGVduzYofz8fN1www2Nibok2e12PfTQQ/J4PFq9enWL90lPT9eFF16o+Pj4kJ73jjvu8EvUJWnevHnq1auXdu3aFfqL6OYCVdbTelmUYPVfEv4Uc9YBAACARlTWEZV27twpScrKyvI713CsMxNnk8kki8Uik8lg37J/2bBhg44fP65evXpp/PjxuuiiizptfB0hUGU9KcasvvFmVTqbJ+ds3QYAAAB8jWQdUSkvL0+SDNvck5OTlZqa2nhNZ/jLX/4ip9OpadOmBbzm9ttvb/bz+PHjtXLlSqWnp4f0HC6Xqz1DDLuSarfhcZu3VufEmnTM2fx4UZUn4l5DZ6itrW32K5ojPsERn8CITXDEJ7hoiE9cXFxXDwFAO5GsIyqVl5dLkpKSkgzP2+32gKuzh9vnn3+uRx55RPHx8VqwYIHf+SlTpuj+++/X2LFjZbfblZeXpxUrVujVV1/VtGnTtHv3bvXq1avF5ykoKFBdXeS0kheciZEU0+yYST6d+fJzJXhtOvvtp7DSrZMnT3beACNMS4sX9nTEJzjiExixCY74BNdd42OxWLr1IrUA6pGsAx2opKREM2fO1FdffaWcnByNGDHC75q777672c9jx45VTk6O6urqtG7dOq1evVpz585t8bkGDBgQtnGHg/dzp6TmFQl7jEmDBw/S4MIK6UxNs3MlbrMGDhwYdKpANKqtrVVRUZHS0tJks9m6ejgRh/gER3wCIzbBEZ/giA+ASECyjqjUUFFvqLCfzel0Bqy6h0tpaammTp2qTz75RM8++6x+9KMfterxc+bM0bp16/Tee++FlKxHWrtblbfS75g9xqy4uDj1T6yV1DxZ9/gklzlWfWJ75rqXNpst4v4MIwnxCY74BEZsgiM+wREfAF2pZ34qRtRrmKtuNC+9tLRUxcXFLW7b1h4lJSW67rrrtH//fi1dulS33nprq++RmpoqSaqqqgr38DpFea3/gnH2mPqqed8447ceVoQHAAAA6pGsIypNmDBBkrRt2za/cw3HGq4Jt5KSEk2dOlX79+/X008/rZ/+9Kdtus/evXslSYMHDw7n8DqN0+2/dVvSv5L1fvEB9lpnRXgAAABAEsk6otQVV1yh9PR0rV+/Xvv372887nQ6tXTpUlmtVs2aNavxeGFhoQ4fPqyysrJ2PW/Tivqvf/1r3XHHHUGvP3bsmAoLC/2OHzp0SE8++aQkyeFwtGtMXcWosp7YUFmPN37r+YrKOgAAACCJOeuIUlarVc8//7wcDoeuvvpqORwO2e12bdq0ScePH9fChQs1fPjwxusXL16stWvXasWKFZo9e3bj8eLiYi1cuLDxZ7fbrTNnzuiuu+5qPPbiiy82/n7OnDk6cOCAzj33XJWUlCg7O9tvbHfddZeSk5Ml1e/1ft999+nyyy9XRkaGEhMTlZeXpy1btsjtduvhhx/WhRdeGNbYdJZglfXAbfBU1gEAAACJZB1RbOLEidq8ebOys7OVm5srt9utUaNGacGCBZo5c2ZI96ioqNDatWubHausrGx2rGmyfuLECUnS4cOHtWTJEsN7zpo1qzFZHzdunBwOhz788EPt3btXVVVVSklJ0VVXXaW5c+cqKyurVa85kgSbs04bPAAAABAcyTqiWmZmptavX9/idS+++GKzpLvBkCFDVFpaGvLzHThwoFXjGzNmjP7zP/+zVY/pDmrqfDLI1RuT9d42k2LMkvusa2iDBwAAAOoxZx1A2BlV1SUpyVafrJtMJsNWeNrgAQAAgHok6wDCzmi+uvT1AnOS1NegFf60i8o6AAAAIJGsA+gAASvrMV+/5fSjsg4AAAAERLIOIOzKao0r6/YWKussMAcAAADUI1kHEHbOs1eO+5dmybpBZb3K41NlgMcCAAAAPQnJOoCwC9QG37yybvz2Q3UdAAAAIFkH0AECLTCX1CRZD7TX+im2bwMAAABI1gGEX6DKemILbfCSdJrKOgAAAECyDiD8QqmsJ8cav/2UB1icDgAAAOhJSNYBhJ1RZT3G5FOs5etkvbfN+O2nLEBVHgAAAOhJSNYBhJ1RZT3R2vzn3jaT3zWSVEqyDgAAAJCsAwg/o8p6gqV5Ak9lHQAAAAiMZB1A2BlW1s9a/N1qNinR6l9dL2POOgAAAECyDiD8DCvrVv8k3Ki6XlZDZR0AAAAgWQcQduWGlXWjZN2/ss6cdQAAAIBkHUAHKHf7J9xnLzAnSb0Ntm+jDR4AAAAgWQcQZl6fT06DhPvsBeakAG3wVNYBAAAAknUA4VXp8cmoNm5YWTdogydZBwAAAEjWAYRZeYA29lAr6+W1Pnl9tMIDAACgZyNZBxBWToP56pL/1m2SlGwwZ92nwAk/AAAA0FOQrAMIK6Nt26TQt26TaIUHAAAASNYBhJXTYNs2KfSt2ySSdQAAAIBkHUBYBaysG7TBB6qsl9IGDwAAgB6OZB1AWAWsrBu0wSfTBg8AAAAYIlkHEFaBEm3jyjpt8AAAAIARknUAYdWaynrANvgaknUAAAD0bCTriGr79u3TjBkzNGTIEA0YMEBZWVlat25dyI/Pz89Xdna2brzxRo0ePVrJyckaO3Zsi49bt26dsrKyNGDAAA0ZMkQzZszQP/7xjw4bZyQJNGe9VyvmrJcxZx0AAAA9nLWrBwB0lHfeeUcOh0M2m03Tp09XUlKSNm3apLlz5+rEiROaP39+i/fYvXu3lixZIovFopEjR6qoqKjFxzzzzDN68sknNXDgQN16662qrKzUG2+8oR/84AfasGGDLr/88rCPM5IY7ZGeYJUsBh3vSTaTTKrfW70p2uABAADQ05GsIyp5PB7de++9MplMevPNNzVu3DhJ0iOPPKLJkycrOztb06ZN07Bhw4LeZ8KECfr73/+uMWPGKD4+XmlpaUGvz8vLU3Z2toYPH66tW7eqd+/ekqQ777xTV155pe6991598MEHslqtYR1nJHG6/RNte4zx3HSzyaQkm8mvkk6yDgAAgJ6OZB1RaceOHcrPz9fs2bMbE2BJstvteuihh3Tbbbdp9erV+tWvfhX0Punp6UpPTw/5eVevXi2Px6P58+c3JuqSNHr0aN14441auXKlduzYoaysrLCOM5IYVdbtMWa9UWhRSo1LMTF1zc5ZTP619YNn3PrDocp2j+WWkQntvgcAAADQFZizjqi0c+dOSWpMiptqOLZr164uf96uGmdHMqqsJwWorEtSvNX/XLWHOesAAADo2aisIyrl5eVJkmH7eHJyslJTUxuvCffzJiYmGrbLN4yl6fOGc5wul6stQw67spo6v2MJlvrk2+Px+J2LM/sn5lUer9xud7vHEikxCaa2trbZr2iO+ARHfAIjNsERn+CiIT5xcXFdPQQA7USyjqhUXl4uSUpKSjI8b7fbVVBQ0CHP27dv34DP2XRsTX8fjnEWFBSors4/Ue5spa54Sc2r5TZvjSSp3Fnud73Fa5XUfKn4SrdXZ0rOtHssJ092fTxCFcrihT0Z8QmO+ARGbIIjPsF11/hYLBYNHTq0q4cBoJ1I1oEoMWDAgK4egiSp6n+L/Y6dkxgvqUJJ9qTGxfUa9C6tkSqbV9xrvCal9Elp91gGDYr8qkJtba2KioqUlpYmm83W1cOJOMQnOOITGLEJjvgER3wARAKSdUSlhkp10yp2U06nM2A1u73PG+w5m46t6e/DMc5IaHdze32qNihm9461SHWS1WpVTExMs3O9YjySmifrtV7JbLHKYg481z0UkRCTUNlstm413s5GfIIjPoERm+CIT3DEB0BXYoE5RCWj+eENSktLVVxc3CHboQ0bNkwVFRWGbXNG89O7apwdxRlgy7VAW7dJUi+DBeYkqbqOReYAAADQc5GsIypNmDBBkrRt2za/cw3HGq7pyuftqnF2lHK3cYLd2tXgJVaEBwAAQM9Gso6odMUVVyg9PV3r16/X/v37G487nU4tXbpUVqtVs2bNajxeWFiow4cPq6ysrF3PO3v2bFmtVj3zzDPN7vXJJ5/o1VdfVUZGhiZOnNjmcUa68gCV9cRgybqFZB0AAAA4G3PWEZWsVquef/55ORwOXX311XI4HLLb7dq0aZOOHz+uhQsXavjw4Y3XL168WGvXrtWKFSs0e/bsxuPFxcVauHBh489ut1tnzpzRXXfd1XjsxRdfbPz98OHD9ctf/lJPPfWUJkyYoKlTp6qqqkobNmyQ2+3Wc88912yBtdaOM9IFrKzbTPoqwGMCVtZpgwcAAEAPRrKOqDVx4kRt3rxZ2dnZys3Nldvt1qhRo7RgwQLNnDkzpHtUVFRo7dq1zY5VVlY2O9Y0WZekBx98UIMHD9aLL76olStXKiYmRhdddJEee+wxjR8/vkPGGSkCzlkPkJBLQeasU1kHAABAD0ayjqiWmZmp9evXt3jdiy++6Jd0S9KQIUNUWlra6uedOXNmqxLtUMcZ6QJV1u22NrTBU1kHAABAD8acdQBhE3g1+MBvNYHa4KuorAMAAKAHI1kHEDYBK+tB2uBZDR4AAADwR7IOIGwq3a3fZ91mlswGp0nWAQAA0JORrAMIG6dBZd0kKT7I6hgmk8lw3jpz1gEAANCTkawDCJsKg2Q9wWqS2RS4si4Zt8IzZx0AAAA9Gck6gLCp9Pi3wScGaYFvYFhZJ1kHAABAD0ayDiBsDCvrISTrRnut0wYPAACAnoxkHUDYVBok64lBtm1rYNQG76KyDgAAgB6MZB1A2DgNVoNPCLJtWwPmrAMAAADNkawDCJtKgwQ72LZtDYzmrHt8kttLwg4AAICeiWQdQNgYz1lvWxu8xCJzAAAA6LlI1gGEjfGc9ba1wUu0wgMAAKDnIlkHEBYer89wBfeQ5qwbtMFLrAgPAACAnotkHUBYGM1Xl9q+GrxEGzwAAAB6LpJ1AGFhNF9dCq0N3mifdYlkHQAAAD0XyTqAsKg02LZNCnHOeqA2eJJ1AAAA9FAk6wDCIlBlPcHajjZ45qwDAACghyJZBxAWFQHnrLd9NXgq6wAAAOipSNYBhEVFO9rgY8wmGa1DR2UdAAAAPRXJOoCwMNpjXQptNXjJeN46+6wDAACgpyJZBxAWgeest1xZl6Q4g+togwcAAEBPRbIOICwqPG1vg5eMK+sk6wAAAOipSNYBhEXgfdZDe5sx2mudOesAAADoqUjWAYRFoDnrobbBG60IT2UdAAAAPRXJOoCwMFoN3maWbAbt7UYCtcH7fCTsAAAA6HlI1gGERaVBFTzUFnjJuA3eK8lV155RAQAAAN0TyTqi2r59+zRjxgwNGTJEAwYMUFZWltatW9eqe9TU1GjJkiXKzMxUWlqaRo4cqXnz5qmoqMjv2rvuukvJyclB//f00083e8yUKVMCXutwONr1+juT06ANPtTF5SQpIcC1gfZvBwAAAKKZtasHAHSUd955Rw6HQzabTdOnT1dSUpI2bdqkuXPn6sSJE5o/f36L9/B6vZo1a5a2bt2qCy64QNdee63y8/O1evVqbd++XVu3blVaWlrj9VOmTNHgwYMN77V8+XJVVlbqyiuvNDz/yCOP+B0bOnRoiK+261UaJNWJIc5XlwJX4SvcPvWNb/OwAAAAgG6JZB1RyePx6N5775XJZNKbb76pcePGSapPiCdPnqzs7GxNmzZNw4YNC3qfNWvWaOvWrXI4HHrppZdkMtUnn6tWrdI999yjRYsWKScnp/H6a665Rtdcc43ffT788EMtWbJE5513njIzMw2f69FHH23ry40IFe1sg7cHqqyzyBwAAAB6INrgEZV27Nih/Px83XDDDY2JuiTZ7XY99NBD8ng8Wr16dYv3eeWVVyRJixYtakzUJWnOnDkaOXKkcnNz5XQ6Q77Pj3/849a+lG7DaDX4QK3tRmiDBwAAAL5Gso6otHPnTklSVlaW37mGY7t27Qp6D5fLpT179mjEiBGGre2TJk1STU2N9uzZE/Q+1dXVWr9+vWJjY3XjjTcGvG7Dhg169tlnlZOTo/fffz/oPSORUVLdmjnridbAbfAAAABAT0MbPKJSXl6eJBm2uScnJys1NbXxmkDy8/Pl9XoDzhtvuHdeXp4mTZoU8D5/+ctfVF5eLofDL79/2AAAIABJREFUoT59+gS87vbbb2/28/jx47Vy5Uqlp6cHHWcDl8sV0nUdxSipjjP55HK5VFtbK6l+ekIgsTJOystrPHK73W0aU1fHJBQNsWn4Fc0Rn+CIT2DEJjjiE1w0xCcuLq6rhwCgnUjWEZXKy8slSUlJSYbn7Xa7CgoKQrpH7969A96j6XWB/OlPf5Ik/eQnPzE8P2XKFN1///0aO3as7Ha78vLytGLFCr366quaNm2adu/erV69egV9DkkqKChQXV3X7HPm80kV7nhJZ1XSayp08mTJv36wqNwZPFaxZptqvM3vcaayRmdKKts0rpMnu8++b0a7C+BrxCc44hMYsQmO+ATXXeNjsVi61SK1AIyRrAMd6OjRo9q9e7eGDBmiiRMnGl5z9913N/t57NixysnJUV1dndatW6fVq1dr7ty5LT7XgAEDwjLmtqj2+OTVGb/jacl2DRqUUF+ZKDytJHuSrNbAbzv2mCrV1DSvsLvNNqX0Mf7SpSWDBkV+VaG2tlZFRUVKS0uTzWbr6uFEHOITHPEJjNgER3yCIz4AIgHJOqJSQ0U9UNXb6XQGrLqffY+ysrKA92h6nZE//elP8vl8mjNnTrMF6kIxZ84crVu3Tu+9915IyXpXtrs5q40r2MnxtmbjslqtiomJCXifRJtZp2ua36uqTkEfE0x3agG02WzdarydjfgER3wCIzbBEZ/giA+ArsQCc4hKTeeTn620tFTFxcUtbtuWkZEhs9mso0ePGp4PNi9ekurq6rR27VpZLBbNnj27NcOXJKWmpkqSqqqqWv3YzlYZYHu1hFbssy4ZL0jnZDV4AAAA9EAk64hKEyZMkCRt27bN71zDsYZrAomLi1NmZqaOHDmiEydO+J3fvn27YmNjA+6bvmXLFhUWFuqqq65qU4v63r17JclwJfpI4wywYntrVoOXjFeEr3D75POxIjwAAAB6FpJ1RKUrrrhC6enpWr9+vfbv39943Ol0aunSpbJarZo1a1bj8cLCQh0+fNiv5f3mm2+WJC1evLhZwrhq1SodOnRI119/fcA2+IaF5ebMmRNwnMeOHVNhYaHf8UOHDunJJ5+UJDkcjpZebperDFD9bnWybnB9nU+q6T7rxAEAAABhwZx1RCWr1arnn39eDodDV199tRwOh+x2uzZt2qTjx49r4cKFGj58eOP1ixcv1tq1a7VixYpmLes33XSTcnNztWHDBh0/flyXXXaZjh07po0bN2rgwIFavHix4fOfOnVKW7ZsUb9+/fTDH/4w4Dh37dql++67T5dffrkyMjKUmJiovLw8bdmyRW63Ww8//LAuvPDC8AWmgwTaCz0xpnXfBwZK7p1ur+KsllaPCwAAAOiuSNYRtSZOnKjNmzcrOztbubm5crvdGjVqlBYsWKCZM2eGdA+LxaI1a9Zo2bJleu211/TCCy8oOTlZs2bN0sKFC5WWlmb4uLVr18rj8eimm24Kuvr5uHHj5HA49OGHH2rv3r2qqqpSSkqKrrrqKs2dO1dZWVlteu2dLXxz1o2T+wqPT31bPSoAAACg+zKVlpYyGRRAu6w6Uql7dpb6Hd9xXV99K9Uml8ul5z4oUEqflKAru39a6lbOx/57qv90VILGpLR+RfhbRia0+jGdzeVy6eTJkxo0aBArDhsgPsERn8CITXDEJzjiAyASMGcdQLtVhqsNPkAlvoIV4QEAANDDkKwDaLfAc9bD0wYfaLV5AAAAIFqRrANot0qPceU7HPusS4G/DAAAAACiFck6gHYzqnybJPVqZbJuNZsUZ7Doe6AvAwAAAIBoRbIOoN2M5qwnxphkMrUuWa9/nP/bEpV1AAAA9DQk6wDazWgBuNbOVw/2OJJ1AAAA9DQk6wDazWif9QRr295ejFaEZzV4AAAA9DQk6wDazajy3fbKunEbvM9HdR0AAAA9B8k6gHYzqnwnhLEN3uOTaiiuAwAAoAchWQfQbsaV9Ta2wQfcvo1sHQAAAD0HyTqAdjOas2409zwUgZJ8FpkDAABAT0KyDqDdwrkafEKAJJ9kHQAAAD0JyTqAdvF4fXLV+R9v65x1O23wAAAAAMk6gPYJVPFu+5x12uABAAAAknUA7WI0X11qz5x12uABAAAAknUA7RKoPb2tlXWr2aQ4i8HzeGiDBwAAQM9Bsg6gXSoDVLzbOmddMk70qawDAACgJyFZB9AuzkBz1tvYBi8ZrwhPsg4AAICehGQdQLtUBmhPb+vWbZLxivCsBg8AAICehGQdQLuEezX4QI+t9Pjk81FdBwAAQM9Asg6gXTpizrrRY91eqYbiOgAAAHoIknUA7RJwNfh2zFkPvH0b2ToAAAB6BpJ1AO1SEWif9Xa0wdsDPJZF5gAAANBTkKwDaJdACXS72uADVOVJ1gEAANBTkKwDaJdKg9b0WIsUY6YNHgAAAGgrknUA7WLUBp9obd9bC23wAAAA6OlI1hHV9u3bpxkzZmjIkCEaMGCAsrKytG7dulbdo6amRkuWLFFmZqbS0tI0cuRIzZs3T0VFRX7XHj9+XMnJyQH/t2HDhg4bZ1cxSqDb0wIvBa6sVwaYHw8AAABEG2tXDwDoKO+8844cDodsNpumT5+upKQkbdq0SXPnztWJEyc0f/78Fu/h9Xo1a9Ysbd26VRdccIGuvfZa5efna/Xq1dq+fbu2bt2qtLQ0v8eNGTNGU6ZM8Ts+evToDhlnVzJqTQ+UbIfKajYp1iLV1DU/7qQNHgAAAD0EyTqiksfj0b333iuTyaQ333xT48aNkyQ98sgjmjx5srKzszVt2jQNGzYs6H3WrFmjrVu3yuFw6KWXXpLJVJ+Erlq1Svfcc48WLVqknJwcv8eNHTtWjz76aKeNsysZVbvb2wbfcI+auubJOW3wAAAA6Clog0dU2rFjh/Lz83XDDTc0JsCSZLfb9dBDD8nj8Wj16tUt3ueVV16RJC1atKgxUZekOXPmaOTIkcrNzZXT6ezycXYlowS6vZV1SUqy+d/jTA2VdQAAAPQMJOuISjt37pQkZWVl+Z1rOLZr166g93C5XNqzZ49GjBihwYMH+52fNGmSampqtGfPHr9zhYWFevnll/Xss89qzZo1+uKLLzpsnF3NaDX49s5Zl6Rz4vzfnk67vKrzUV0HAABA9KMNHlEpLy9Pkgzbx5OTk5Wamtp4TSD5+fnyer0aOnSo4fmGe+fl5WnSpEnNzm3fvl3bt29v/NlqterOO+/Uk08+KbP56yQ0HONs4HK5Qrou3JwGlfV4s6/ZeGprayXVt/2HKtXmf8zrk4oqatXXIJE30lUxaY2G2DT8iuaIT3DEJzBiExzxCS4a4hMXF9fVQwDQTiTriErl5eWSpKSkJMPzdrtdBQUFId2jd+/eAe/R9DpJ6tWrlx555BFdc801Sk9PV01NjT744AM98cQTWrFihWw2mxYtWhTWcTYoKChQXV1dyxeGkc8nVbrjJTWvpPtcFTp5suSsqy0qd5YrVPF1ZkkxfsePni6XJSG0dviTJzs3Hu1htLsAvkZ8giM+gRGb4IhPcN01PhaLJWCxAUD3QbIOhFHfvn2bLSxnt9v1wx/+UOPHj9cll1yiFStW6L777lNycnLYn3vAgAFhv2dLqjw+eXXG73j/ZLsGDUpo/Lm2tlYqPK0ke5Ks1tDedobGeqXCar/jLmuCUvr4J/FGBg2K/KpCbW2tioqKlJaWJpvNoJ2ghyM+wRGfwIhNcMQnOOIDIBKQrCMqNVSqm1a9m3I6nQGr2Wffo6ysLOA9ml4XTFpamr7//e/rtdde0759+xrno4djnA26ot3NWW1cue4dbzMcj9VqVUxMaIl2WqJPJlXr7Cb74lqFfI/u1AJosxnHDPWIT3DEJzBiExzxCY74AOhKLDCHqNR0PvnZSktLVVxc3OJ2aBkZGTKbzTp69Kjh+WDzzY2kpqZKkqqqqsI6zq4UaCu1cKwGb7OY1CfW/y3qq2pWhAcAAED0I1lHVJowYYIkadu2bX7nGo41XBNIXFycMjMzdeTIEZ04ccLv/Pbt2xUbG6vMzMyQxrRv3z5JarayfDjG2ZUqDPZYl6QEa/uTdUnqG+//FnUqQDUfAAAAiCYk64hKV1xxhdLT07V+/Xrt37+/8bjT6dTSpUtltVo1a9asxuOFhYU6fPiwX8v7zTffLElavHixfE22DFu1apUOHTqk66+/vlmb+t69e+V2u/3Gs3z5cr377rsaNWqUxo4d2+ZxRpryWuMqtz0mPG8t/QxWfS93++QK8CUBAAAAEC2Ys46oZLVa9fzzz8vhcOjqq6+Ww+GQ3W7Xpk2bdPz4cS1cuFDDhw9vvH7x4sVau3atVqxYodmzZzcev+mmm5Sbm6sNGzbo+PHjuuyyy3Ts2DFt3LhRAwcO1OLFi5s9769+9SsdOXJEEyZM0De/+U25XC69//772r9/v5KTk5WTkyOTydTmcUaaYpdxsm7Uvt4W/eIthse/ctVpUCJvXwAAAIhefNpF1Jo4caI2b96s7Oxs5ebmyu12a9SoUVqwYIFmzpwZ0j0sFovWrFmjZcuW6bXXXtMLL7yg5ORkzZo1SwsXLlRaWlqz63/0ox9p48aNev/991VcXCxJGjRokH72s59p3rx5+uY3v9kh4+wqJTXGyXpqiPugt6SfQRu8JJ2q9mpQYlieAgAAAIhIptLSUvpJAbTJb/c7tXiv/0r2H8/srwEJX1fFXS6XnvugQCl9UkJeyV2q/zLA6P4/GBirHw6Ob/Hxt4xMaPGaruZyuXTy5EkNGjSIFYcNEJ/giE9gxCY44hMc8QEQCZizDqDNzgSorKeEqQ2+t80ko+nvpwK03wMAAADRgmQdQJsZJeu9rCbFhWk1eLPJpL4GLfVs3wYAAIBoR7IOoM2MFpgLV1W9gdEic6eq65qtzg8AAABEG5J1AG1mtMBcuJN1o73Wa71SWS3JOgAAAKIXyTqANjNqg08J00rwDfrFBd6+DQAAAIhWJOsA2uxMJ7TBG1XWpfrt2wAAAIBoRbIOoE28Pp9KajtjzjrJOgAAAHoeknUAbVJW65PXYNp4nzAn672sZiXG+K8u/1U1bfAAAACIXiTrANrEqAVeklLDPGddkvoZ3JO91gEAABDNSNYBtInR4nJS+NvgJePt24pdXnmMSvsAAABAFCBZB9AmnZmsGy0y55N0muo6AAAAohTJOoA26erKuiSdYt46AAAAohTJOoA2KQ6wz3m491mXpL4B7vl5Jck6AAAAohPJOoA2KenEyvo5cWbFGNz2n8XusD8XAAAAEAlI1gG0iVEbvNUk2Q22WWsvq9mkkb2tfseLqr36sorqOgAAAKIPyTqANjFK1lPizDKZwp+sS9K3z7EZHv/wdG2HPB8AAADQlUjWAbSJ0T7rHdEC3+D8PjGyGHwPQCs8AAAAohHJOoA2KTaqrHdgsh5vNWlUsn8rfGG1V4W0wgMAACDKkKwDaBOjBeY6MlmXpO8EaoWnug4AAIAoQ7IOoNV8Pl/AOesdKVArPPPWAQAAEG1I1gG0WpXHpxqDzvOOrqzTCg8AAICegmQdQKsZzVeXpNQOTtYl6duptMIDAAAg+pGsA2g1o5XgJalPB7fBS9KYFFrhAQAAEP1I1gG0mtHiclLHt8FLwVvhPy6hug4AAIDoQLIOoNWMFpeTOidZlwK3wufmV8vj9XXKGAAAAICORLIOoNW6Oln/VmqMEqz+vfBfubza8WVNp4wBAAAA6Egk64hq+/bt04wZMzRkyBANGDBAWVlZWrduXavuUVNToyVLligzM1NpaWkaOXKk5s2bp6KiIr9r9+/fr6eeekpXXXWVhg8frn79+mncuHGaP3++CgoKDO8/ZcoUJScnG/7P4XC06XV3tOIAc9ZTO2HOuiTFWky6enCc4bm/nXSprNZ4fAAAAEB34T/xE4gS77zzjhwOh2w2m6ZPn66kpCRt2rRJc+fO1YkTJzR//vwW7+H1ejVr1ixt3bpVF1xwga699lrl5+dr9erV2r59u7Zu3aq0tLTG6x944AHt3btX48eP1/Tp0xUbG6s9e/bo5Zdf1p///Gf99a9/1bnnnmv4XI888ojfsaFDh7Y9AB3IqLJukpRs67zv/y5Js2l3Ua2+qGy+ZVuNV/qv49WaPSKh08YCAAAAhBvJOqKSx+PRvffeK5PJpDfffFPjxo2TVJ8QT548WdnZ2Zo2bZqGDRsW9D5r1qzR1q1b5XA49NJLL8lkqm+9XrVqle655x4tWrRIOTk5jdfPnDlTv//975WRkdHsPsuWLdMTTzyhhQsX6vXXXzd8rkcffbQ9L7lTGS0w19tmksVssEx7BzGbTHJkxOv5gxV+5z74yq0J/T2dNhYAAAAg3GiDR1TasWOH8vPzdcMNNzQm6pJkt9v10EMPyePxaPXq1S3e55VXXpEkLVq0qDFRl6Q5c+Zo5MiRys3NldPpbDx+xx13+CXqkjRv3jz16tVLu3btas/LihhGlfXOmq/e1NAkqzLPiTE8l5tfLZ+PxeYAAADQPZGsIyrt3LlTkpSVleV3ruFYS4mzy+XSnj17NGLECA0ePNjv/KRJk1RTU6M9e/a0OB6TySSLxSKLxRLwmg0bNujZZ59VTk6O3n///Rbv2ZWMkvXOmq9+tmuHxMuo+/54RZ3+64Sr8wcEAAAAhAFt8IhKeXl5kmTY5p6cnKzU1NTGawLJz8+X1+sNOG+84d55eXmaNGlS0Hv95S9/kdPp1LRp0wJec/vttzf7efz48Vq5cqXS09OD3rsrGC0w1xWVdUlKjjVr8sA4w8T8qb3l+uGgOFk7sT0fAAAACAeSdUSl8vJySVJSUpLhebvdHnB19rPv0bt374D3aHpdIJ9//rkeeeQRxcfHa8GCBX7np0yZovvvv19jx46V3W5XXl6eVqxYoVdffVXTpk3T7t271atXr6DPIdV3AnSWMwbJepI18Bhqa2sl1a8l0BEu7WvWO1+aVOZu3vZ+qMyjVz4p06xhxivHR4KG2DT8iuaIT3DEJzBiExzxCS4a4hMXF7n/9gEIDck60IFKSko0c+ZMffXVV8rJydGIESP8rrn77rub/Tx27Fjl5OSorq5O69at0+rVqzV37twWn6ugoEB1dXUtXtdebq9U4fH/8iCmtkInT5YEeaRF5c7gX2y0x4Q+Zv33Kf/567/+0KmLrF+piwr/ITPaChBfIz7BEZ/AiE1wxCe47hofi8USsTvKAAgdyTqiUkNFPVDV2+l0Bqy6n32PsrKygPdoet3ZSktLNXXqVH3yySd69tln9aMf/SiksTeYM2eO1q1bp/feey+kZH3AgAGtun9bnar2SvJPygefk6RBg4w7AGpra6XC00qyJ8lq7Zi3nYnJPu0pr9YpV/Pq+qlas/5edY7uGh3fIc/bXrW1tSoqKlJaWppsNltXDyfiEJ/giE9gxCY44hMc8QEQCUjWEZWazif/9re/3excaWmpiouLdfHFFwe9R0ZGhsxms44ePWp4Pti8+JKSEk2dOlX79+/Xb37zG916662tfg2pqamSpKqqqpCu76x2t8pqt+HxtITYFsdgtVoVE2O8ent7xUi6Zoi08pB/vJ7/pFq3nd9bvTtxH/jWstlstCwGQXyCIz6BEZvgiE9wxAdAV4rcT65AO0yYMEGStG3bNr9zDccargkkLi5OmZmZOnLkiE6cOOF3fvv27YqNjVVmZmaz400T9aefflo//elP2/Qa9u7dK0mGK9F3pWKDleAlKaWLVoNvamxKjIYk+q+4X1Lj0wsf+e/HDgAAAESqrv90DXSAK664Qunp6Vq/fr3279/feNzpdGrp0qWyWq2aNWtW4/HCwkIdPnzYr+X95ptvliQtXry42Z7dq1at0qFDh3T99dc3a4MvKSnRddddp/379+vXv/617rjjjqDjPHbsmAoLC/2OHzp0SE8++aQkyeFwtOKVdzyjxeUkqU8ETAo3mUy6ZohxBeT3n1Sq0m08dgAAACDS0AaPqGS1WvX888/L4XDo6quvlsPhkN1u16ZNm3T8+HEtXLhQw4cPb7x+8eLFWrt2rVasWKHZs2c3Hr/pppuUm5urDRs26Pjx47rssst07Ngxbdy4UQMHDtTixYubPe+cOXN04MABnXvuuSopKVF2drbf2O666y4lJydLqt/r/b777tPll1+ujIwMJSYmKi8vT1u2bJHb7dbDDz+sCy+8sIOi1DYlgSrrEZCsS9KI3jEalWzVp6XNV54/U+PV6iNVuuO8xC4aGQAAABA6knVErYkTJ2rz5s3Kzs5Wbm6u3G63Ro0apQULFmjmzJkh3cNisWjNmjVatmyZXnvtNb3wwgtKTk7WrFmztHDhQqWlpTW7vqFd/vDhw1qyZInhPWfNmtWYrI8bN04Oh0Mffvih9u7dq6qqKqWkpOiqq67S3LlzlZWV1Y4IdIwzEZ6sS9LkgXH6tNS/7X35RxW6bVQC+64DAAAg4plKS0t9LV8GAPUe/6BM//egfyJc+OMBirMaJ8Eul0vPfVCglD4pHbbAXFM+n0/PHazQMaf/VnYvX9FHjqEt71vfWVwul06ePKlBgwaxiJEB4hMc8QmM2ARHfIIjPgAiQeSUwgB0C8UGc9YTrKaAiXpXMJlMyhoQa3ju+YMVzdYfAAAAACIRyTqAVjFqg4+ExeXONiYlRsOT/Gf6/LPYrR1f1nTBiAAAAIDQRd4nbAARzWiBuUiar97AbDJp3hjjxeSeO8A2bgAAAIhskfcJG0BEM6qsp0bAHutGfjSsl/rF+49tW0GN/llc2wUjAgAAAEITmZ+wAUQsoznrkVhZl6Q4q0k/C7BV27P7nZ08GgAAACB0kfkJG0BEKnbVGVbW+xpUryPFbSMTlGiw+N3GYy4dKnV3wYgAAACAlkXuJ2wAEeejEo/h8fP6dPx2bG2VHGvWbaMS/I77JP2W6joAAAAiFMk6gJAdPGNciT4/gpN1Sfr5+YmKtfgfX3e0Wsecxl9AAAAAAF2JZB1AyD4q8U/WTZJG9/HfIi2SpPWy6Ccj/KvrdT7peVaGBwAAQAQiWQcQMqPK+rAkq3pZI/+t5N6xiTKYuq5VRyr1ZVVd5w8IAAAACCLyP2EDiAger0+fGizIdn5KZFfVGwxKtOrG4b38jtd6peUHqa4DAAAgspCsAwjJZ+Ue1RgUoMdE+Hz1pu4fa5fZoLr+/w5VqpDqOgAAACIIyTqAkHwUaHG5lO6TrA/rbdX16fF+x6s8Pi39JyvDAwAAIHKQrAMIidHicpI0phsl65L0wLfshsf/eKhSR8tZGR4AAACRgWQdQEiMFpdLspk0KMFgT7QIdn5KjG4Y6l9d9/ikp/aVd9o4ymu9emJPmcb9+Ywmvxevn+wo18ufVug4W8kBAABAJOsAQvTRGf8k8vw+MTKZDCaBR7gF30kyXBn+jfxqfXi6tkOf2+fzaf3RKl30RpGWHahQUbVPJW6Ttnzh1vz/LdO49UW67C+n9Nbnrg4dBwAAACIbyTqAFpXUePWFwQJs3WlxuaYykqy6daT/vuuS9MTejquu55d7dN3m0/rp/5SosNob8LqDZ9y64e/FWn6QefQAAAA9Fck6gBYZtcBL3WtxubM99G27EgzK628X1OjtgvBXtT88Xaus/zqldwpDr9wv/KBcC98vk9fnC/t4AAAAENlI1gG0KFoWl2uqX7xFd5+faHjul++VqbYufAnyh6drNfVvp1VS0/p7Lv+oQnfuKAnreAAAABD5SNYBtMiosm6SNDrZ2vmDCaN5YxKVGuv/NvhpqUe/PRCeFvR9X9Xqur+dVllt4GS7f2zglnhJWne0WjPfKpbTHfw6AAAARI/u/UkbQKcwqqwPTbIoIaZ7f9+XZDPrwXF2Pfp+md+5Z/7p1LT0eI1Mbl33wB8OVTb+/pjTo5yPK+Tyn+4vSeptM+naQTYNVLls9j7ac8ar/z5h3IL/dkGNLsk9pTtGJyjJ1jzutwSYfw8AAIDuq3t/0gbQ4Txenz4xSNbP76aLy53tp6MTDNv5a73SfbtK2zxf/OAZt1Z8FDhRH51s1WPfSdK4FKtMJskeY9LkgXH6ybm9ZAmwwP7nlXV67kCFvqoOcFMAAABEDZJ1AEEdLfcYJpzdeb56UzFmk/7vhGSZDRLkd0/V6v81qZSH6p0va/Typ5UK1LV+Xh+rbh+VoFiDrHz8OTbdeV6CYgNsX19c49WyAxX6rIz92AEAAKIZbfAAggq0uFx3qKz/oRWJ9hXfiNX2ghq/44+9X6avqr3q3ytA9tyE1+fTfx13aZvBfRqc36d+2zir0bcD/3Ju7xjNG5Oo331cqXK3f2W/0uPTio8q9MNBcbpqYGyL4wIAAED3Q2UdQFB7v4q+bduM/NugOMPF5mrq6ldk/9Jgn/mmiqrr9PzBiqCJ+piUlhP1BgMTrLpvbKL6xRu/Tfsk/fdJl/7z40ra4gEAAKIQlXUAAX1W5tZLn1b4HbfHmDQkseVKc3cSazFp5rB4vfixfzW+wu3T8oMV+vn5iRqQ0Px1e30+7fiyRm+ecAVse5ekS9NscgyNl8XUcqLeIDXOonvHJOr3n1TqeIVxQn6ozKNL/3xKv764t6ZnxMvUivt3VzV1Pr31uUv/PONWea1XVR6fKt0+2SwmZZ4ToylD4vWNEDohjLi9Pn1wqlYHz7j1cYlbH5d49GV1nfrGmTWhf6wmfiNWl6TZlBgBiytWuL166/NabfwsRic/LZPF7NQQu0XpdqvS7VZdkmZTuj0y/pn3+nz6Z7FbH55265/FtfrnGbcKq+p0TpxFl6bZdPk3YnVZ/1glG3xhBkSCkhqv3vmyRu98WaP/PVUrZ61XgxMtOj8lRuf3idF3zrHp/D7WHvEeDKDzmEpLS9m8F1Fr3759ys7O1vvvvy+3261Ro0bprrvu0owZM0K+R01NjZYtW6bXX39dn3/+uZKTkzV58mQtXLhQaWlpho9Zt26dXnzxRX0Lp0wVAAAf40lEQVT66aeKiYnRRRddpMcee0zf+c53Omyc4eb1+TTlr6f1v0W1fuf+bVCcXr0qNeR7uVwuPfdBgVL6pCgmJrIr8muOVOn9r/xfsyT1spr0vQGxGpZkVaxF+vC0W/tOu3WmJviWatcMjtOV34w1/BDndrt1puRM0NjU1vn0ypFKHTwTfJ76978Zq99ckqwhEZKghYPL5dLJkyc1cOBAHaq0aPVnVVp/tKrFPesv6mvTtUPidH1GvAYmthyPgso6/fFwpf54qFKF1cH/PGPM0qQBsfr5+Yma+A3jP9eO4vb6lJtfrdVHqrS7qCboF0SS9L0BsZo7KkH/NihOlhA6OsKt0u3VqiNVeuGjioBfODUwSbqsv03zxtj1/YHtj2vD351BgwYpLi6uXfeKRsQnOJfLpWMnTuqA+mnlZ7V6t6hWLX1gPi/ZqrvHJGrG0F6Ga5IAQGuRrCNqvfPOO3I4HLLZbJo+fbqSkpK0adMmHT9+XI8//rjmz5/f4j28Xq9mzJihrVu36oILLtBll12m/Px8bdq0SQMGDNDWrVv9EvZnnnlGTz75pAYOHKipU6eqsrJSb7zxhlwulzZs2KDLL7887OPsCDkfV+iX7/lvaWaS9Nerz9F300KfK92dkvWaOp9+90mF8srb31puMUk3De+lC/raAl4TSrIu1X958nZBjf7ruEvB8rNeVpPuGJ2guaMT9c2E7t/9UOys1kv/KNTG4nh9VNr6PxOTpCsGxOqm4b00ZXBcs4p4QWWd/ufLGv31RLXePOFSXRv+Nfx2aozuG5uoa4fEhzS9oa3Kar165VClcj6u1BctTMkwMijRoltHJmjW8F4hrb/QXp9XePSHw1V6+dOKFr9YMXJeH6t+Mdau6zPiFdPGuJKMBkd8AnN5fHrl0zL93wNOnXS1vtsjLd6suaMTNWdE5/z3BiB6kawjKnk8Hl144YUqKCjQli1bNG7cOEmS0+nU5MmTdeTIEb333nsaNmxY0PusWrVK99xzjxwOh1566aXGSk/D8RtvvFE5OTmN1+fl5eniiy9Wenq6tm7dqt69e0uSPvnkE1155ZVKS0vTBx98IKvVGtZxhlt+uUcT/nJKVR7/t4c7Rifo6e8mt+p+3SlZl+oT9pc+qdSR8ravuH5OnFmzh/dSRlLwqm6oyXqDo+Ue/fFwpcpqg791W03S9KHxuvncBGWeY1OctftUeZxur94/Vau/nnDp1c+qVGHw97CtettM6h9vUZ1P+qwdf75nS4s3a2p6vBwZ8bqwn03mMFTbK91evfVFjTYdr9bmE66wxMFikiYPjNOcEb102Tdi1dsWvrbzKo9Xbx53ac1nVXq7oKbFKmQoUmPNuj4jXo6h8bq4lXElGQ2O+DTn8dZPadqQX61Nx6tV3sJ7bCjMJumqb8Zq9ogETR4Yp/hu9D4MIDKQrCMqbdu2TdOnT9fs2bO1YsWKZufeeOMN3XbbbXrggQf0q1/9Kuh9Jk+erPfff1/79+/X4MGDm527+OKLdezYMX322Wey2+2SpH//93/Xs88+qxdffFE33XRTs+sfeOABrVy5Um+88YaysrLCOs5wc2w5ra1f+C+UNiTRot3T+imhlfN1u1uyLtW3nr/0aaUOt2GLtInfsGnK4PiQ2iBbm6xL9XOV135WpY9KQhtbjFn6VkqMxve1aUAvi1JizUqJMyvBapLFJJlNDb9Klia/N2L0D0agregNr1X9h+Jab31Ld5XHp9PVXhVV16mouk7/LHbrn8XuNlW5I0X/eLNG94nR8CSrhvW26pw4s+ItJvWymhRnNalpaD0+qdrjU7XHp0qPTycrPMp31umY06N/nK413DYxnEb0tuo758RoRJJVfWLN6hNrVnKsWQ1/dU3/+n9Tk59ddT5VuL1yun067fLq4xK3Pjrj1uEyT4f+ufWLN2t0coxG9LZqeG+rUmPN6mU1KSHGpDjL13FtGKu7tlanTp1SWr9+io2NbfJ61Oz1tEd7X26g/3Y6+rl9kmpralT0r/jYYlu3q0T7xt2+qLXnub2q/++t6l//zRVW1ymvzKPPyj36qMTdpi6QUFlM9f+9jUmp/zscbzEpxlz/9/cn5yZ02PMC6N6iZ2Ij0MTOnTslqTEpbqrh2K5du4Lew+Vyac+ePRoxYoRfoi5JkyZNUk5Ojvbs2aNJkyaF9LwrV67Url27Gs+HY5wdIfui3nrkvVLtL26eDOZM7NPqRL1BfIxZcRaTrN2kIzDWYtI95ydo7WdVOlwWWsaUGmfW1PQ4DW2hmt6UxWtqdWxiLWb9/PwEHTzj0ZsnXKow2N7tbMecdTrmrA55XF0puZXVXptZsppNhp0g7dUn1qw+NpO+rPaqOsT7u73S/mK39hcb76QQqgSrWQkh/lVKjKn/IqDU5VVrwnDG5dXWz2u0VYF3MQhVa/7cUmLNSok1qaTWp2JXCxPv/6XOKx0849bBM62Jay/p0wpJ/gtlQiI+XzPLpNTY0L6+sceYdE6cWc5/fdkYqq+qvdr+RY22N/ky3G4jWQcQGMk6olJeXp4kGbaPJycnKzU1tfGaQPLz8+X1ejV06FDD8w33zsvLa0zW8/LylJiYaLjwXNPrwznOjnBucoxyf9A3bPeLi4vTvd9ND9v9OtNPRiZ2wrP0bvMj/+PiMA4DAAAAEYM9UhCVysvLJUlJSUmG5+12e+M1Ld2jYd650T2aXtfw+2DPaXR9e8cJAAAAIPqQrAMAAAAAEGFI1hGVGirVgarSTqczYDX77HuUlflvX9Zwj6bXNfw+2HMaXd/ecQIAAACIPiTriEpG88MblJaWqri4uMXt0DIyMmQ2m3X06FHD80bzzYcNG6aKigoVFRWFfH17xwkAAAAg+pCsIypNmDBBUv3WaGdrONZwTSBxcXHKzMzUkSNHdOLECb/z27dvV2xsrDIzM9v8vOEYJwAAAIDoQ7KOqHTFFVcoPT1d69ev1/79+xuPO51OLV26VFarVbNmzWo8XlhYqMOHD/u1vN98882SpMWLF8vXZHPXVatW6dChQ7r++uubtanPnj1bVqtVzzzzTLN7ffLJJ3r11VeVkZGhiRMntnmcAAAAAHoGU2lpafg3pgUiwI4dO+RwOBQbGyuHwyG73a5Nmzbp+PHjWrhwoR588MHGa++66y6tXbtWK1as0OzZsxuP19XVaebMmdq6dasuuOACXXbZZTp27Jg2btyoAQMGaOvWrX7btP3mN7/RU089pYEDB2rq1KmqqqrShg0bVF1drQ0bNjRL1ls7TgAAAAA9A5V1RK2JEydq8+bN+u53v6vc3Fy9/PLLSklJ0e9+97uQE2CLxaI1a9bo0Ucf1ZkzZ/TCCy9o9+7dmjVrlt566y3D/dQffPBB/e53v1Pfvn21cuVKbdiwQRdddJH+9re/+SXq4RpnpNq3b59mzJihIUOGaMCAAcrKytK6deu6elidoqCgQC+88IKuv/56jRkzRn379tW5556rH//4x9qzZ4/hY8rLy/XYY49pzJgx6tevn8aMGaPHHnusx2zf99xzzyk5OVnJycn64IMPDK/piTHatGmTpk2bpoyMDPXv31/f+ta3dPvtt+vzzz9vdl1Pio3P59PGjRt1zTXXaOTIkfrGN76hCy64QL/4xS907Ngxv+tramq0ZMkSZWZmKi0tTSNHjtS8efMM1xfpTl577TX94he/0Pe+9z3169dPycnJWr16dcDri4qKNG/ePI0cOVJpaWnKzMzUkiVLVFtba3h9d49bqPGprKzUa6+9pltuuUWZmZnq37+/Bg8erKuvvlrr168PeH+v16vf/e53uvTSS9W/f38NGzZMt9xyi+E6NADQFlTWAXSId955Rw6HQzabTdOnT1dSUlJjx8Djjz+u+fPnd/UQO9QTTzyhZcuWKSMjQxMmTFDfvn2Vl5enN998Uz6fTy+//LKuv/76xusrKyv1b//2bzpw4IAmTZqkcePG6eDBg3rrrbc0duxYbd68WQkJCV34ijrWoUOHNHHiRFmtVlVWVurvf/+7LrzwwmbX9LQY+Xw+3X///frDH/6gjIwMXXnllUpMTNSXX36pXbt26fe//70uueQSST0vNgsWLNCKFSvUv39/XX311bLb7Tp48KC2bdumxMRE/e1vf9N5550nqT6hmjFjRrMOqfz8fG3atClgh1R3MXbsWJ08eVKpqanq1auXTp486dch1qCoqEhXXnmlvvjiC02ZMkXDhw/Xu+++q3fffVdXXXWVXn/9dZnNX9dwoiFuocbnrbfe0g033KCUlJTG6WlfffWVNm3apLKyMs2dO1dLly71u/99992nP/7xjxo1apQmT56sU6dOKTc3V7GxsdqyZYtGjRrVWS8VQJQiWQcQdh6PRxdeeKEKCgq0ZcsWjRs3TlL9XPzJkyfryJEjeu+996J6pfuNGzfqnHPO0aWXXtrs+O7duzV16lQlJibq008/VWxsrCTpP/7jP/T000/rvvvu0+LFixuvbzj+8MMP67HHHuvU19BZ6urq9P3vf18mk0nDhg3T66+/bpis97QY5eTk6Je//KXmzp2rX//617JYLM3OezweWa1WST0rNkVFRRo9erQGDhyonTt3Nls35IUXXtBjjz2m2bNna8WKFZLq1xi555575HA49NJLL8lkMjU7fuONNyonJ6dLXkt7vf322xo6dKgGDx6s3/72t1q8eHHAZP1nP/uZXn31VT3zzDO6/fbbJdV/IXT33Xdr7dq1Wr58uebMmdN4fTTELdT4HDhwQJ9++qmmTZummJiYxuOnTp3SlVdeqZMnT2rbtm0aP35847kdO3bouuuu0yWXXKI///nPje/l//M//6Np06bpkksu0X//9393zgsFELVogwcQdjt27FB+fr5uuOGGxkRdkux2ux566CF5PJ6grZrR4LrrrvNL1CXp0ksv1eWXX66SkhJ9/PHHkuo/MP/pT39SYmKiHn744WbXP/DAA0pOTtaqVauaLXIYTZYtW6aDBw9q+fLlfglpg54Wo+rqai1ZskTp6enKzs42jEtDot7TYnPixAl5vV5997vfbZaoS9IPfvADSdLp06cbj73yyiuSpEWLFjUmnJI0Z84cjRw5Urm5uXI6nZ0w8vD73ve+p8GDB7d4ndPpVG5urtLT03Xbbbc1HjeZTFq0aJHMZnNjnBpEQ9xCjc/YsWM1Y8aMZom6JPXr10+33nqrJGnXrl3NzjXEZ+HChY2JulS/cOyVV16p3bt367PPPmvvSwDQw5GsAwi7nTt3SpKysrL8zjUcO/uDT0/S8IGwIQHLy8vTl19+qYsvvtivVTkuLk6XXnqpCgoKdPTo0U4fa0f7+OOPtWTJEj344IMaPXp0wOt6Woy2b9+ukpISTZkyRXV1ddq4caN++9vfauXKlX6vsafFZtiwYbLZbHr33Xf9ksUtW7ZIki6//HJJksvl0p49ezRixAjDpG3SpEmqqakJuI5EtPjggw9UU1OjSZMmNUu8Jal///4677zztGfPHrlcLknEramz368b7Ny5UwkJCfrud7/r9xj+nQMQLtauHgCA6NOwuI5Rm3tycrJSU1N77AI8J0+e1Ntvv620tDSdf/75kr6O19ChQw0f0xDHvLy8qJo64PF4dPfdd+vcc8/V/fffH/Tanhajf/zjH5Lqq+eXXXaZjhw50njObDbr7rvv1lNPPSWp58UmJSVFjz/+uB5//HFdfPHF+uEPf6jExER9/PHHevvtt3XLLbfozjvvlCTl5+fL6/WGFJtJkyZ12mvobKH8HTl48KCOHTumUaNGEbd/qaur09q1a2UymfS9732v8XhlZaUKCwt13nnnGXa9NI0PALQHyTqAsGtYffrsFtUGdrtdBQUFnTmkiOB2u3XnnXeqpqZGixcvbvyQ1xCv3r17Gz7Obrc3uy5aPPPMM42LoJ3dfnq2nhajhjbu5cuXa9y4cdq2bZvOPfdc7d+/X7/4xS+0fPlyZWRk6Pbbb+9xsZGkefPmqX///rr//vv18ssvNx6/+OKLNXPmzMa/Tz0xNkZaGwfiVu///J//o48//lhz5sxpXLBQCu3fuKbXAUBb0QYPAJ3A6/Xq5z//uXbv3q2bb75ZN954Y1cPqUsdOHBAv/nNbzRv3jx9+9vf7urhRByv1ytJstlsWr16tcaPH6/ExERdeuml+uMf/yiz2azly5d38Si7ztKlS3X33Xfr/vvv10cffaQvvvhCmzdvlsfj0bX/v717j6m6/uM4/oIoUIyORiJKBk4BTQ3z5CXUn5VABL/UMC2dmc00RqJOE92szdKQbOqmRtRyDiRTA+WYzWngwBtIdiFnZYrSqFCmglfA4Pz+cOf8POJRSvAcOc/HdjbO9/P5fr+f73uMw/t8bv/9r0wmk6ObiLvc2rVrtWzZMvXt21dLlixxdHMAuCiSdQDNztLbYK9X4fz583Z7JFojs9msxMREbdy4UWPHjtXy5cttyi2xqK6uvuH5lnm5rSlm8fHxCgoK0rx585pU39ViZHmOsLAw+fv725T17NlTgYGBOn78uKqqqlwuNvn5+Vq8eLFef/11zZ49W126dLHOHd6wYYPatGljXfne1WJjzz+Ng6vHbd26dZo1a5Z69eqlLVu2qF27djblTfmMu7YeAPxbDIMH0Oyuna93fa9pVVWVTp8+rYEDBzqiaXdcQ0ODpk+frszMTI0ZM0apqak2exlL/4+XvQXAbrYGwN3q0KFDkmR3n+aIiAhJV/9pjo2NdbkY9ejRQ5L9YciW4zU1NS4Xm+sXkbuWr6+vevXqpQMHDuj06dMKCgqSu7u7y8TGnqb8jri7uyswMFCSXDpuGRkZmjFjhkJDQ2UymdShQ4dGdby9vdWpUyeVlZWpvr6+0bz11hwfAHcWyTqAZhceHq5ly5YpLy9PcXFxNmV5eXnWOq3dtYn6Cy+8oLS0NLuLEfn7+6uoqEgXL160WdG7pqZG+/btk7+/v93Fnu5GEydOvOHxffv26dixY4qOjpavr691JWpXi5ElET1y5EijsitXrqi0tFTe3t7y9fWVn5+fS8Wmrq5Oku32bNeyHL/vvvvk5eWl/v37q7i4WL///nujlc137dolT09P9e/fv2Ub7WBGo1Genp7atWuXzGazzYrwFRUVOnz4sIxGo7y8vCTJZeOWkZGhxMREhYSEyGQyydfX127d8PBwZWVlqbCwsNHnmSt9zgFoWQyDB9Ds/vOf/ygwMFBffvmlSkpKrMfPnz+vpUuXysPDQ+PHj3dgC1teQ0OD3nzzTWVmZmrUqFH65JNP7O4h7ubmpokTJ+rChQv64IMPbMqWLVumqqoqTZw4sdGWS3ezlStX3vA1YMAASVf