DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Behavioural Economics Experiment\n",
    "\n",
    "In this notebook, we performed all the statistical analysis and data visualization for the experiment conducted during the course [Behavioural Economics](https://coursecatalogue.uva.nl/xmlpages/page/2018-2019-en/search-course/course/58872) (6012B0265Y) at the [University of Amsterdam](https://www.uva.nl/en/home).\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "As part of an assignment for the course we were instructed to perform a behavioural economics experiment for the class (using the rest of the students are subjects).\n",
    "\n",
    "For our experiment, we recreated Nagel's (1995) experiment, but added a treatment group where participants were grouped in groups of three and participated as a single individual competing in a market with 5 other groups of three. At the begining of the each round/stage we asked all the members of a group to log their individual response before discussing with the rest of the group about their final response.\n",
    "\n",
    "\n",
    "## Data\n",
    "\n",
    "The data contains the data for each subject that participated in the experiment\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**References**\n",
    "\n",
    "Nagel, R. (1995). Unraveling in guessing games: An experimental study. The American Economic Review, 85(5), 1313-1326.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"margin-top: 100px;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Libraries Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data handling\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.style as style\n",
    "\n",
    "# Statistical tests\n",
    "from scipy.stats import wilcoxon\n",
    "from scipy.stats import mannwhitneyu\n",
    "from scipy.stats import ttest_ind\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import ttest_1samp\n",
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "\n",
    "# Style globals\n",
    "style.use('seaborn-pastel')\n",
    "colors = [\"#E4CA99\", \"#A1B6B4\", \"#AEDDE0\", \"#A63D11\", \"#E97140\", \"#2D7F9D\", \"#DC7684\", \"#A4C9D7\", \"#EAF2F4\"]\n",
    "\n",
    "# Jupyter configuration\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"margin-top: 100px;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"DataSubjects.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Individual Code</th>\n",
       "      <th>Seat</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Market</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Number of Answers</th>\n",
       "      <th>Answers Missing</th>\n",
       "      <th>Individual Answer Stage 1</th>\n",
       "      <th>Individual Answer Stage 2</th>\n",
       "      <th>...</th>\n",
       "      <th>Time Individual Stage 1</th>\n",
       "      <th>Time Individual Stage 2</th>\n",
       "      <th>Time Individual Stage 3</th>\n",
       "      <th>Time Individual Stage 4</th>\n",
       "      <th>Time Group Stage 1</th>\n",
       "      <th>Time Group Stage 2</th>\n",
       "      <th>Time Group Stage 3</th>\n",
       "      <th>Time Group Stage 4</th>\n",
       "      <th>Experience</th>\n",
       "      <th>Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1806G</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>12.07</td>\n",
       "      <td>19.3</td>\n",
       "      <td>...</td>\n",
       "      <td>11/6/2019 16:07:43</td>\n",
       "      <td>11/6/2019 16:22:40</td>\n",
       "      <td>11/6/2019 16:35:10</td>\n",
       "      <td>11/6/2019 16:45:53</td>\n",
       "      <td>11/6/2019 16:07:43</td>\n",
       "      <td>11/6/2019 16:22:40</td>\n",
       "      <td>11/6/2019 16:35:10</td>\n",
       "      <td>11/6/2019 16:45:53</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1706G</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>37.50</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11/6/2019 16:07:44</td>\n",
       "      <td>11/6/2019 16:22:41</td>\n",
       "      <td>11/6/2019 16:35:10</td>\n",
       "      <td>11/6/2019 16:45:58</td>\n",
       "      <td>11/6/2019 16:07:44</td>\n",
       "      <td>11/6/2019 16:22:41</td>\n",
       "      <td>11/6/2019 16:35:10</td>\n",
       "      <td>11/6/2019 16:45:58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1606G</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>43.00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11/6/2019 16:07:45</td>\n",
       "      <td>11/6/2019 16:22:42</td>\n",
       "      <td>11/6/2019 16:35:10</td>\n",
       "      <td>11/6/2019 16:45:45</td>\n",
       "      <td>11/6/2019 16:07:45</td>\n",
       "      <td>11/6/2019 16:22:42</td>\n",
       "      <td>11/6/2019 16:35:10</td>\n",
       "      <td>11/6/2019 16:45:45</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5117G</td>\n",
       "      <td>51</td>\n",
       "      <td>17</td>\n",
       "      <td>G</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>29.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11/6/2019 16:08:29</td>\n",
       "      <td>11/6/2019 16:23:35</td>\n",
       "      <td>11/6/2019 16:37:20</td>\n",
       "      <td>11/6/2019 16:45:01</td>\n",
       "      <td>11/6/2019 16:08:29</td>\n",
       "      <td>11/6/2019 16:23:35</td>\n",
       "      <td>11/6/2019 16:37:20</td>\n",
       "      <td>11/6/2019 16:45:01</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4917G</td>\n",
       "      <td>49</td>\n",
       "      <td>17</td>\n",
       "      <td>G</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>17.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11/6/2019 16:08:30</td>\n",
       "      <td>11/6/2019 16:23:34</td>\n",
       "      <td>11/6/2019 16:37:18</td>\n",
       "      <td>11/6/2019 16:44:45</td>\n",
       "      <td>11/6/2019 16:08:30</td>\n",
       "      <td>11/6/2019 16:23:34</td>\n",
       "      <td>11/6/2019 16:37:18</td>\n",
       "      <td>11/6/2019 16:44:45</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID  Individual Code  Seat Treatment  Market  Gender  Number of Answers  \\\n",
       "0  1806G               18     6         G       2  Female                  4   \n",
       "1  1706G               17     6         G       2  Female                  4   \n",
       "2  1606G               16     6         G       2    Male                  4   \n",
       "3  5117G               51    17         G       4  Female                  4   \n",
       "4  4917G               49    17         G       4    Male                  4   \n",
       "\n",
       "   Answers Missing  Individual Answer Stage 1  Individual Answer Stage 2  ...  \\\n",
       "0            False                      12.07                       19.3  ...   \n",
       "1            False                      37.50                       68.0  ...   \n",
       "2            False                      43.00                       26.0  ...   \n",
       "3            False                      29.00                       16.0  ...   \n",
       "4            False                      17.00                        3.0  ...   \n",
       "\n",
       "   Time Individual Stage 1 Time Individual Stage 2  Time Individual Stage 3  \\\n",
       "0       11/6/2019 16:07:43      11/6/2019 16:22:40       11/6/2019 16:35:10   \n",
       "1       11/6/2019 16:07:44      11/6/2019 16:22:41       11/6/2019 16:35:10   \n",
       "2       11/6/2019 16:07:45      11/6/2019 16:22:42       11/6/2019 16:35:10   \n",
       "3       11/6/2019 16:08:29      11/6/2019 16:23:35       11/6/2019 16:37:20   \n",
       "4       11/6/2019 16:08:30      11/6/2019 16:23:34       11/6/2019 16:37:18   \n",
       "\n",
       "   Time Individual Stage 4  Time Group Stage 1  Time Group Stage 2  \\\n",
       "0       11/6/2019 16:45:53  11/6/2019 16:07:43  11/6/2019 16:22:40   \n",
       "1       11/6/2019 16:45:58  11/6/2019 16:07:44  11/6/2019 16:22:41   \n",
       "2       11/6/2019 16:45:45  11/6/2019 16:07:45  11/6/2019 16:22:42   \n",
       "3       11/6/2019 16:45:01  11/6/2019 16:08:29  11/6/2019 16:23:35   \n",
       "4       11/6/2019 16:44:45  11/6/2019 16:08:30  11/6/2019 16:23:34   \n",
       "\n",
       "   Time Group Stage 3  Time Group Stage 4  Experience Skills  \n",
       "0  11/6/2019 16:35:10  11/6/2019 16:45:53         Yes    3.0  \n",
       "1  11/6/2019 16:35:10  11/6/2019 16:45:58         Yes    2.0  \n",
       "2  11/6/2019 16:35:10  11/6/2019 16:45:45          No    3.0  \n",
       "3  11/6/2019 16:37:20  11/6/2019 16:45:01         Yes    2.0  \n",
       "4  11/6/2019 16:37:18  11/6/2019 16:44:45         Yes    4.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                           80\n",
       "Individual Code              80\n",
       "Seat                         80\n",
       "Treatment                    80\n",
       "Market                       80\n",
       "Gender                       80\n",
       "Number of Answers            80\n",
       "Answers Missing              80\n",
       "Individual Answer Stage 1    80\n",
       "Individual Answer Stage 2    80\n",
       "Individual Answer Stage 3    80\n",
       "Individual Answer Stage 4    80\n",
       "Group Answer Stage 1         60\n",
       "Group Answer Stage 2         60\n",
       "Group Answer Stage 3         60\n",
       "Group Answer Stage 4         60\n",
       "Final Answer Stage 1         80\n",
       "Final Answer Stage 2         80\n",
       "Final Answer Stage 3         80\n",
       "Final Answer Stage 4         80\n",
       "Time Individual Stage 1      80\n",
       "Time Individual Stage 2      80\n",
       "Time Individual Stage 3      80\n",
       "Time Individual Stage 4      80\n",
       "Time Group Stage 1           80\n",
       "Time Group Stage 2           80\n",
       "Time Group Stage 3           80\n",
       "Time Group Stage 4           80\n",
       "Experience                   66\n",
       "Skills                       66\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            object\n",
       "Individual Code                int64\n",
       "Seat                           int64\n",
       "Treatment                     object\n",
       "Market                         int64\n",
       "Gender                        object\n",
       "Number of Answers              int64\n",
       "Answers Missing                 bool\n",
       "Individual Answer Stage 1    float64\n",
       "Individual Answer Stage 2    float64\n",
       "Individual Answer Stage 3    float64\n",
       "Individual Answer Stage 4     object\n",
       "Group Answer Stage 1         float64\n",
       "Group Answer Stage 2         float64\n",
       "Group Answer Stage 3         float64\n",
       "Group Answer Stage 4          object\n",
       "Final Answer Stage 1         float64\n",
       "Final Answer Stage 2         float64\n",
       "Final Answer Stage 3         float64\n",
       "Final Answer Stage 4          object\n",
       "Time Individual Stage 1       object\n",