3B1+5cqX69u0ryfViFBQUpKefflqlpaWN9r9evny5qqurFRMTIw8PD5eLjWWrrI8++qjRMO3PP/9cpaWlCgsLsy7yNWnSJEnSwoULbfaaX7dunX799VeNHj261Q9X9vHx0ejRo3XixAmtWbPGetxsNmvhwoVqaGjQK6+8YnOOq8UtPT1diYmJCg4Olslk0kMPPXTT+pb4LFq0yPoFknR1mkZubq6efPJJde/evUXbDKD1c6uqqjLfuhoA/DMFBQWKi4uTp6en4uLidP/992vr1q0qKyvTggULNGfOHEc3sUUlJycrJSVF7dq10xtvvHHDRD0mJsaajF68eFHPPvusfvrpJz311FMKCwvToUOHtHPnTvXp00fbt29vtId2axQfH6/169dr586deuKJJ2zKXC1Gx48fV2RkpCorKxUVFaUePXqopKREBQUFevjhh/XNN99YpxG4Umzq6+s1cuRI7dmzR76+voqOjpbBYNChQ4esPb5btmzR4MGDrfXHjh2r3NxcGY1GDRkyRCdOnJDJZFLnzp2Vm5trdzqGs0tPT9f+/fslSYcPH9aPP/6oQYMGKSgoSNLVvzGxsbGSrvagjxgxQn/88YdiY2PVvXt37d+/X4WFhXrmmWe0adMmmyk6rSFuTY1Pfn6+Ro0aJbPZrMmTJ6tjx46NrtWnTx9rLC0SExOVnp6u0NBQRUZG6tSpU9q8ebM8PT21Y8cOhYaGtvxDAmjVSNYBtJiDBw8qOTlZBw4c0JUrVxQaGqr4+HiNHTvW0U1rcZak82ZWr16tCRMmWN9XV1crJSVFJpNJJ0+elJ+fn55//nklJSXZnbvc2twsWZdcL0bl5eV6//33lZubqzNnzsjPz0/R0dGaO3duo54/V4pNbW2tPv74Y2VnZ+u3335TXV2dOnbsqPDwcOvCYNfXX7FihTZs2KDy8nIZDAZFRkZqwYIF6tSpk4Oe4vbd6u9MUlKS5s+fb31fUVGhRYsWaceOHaqqqlJAQIDGjRunmTNnytPTs9H5d3vcmhqfzMxMJSQk3PRaL7/8slJTU22ONTQ06NNPP9XatWutU1OGDh2qt99+m151AM2CZB0AAAAAACfDnHUAAAAAAJwMyToAAAAAAE6GZB0AAAAAACdDsg4AAAAAgJMhWQcAAAAAwMmQrAMAAAAA4GRI1gEAAAAAcDIk6wAAAAAAOBmSdQAAAAAAnAzJOgAANxETEyODwaDk5ORb1jUYDDIYDNq9e7f12O7du63Hr3116tRJvXv31qRJk5Sbm9uSj9CsvvrqKyUnJ9s8IwAAaH4ejm4AAACuYtCgQdafz5w5o7KyMuXk5CgnJ0cJCQlavHixA1vXNNu2bdP69eslSUOHDnVwawAAaL1I1gEAuEO2b99u8/7s2bOaO3euNm3apNWrVys6OlpDhgxxUOsAAIAzYRg8AAAO0r59e61atUqdO3eWJG3evNnBLQIAAM6CZB0AAAfy9PRUWFiYJKmsrOy2r5ecnCyDwaD4+HjV1NQoJSVFgwcPVufOndW1a1drvaNHj2r58uWKjY1V79695efnp65duyoqKkppaWm6cuWKzXXLyspkMBisQ+BTUlJs5uD36dOnUVvKysr01ltvyWg0yt/fXwEBARo+fLhWrlypmpqa235WAABaM4bBAwDgYJcvX5YkeXt7N9s1a2trFRMTo4MHD6pbt24KDg5WZWWltfzdd9+VyWRSu3bt1LFjRz366KOqrKxUUVGRioqKtG3bNmVlZenee++VJHl5eWnQoEE6duyYKisrFRAQoICAAOv1/Pz8bO6/detWTZ06VZcvX5aXl5cCAwNVW1urkpIS/fDDD8rJyVF2drZ8fHya7ZkBAGhNSNYBAHCgiooKHTx4UJL02GOPNdt1c3Jy1KVLF+Xn51uva/lSQJLGjRunGTNm6PHHH5ebm5v1+JEjR5SQkKCCggKlpqYqMTFR0tVkfPv27YqPj9f69es1YcIEzZ8//4b3Likp0ZQpU1RXV6cFCxYoISFBbdq0kSSVlpZqypQp+vbbb5WUlKTU1NRme2YAAFoThsEDANAE1w/7vtHrnzh79qzy8vI0ZswYnTt3Tn5+fpo8eXKztbe+vl5r1qyx+QLAkjBLV7ek69+/v02iLknBwcFKS0uTJGVmZv6re7/33nuqra3VzJkzNWfOHJv7duvWTenp6fL29tbGjRv1559//qt7AADQ2tGzDgBAE1w/7PtGCgsLb1puL6EfPny4PvzwQ7Vv3/5ft+96oaGhMhqNN61TWVmprKwsfffddzp16pRqa2tlNput5UeOHNHly5dtku1bOXfunPLy8iRJr7766g3rBAQEqF+/ftqzZ4/27t2rF198scnXBwDAVZCsAwDQBDcb9m1xq951yz7r9fX1+uuvv1ReXq577rlHISEhCgoKara2SlJISMhNyy17u1+4cMFuHbPZrLNnz/6jZP3nn39WfX293NzcNG3aNLv1jh49Kkn0rAMAYAfJOgAAd8j1+6wXFBRo8uTJSktLk7u7u5KTk5vtXm3btrVbVlZWpqlTp6q2tlajR4/WtGnTFBwcLB8fH3l4eKihoUEdOnSQpEarwt9KVVWVpKuJ/q1GGkjSpUuX/tH1AQBwFSTrAAA4yLBhw5SWlqYxY8YoNTVVcXFxtxy63hyys7NVW1sro9Gozz77TO7utkvYnDlz5l9f27Ki/QMPPNAsW9EBAOCqWGAOAAAHGjFihKKioiRJ77zzzh25pyWJHjhwYKNEXZKKiorsnnv9gnTX69mzp9zc3FRdXa1ffvnl9hoKAIALI1kHAMDBLHPh9+3bp/z8/Ba/n2UOekVFRaMys9msVatW2T3XMry+pqbmhuUPPvighg0bJklaunTp7TYVAACXRbIOAICDhYWFWXvXlyxZ0uL3Cw8Pl3R1kbmvv/7aevz8+fNKSEjQ999/b/dcy0J4hYWFduezL1y4UF5eXsrKytL06dN18uRJm/K6ujrl5uZq0qRJt/soAAC0WiTrAAA4gXnz5kmS9u/f3+K9688995yGDBmiv//+W+PHj1ffvn01fPhwhYSE6IsvvtCKFSvsnjty5Ei1bdtWRUVF6tWrl6KiohQTE6PXXnvNWicsLEzp6eny8fFRRkaGevbsqQEDBigiIkIDBw5Uly5dFBcXp5ycnBZ9TgAA7mYk6wAAOIF+/frdsd51d3d3bdy4UbNmzdIjjzxi3UZu6NChMplMeumll+yeGxAQoOzsbEVERKihoUHFxcXau3eviouLbepFRkaquLhYs2fPVu/evVVRUaGSkhJdunRJRqNRSUlJKigoaNHnBADgbuZWVVVldnQjAAAAAADA/9GzDgAAAACAkyFZBwAAAADAyXg4ugEAAKCxjIwMZWZmNrl+RESEZs+e3YItAgAAdxLJOgAATqi8vFyFhYVNrm/ZUg0AALQOLDAHAAAAAICTYc46AAAAAABOhmQdAAAAAAAnQ7IOAAAAAICTIVkHAAAAAMDJkKwDAAAAAOBkSNYBAAAAAHAyJOsAAAAAADgZknUAAAAAAJwMyToAAAAAAE7mf6lW7srA9ASEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a distribution plot.\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    plt.figure(figsize=(6,3), dpi= 100)\n",
    "    ax = sns.distplot(HR_2016_data['HR_rate'], bins=15)\n",
    "    plt.title(label='Distribution of the HR_rate \\n (workers in the mental health sector per '\n",
    "              '100 000 population)', fontweight='bold')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.283803413572262"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measuring skewness\n",
    "stats.skew(HR_2016_data['HR_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "HR_numpy = HR_2016_data['HR_rate'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using interquartile range (IQR) to eliminate outliers and fix distribution in order to proceed with Pearson's r correlation.\n",
    "HR_IQR = stats.iqr(HR_numpy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating variables for Q1 and Q3\n",
    "Q1 = HR_2016_data['HR_rate'].quantile(0.25)\n",
    "Q3 = HR_2016_data['HR_rate'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating mask.\n",
    "iqr_mask = (~((HR_2016_data['HR_rate'] < (Q1 - 1.5 * HR_IQR)) | (HR_2016_data['HR_rate'] > (Q3 + 1.5 * HR_IQR))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "HR_2016_data['mask'] = iqr_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     72\n",
       "False    10\n",
       "Name: mask, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HR_2016_data['mask'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "HR_2016_data = HR_2016_data.where(HR_2016_data['mask'] == True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping data related to outliers.\n",
    "HR_2016_data = HR_2016_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetting index and dropping redundant columns.\n",
    "HR_2016_data = HR_2016_data.reset_index().drop(columns=['index', 'mask'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>Year</th>\n",
       "      <th>Psychiatrists</th>\n",
       "      <th>Psychologists</th>\n",
       "      <th>HR_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.2635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.471</td>\n",
       "      <td>1.231</td>\n",
       "      <td>1.3510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.840</td>\n",
       "      <td>0.788</td>\n",
       "      <td>2.3140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.452</td>\n",
       "      <td>1.165</td>\n",
       "      <td>2.3085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country    Year  Psychiatrists  Psychologists  HR_rate\n",
       "0  Afghanistan  2016.0          0.231          0.296   0.2635\n",
       "1      Albania  2016.0          1.471          1.231   1.3510\n",
       "2       Angola  2016.0          0.057          0.179   0.1180\n",
       "3      Armenia  2016.0          3.840          0.788   2.3140\n",
       "4   Azerbaijan  2016.0          3.452          1.165   2.3085"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HR_2016_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a distribution plot.\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    plt.figure(figsize=(6,3), dpi= 100)\n",
    "    ax = sns.distplot(HR_2016_data['HR_rate'], bins=15)\n",
    "    plt.title(label='Distribution of the HR_rate \\n (workers in the mental health sector per '\n",
    "              '100 000 population)', fontweight='bold')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5402064611116548"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measuring skewness\n",
    "stats.skew(HR_2016_data['HR_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating variable for age-standarized suicide rates dataframe.\n",
    "agestd_data = pd.read_csv('agestd.csv', header=0, engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Age-standardized suicide rates (per 100 000 population)</th>\n",
       "      <th>Age-standardized suicide rates (per 100 000 population).1</th>\n",
       "      <th>Age-standardized suicide rates (per 100 000 population).2</th>\n",
       "      <th>Age-standardized suicide rates (per 100 000 population).3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Country</td>\n",
       "      <td>Sex</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>14.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.3</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Unnamed: 1  \\\n",
       "0      Country         Sex   \n",
       "1  Afghanistan  Both sexes   \n",
       "2  Afghanistan        Male   \n",
       "3  Afghanistan      Female   \n",
       "4      Albania  Both sexes   \n",
       "\n",
       "   Age-standardized suicide rates (per 100 000 population)  \\\n",
       "0                                             2016.0         \n",
       "1                                                6.4         \n",
       "2                                               10.6         \n",
       "3                                                2.1         \n",
       "4                                                5.6         \n",
       "\n",
       "   Age-standardized suicide rates (per 100 000 population).1  \\\n",
       "0                                             2015.0           \n",
       "1                                                6.6           \n",
       "2                                               10.9           \n",
       "3                                                2.1           \n",
       "4                                                5.3           \n",
       "\n",
       "   Age-standardized suicide rates (per 100 000 population).2  \\\n",
       "0                                             2010.0           \n",
       "1                                                7.4           \n",
       "2                                               12.5           \n",
       "3                                                2.1           \n",
       "4                                                7.7           \n",
       "\n",
       "   Age-standardized suicide rates (per 100 000 population).3  \n",
       "0                                             2000.0          \n",
       "1                                                8.1          \n",
       "2                                               14.3          \n",
       "3                                                1.7          \n",
       "4                                                5.8          "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agestd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unnecessary columns.\n",
    "agestd_data = agestd_data.drop(agestd_data.columns[[3, 4, 5]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming columns.\n",
    "agestd_data = agestd_data.rename(columns={'Unnamed: 0': 'Country', 'Unnamed: 1': 'Sex', \n",
    "                            'Age-standardized suicide rates (per 100 000 population)': 'Suicide_rate'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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>Sex</th>\n",
       "      <th>Suicide_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Country</td>\n",
       "      <td>Sex</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country         Sex  Suicide_rate\n",
       "0      Country         Sex        2016.0\n",
       "1  Afghanistan  Both sexes           6.4\n",
       "2  Afghanistan        Male          10.6\n",
       "3  Afghanistan      Female           2.1\n",
       "4      Albania  Both sexes           5.6"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agestd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "agestd_data = agestd_data.drop([0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\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>Sex</th>\n",
       "      <th>Suicide_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Female</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Both sexes</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Albania</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country         Sex  Suicide_rate\n",
       "1  Afghanistan  Both sexes           6.4\n",
       "2  Afghanistan        Male          10.6\n",
       "3  Afghanistan      Female           2.1\n",
       "4      Albania  Both sexes           5.6\n",
       "5      Albania        Male           7.0"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agestd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering data with respect to the constraint.\n",
    "agestd_data = agestd_data[agestd_data['Sex'] == 'Both sexes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suicide_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.682514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.529334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suicide_rate\n",
       "count    183.000000\n",
       "mean       9.682514\n",
       "std        5.529334\n",
       "min        0.400000\n",
       "25%        5.750000\n",
       "50%        9.100000\n",
       "75%       12.500000\n",
       "max       30.200000"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a look at distribution parameters.\n",
    "agestd_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking number of nan values in Suicide_rate column.\n",
    "agestd_data['Suicide_rate'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging both dataframes in order to perform further analysis.\n",
    "suicide_data = pd.merge(HR_2016_data, agestd_data, on='Country', how='inner').drop(['Sex'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Psychiatrists</th>\n",
       "      <th>Psychologists</th>\n",
       "      <th>HR_rate</th>\n",
       "      <th>Suicide_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.231</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.2635</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>1.471</td>\n",
       "      <td>1.231</td>\n",
       "      <td>1.3510</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.179</td>\n",
       "      <td>0.1180</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.840</td>\n",
       "      <td>0.788</td>\n",
       "      <td>2.3140</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.452</td>\n",
       "      <td>1.165</td>\n",
       "      <td>2.3085</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country    Year  Psychiatrists  Psychologists  HR_rate  Suicide_rate\n",
       "0  Afghanistan  2016.0          0.231          0.296   0.2635           6.4\n",
       "1      Albania  2016.0          1.471          1.231   1.3510           5.6\n",
       "2       Angola  2016.0          0.057          0.179   0.1180           8.9\n",
       "3      Armenia  2016.0          3.840          0.788   2.3140           5.7\n",
       "4   Azerbaijan  2016.0          3.452          1.165   2.3085           2.6"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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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       "\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>Year</th>\n",
       "      <th>Psychiatrists</th>\n",
       "      <th>Psychologists</th>\n",
       "      <th>HR_rate</th>\n",
       "      <th>Suicide_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>71.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>2.337577</td>\n",
       "      <td>2.031507</td>\n",
       "      <td>2.184542</td>\n",
       "      <td>9.230986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.622944</td>\n",
       "      <td>2.805171</td>\n",
       "      <td>2.766721</td>\n",
       "      <td>5.468314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016500</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.177000</td>\n",
       "      <td>0.226250</td>\n",
       "      <td>5.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>0.839000</td>\n",
       "      <td>0.788000</td>\n",
       "      <td>1.048500</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>2.766500</td>\n",
       "      <td>2.406000</td>\n",
       "      <td>2.890000</td>\n",
       "      <td>11.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>16.188000</td>\n",
       "      <td>12.368000</td>\n",
       "      <td>11.324000</td>\n",
       "      <td>30.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year  Psychiatrists  Psychologists    HR_rate  Suicide_rate\n",
       "count    71.0      71.000000      71.000000  71.000000     71.000000\n",
       "mean   2016.0       2.337577       2.031507   2.184542      9.230986\n",