       "Time Individual Stage 2       object\n",
       "Time Individual Stage 3       object\n",
       "Time Individual Stage 4       object\n",
       "Time Group Stage 1            object\n",
       "Time Group Stage 2            object\n",
       "Time Group Stage 3            object\n",
       "Time Group Stage 4            object\n",
       "Experience                    object\n",
       "Skills                       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Individual Code</th>\n",
       "      <th>Seat</th>\n",
       "      <th>Market</th>\n",
       "      <th>Number of Answers</th>\n",
       "      <th>Individual Answer Stage 1</th>\n",
       "      <th>Individual Answer Stage 2</th>\n",
       "      <th>Individual Answer Stage 3</th>\n",
       "      <th>Group Answer Stage 1</th>\n",
       "      <th>Group Answer Stage 2</th>\n",
       "      <th>Group Answer Stage 3</th>\n",
       "      <th>Final Answer Stage 1</th>\n",
       "      <th>Final Answer Stage 2</th>\n",
       "      <th>Final Answer Stage 3</th>\n",
       "      <th>Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>80.0000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.5000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.825000</td>\n",
       "      <td>36.126765</td>\n",
       "      <td>15.340342</td>\n",
       "      <td>7.669050</td>\n",
       "      <td>24.580000</td>\n",
       "      <td>7.407500</td>\n",
       "      <td>1.069500</td>\n",
       "      <td>28.755788</td>\n",
       "      <td>11.240625</td>\n",
       "      <td>4.539800</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.2379</td>\n",
       "      <td>5.802662</td>\n",
       "      <td>1.125088</td>\n",
       "      <td>0.382364</td>\n",
       "      <td>23.635821</td>\n",
       "      <td>17.060620</td>\n",
       "      <td>19.154481</td>\n",
       "      <td>21.226158</td>\n",
       "      <td>8.107879</td>\n",
       "      <td>1.288075</td>\n",
       "      <td>23.001638</td>\n",
       "      <td>12.393299</td>\n",
       "      <td>11.857805</td>\n",
       "      <td>0.808608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.7500</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.585000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.150000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>3.525000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.5000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>33.331500</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.665000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.2500</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>43.360000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.962500</td>\n",
       "      <td>27.750000</td>\n",
       "      <td>9.812500</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>35.250000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.0000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>4.330000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Individual Code       Seat     Market  Number of Answers  \\\n",
       "count          80.0000  80.000000  80.000000          80.000000   \n",
       "mean           40.5000  10.500000   2.500000           3.825000   \n",
       "std            23.2379   5.802662   1.125088           0.382364   \n",
       "min             1.0000   1.000000   1.000000           3.000000   \n",
       "25%            20.7500   5.750000   1.750000           4.000000   \n",
       "50%            40.5000  10.500000   2.500000           4.000000   \n",
       "75%            60.2500  15.250000   3.250000           4.000000   \n",
       "max            80.0000  20.000000   4.000000           4.000000   \n",
       "\n",
       "       Individual Answer Stage 1  Individual Answer Stage 2  \\\n",
       "count                  80.000000                  80.000000   \n",
       "mean                   36.126765                  15.340342   \n",
       "std                    23.635821                  17.060620   \n",
       "min                     0.000000                   0.000000   \n",
       "25%                    21.585000                   4.300000   \n",
       "50%                    33.331500                  10.000000   \n",
       "75%                    43.360000                  20.000000   \n",
       "max                   100.000000                 100.000000   \n",
       "\n",
       "       Individual Answer Stage 3  Group Answer Stage 1  Group Answer Stage 2  \\\n",
       "count                  80.000000             60.000000             60.000000   \n",
       "mean                    7.669050             24.580000              7.407500   \n",
       "std                    19.154481             21.226158              8.107879   \n",
       "min                     0.000000              0.000000              0.000000   \n",
       "25%                     0.000000             17.150000              2.250000   \n",
       "50%                     1.000000             21.150000              5.000000   \n",
       "75%                     4.962500             27.750000              9.812500   \n",
       "max                   100.000000            100.000000             29.000000   \n",
       "\n",
       "       Group Answer Stage 3  Final Answer Stage 1  Final Answer Stage 2  \\\n",
       "count             60.000000             80.000000             80.000000   \n",
       "mean               1.069500             28.755788             11.240625   \n",
       "std                1.288075             23.001638             12.393299   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000             18.750000              3.525000   \n",
       "50%                0.665000             22.200000              6.500000   \n",
       "75%                2.050000             35.250000             18.000000   \n",
       "max                4.330000            100.000000             50.000000   \n",
       "\n",
       "       Final Answer Stage 3     Skills  \n",
       "count             80.000000  66.000000  \n",
       "mean               4.539800   2.500000  \n",
       "std               11.857805   0.808608  \n",
       "min                0.000000   1.000000  \n",
       "25%                0.000000   2.000000  \n",
       "50%                0.800000   3.000000  \n",
       "75%                3.200000   3.000000  \n",
       "max               82.000000   4.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"margin-top: 100px;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            object\n",
       "Individual Code                int64\n",
       "Seat                           int64\n",
       "Treatment                     object\n",
       "Market                         int64\n",
       "Gender                        object\n",
       "Number of Answers              int64\n",
       "Answers Missing                 bool\n",
       "Individual Answer Stage 1    float64\n",
       "Individual Answer Stage 2    float64\n",
       "Individual Answer Stage 3    float64\n",
       "Individual Answer Stage 4     object\n",
       "Group Answer Stage 1         float64\n",
       "Group Answer Stage 2         float64\n",
       "Group Answer Stage 3         float64\n",
       "Group Answer Stage 4          object\n",
       "Final Answer Stage 1         float64\n",
       "Final Answer Stage 2         float64\n",
       "Final Answer Stage 3         float64\n",
       "Final Answer Stage 4          object\n",
       "Time Individual Stage 1       object\n",
       "Time Individual Stage 2       object\n",
       "Time Individual Stage 3       object\n",
       "Time Individual Stage 4       object\n",
       "Time Group Stage 1            object\n",
       "Time Group Stage 2            object\n",
       "Time Group Stage 3            object\n",
       "Time Group Stage 4            object\n",
       "Experience                    object\n",
       "Skills                       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to make the following type adjustments\n",
    "\n",
    "**To Float**\n",
    "- Individual Answer Stage 4\n",
    "- Group Answer Stage 4\n",
    "\n",
    "**To DateTime**\n",
    "- Time Individual Stage 1\n",
    "- Time Individual Stage 2\n",
    "- Time Individual Stage 3\n",
    "- Time Individual Stage 4\n",
    "- Time Group Stage 1\n",
    "- Time Group Stage 2\n",
    "- Time Group Stage 3\n",
    "- Time Group Stage 4\n",
    "\n",
    "**To Bool**\n",
    "- Experience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But first, we will change the name of some columns for convenience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(\n",
    "    columns={\n",
    "        \"Time Individual Stage 1\": \"time_ind_stage1\",\n",
    "        \"Time Individual Stage 2\": \"time_ind_stage2\",\n",
    "        \"Time Individual Stage 3\": \"time_ind_stage3\",\n",
    "        \"Time Individual Stage 4\": \"time_ind_stage4\",\n",
    "        \"Time Group Stage 1\": \"time_group_stage1\",\n",
    "        \"Time Group Stage 2\": \"time_group_stage2\",\n",
    "        \"Time Group Stage 3\": \"time_group_stage3\",\n",
    "        \"Time Group Stage 4\": \"time_group_stage4\",\n",
    "    },\n",
    "    inplace=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Individual Code', 'Seat', 'Treatment', 'Market', 'Gender',\n",
       "       'Number of Answers', 'Answers Missing', 'Individual Answer Stage 1',\n",
       "       'Individual Answer Stage 2', 'Individual Answer Stage 3',\n",
       "       'Individual Answer Stage 4', 'Group Answer Stage 1',\n",
       "       'Group Answer Stage 2', 'Group Answer Stage 3', 'Group Answer Stage 4',\n",
       "       'Final Answer Stage 1', 'Final Answer Stage 2', 'Final Answer Stage 3',\n",
       "       'Final Answer Stage 4', 'time_ind_stage1', 'time_ind_stage2',\n",
       "       'time_ind_stage3', 'time_ind_stage4', 'time_group_stage1',\n",
       "       'time_group_stage2', 'time_group_stage3', 'time_group_stage4',\n",
       "       'Experience', 'Skills'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parsing Floats**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df[\"Individual Answer Stage 4\"] == \"-\", \"Individual Answer Stage 4\"] = np.nan\n",
    "df['Individual Answer Stage 4'] = pd.to_numeric(df['Individual Answer Stage 4'],errors='coerce')\n",
    "\n",
    "df.loc[df[\"Group Answer Stage 4\"] == \"-\", \"Group Answer Stage 4\"] = np.nan\n",
    "df['Group Answer Stage 4'] = pd.to_numeric(df['Group Answer Stage 4'],errors='coerce')\n",
    "\n",
    "df.loc[df[\"Final Answer Stage 4\"] == \"-\", \"Final Answer Stage 4\"] = np.nan\n",
    "df['Final Answer Stage 4'] = pd.to_numeric(df['Final Answer Stage 4'],errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parsing Dates**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"time_ind_stage1\"] = pd.to_datetime(df[\"time_ind_stage1\"])\n",
    "df[\"time_ind_stage2\"] = pd.to_datetime(df[\"time_ind_stage2\"])\n",