       "std       0.0       3.622944       2.805171   2.766721      5.468314\n",
       "min    2016.0       0.014000       0.000000   0.016500      2.400000\n",
       "25%    2016.0       0.165000       0.177000   0.226250      5.550000\n",
       "50%    2016.0       0.839000       0.788000   1.048500      8.000000\n",
       "75%    2016.0       2.766500       2.406000   2.890000     11.650000\n",
       "max    2016.0      16.188000      12.368000  11.324000     30.200000"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a distribution plot.\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    plt.figure(figsize=(8,4), dpi= 100)\n",
    "    ax = sns.distplot(suicide_data['Suicide_rate'], bins=15)\n",
    "    plt.title(label='Distribution of the Suicide_rate \\n (per 100 000 population)', fontweight='bold')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3699754653007674"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Measuring skewness\n",
    "stats.skew(suicide_data['Suicide_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REREREa1NbHQmoa2tDftravBUfknSk53lhjxU5xdjf01NWtLE07XNfqfVjiujQ0knunuDMoyiBFWS7x8NBeEJ+FFbW5tkBSIiIiIiIiIiWo/Y6ExSS0sLnqyqQum0gF0We9yTk7miBrstdpROC/h21U60tLSkbY1Kb7OvMOYjO1PE5eGBpGvEGqRCConwvdMB7H52T9LPExERERERERHR+sNGZwqcTicqysrQ+Nbb2FdQij1mO7YZzbBIurlkcbUqAxZJh21GM/aY7dhXUIpTb72NirKytExyzqf0NvsnrDa839+NSDTRvPXbYt9LJMmJUGB2+7ujsjLp54mIiIiIiIiIaP1RAUi+a/UAyM7OxsTEBHJycjA5ObnofZIkoba2Fruf3QNHZSVshUVz11wD/XC2teFc8xk0NDQknK4uCAJEUUQoFEIkEkn4M1RXV+NUUxMujQ2i0zeS8PNbjWZ8y1KC8/3Xkw4hirFIOnyvaCNOXP/XpGuoVRn4cfljUKvVSX0fRERERERERER0/8TbX0sVG53LSPaHSKU5GWuaPrN3LxwOB0oKC+eu9Q0MwOl04uzp0wk1TR0OBxpOnkQkS8LHviGMhoLLPpMrarDTakd2poj3+7vhDvoT+hwL2WY0o0iXg1/3d6dU5ycV34ROp0MgEEh5TURERERERERElD5sdK4S9+uHAABRFHG0rg6HjxyBOzAJ17QMt+yHNyQjHJ2BWpUBkyjBIulgz9TCrNWh/ng9Xquriyu1/Xb9w/AE/OidDsAd9MMbnFdfI8Gi0eEPDPnIFSVcGR3C5eGBlLarz/ecvQxdvhF0+oaTrsGJTiIiIiIiIiKitYONzlXifv0QlZWVePe99xKeuNxusEKYklF74EDcZ34ut83+yv/8n3jq31bjV/3XMBxMbJv9cuut3VCBX1z7XdKp7cDs9vfHNUbYi4oVWxsREREREREREaXH/eqvMYxoFaiursaF1lb0ZEZw3t0bV5MTAEZDQZx396InM4ILH7aiuro6rudkWcaJEyewf28N7EXFUKvV0Ol0UKvVsBcVY+8ze/Dzn/8cj+ktqXyse+y02nFldCilJicAWDQ6ONvaFFoVERERERERERGtB2x0rrDKykqcamrCB8N9SQUFAUCnbwQtwzdwqqkJDocDwOwZoVqtFoIgLPt8JBJBIBC4Yxv4a3V1EKZklBvyklrT3SqM+cjOFHF5eCDlWvZMLc41n1FgVUREREREREREtF5w6/oy0jlaq9Fo0NH1GbrVkaSbnPNVGPLxDYMF4+PjigQYVVZW4kJrKz4Y7kspbd2WpcezJVugzsiALxSEO+hH76QP18a9CU935ooa7CsohdVsSTi9noiIiIiIiIiI7j9uXX8AvHr0KMJajSJNTgDo8A3DhzDGJTXe7PwUP+v4BG92foqL8iiyHRV45Y3XMeRx4/VjxyCK4rL12trasL+mBk/llyQ92bnVaMauok1o7ruGNzs/xfkb13Fjahxlhjz8aMuj2GEugqBSxV1vh8GK+uP1bHISEREREREREdEdONG5jHR1nLVaLQbdbjQOdsd9Jmc8lgv8SSbAyOFwoOHkyYSDknZa7cjOFPF+fzfcQf+C91RZ7chZ4p75yg15KJ0WUFFWFlfKPBERERERERERrTxOdK5zBw8ehDswqWiTE5gNKPIE/diiNy16PdEAI6fTiYqyMjS+9Tb2FZRij9mObUYzLJIOatXs/0JqVQYskg7bjGYcsJehdkMFhuQpvNPTvmgDczQURJPrKpzem9hvL4MtS7/oGmxZeuwwFqD2wAE2OYmIiIiIiIiI6B6c6FxGujrOjc3NyHZUoH3Mo1jNmG1GM4p0Ofh1f/eS99my9HgqvwRPVlXFNdkJAJIkoba2Fruf3QNHZSVshUVz18ZCMtyyH66p8YTP37Rl6bGreBNOfdl1T2O03JCH7UYrnqvZh5aWlrhrEhERERERERHRyrtfE51sdC4jXT+Eq78fF+XRZbdrJ8Mi6fC9oo04cf1fl703le3gsTClLzNn8Pux4ZT/R6ow5sNhehjv9LQjEo0iV9Rgh8GKjAS22RMRERERERER0erCretrkCAI0Gq1EARh2ftKCgvhDaUnUMcblGEUJcQT8dPpG0EkS8LRurqE3xMLU2pXoMkJAB1jw5icDuGZ4s3YY7ZjX0EpTr31NirKytjkJCIiIiIiIiKiJbHRmQJJknDo0CE0NjfD1d+PcDgMv9+PcDgMV38/GpubcejQIUiSdMdzscTzRLZ2JyJWV1DF9/N+7BvC4SOH71nnUrRaLQ4fOYKPx91JrXExrUO9KJSy8H++Wger2YJXXn6ZZ3ISEREREREREdGy2OhMgiiKeP3YMQx5PHj5jZ8i21GBi/Io3uz8FD/r+ARvdn6Ki/Iosh0VeOWN1zHkceP1Y8fmGpyxxp06zkZkomJ1I3E2UkdDQXgCftTW1sb9jnSGKd0Y8+LWrVuQ5fRMvBIRERERERER0frDRmeCKisr0dHVhX0vvYjGwW6c9bjQPuaBO+ifm6QMR2fgDvrRPubBGU8vGgd7sO+lF9HR1QWHw4FIJIK+gQGYxPgnKBNh0kgYC8kJbSfvnQ5g97N74r7/mb174ZpOTyMy0bUQERERERERERGx0ZmA6upqXGhtRU9mBOfdvXFPM46Ggjjv7kVPZgQXPmxFdXU1nE4nLJIuLeu0aHRwy4mFHLmDfjgqK+O+3+FwJPyOdK2FiIiIiIiIiIiIjc44PfroozjV1IQPhvvQ6RtJqkanbwQtwzdwqqkJv79yBbbM9Ex0btab4JoaT+gZb1CGrbBo2SAlQPkwJRVmt9vHwpMSWQsREREREREREREAqFd6AWvF//jbt3FpbDDhBuLdXFPjuKQexL/74Q9h1mUjV9Qoes5lrqiBWaPD6fFrCT0X23YviiICgcCS96YapqRWZWCL3gR7tgEWjQ4GUTN3zRcKwh2cnRQ1GAzwer1JvYOIiIiIiIiIiB4sbHTGKaLVoNPdq0itTt8I7BY72v+1Hds3fQ3nFaoLADutdlwZHUq4CRkLMIon4Xx+mFIi7xFUKnwjvxCPmKzwyH58Pu5F2/BNeEMywtEZqFUZMIkSLJIOOZkivuj9EvXH6/FaXR2T14mIiIiIiIiIaEkqIKHMmgdOdnY2JiYm8Pc97XDLS086JiJX1GBfQSmGR7zoVoeT3g4/X4UxHw7Tw3inpx2RaGI/q0XS4XGNEfai4rjud/X346I8Ojd9GU/9pws3YmI6hA+H4jvfNFfUYLvBCmFKRu2BA3A6nXG9i4iIiIiIiIiIVo9Yfy0nJweTk5Npew/P6IzTmMIThaOhIDwBP/7+l7/EDmMBbFn6lOrZsvR4wmrD+/3dCTc5gdkAI2dbW9z3JxKmZMvSY7+9DE7vTTS5rqYU4kRERERERERERLQQNjpXUO90ABX/Zhv219TgqfwSlBvykqqz1WjGrqJNOH/jetwTlnezZ2pxrvlM3PefPX06rjAli6TDruLZtXWMDSe1tvkhTg6HI6kaRERERERERES0vrHRuYLcQT8clZVoaWnBk1VVKJ0WsMtiR+68cJ6l5Ioa7LN9HY+aCnCqtyvpoKRcUQOzVoeGhoa4n2loaIBFm73kWtUqFb5btBEfDbmUCXEaG0TDyZNzYUhEREREREREREQxbHSuIG9Qhq2wCIIgwOl0oqKsDI1vvY19BaXYY7Zjm9EMi6SbCwpSqzJgkXTYZjTjgL0MtRsqMCRP4Z2e9qQnOQFgh8GK+uP1kGU57mdkWUb98ePYbrAues9j+YUYvxVMepLzbp2+EUSyJBytq1OkHhERERERERERrR8MI1pG7LDU45/9C27NRJa8VwVAUGUgEp2J+0v9ScU3odPpEAjcDjqSJAm1tbXY/eweOCorYSssmrvWNzCAnJwc9E8HcGHQlXC6+t1MrH3bAAAgAElEQVTKDXkonRZQUVaWcLK5KIro6OpCT2bknjAltSoDP/r6o2j4oiPuMznjEQtxspotCTVmiYiIiIiIiIhoZdyvMCJ12io/ANSqDGzRm2DPNsCi0cEwbxu3LxSEO+hH76QP18a9CzYkY5OadzcYZVnGiRMncOLECQCAIAgQRRGhUAiRSASVlZW40NqKQl12SlvCbVl67DAW4MmqqoSbnLF1v/D887jQ2oqp8PQda9miN8Ej+xVtcgK3Q5xqa2vnvh8iIiIiIiIiIiI2OpMgqFT4Rn4hHjFZ4ZH9+Hzci7bhm/CGZISjM1CrMmASJVgkHcoMeaiy2nBldAiXhwfuSEQ3aSS4BvoRiSw9KRqJRO6Y+Gxra8P+mhqcamrCJfXgPdOU8Sg35GG70Yr9NTVwOp0JP7/cWuzZBnw+7k267lJ6pwPY/eweNjqJiIiIiIiIiGgOz+hMkEXS4Qel22CRstDwRQd+1duF9jEP3EH/3NRmODoDd9CP9jEPftXbhYYvO2CRsmaf0+hu19Lo4GxrS2odqQQY7bbYUTot4NtVO9HS0pLU+5dbi0Wjg1tO/tzQpcRCnIiIiIiIiIiIiGLY6EyALUuP/fYyOL030eS6Gve27NFQEE2uq3B6b2K/vQy2LD0AoNyYjy+7e5JeT6IBRnvMduwrKMWpt95GRVlZSpOcy63FIGrgDaXnDM35IU5EREREREREREQAw4iWFTss9R9vXMeTD2/A+RvXUz4Xc1fxJrQM9OCpwlLcCobwZFXVXNPx7vM447VUgJFroB/Otjacaz6DhoaGtIf4mEwmjIyM4Gcdn6TtHQuFOBERERERERER0epzv8KI2OhcRuyHCEzfwm88N9AxNpxyzQpjPr5lKUH7qBsT0yFseygHn1y+jD/8wz9ESWHh3H19AwNwOp04e/p0wg3KZBumShAEAeFwGG92fppyKvxC1KoM/Lj8MajV6vv+2YiIiIiIiIiIKDFsdK4SsR/ixqQPJ11XFav7nL0MKgD5kg7eYABdvhG4Zf+CgUb2TC3MWh3qj9fjtbq6pBLS7zdXfz8uyqNwB5U/p9Mi6fC4xgh7UbHitdNlJRvPREREREREREQr6X41OnlGZ5w+8vQpWu/C4JewarNw8stO/MOXny0ZaHTG04vGwR7se+lFdHR1weFwKLqWdHA6nbBIuuVvTEIqIU73iyRJOHToEBqbm+Hq70c4HIbf70c4HIarvx+Nzc04dOgQJEla6aUSEREREREREa0LbHTGaUzhKcrRUBCDgSmYNVlx33/e3YuezAgufNiK6upqRdejtLOnT8OWmZ4mnj1Ti3PNZ9JSO1WiKOL1Y8cw5PHg5Td+imxHBS7Ko3iz81P8rOMTvNn5KS7Ko8h2VOCVN17HkMeN148dgyiKK710IiIiIiIiIqI1jVvXlxEbrT3+2b/g1oyyW463Gc0o0uXg1/3dCT1ny9LjqfySO0KMVhtJkjDk8aBxsDvudPp45Ioa7CsohdVsSXuoUqIqKyvx7nvvIZIl4WPfUFyfO1fUYLvBCmFKRu2BA6v29yQiIiIiIiIiSha3rj8A3EF/Utu7XVPjuDQ2iIaTJ1ftJKAsy6g/fhzbDVZF6+4wWFF/vH7VNTmrq6txobUVPZkRnHf3xt3cXWuTukREREREREREqxUbnSvIG5RhFCWokni20zeCSJaEo3V1Si8raYIgQKvVQhAEAMBrdXUQpmSUG/IUqV9uyEPGlIzXVtFnBmYnOU81NeGD4T50+kaSqtHpG0HL8A2campaE2ewEhERERERERGtNmx0rqBY8JCgSu5n+Ng3hMNHDq9YoM1ygTvvvvce/p+/+zvsyC2ALUuf0rtsWXrsMBag9sCBVZU6r9Fo0HDyPVwaG4RrajylWmthUpeIiIiIiIiIaLVio3MFqb9qcEa+angmajQUhCfgR21t7T3X7p6uVFIigTvf/w8/QoZKhV0PlyY92VluyEN1fjH219SsujMsXz16FGGtJulJzrutxkldIiIiIiIiIqK1gGFEy0hnGJFF0uE5exn+76u/m5vuTNQ2oxmq63145nvfQ21tLZ7ZuxcOhwMlhYVz9/QNDMDpdOLs6dNoaGhI6XzLZAN3duYVwaLLRv/kOH47ejPu53YYrMhYpUE9Wq0Wg273AxW4RERERERERESUqPsVRqROW2ValkWjQzASxha9CZ2+4aRqDIcC2P+txzHs9eLmpA+uaRkX5VF4OwcQjs5ArcqASZRgcVTglW8+hp+/+d9Rf7wer9XVJbwFvLq6GqeamnBpbBCd7t64nxsNBdE40I2thnw8nl+EA1IWBqcm0DsdgDvohzco316rRoJFo4M9UwuzVpf0Wu+HgwcPwh2YVLTJCdw5qXvixAlFaxMRERERERERrVec6FxGOic6n7OXYWr6FjJUKvy6vzvh5y2SDt8t3Ah/eBr/NPhF3FOS2w1WCAlOSVZWVuJCays+GO5L6SxKW5YeT+WX4L/91/+Kin+zDY7KStgKi+auuwb64Wxrw7nmMylPn6ZbY3Mzsh0VaB/zKF57m9GMibZ27N9bo3htIiIiIiIiIqL7iROdq4xRFOGWA4rVyxU1MGt0uOS5ge8Ulib8vC1Lj13Fm/DRkAsdY/FPg46Ggjjv7kWFIR8f/eY3OHjgAM6dO7fkM4oH7qgH8f1//+9RUVaGUCgEQRAgiiJCoRAiEWWbyenkcDhwUR5NS2130I/HKyvTUpuIiIiIiIiIaD1iGFGc/shcomi9nVY7rowOwSMHYBQlqBJ41iLpsKt4E87fuJ5Qk3O+Dt8w/nHoSzQ2n8b/OHFiyZTvdAfuRCIRBAKBNdXkFAQBJYWF8IbSM3HqDcqwFRalJUyKiIiIiIiIiGg9YqMzTjmZIiqM+YrUqjDmIztTxOXhgbkQIkEV30+hVqnw3aKN+GjIpch05T8P9uLgv/s+Oq9ehcPhuOcerVaLw0eO4ONxd0rvutvHviEcPnIYkiQpWvd+iTWGkw2RWk6s7lINaCIiIiIiIiIiuo2NzjhduNmLJ6w22LL0KdWxZenxhNWG9/u7EYlGof6qwRmJs2H2WH4hxm8Fk57kvFvH2DDcwQCixhxc+LAV1dXVd1y/H4E7a1EsHEkdZ4M6UbG6qzGEiYiIiIiIiIhoNWKjM06R6AzO37iOXcWbkp7s3Go0Y1fR7JZzd9APADBpJIyF5LgSodSqDDxisuLDIVdS71/Mh0O9KNLl4MJIP041Nd0x2fnM3r1wTadne3bvdAC7n92TltrpFolE0DcwAJOYnolUk0aCa6B/TW3nJyIiIiIiIiJaSWx0xsksaeGaGsepL7vgMD2MGtvXkStq4no2V9Rgn+3reNRUgFO9XXdsObdodHDL/rjqbNGb4JH96ZmuDPohCZm4NDaIhpMn57ZMOxyOuNeXKHfQD8caDtxxOp2wSLq01LZodHC2taWlNhERERERERHResRGZ5y26E0AZptz7/S0wy1PoXZDBZ6zl2Gb0QyLpJvbbqxWZcAi6bDNaMYBexlqN1RgSJ7COz3t8AT9UKsy5sKHNutNcZ+1ac824PNxbzo+Hj4f98KWpb8jKIiBO0s7e/o0bJnpmei0Z2pxrvlMWmoTEREREREREa1HKiCuXdMPrOzsbExMTCA8E8E7Pb+/Y5pSrcrAFr0Jtiw9LJIOxnnbmMdCMtyyHzf8E1ABKM7Sw6LRwTBvCnT8VhBihhq/cffhM9/IssE2f7LpkTu2vSvJIunwvaKNOHH9X2cnUAtK8TX7BoyMjOBnHZ8o/r6Yn1R8EzqdDoFAIG3vSBdJkjDk8aBxsFvRKdvY9281WyDL6WkyExERERERERHdL7H+Wk5ODiYnJ9P2HnXaKq8zbf/Shm+Xfx2/6u2a+1s4OoNO3zA6fbPBQCrMpqdHojPIUKnwjfxCfMtaAo/sx+fjXrQN34Q3JCMcnYFalQGTKMEi6bBZb8LjlhJcGR3C5eEBRKL39p5VAAyiJq3TlUZRggq3g4L27t0LYLahm4508bUeuCPLMuqPH8e+l17EeXevYnV3GKyoP17PJicRERERERERUQK4dT1Oz+3fj/yHNEsGEUUx2/w0Szr8oHQbLFIWGr7owK96u9A+5oE76J9rGIajM3AH/Wgf8+BXvV1o+LIDFilr9jnNvec+Cl81BdPRcJxfN/ae3ukAvvfMbgbuLOO1ujoIUzLKDXmK1Cs35CFjSsZrdXWK1CMiIiIiIiIielCw0RmniYkJ/Ic/+xGeLLDDlqVf9D5blh777WVwem+iyXU17i3No6EgmlxX4fTexH572T3v0D/0EIDbU5BKi9WNfNXwjAUFMXBnaaFQCC88/zx2GAuW/P8iHrYsPXYYC1B74MCanXIlIiIiIiIiIlopbHQm4Je//CVe/T/+AntKNi842WmRdNhVvAnnb1xHx9hwUu/oGBvG+f7r2FW8CWWGvLlAo4MbKuCfvpXW6cqxkDx3YGssKOj8mTMM3FlGW1sb9tfU4Kn8kqQnO8sNeajOL8b+mho4nU6FV0hEREREREREtP6x0ZmgN954A3/yxy/iW/lFOGD/A+R+FS6kVqnw3aKN+GjIFXeK+mJcU+P4aMiFbxfYUazLwWe+Efzi2u/QH5hM63SlW74dchTbyn769GlYtNlzn1MpuaIGZq0ODQ0NitZdKS0tLXiyqgql0wJ2Wexxf1+5oga7LXaUTgv4dtVOtLS0pHmlRERERERERETrExudSXjnnXfwn/63nyBLUKN2QwWes5dhd/FmTNwKJT3JebeOsWHcDExh7FYQnb5hhKMz6J30YbPepEj9u23Wm+5o0Ma2svt8PtQfP47tBqui71uPgTtOpxMVZWVofOtt7CsoxR6zHduMZlgk3dz3qVZlwCLpsM1oxh6zHfsKSnHqrbdRUVbGSU4iIiIiIiIiohSoANwb8U1zsrOzMTExgZycHExOTs79vbG5GdmOCnzmG8EfGPJQVWDD3/f8Pu4zOeORK2pQu6ECv7j2O4SjM3hYm4Xn7H+Ad3ra0/oeYHYb/uMaI+xFxRBFER1dXejJjKDTN5Ly+8oNeSidFlBRVraiZ1EKggBRFBEKhRQPRJIkCbW1tdj97B44KithKyyau+Ya6IezrQ3nms+goaFhXTV7iYiIiIiIiIjutlh/TWnqtFVe5xwOBy7KowhHZxCJRjEYmFK0+QjMBhR5gn5s0ZswFb6FXcWb4JryocpqR5PrqmLv2Wm148ro0B2J7vODgmKBOxdaWzEVnk5pa34scOfJqqr73uSMNR+f2bsXDocDJYWFc9f6BgbgdDpx9vRpRZqPsizjxIkTOHHiBID0NlWJiIiIiIiIiIhb15MiCAJKCgvhDc02w+zZBnw+7k3Luz4f92KzPncu5OjcjevIyRQXDENKRoUxH9mZIi4PD9zx97uDgtZy4I4oinj92DEMeTx4+Y2fIttRgYvyKN7s/BQ/6/gEb3Z+iovyKLIdFXjljdcx5HHj9WPHIIqiYmuIRCIIBAJschIRERERERERpQkbnUmINcDmtnnfFeSjJHfQjwJt9lzIUSQaxfsD3XjCaoMtS59SbVuWHk9YbXi/vxuR6O0TDBYLClqLgTuVlZXo6OrCvpdeRONgN856XGgf88Ad9M/9fuHoDNxBP9rHPDjj6UXjYA/2vfQiOrq64HA47ttaiYiIiIiIiIgoeWx0JiG25VqtyoAKgEHUzE13Ks0blKER1OicF3Lklv04f+M6dhVvSnqyc6vRjF1Fs1Oi7uCdTdqlgoLWUuBOdXU1LrS2oiczgvPu3riPFhgNBXHe3YuezAgufNiK6urqNK+UiIiIiIiIiIhSxTCiZSx2WKqrvx8X5VF4QzJ+XP4YftbxSdrW8JOKb+LNzk/vOEMTmJ0kfbpoIyamQ/hwKL5GXq6owU6rHQ9rs9HYexU35TsPgE0kKGg1B+5UVlbiQmsrPhjuS/lM0afyS/BkVRVT0YmIiIiIiIiIksAwolXO6XTC4qiA56tpSLUq455GpBJiU5KRBWq7g36809OOb+QXonbD7Fo+H/fCHfTDG5QRjs5ArcqASSPBotFhi96EfI0OV0aHkKFSwShq7mh0JhoUtFoDdzQaDRpOvodLY4MpNTkBwDU1jkvqQTScPLniKfFERERERERERLQ4NjqTdPb0abz8zf8F7QB8oSBMonTPFnAlmDQSxkLyomO3kWgUlzz9+HT4JrboTbBl6eHIK4BRlObuGQvJcMt+fOYbwbXxawhHZ7DNaIYtS49O3+yW+HJDHrYbrSkFBcUCd1baq0ePIqzVoNPjUqRep28EdosdR+vq8MrLLytSk4iIiIiIiIiIlMWt68tYbLRWkiQMeTxoHOzGdnMxbkyNo33Mo/j7txnNKNLl4Nf93Qk9pwIgqDIQic4s+ANbJB2+V7QRzX3XsMNgRcaUjNoDB9b89mytVotBtxuNg91xn8kZj1xRg30FpbCaLfd9Gz4RERERERER0Vp2v7auM4woSbIso/74cWw3WNE76cNmvSkt79msNyW1/TqK2TTxxbrY3qAMoyitWFBQuhw8eBDuwKSiTU5gNqDIE/CjtrZW0bpERERERERERKQMNjpT8FpdHYQpGRkqwCzpkCtqFK2fK2pg1uhwbdyraF0Ac+eJfs2+Aa+8/PK6OXvymb174ZpOz8Rl73QAu5/dk5baRERERERERESUmnXd6Dx69Cii0egd/wYHBxWrHwqF8MLzz+ObBiu+mBxDldWuWG0A+LcPfw3to+60hhz5fD7Fa68kh8MBt6z8WanAbPiTo7IyLbWJiIiIiIiIiCg167rRCQAdHR2wWq1z/7Zu3apo/ba2NuyvqUFxphYmUUKFMV+RuhWGfOSLWvRMjilS724mjQTXQP+KpqMrTRAElBQWwhtKz0SnNyjDVlgEQRDSUp+IiIiIiIiIiJK37hud4XAYbrd77t/IyIji72hpacGTVVUYHx7Bk1Y7bFn6lOrZsvTYbrTi0m9/i3yNVqFV3smi0cHZ1paW2itFFEUASMsE7Py6sfcQEREREREREdHqse4bnZs2bcLAwAC++OILvPvuu9iwYcOS9z/00EPIzs6+4188nE4nNpWW4tTJk9hTsjnpyc5yQx6q84uxv6YGf/e3fwtbppRUneXYM7U413wmLbVXSuyc0di2fKXF6q6X80yJiIiIiIiIiNaTdd3ovHz5Mn74wx/iO9/5Dv7sz/4MVqsVly5dQm5u7qLPvPzyy5iYmJj7NzAwEPf7QqEQXnjhBTz5RBW2PZSNfSWb4w4oyhU12G2xo3RawLerdqKlpQUNDQ2waLPTE3Kk1aGhoWHZewVBgFarXRPbtSORCPoGBmAS09McXo/b/YmIiIiIiIiI1ot13eh8//330djYiI6ODvzTP/0Tdu3aBQD44z/+40WfOXbsGHJycub+FRYWJvze3/72t7AXl+Dd/+sX2FdQij1mO7YZzbBIurmpQLUqAxZJh21GM/aY7dhXUIpTb72NirIyOJ1OAIAsy6g/fhzbDdYkPv3idhisqD9eD1m+9yxLSZJw6NAhNDY3w9Xfj3A4DL/fj3A4DFd/Pxqbm3Ho0CFIUnqaialyOp2wSLq01F6P2/2JiIiIiIiIiNYLFYDoSi/ifvrggw/Q3d2NP//zP4/r/uzsbExMTCAnJweTk5MJv0+SJNTW1mL3s3vgqKyErbBo7pproB/Otjacaz6DkydPYmZmBqFQ6I6JQVEU0dHVhZ7MCDp9qZ8vWm7IQ+m0gIqysju2YIuiiKN1dTh85AjcgUm4pmW4ZT+8IRnh6AzUqgyYRAkWSQd7phZmrQ71x+vxWl3dqtrKfejQIbz8xk9x1uNSvPYesx2v/5dXcOLECcVrExERERERERGtV6n21+L1QDU6H3roIfT09OAXv/gF/vqv/zquZ5T+IQRBgCiKyMjIwIEDB/DM3r1wOBwomTc52jcwAKfTibOnT6OhoQHl5eW40NqKD4b74JoaT/rdtiw9nsovwZNVVXNTowBQWVmJd997D5EsCR/7hjAaCi5bK1fUYLvBCmFKRu2BA3fUWwmxhvLe/fvx3aefxt9/8fu4Pke8ckUN9hWUwmq2LDgJS0REREREREREC2OjUwF/8zd/g7Nnz6Kvrw9msxl/8Rd/gaqqKmzduhV9fX1x1VD6h0h2cvK3v/kN3v2Hf8ClscGkJjvLDXnYbrTiuZp9aGlpmft7dXU1TjU1KV73flno+7RIOmRlimhyXVXsPbstdpx662288vLLitUkIiIiIiIiInoQsNGpgHfffRdPPPEE8vLyMDw8jE8++QR/+Zd/ia6urrhrKPlDpDo5+dqrr+LoX/1Vws/vMFiRscDkZWVlZVonRdNtse9TUKnwg9JtcHpvomNsOOX3LLbdn4iIiIiIiIiIlsdG5yqh1A+h1OTkC88fxOPf+hYOHzkMT8CP3ukA3EE/vMF5E6EaCRbN0mdpajQadHR9hm51es/+TJflvk+LpMN+exnO37i+Jpu4RERERERERETrBRudq4QSP0Q6Jic/++yzuEKOGhoaFjxT8vVjx7DvT/4Y5xUM7dllsaPxPmzvjvf7tGXpsat4Ez4aciU12bnS2/KJiIiIiIiIiNYDNjpXiVR/iPs1ORkLObo7tX0hWq0Wg243Gge711xgT6Lfp0Wjw9NFGzExHcKHQ70pb/cnIiIiIiIiIqLE3K9GZ0baKhMA4NWjRxHWahRpcgJAp28EkSwJR+vq7vh7JBJBIBBYtskJAAcPHoQ7MKlokxMARkNBeAJ+1NbWKlp3vkS/T3fQj3d62uGWp1C7oQLP2cuwzWiGRdJBrZr931+tyoBF0mGb0Yw9Zjv2FZTi1Ftvo6KsjE1OIiIiIiIiIqI1ghOdy0il47xaJycbm5uR7ahA+5hHsTXFbDOaMdHWjv17axSvner3qVZlYIveBFuWHhZJB6MozV0LhcN4//1/RHNj06Lb/YmIiIiIiIiIKHH3a6JTnbbKdF8mJ0+cOJHw8w6HAxflUUXXFOMO+vF4ZWVaaqf6fYajM+j0DaPTN3tepwqAoMpAJDqDZ8x2NDc2JfV9EhERERERERHRyuPW9TR6Zu9euKbTMxnYOx3A7mf3JPSMIAjIzs5GSWEhvKH0rMsblGErLIIgCIrXVvr7jGK2+RlFct8nERERERERERGtHmx0ppHD4YBb9qeltjvoh2OZyUlJknDo0CE0NjfD1d+PcDiMiYkJAMAPN27DruJNKDfkz51VqYRwdAYAIIqiYjVjVvr7JCIiIiIiIiKi1Ytb19NEEITZycnOgbTU9wZl2Eq3QhCEewKIRFHE0bo6HD5yBO7AJFzTMi7Ko/B2DiAcnYFalQGTKMEi6VBmyEOV1YYro0O4PDyASDS1I1tjTdP5ifBKWMnvk4iIiIiIiIiIVj82OtMkNtEYm3BU2vzJyUAgMPf3yspKvPvee4hkSYuG9oSjM3AH/XAH/Wgf8yBX1KDKascPSrfh/f5uuIPJT02aNBJcA/2KNwtX6vskIiIiIiIiIqK1gVvX0yQ20ajktvD5FpqcrK6uxoXWVvRkRnDe3Rt3aM9oKIgm11U4vTex314GW5Y+6XVZNDo429qSfn4xK/F9EhERERERERHR2sFGZ5pEIhH0DQzAJEppqX/35GRlZSVONTXhg+E+dPpGkqrZMTaM8/3Xsat4EywaXVI17JlanGs+k9SzS7nf32eiBEGAVqtNSwgTEREREREREREtj43ONHI6nbBIyTUMlzN/clKj0aDh5Hu4NDYI19R4SnVdU+P4aMiFp4s2QlCpEno2V9TArNWhoaEhpTUs5n59n/FYKOjJ7/cjHA7D1d+PxuZmHDp0CJKUnsYsERERERERERHdiY3ONBEEAf/vr38NW+bCjS4VZrdLL9VKXOqe+ZOTrx49irBWk/Qk5906xoYxMR3CN/ILE3puh8GK+uP1kGVZkXXc7ezp04t+n6mKdxJVFEW8fuwYhjwevPzGT5HtqMBFeRRvdn6Kn3V8gjc7P8VFeRTZjgq88sbrGPK48fqxY2lJoSciIiIiIiIiottUAFKL2V7nsrOzMTExgZycHExOTi56nyRJqK2txTN798LhcKCk8HaTcPxWEG7Zj1uRCB4SBJg1OhhEzdx1XygId9CPG1PjiAIoydLDssg9vZM+eIJTeNb6NVjNFqhUKgy63YsGDyUrV9SgdkMFfnHtd3EFAJUb8lA6LaCirCxt51xKkoQhjyctn3VfQSmsZsuSTdr5QU8f+4biWkOuqMF2gxXClIzaAwfgdDoVWzcRERERERER0VoQb38tVUxdT5EoijhaV4fDR47AHZiEa1rGRXkU3s4BhKMzEDMEPGEtwRZ9HkaCAXzmG8a/DN+ENyQjHJ2BWpUBkyjBIulQbsxHnkaLG1MT+Mf+bgwHA/fcU2bIw7clOz69fBkzMzP4/ve/D3dgUtHGHzAbUOQJ+rFFb0Knb3jJe21ZeuwwFuDJqqq0hvnIsoz648ex76UXcd7dq1jdeCZRq6urcaqpCZfGBtGZwLtHQ0Gcd/ei3JCHCx+24rmafWhpaVFg1URERERERERENB8nOpexVMd5uQk/i6TD04UbMTEdwodD8aWg54oaVFntyMkU8X5/N9xB/4L37DBYkTElo6+vDzMbi9E+5kntgy5gm9GMIl0Oft3fveg95YY8bDda71sDTxRFdHR1oSczoshW/XgmUSsrK3GhtRUfDPeldAaqLUuPp/JL8GRVFSc7iYiIiIiIiOiBcb8mOnlGZ5Kqq6txobUVPZkRnHff28S0Zemx314Gp/cmmlxX4564HA0F0eS6Cqf3Jvbby2DL0i94zzl3L3oyI/ijP/ojuOV7m6FKcAf9i4b/5Ioa7LbYUTot4NtVO+/blGIoFMILzz+PHcaCBb+bRMQmUWsPHFi0yal00NOlsUE0nDzJMzuJiIiIiIiIiBTGRmcSKraEF4gAACAASURBVCsrcaqpCR8M9y04VWiRdNhVvAnnb1xHx9jS274X0zE2jPP917GreBMsmoWbjZ/5RiCq1fCG0hP+4w3KMIrSXCiSRdJhm9GMPWY79hWU4tRbb6OirOy+Tye2tbVhf00NnsovQbkhL6ka5YY8VOcXY39NzZLrVzroqdM3gkiWhKN1dYrUIyIiIiIiIiKiWdy6voy7R2s1Gg06uj5Dt3rhrdNqlQo/2LgNbSM3k25yzldhzIfD9DDe6WlHJHrnT6VWZeDH5Y/hZx2fpPyexfyk4ptz/+0a6IezrQ3nms+goaEhbenq8XI4HGg4eTLhcKDYtv/lwoG0Wm3agp7iCT8iIiIiIiIiIloPGEa0Ss1N+HlcC15/LL8Q47eCijQ5gdnJzk05JnwjvxCXPP13XIt8lYauVmXElYyeKLVqduA3JycHgUAAkUhE8Xekwul0oqKs7KswqMPwBPzonQ7AHfTDG5wX9qSRYNHoYM/UwqzVof54PV6rq1s2OOngwYPpC3oK+FFbW4sTJ04oWpuIiIiIiIiI6EHFic5lzO84RyKRJSf81KoM/Ojrj6Lhiw7FJwBrN1TgF9d+d0dDUwXg0KZHcP7G9QVDi1JlkXR4XGOEvahY8dpKkyQJtbW12P3sHjgqK2ErLJq7luwkamNzM7IdFWkLeppoa8f+vTWK1yYiIiIiIiIiWk040bkKLTfht0Vvgkf2p2cCMOhHmSEPM9Eo7NkGWDQ6GEQNbkUisEi69DQ6NTo429oUr5sOsizjxIkTcxOSgiBAFEWEQqGkJ1EdDgcuyqNKLnOOO+jH45WVaalNRERERERERPQgYqMzAc/s3QvX9OLTgPZsAz4f9yr+XkGlQnhmBjsLbBgMTOHzcS/ahm/CG5KxRW9CmSEvLVOH9kwtXm8+o3jd+yESiSAQCCT9vCAIKCkshLdzQMFV3eYNyrCVboUgCKvuSAAiIiIiIiIiorWIjc4ELDfhZ9Ho0DZ8U9F3WiQdni7ciMnpEP6+5/f3TIteG/eiqsCGXFGj+HZ5s1aHhoYGxWquJaIoAkBazj6dX1cUxZQaskRERERERERENCtjpRewVsxN+IUWnuhUATCImkWvJ8OWpcd+exmc3ptodF1dsJEZjs7gincIVVa7Yu8FgB0GK+qP1z+wqeCxoKJYIJPSYnWXC0QiIiIiIiIiIqL4sNEZp+Um/ISvGldKTQBaJB12FW/C+RvXl01wvzw8gJxMERXGfEXeXW7IQ8aUjNfq6hSptxZFIhH0DQzAJEppqW/SSHAN9HPbOhERERERERGRQtjojNNyE36RrxqcSkwAqlUqfLdoIz4acsE1Nb7s/ZFoFO8PdOMJqw22LH1K77Zl6bHDWIDaAwce+GlDp9MJi6RLS+21FPRERERERERERLQWsNEZp+Um/KIAfKGgIhOAj+UXYvxWcNlJzvncsh/nb1zHruJNSU92lhvyUJ1fjP01NXA6nUnVWE/Onj4NW2Z6JjrtmVqcW6NBT0REREREREREqxEbnQlYbsLPHfSnPAGoVmXgEZMVHw65En7WNTWOU192wWF6GDW2ryNX1MT1XK6owW6LHaXTAr5dtRMtLS0Jv3s9amhogEWbHff3GK8HPeiJiIiIiIiIiCgd2OhMwHITfr2TPmzWm1J6xxa9CR7Zn3SCujvoxzs97XDLU6jdUIHn7GXYZjTDIunmttWrVRmwSDpsM5qxx2zHvoJSnHrrbVSUlXGScx5ZllF//Di2G6yK1n3Qg56IiIiIiIiIiNJBhdld17SI7OxsTExMICcnB+FwGEMeDxoHuxdsRKpVGfjR1x9FwxcdSTcqdxVvwo2pcbSPeVJdOtSqDGzRm2DL0sMi6WCct63eNdAPZ1sbzjWfQUNDA5tuixBFER1dXejJjKDTN5JyvXJDHkqnBVSUlT3wZ6ASERERERER0YNhfn9tcnIybe9Rp63yOhSb8Nv30os47+6953o4OoMr3iFUWe1ocl1N6h0WjQ5twzdTW+i89XT6htHpmz3r0yrp8LiUi6+V2Jj2HadQKIQXnn8eF1pbMRWejiscajGxoKcnq6rY5CQiIiIiIiIiUhi3rifotbo6CFMyyg15C16/PDwA40OapAKBVAAMogbeUHqmK0eCMkoeLkxL7fWsra0N+2tq8FR+yaK/+3IY9ERERERERERElF5sdCYoNuG3w1gAW5b+nusqACoVUGW1L3h9KcJXZ2iGozNKLPUesbo5OTlpqb+etbS04MmqKpROC9hlsTPoiYiIiIiIiIholWGjMwlLTfg9ll+I0ZCMczc+x67iTQlNdka+akTGQoOUFqv7v//n/5yW+uud0+lERVkZGt96G/sKSrHHbGfQExERERERERHRKsEwomUsdViqw+FAw8mTiGRJ+Ng3hIlbt+4II7JodHi6aCMmpkP4cKg3roCilzY/gnN91+EO+hX/LBZJh93Fm6C6NQ2r2cIAohRIkoTa2lrsfnYPHJWVsBUWzV1j0BMRERERERER0W33K4yIjc5lLPdDiKKIo3V1OHzkMOSZGUwLKrz35Wdz1wWVCt/IL8QjuVZ4gn58Pu6FO+iHNygjHJ2BWpUBk0aCRaPDFr0J+RodfuPuUyR1/W7bjGYU6XKgvhXG6//lFZw4cULxdzyoBEGAKIoIhUIMeiIiIiIiIiIimoeNzlUi3h9CkiR89NvfwGc2LNikVKsysEVvgi1LD4ukg1GU5q6NhWS4ZT9cU+MQVCps1pvwq94uqDB7bmckOqPIj/ScvQxdvhEIKhUm2tqxf2+NAlWJiIiIiIiIiIgWd78aneq0VX7AyLIMs9mCa/LogtfD0Rl0+obR6RsGgAWbmGpVBv7AkAezRoeXNj8C/UO3A298oSDcQT96J324Nu5NOLAoV9TArNHh9Pg1mDQSHq+sTOZjEhERERERERERrUpsdCpEEASUFBbC2zkQ1/1R3E5Bn9vebrLCI/vxG3cf3LIf3tC87e2iBIukQ5khD1VWG66MDuHy8AAi0fhmPXda7bgyOoRwdAbeoAxb6VYIgsBt1kREREREREREtC6w0akQURQBIOFJS4ukw9OFs4FFsRCju4WjM3AH/XAH/Wgf8yBX1KDKascPSrfh/f7uZYOLKoz5yM4U0dx37Y41/umf/il++ctfMiyHiIiIiIiIiIjWvIyVXsB6EQqFAMxuP4+XLUuP/fYyOL030eS6GlcqOwCMhoJocl2F03sT++1lsGXpl3zHE1Yb3u/vnpv+jK3xP/1VHYY8brx+7Nhco5aIiIiIiIiIiGgtYqNTIZFIBH0DAzDNCxlaikXSYVfxJpy/cR0dY8NJvbNjbBjn+69jV/EmWDS6e65vNZqxq2j2HfOnPk0aCWMhGWc8vWgc7MG+l15ER1cXHA5HUusgIiIiIiIiIiJaaWx0KsjpdMIi3dtwvJtapcJ3izbioyEXXFPjKb3TNTWOj4ZceLpoIwSVCsBs8NA+29fxqKkAp3q77nmHRaODW55tfI6Ggjjv7kVPZgQXPmxFdXV1SushIiIiIiIiIiJaCWx0Kujs6dOwZS4/0flYfiHGbwWTnuS8W8fYMCanQ3imeDMO2MtQu6ECQ/IU3ulpX/D8zs160z3Nz07fCFqGb+BUUxMnO4mIiIiIiIiIaM1ho1NBDQ0NsGizkStq7rmmVmWg3JCP3cWbUJn3MD4ccin67tahXhRn5eDquBe/uPY7XPL0L5jInitqYNbocG3ce88119Q4Lo0NouHkSZ7ZSUREREREREREawobnQqSZRn1x49ju8E69zdBpcIOcxF+9PVHUWbIQ3hmBoOBybiDh+I1GgpiMDCFmWh0yeT3nVY7rowOLXpPp28EkSwJR+vqFF0fERERERERERFROrHRqbDX6uogTMkoN+TBIunwg9JtsEhZaPiiA7/q7YKQkbHgNKUSPh/3LpnAXmHMR3amiMvDA0vW+dg3hMNHDkOS4gtWIiIiIiIiIiIiWmlsdCosFArhheefx7fyivCcvQxO7000ua7OTXDODwJSmjvoXzQMyZalxxNWG97v715wS/t8o6EgPAE/amtr07FMIiIiIiIiIiIixbHRmSZRRHHuxvU7AodUAAyiBt6QnJZ3eoMyjKIE1V1/32o0Y1fRJpy/cX3BcKKF9E4HsPvZPcovkoiIiIiIiIiIKA3UK72A9Uaj0aDh5Hv4zcjAPcnmgmq2r7zUGZqpiNUVVBkIR2eQK2qw02pHdqaIU71dcTc5gdnp0McrK9OyTiIiIiIiIiIiIqWx0amwV48eRVirQafn3lT1yFeNSPVXjUilqb9qpJYb8rBZb0K+Rocro0No7ru27Hb1u3mDMmylWyEIAiKRiOJrJSIiIiIiIiIiUhIbnQrSarU4fOQIGge7F7weBeALBWESpYSmK+Nl0kgIRsIo1OXgM98Iro1fS7qhGntOFEUEAgEll0lERERERERERKQ4NjoVdPDgQbgDk3PBQwuJBQalo9Fp0ejQO+nDr/sXbrQmIjYdGgqFUq5FRERERERERESUbgwjUtAze/fCNb100FDvpA+b9aa0vH+z3nTPuaDJMmkkuAb6uW2diIiIiIiIiIjWBDY6FeRwOOCWl57UvDbuhVnSIVfUKPruXFEDs0aHa+NeRepZNDo429oUqUVERERERERERJRubHQqRBAElBQWwhtaeqIzHJ3BFe8Qqqx2Rd+/02rHldEhxUKO7JlanGs+o0gtIiIiIiIiIiKidGOjUyGiKAJAXI3Gy8MDyMkUUWHMV+TdFcZ8ZGeKuDw8oEi9XFEDs1aHhoYGReoRERERERERERGlGxudComF9sRCfJYSiUbx/kA3nrDaYMvSp/ReW5YeT1rteL+/G5FoNKVaMTsMVtQfr4csLz2dSkREREREREREtFqw0amQSCSCvoEBmEQprvvdsh/nb1zHruJNSU92lhvyUJ1fDM/wMPI08b03npoZUzJeq6tTpB4REREREREREdH9oF7pBawnTqcTFkcF3MGlA4liXFPjOPVlF54u2ohNOSZ8ONSL0VBw2edyRQ12GKzImJLx7aqdUKlUuNDaiqnwdEqp67YsPXYYC/BkVdXchCoREREREREREdFawIlOBZ09fRq2zMQmK91BP97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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a scatterplot.\n",
    "import matplotlib.ticker as ticker\n",
    "with plt.style.context('dark_background'):\n",
    "    fig, ax = plt.subplots(figsize=(16, 10), dpi=100)\n",
    "    sns.scatterplot(suicide_data['HR_rate'], suicide_data['Suicide_rate'], s=300, ax=ax)\n",
    "    ax.xaxis.set_ticks(np.arange(0, 80, 10))\n",
    "    ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))\n",
    "    ax.set_xlabel('HR_rate', fontsize='22')\n",
    "    ax.set_ylabel('Suicide_rate', fontsize='22')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Pearson's r and related p-value.\n",
    "r_pearson, p_value  = stats.pearsonr(suicide_data['HR_rate'], suicide_data['Suicide_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08808655853799455"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46509827577856044"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning sample size to a variable.\n",
    "N = suicide_data['Suicide_rate'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "def r_to_z(r):\n",
    "    '''Converting r value to z value - Fisher's transformation'''\n",
    "    return math.log((1 + r) / (1 - r)) / 2.0\n",
    "\n",
    "def z_to_r(z):\n",
    "    '''Converting back z value to r value'''\n",
    "    e = math.exp(2 * z)\n",
    "    return((e - 1) / (e + 1))\n",
    "\n",
    "def r_confidence_interval(r, alpha, n):\n",
    "    '''Calculating Pearson's r confidence interval for the two-tailed test'''\n",
    "    z = r_to_z(r)\n",
    "    se = 1.0 / math.sqrt(n - 3)\n",
    "    z_crit = stats.norm.ppf(1 - alpha/2)  # 2-tailed z critical value\n",
    "\n",
    "    low_interval = z - z_crit * se\n",
    "    high_interval = z + z_crit * se\n",
    "\n",
    "    return (z_to_r(low_interval), z_to_r(high_interval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning 95% confidence interval to a variable.\n",
    "CI_95 = r_confidence_interval(r_pearson, 0.05, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.14826414047275943, 0.314918428231186)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CI_95"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "**Pearson's r** = *0.08808655853799455*   \n",
    "**p-value** = *0.46509827577856044*   \n",
    "**95% confidence interval** = *(-0.14826414047275943, 0.314918428231186)*   \n",
    "\n",
    "## Conclusions\n",
    "\n",
    "Based on the Pearson's r with respect to p-value and taking into account the sampling error we retain the null hypothesis. \n",
    "\n",
    "There is no statistically significant correlation between suicide rate across the world and a number of psychiatrists and psychologists working in the mental health sector.\n",
    "\n",
    "As an alternative solution, I would recommend applying similar analysis on the data including counseling rate with additional constraints like a number of sessions/overall length of a therapy per patient. I can only speculate that this would have a higher chance of showing a significant correlation than simply the number of workers in the mental health sector, although acquiring this type of data may not be accessible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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