    "df[\"time_ind_stage3\"] = pd.to_datetime(df[\"time_ind_stage3\"])\n",
    "\n",
    "df.loc[df[\"time_ind_stage4\"] == \"-\", \"time_ind_stage4\"] = np.nan\n",
    "df[\"time_ind_stage4\"] = pd.to_datetime(df[\"time_ind_stage4\"])\n",
    "\n",
    "df[\"time_group_stage1\"] = pd.to_datetime(df[\"time_group_stage1\"])\n",
    "df[\"time_group_stage2\"] = pd.to_datetime(df[\"time_group_stage2\"])\n",
    "df[\"time_group_stage3\"] = pd.to_datetime(df[\"time_group_stage3\"])\n",
    "\n",
    "df.loc[df[\"time_group_stage4\"] == \"-\", \"time_group_stage4\"] = np.nan\n",
    "df[\"time_group_stage4\"] = pd.to_datetime(df[\"time_group_stage4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Parsing Booleans**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No', nan], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Experience\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"experience\"] = np.nan\n",
    "df.loc[df[\"Experience\"] == \"Yes\", \"experience\"] = True\n",
    "df.loc[df[\"Experience\"] == \"No\", \"experience\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                   object\n",
       "Individual Code                       int64\n",
       "Seat                                  int64\n",
       "Treatment                            object\n",
       "Market                                int64\n",
       "Gender                               object\n",
       "Number of Answers                     int64\n",
       "Answers Missing                        bool\n",
       "Individual Answer Stage 1           float64\n",
       "Individual Answer Stage 2           float64\n",
       "Individual Answer Stage 3           float64\n",
       "Individual Answer Stage 4           float64\n",
       "Group Answer Stage 1                float64\n",
       "Group Answer Stage 2                float64\n",
       "Group Answer Stage 3                float64\n",
       "Group Answer Stage 4                float64\n",
       "Final Answer Stage 1                float64\n",
       "Final Answer Stage 2                float64\n",
       "Final Answer Stage 3                float64\n",
       "Final Answer Stage 4                float64\n",
       "time_ind_stage1              datetime64[ns]\n",
       "time_ind_stage2              datetime64[ns]\n",
       "time_ind_stage3              datetime64[ns]\n",
       "time_ind_stage4              datetime64[ns]\n",
       "time_group_stage1            datetime64[ns]\n",
       "time_group_stage2            datetime64[ns]\n",
       "time_group_stage3            datetime64[ns]\n",
       "time_group_stage4            datetime64[ns]\n",
       "Experience                           object\n",
       "Skills                              float64\n",
       "experience                           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "\n",
    "Beside all this type parsing, the data is in general clean because the collection method was designed in such a way to prevent inconsitencies. In any case, small inconsitencies were solved (either straight away during the experiment or in a following data cleaning process)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"margin-top: 100px;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2016 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8)) = plt.subplots(4, 2, figsize=(20, 28))\n",
    "\n",
    "sns.countplot(\"Gender\", data=df, ax=ax1, palette=colors)\n",
    "ax1.set_title(\"Distribution of Gender\\n\", fontsize=14)\n",
    "ax1.set_xlabel(\"\\nGender\")\n",
    "ax1.set_ylabel(\"Total Count\\n\")\n",
    "\n",
    "sns.countplot(\"experience\", data=df, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"Have you taken a course on Game Theory/Strategy\\n or a similar subject before Behavioural Economics?\", fontsize=14)\n",
    "ax2.set_xlabel(\"\\nExperience\")\n",
    "ax2.set_ylabel(\"Total Count\\n\")\n",
    "\n",
    "sns.countplot(\"Skills\", data=df, ax=ax3, palette=colors)\n",
    "ax3.set_title(\"Identify your knowledge in strategy and decision\\n making on a scale from 1 (low) to 4 (high)\", fontsize=14)\n",
    "ax3.set_xlabel(\"\\nSkill\")\n",
    "ax3.set_ylabel(\"Total Count\\n\")\n",
    "\n",
    "df.groupby(\"Skills\")[\"Individual Answer Stage 1\"].mean().plot(kind=\"bar\", ax=ax4, color=colors)\n",
    "ax4.set_title(\"Average First Individual Response by Self-Reported Skill Level\", fontsize=14)\n",
    "ax4.set_xlabel(\"\\nSkill\")\n",
    "ax4.set_ylabel(\"Average Response\\n\")\n",
    "ax4.tick_params(labelrotation=0)\n",
    "\n",
    "df.groupby(\"experience\")[\"Individual Answer Stage 1\"].mean().plot(kind=\"bar\", ax=ax5, color=colors)\n",
    "ax5.set_title(\"Average First Individual Response by Reported Experience\", fontsize=14)\n",
    "ax5.set_xlabel(\"\\nPrevious Experience\")\n",
    "ax5.set_ylabel(\"Average Response\\n\")\n",
    "ax5.tick_params(labelrotation=0)\n",
    "\n",
    "df.groupby(\"Treatment\")[\"Individual Answer Stage 1\"].mean().plot(kind=\"bar\", ax=ax6, color=colors)\n",
    "ax6.set_title(\"Average First Individual Response by Treatment Group\", fontsize=14)\n",
    "ax6.set_xlabel(\"\\nTreatment Group\")\n",
    "ax6.set_ylabel(\"Average Response\\n\")\n",
    "ax6.tick_params(labelrotation=0)\n",
    "\n",
    "sns.countplot(\"Gender\", data=df[df[\"Treatment\"] == \"I\"].sort_values(\"Gender\"), ax=ax7, palette=colors)\n",
    "ax7.set_title(\"Distribution of Gender (Individuals Treatment)\", fontsize=14)\n",
    "ax7.set_xlabel(\"\\nGender\")\n",
    "ax7.set_ylabel(\"Total Count\\n\")\n",
    "\n",
    "\n",
    "sns.countplot(\"Gender\", data=df[df[\"Treatment\"] == \"G\"].sort_values(\"Gender\"), ax=ax8, palette=colors)\n",
    "ax8.set_title(\"Distribution of Gender (Group Treatment)\", fontsize=14)\n",
    "ax8.set_xlabel(\"\\nGender\")\n",
    "ax8.set_ylabel(\"Total Count\\n\")\n",
    "\n",
    "\n",
    "fig.subplots_adjust(hspace=0.4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(25, 8))\n",
    "\n",
    "df.groupby(\"Treatment\")[\"Final Answer Stage 1\"].mean().plot(kind=\"bar\", ax=ax1, color=colors, rot=0)\n",
    "\n",
    "ax1.set_title(\"Average Final Response by Treatment Group (Round 1)\\n\", fontsize=16)\n",
    "ax1.set_xlabel(\"Treatment Group\")\n",
    "ax1.set_ylabel(\"Average Response\")\n",
    "ax1.set_ylim(0, 100)\n",
    "\n",
    "\n",
    "df.groupby(\"Treatment\")[\"Final Answer Stage 2\"].mean().plot(kind=\"bar\", ax=ax2, color=colors, rot=0)\n",
    "\n",
    "ax2.set_title(\"Average Final Response by Treatment Group (Round 2)\\n\", fontsize=16)\n",
    "ax2.set_xlabel(\"Treatment Group\")\n",
    "ax2.set_ylabel(\"Average Response\")\n",
    "ax2.set_ylim(0, 100)\n",
    "\n",
    "\n",
    "df.groupby(\"Treatment\")[\"Final Answer Stage 3\"].mean().plot(kind=\"bar\", ax=ax3, color=colors, rot=0)\n",
    "\n",
    "ax3.set_title(\"Average Final Response by Treatment Group (Round 3)\\n\", fontsize=16)\n",
    "ax3.set_xlabel(\"Treatment Group\")\n",
    "ax3.set_ylabel(\"Average Response\")\n",
    "ax3.set_ylim(0, 100)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"margin-top: 100px;\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internal Validity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gender Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a table of proportions and total for gender for each treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "genders = df[\"Gender\"].unique()\n",
    "treatments = df[\"Treatment\"].unique()\n",
    "gender_proportions = {\n",
    "    \"I\": {},\n",
    "    \"G\": {}\n",
    "}\n",
    "gender_totals = {\n",
    "    \"I\": {},\n",
    "    \"G\": {}\n",
    "}\n",
    "\n",
    "for gender in genders:\n",
    "    for treatment in treatments:\n",
    "        total = df[(df[\"Treatment\"] == treatment) & (df[\"Gender\"] == gender)][\"ID\"].count()\n",
    "        proportion = total / df[(df[\"Treatment\"] == treatment)][\"ID\"].count()\n",
    "        gender_proportions[treatment][gender] = proportion\n",
    "        gender_totals[treatment][gender] = total\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
    "\n",
    "sns.barplot([\"Female\", \"Male\"], list(gender_proportions[\"I\"].values()), ax=ax1, palette=colors)\n",
    "ax1.set_title(\"Distribution of Gender (Individuals Treatment)\\n\", fontsize=22)\n",
    "ax1.set_xlabel(\"\\nGender\", fontsize=16)\n",
    "ax1.set_ylabel(\"Proportion\\n\", fontsize=16)\n",
    "\n",
    "ax1.spines['bottom'].set_color('white')\n",
    "ax1.spines['top'].set_color('white') \n",
    "ax1.spines['right'].set_color('white')\n",
    "ax1.spines['left'].set_color('white')\n",
    "ax1.tick_params(axis='x', colors='white')\n",
    "ax1.tick_params(axis='y', colors='white')\n",
    "ax1.yaxis.label.set_color('white')\n",
    "ax1.xaxis.label.set_color('white')\n",
    "ax1.title.set_color('white')\n",
    "ax1.set_ylim(0, 1)\n",
    "\n",
    "\n",
    "sns.barplot([\"Female\", \"Male\"], list(gender_proportions[\"G\"].values()), ax=ax2, palette=colors)\n",
    "ax2.set_title(\"Distribution of Gender (Group Treatment)\\n\", fontsize=22)\n",
    "ax2.set_xlabel(\"\\nGender\", fontsize=16)\n",
    "ax2.set_ylabel(\"Proportion\\n\", fontsize=16)\n",
    "\n",
    "ax2.spines['bottom'].set_color('white')\n",
    "ax2.spines['top'].set_color('white') \n",
    "ax2.spines['right'].set_color('white')\n",
    "ax2.spines['left'].set_color('white')\n",
    "ax2.tick_params(axis='x', colors='white')\n",
    "ax2.tick_params(axis='y', colors='white')\n",
    "ax2.yaxis.label.set_color('white')\n",
    "ax2.xaxis.label.set_color('white')\n",
    "ax2.title.set_color('white')\n",
    "ax2.set_ylim(0, 1)\n",
    "\n",
    "plt.savefig('gender_distribution_treatments.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test for difference in proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I': {'Female': 0.4, 'Male': 0.6},\n",
       " 'G': {'Female': 0.4166666666666667, 'Male': 0.5833333333333334}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I': {'Female': 8, 'Male': 12}, 'G': {'Female': 25, 'Male': 35}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z-stat: 0.1311228762747762\n",
      "p-value: 0.8956781047476398\n"
     ]
    }
   ],
   "source": [
    "yeses_i = gender_totals[\"I\"][\"Male\"]\n",
    "totals_i = yeses_i + gender_totals[\"I\"][\"Female\"]\n",
    "\n",
    "yeses_g = gender_totals[\"G\"][\"Male\"]\n",
    "totals_g = yeses_g + gender_totals[\"G\"][\"Female\"]\n",
    "\n",
    "z, p = proportions_ztest([yeses_i, yeses_g], [totals_i, totals_g])\n",
    "print(f\"z-stat: {z}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average First Individual Response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because for the first individual answer in the groups treatment, they didn't discussed with the group yet, it reflects their true intent.\n",
    "\n",
    "If the difference is not significant, then we can assume the treatments were different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eotqXW883/VS2hfA4cXxqsihzHhvLoVNNoceIWiFNPlQ83kf812vn01T/nz7c4b364bPEsapV/T/B7iNcBnVhMK3x5O1U1TrbVRktx21KVJ1qVW8g413M+YcJoipU+ae49f03JKpdQTRm1e4AvlhRvhtqy7Qzm/iD2c004qD/R+Ik9hxVwxYzavOt1bJc2ajPtcQJoZ1HiYsL7axI1UjbH2j+rosRDWFA83cdS52qnyaq3+kw2jdkNkjaBZQQAcnjxfPWRoLK7eBqmreD/xDb11DMIKoIQ/Wntm4Tqv0wp4r3ilT9hP6qw3yj1S/tTsTFu38RF8DqjeqUF51a98F+WpA5Ly7dS1T7ezURUK5F82+4dfF4P3GRr2k/vpvqD359X76+eHw7cTGxKWDtpNffsF61swxcniMaP2xSVrlcmOZj0KW0D16gCioXBxapjb+bqm/ko+neQFvpdUQw+hxR9bdpfT9PFSwN59jZy7qdytznwxOKx/cSVYLrtiaqO9fnGyvndpi2BXHRGuKCb9O6XpBoiAvar+vFiFt3LqRqULS+j69azNePffw5olp6O/+qPW/6f/AvquByWsu0cl+/itgGm9ZH2eDjojQ3xtp0voFqnxnJRunKRj87BdIQ56B2F+BP7LDMJTQfD6A69vya5gvNDxO3cgC8pksZh+N2qv8prZ6m+p02b5hHo8RgWuNJebX8DtpnS08h/qTU521V/qmfytzZpHcRV2pnEVm3uvq9iVfQOZNatrS9fEMZ7ieuHneyNXFf1deIll+XYe7gv7Rky+sXFI+3dvmMvzeMr3/X0+j8Xfct5mv6rr04jjh5Ng1D7Zf09i7TP0/8uXkp8ZveRVyN35PO95ONtueIVjyblNtSa6CzevHYqbXsXqZ3Uu5PryJakK0rA+y7iICmV6vXnncq2x301rftUC1ABCRnElmcFzJnwFXXug+Ols1orn0C1b48lc778eNU2099X/4hcRyan6il8iARyH+F+NPZy3+ITr/h3VT79wtq48vnd9D5j+9NteerNczTdCEJ5jwO1/efRNynD5Elv4k4nv6QqPnUGsiUyvU9hagZ02l9l4HMUI+dieZjOMy53l/QMu1E4qLu8szdInmZrb4RuG6IZeuXTsfw+nnqVjqv6zLYaF3XaxK12r5FXAhZgbkvLpT6sY8/SvMxq76dT2+Y53mqmgKtx/tyfbyOzuvixtoyTdteL/vMUC6s9ars2moJqsC6XzptUwtS9Yrwtw7zQXXsaTru9EOv5+7W/VujzGBa48U0quD3D0TVxVe0DMtTnSiagukba/O0Vk0uX/+GuavnDeVE2lTVqNOfQ4jA+WdEla37iD91GxMZuyWJLEq9UZnWqlpl9r5bNfKmILWf33UsdVvPlxB/ss4hAtZViADwGCIDcBHRYNtYa1fFq53WPx3D3Q56cQ7VvlLfnyYDuxTPT6V7hqGu3qhYt7IP51aJbr5K1aDc6UTDhC8mGuMpG9X532J6U3XJfphFdWFpPiIwfhdVJutTNDdkNdx9eSaxjxxJZGoXIaqYH0gch++k6h6oSa+/Yf12jvJ5t23z8drzpi7ahrr/nEgEmpcR2++aRFX5HxMXAX7BnBd+YHSPnbPo/N3q6671Vpl7qBpc+kDLfG8vnp8wxHL1U6dj+HDX9XzEefYFxDZ4IJFlXIU495b7eNl1ZD/28V63xV7ma91e+7ntDeXz++mO4nEynbsCm8KcF97/0mHeUqdtqn4M6fXY061ryOHo9dy9EM3JFo0Cg2mNF++lOljsRvQz2m4o7w98Ec19eZbZtLdR9QO4BlVV33ZVUusHtcXpnEkth079W3ayC3HP0HPE/XRHElW2pxP3+zxB5wNnWdZuLR033Ytc/67r09t3bVetfjy4mriPbhmi5dNvENUzIdb9ZfTWp/EgGu520ItZVK0D14PpbaiydzlVvGHO7a9b2TtN7yWAb/qDPImqjYTjiRaEzyaq5j5M7INPMLLZmXYSUTX/p8T2WVZFPpL2f+rKdXk5ve3Hk4gW4OseIS7orUBUk/04EUg+TQQeR9O+5eJSr79h/c9r+bzbtlmf/njjXEN3LhFgrUBcWDmcyNRPJs4fVzBndddyfd9D7+t7X4ZmQTqfB+rrpl1gcHzx+GailWCIdj4WJc497e6ZHSTlun6a+C/by7qutwT+aqJlZIieLb5KHO/voepX+QlGNljqp3J9HEPv2955o1/MntRrHo5mFeb6MaTXY0/rcafXC8e9XJzp9dw9k94v1GgEGExrvGhqVGwoy5SZsiWoGospA4H6faB19fuZRjpbWZ7gr6e5qvZ6HZa/s3jsdo9X0/TR/K6D4nEiU/M14l7fbYhAcRGaG+8adHcUj033xdHj9G7KYHkdooYIVPvTX2m+56vJHbXnncq2OtXFsHbK6pSdAt6mLpVWpKoC+dMOy3faD0faXURDghDZ8k+2mafcl9dj+Jm154lqv0cBOxPVG8sLT1+gOcvV6TdcheoP4Z218XcUj6vT+fdbt/b8zsa5hu9Borr/54jbQnYvxk+juugC1fpeiaq66EiZxJxVnVvV13u7dXMW8b3mp+omscxSn0NctBlk5bpemKEdw8rz7FO0P+dDXGQYyXuD+6lcHxPhnH0O1f3KHxzFz51F3IYHczaI2E557Gndt+rV+JuOXWUNo256PXeP5LFPPTCY1niwLtWf9P3pfrW1bBVzF9rf//Rvqsa3yvs6660Ot2sV949UV353H8J3yFH+Ke2UdejUenbZGNFGNN/btxTRp2U7/6I6Me/e4XMmsgup+lFuPaGVJ/lBr1ZVbgeb0Bw0rkDVUNlQXULV4u77iAC3bHU/NysNcWvDHcXzTv12d+vTu7zvcM0O82zbML4eGDb9zi+kufbLaDmeqhrqp5g7k1E2ZLQk1W/SLw9S9bO6CM33Dvb6G9azUZcVj1Oo2qBop+wx4WmiD93RciJVA2L148PvisdJdO7HuV96Wbf3U20jdc9Q7Z+7E9Wdy751T+hD2UbaRVQNTO4+hOXLfbzT/+BuvVQM0rmg3Nc3Zs6LTKOp3mDXcNbJfVTb5pZEmxWjpTz27Ejz/fNLU912eFnLtPr97k3nnk3prUbYC6ku+rRamKhNB/3vi1uZDKY1HpQZ5kTVNUQnZYvdyzJ34yql8kC9A9Fgxzot41vNJKozQpy4u/2RX4GhN1hSNpCxLnM36gQRBH+ozfhS2RDbZJq7DzqYzlm9bxWPWxNdvHSyBM1B+6BanMgeNZlMdT/kQy3TytdTqKpHDqKTqLaD1qq7pcNp/sPQq3or+e8G3kJsE/XxucrWWHeg+sNQtyJVVrZJmTV9I1VXK3Xvobl7l/uoLqrt2Gb6FOJ4MNbn0NlUXd0sC+zdMv13RO0AiPu7V+/yfq2Zzk6ZT5jzfsaHG+bZlvbH4anAl4rnVxPVp0uXUgWrB9L+WPoqqiDqFPrbGN1U4oJjk8WoMkv148OtVFnOrzJ390WtXszwtqH9qFqbrtuS6t7nTi0bl1W9Xwl8k7gI8CBxS8Oge4DqfP0puncxtipzZgrL8+xCtL+o9kKiplIn5W/fdLFyNP0fUbNuEnExZIku849EDwT1fWG462R/qsD0BDpfVOuncp9Yier41OoIqm3puJZpN1Idi9r1crEA0YNIrw6n/YWJr1Mdo07IeD+NgLH+IyB1M4nq6vAV9Fad5VyqRpGaqnqfQVyZX5A4CUHcK3VJh/c9gGjhcT6ite+jiRZtVyDuuV2b+IN+SlHOobYI/XMiEFmA+GP2JuKP2xpENcNz6NxS97+oAv8PEH84XkkEFC8nDv77MGd17lZHU3Xh8W0i278tETQvTfwJfBvRuu1dDH7XUq1WJrKfPyMab3op8RuuRFxcOYsqkGi9gPPn2vOvE1VV5ycCrEE6pv6T6K8UYh86nbjndRlieziV2D/u6MNnlX9qV6H6o3AZQ69+diRVP+8/J/r1XI3Y195O1CyZTOdGYn5M7EdLEvvRpsS2uzZxb/yJNO8DM4muUSAa2Dq0WG454gLT74E3MGcAOFZOovoen2HOrHoiAs6ZxLb9J+JP6vrEuphK1GD5GFET4+KW9z6ZqNr9BeLexWlE0L4eEWh8uZjvAua+6FS6gzje7kdkP5cnLrhcSvzhTsx9K8XzVBfx1iQyLzsWy65alPe3xD73EFU/2f2yMXFcO4HIiq1JrK9ViVuDfkeVWTq9ZdmPEQHpYsR2ejCx3y1blP9lRN+0ZxOtcS/A0DxDXPC5tCjjCkX59iWOX5OIqtqd/rjfQJXRLxsMPJmh9z0/2j5H1DRbgLhN50hiP1+uGNYlju8/J46Hy9aW/T1V/+U/Jo6FqxDnhg8S21zZRkGTsrXzlYntdXlimxyLc8GjVI0BbkScp/Yitt2liP1/C2L//wvVuaGfbqCqLfBVYn9fgFgfuZnq+4lj/cNEzZczif92uxHn5mWI33Nt4n/O76myuM+3vlmGc6guiB0AfI84Xi5DrNfTqKqeH0vVfWDpKap2RPYlej5YldgetyVqva1P8/Gy7k6i7Z3fEDXIliGOvd8ntn2I7vGaujnVaEkpOTgM8vC6VNk3Y7njimVmppSWapjnV2lO/9PD+05LKV2Wuns+pbRuy7IHFdNu6+FzDujw3ncX713atc3yC6WUzu/wHieklA4unt/SUIbFU0q/7PZFC2/s4TvVhym1ZX+UuezatWU3bTN9ejFt/x7fo5OjG5Zv3XZK52V+l+kdltu7mDazx/do930XTCmd2+H7nZBSOrR43m472L4277Qu5fhry3vv1WX+Tp9LSmmDlNJDDeV+OqW0Q5fvTkrpiA7f/fSU0j7F83breLWU0r0dlj+0y3fIWXfthtM6lK11+FDts/ZuM/01qVpXndzZsty1PSxzc0pp1Zbl1qtNf3NK6Z8Ny85OKX204TuV+8BzHT77/pTSRg3LlmU/qsP7v7n2Xss1jG8yO6X0hYb3XTeldGsP7/FMin00Z7vYt1j2iZTSVsVjO4+mlF7Vw/vt07LcKzLL08vwYPHeZ/cw77trZVmoh/lfmFK6vmEd1D2XUlqxZdl3pebt6/GU0rYp9u2UYl9v/ewFUvPvXJ9//2Lc9A7fY9Pasmt3mG9mMU+7/ZyU0vtSSk82r4b/7zdDeO9evssJDZ93ZYf37DSsmVK6tIfvk1LsT0enOfflcuj0O7YOS6aULu7yWWek5n13pZTSvxuWeyKltF2X8pxXTDstpfT1DmW4pihrv/dXh8xhkLIoUjtlZnk2kd3oVVm9dEGa77dprdJ9Sg/vO51o3XVn4urjXUR2YBZx3+hviSvUqzBnH6i5DiC6v7mcuFf7KSKLcTjRwEin/kUhMlE7EFdGryuWn0Fk9z9EZKuaWqOkNv5txJXRk4hqcU8TWYvpRBZrf+LK9zk5X24A/IOoLn8gURuh/G6ziuenEvdEtVabLb0XOIioQtutz/CxNIuo2bAPkYEqt4MriezY7n38rPr+9Cx5+2s7fyauwv+AyD49A9xLZAY2JbIU3exHZBGuJvajx4vnHyb2r04ZjH8TmYhjiH37WSJLdR5R5XD/3C80gn5MVW3188zd2NgfiNoknyFqnDxAZJCeJGq5nEpkJl/astwuxLbzC6JWziNU/Z5fTBzrNiCOg02mE+vxf4j9biaRlTmLOJb+oMOyRxPZph8S2feZRG2E64naBWsTPR302wXA9sTx9gpiW5hFHCP+DvyIyF43ZX1vIrbdjxDb6XRi+3ma+J1+RWyXU2nfRkevLibaRDiJqMnxDLGt/pDIfl3Vw3vUq8j/hbkzbYPudiLzvyuRvbyXWA8zid/tbCJDuxJz9598OlETqazNNouoSfGj4j0voLNniCr1RxOZ7+H8lv1yMlGL7RvE7/8I8f/pMaIK8rHEtt3tVrWh2pPIxl5Pf7ouLM/VWwPfJbbRB4nj0GPEev850QDjysQ5+8FhfuaM4vN2J2qhPETsv/cT29PbiP+VTb/3vcRtKEcTx8by3HUScSz8bUZZvkZk6C8ksvQzif8dXyRqGszIeC+NkEkp5XT/KWkCOZsItH5F/xsnkjTvWo/44w4RdI5EwKv+WIwI9hclbqf4386zSxph5xFthZzOnK/K/AkAACAASURBVN25aUCZmZbmTYtTtdw6mq3gSpIGR9m39LMMrfV9SZqnGUxLE9NCdO6f9TCqat4/6zCfJGni2rd4/BVR/V+SlKH1vipJE8MqxL3ARxP3g91O7O/rEfcWld39nALcMhYFlCSNuklEy8pLEW0KbFiMP2LMSiRJ45jBtDRxrUQ0QvKNhumXEo0LSZLmDW+i6vatdAK9NVYmSWphMC1NTHcTLRZvR/QtvQJRrfthopXNU4mWJWePVQElSWNmNtGP7U+AQ8a4LJI0btmatyRJkiRJmWyATJIkSZKkTAbTkiRJkiRlMpiWJEmSJCmTwbQkSZIkSZkMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpTJYFqSJEmSpEwG05IkSZIkZTKYliRJkiQpk8G0JEmSJEmZDKYlSZIkScpkMC1JkiRJUiaDaUmSJEmSMhlMS5IkSZKUyWBakiRJkqRMBtOSJEmSJGUymJYkSZIkKZPBtCRJkiRJmQymJUmSJEnKZDAtSZIkSVImg2lJkiRJkjIZTEuSJEmSlMlgWpIkSZKkTAbTkiRJkiRlMpiWJEmSJCmTwbQkSZIkSZmmjHUBJoA01gWQJEmSJI2IB4Hl200wMy1JkiRJUnt3Nk0wmJYkSZIkKZPBtCRJkiRJmQymJUmSJEnKZDAtSZIkSVImg2lJkiRJkjIZTEuSJEmSlMlgWpIkSZKkTAbTkiRJkiRlMpiWJEmSJCmTwbQkSZIkSZkMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpTJYFqSJEmSpEwG05IkSZIkZTKYliRJkiQpk8G0JEmSJEmZDKYlSZIkScpkMC1JkiRJUiaDaUmSJEmSMhlMS5IkSZKUyWBakiRJkqRMBtOSJEmSJGWaMtYFkCRJ0uj48bm/GesiSPOk9+/wprEugkaAmWlJkiRJkjIZTEuSJEmSlMlgWpIkSZKkTAbTkiRJkiRlMpiWJEmSJCmTwbQkSZIkSZkMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpRpXgqmzwMScFDL+KWBHwEPAk8CvwPWH92iSZIkSZLGk3klmH4P8PI24ycBZwHbAx8HdgbmBy4CVhm10kmSJEmSxpV5IZheCvgW8Jk203YCtgB2A04lstc7Eevl86NVQEmSJEnS+DIvBNPfBG4iguVWOwH3Epno0gzg18BbRr5okiRJkqTxaKIH01sA7wf2aZi+LvDXNuNvAlYDFhuhckmSJEmSxrGJHEzPDxwDHA78vWGeZYBH2ox/uHhcumG5PYFri0GSJEmSNI+ZMtYFGEFfABYGDu4wzySihe924zs5thhoWF6SJEmSNIFN1GB6NeDLwB7AgsVQWpBolOxxIgO9TJvly4x0u6y1JEmSJGkeN1Grea8BLAScRATE5QDw2eL5+sS90eu2Wf6lwL+BJ0a8pJIkSZKkcWeiZqavB17XZvxFRIB9HHAb0cf0B4EtgUuKeZYAdgROGfliSpIkSZLGo4kaTD8KXNww7c7atLOAK4gA+3NExvqLxD3T3xzREkqSJEmSxq2JWs27V88DbwYuAL4P/BKYTWS17xrDckmSJEmSBthEzUw3addK98PAh4pBkiRJkqSu5vXMtCRJkiRJ2QymJUmSJEnKZDAtSZIkSVImg2lJkiRJkjIZTEuSJEmSlMlgWpIkSZKkTAbTkiRJkiRlMpiWJEmSJCmTwbQkSZIkSZkMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpTJYFqSJEmSpEwG05IkSZIkZTKYliRJkiQpk8G0JEmSJEmZDKYlSZIkScpkMC1JkiRJUiaDaUmSJEmSMhlMS5IkSZKUyWBakiRJkqRMBtOSJEmSJGUymJYkSZIkKZPBtCRJkiRJmQymJUmSJEnKZDAtSZIkSVImg2lJkiRJkjIZTEuSJEmSlMlgWpIkSZKkTAbTkiRJkiRlMpiWJEmSJCmTwbQkSZIkSZkMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpTJYFqSJEmSpEwG05IkSZIkZTKYliRJkiQpk8G0JEmSJEmZDKYlSZIkScpkMC1JkiRJUiaDaUmSJEmSMhlMS5IkSZKUyWBakiRJkqRMBtOSJEmSJGUymJYkSZIkKZPBtCRJkiRJmQymJUmSJEnKNJGD6e2AC4HpwCzgbuCnwEtr82wFpDbDo6NZUEmSJEnS+DJlrAswgpYB/gR8H3gAWA3YH7gSWB+4szbvJ4Braq+fG6UySpIkSZLGoYkcTJ9aDHVXA7cA7wCOqI2/mQiyJUmSJEnqaiJX827noeLx2TEthSRJkiRpXJsXgunJwALAmsAxxD3Up7XMczIwmwi2TyGqhEuSJEmS1NZEruZdugrYsHh+G7A18J/i9QyiuvclwGPABsCXgCuK5/+hvT2LQZIkSZI0D5oXgundgCWANYDPAhcAWwB3AH8uhtIlwKXEvdWfAL7S8J7HFgNE69+SJEmSpHnIvFDN+2YiO30qsA2wGNGqd5PrgFuBjUe+aJIkSZKk8WheCKbrHiWqer+4y3yTMOMsSZIkSWowrwXTU4G1gX92mGcjYC0imy1JkiRJ0lwm8j3TvySqbN9ANC62FvBp4DmqPqZPBm4v5nuUaHTsi8A9wHdHubySJEmSpHFiIgfTVwK7APsRXWPdBVwMHEI0PgbwV+A9wMeBRYhus34BfA14cFRLK0mSJEkaNyZyMH1YMXRySDFoHnTPNcd2n0lS3628sT0LSpKk8W9eu2dakiRJkqRhM5iWJEmSJCmTwbQkSZIkSZkMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpTJYFqSJEmSpEwG05IkSZIkZTKYliRJkiQpk8G0JEmSJEmZDKYlSZIkScpkMC1JkiRJUiaDaUmSJEmSMhlMS5IkSZKUyWBakiRJkqRMBtOSJEmSJGUymJYkSZIkKZPBtCRJkiRJmQymJUmSJEnKZDAtSZIkSVImg2lJkiRJkjIZTEuSJEmSlMlgWpIkSZKkTAbTkiRJkiRlMpiWJEmSJCmTwbQkSZIkSZkMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpTJYFqSJEmSpEwG05IkSZIkZTKYliRJkiQpk8G0JEmSJEmZDKYlSZIkScpkMC1JkiRJUiaDaUmSJEmSMhlMS5IkSZKUyWBakiRJkqRMBtOSJEmSJGUymJYkSZIkKZPBtCRJkiRJmQymJUmSJEnKZDAtSZIkSVImg2lJkiRJkjIZTEuSJEmSlMlgWpIkSZKkTAbTkiRJkiRlMpiWJEmSJCnTRA6mtwMuBKYDs4C7gZ8CL22Zb1XgZ8AM4DHgF8Bqo1dMSZIkSdJ4M2WsCzCClgH+BHwfeIAIkPcHrgTWB+4EFiEC7lnAB4AEHARcBLwMeHLUSy1JkiRJGngTOZg+tRjqrgZuAd4BHAF8BFgDeAlwWzHPDcA/gL2AI0elpJIkSZKkcWUiV/Nu56Hi8dnicSciU31bbZ7bgcuBt4xiuSRJkiRJ48i8EExPBhYA1gSOIe6hPq2Yti7w1zbL3MTc91ZLkiRJkgRM7GrepauADYvntwFbA/8pXi8DPNJmmYeBpTu8557FIEmSJEmaB80LmendgE2B9xKtdV8ArF6bntosM6nLex4LbFQMkiRJkqR5zLwQTN9MZKdPBbYBFiNa9YbISi/TZpmlaZ+xliRJkiRpngim6x4lqnq/uHh9E3HfdKuXAn8brUJJkiRJksaXeS2YngqsDfyzeH0WUQV8jdo8qwObF9MkSZIkSZrLRG6A7JfAdUS/0Y8BawGfBp4j+pgG+CGwL3Am8BXi/ukDgbuIlr8lSZIkSZrLRM5MXwm8FTgR+A3wGeAS4BXArcU8TxKte98K/AQ4mehnemvgiVEuryRJkiRpnJjImenDiqGbfwM7j3BZJEmSJEkTyETOTEuSJEmSNCIMpiVJkiRJymQwLUmSJElSJoNpSZIkSZIyGUxLkiRJkpRpOMH04sCK/SqIJEmSJEnjRW4wvSjR3dTdwKPAXbVpmwBnEf04S5IkSZI0YeX0M704cBmwPvBX4DHgJbXpNwFbA7cA1/ergJIkSZIkDZqczPRXiEB6D+BlwE9bpj8JXAJs05+iSZIkSZI0mHKC6Z2B84Hji9epzTx3AKsMs0ySJEmSJA20nGB6FeAvXeZ5Alhy6MWRJEmSJGnw5QTTTwDLd5nnhcCDQy+OJEmSJEmDLyeYvgZ4M7BYw/RpwA7AH4dbKEmSJEmSBllOMP0dYDngbGDNlmlrAqcDCxfzSZIkSZI0YeV0jXUucBDRqvctwKxi/HSi+vck4MtE91mSJEmSJE1YOZlpgK8C2wHnEF1hASxItPK9HXBI/4omSZIkSdJgyslMly4oBkmSJEmS5km5mWlJkiRJkuZ5OcH0asAbgEVr4yYD/wX8CbgU2LF/RZMkSZIkaTDlVPM+AHgrMLU27svF+NJmwObA1cMtmCRJkiRJgyonM70Z8Hvg2eL1JOBjwK3AGsCrgaeAT/ezgJIkSZIkDZqcYHoacGft9SuILrGOAu4ArgTOBF7Vr8JJkiRJkjSIcoLp+YHZtdebAwm4sDbuLiLoliRJkiRpwsoJpu8GXlZ7/UbgQeBvtXHLA4/3oVySJEmSJA2snAbIzgE+ARwKzCRa9j6hZZ61mbMquCRJkiRJE05OMH0Y0Zr354vX9zFnS96rEY2QfbsvJZMkSZIkaUDlBNP3A+sRGWmAi4AZtelLAF8gMtiSJEmSJE1YOcE0RNdXv2qY9tdikCRJkiRpQstpgEySJEmSJJGfmV4K2B3YBFgamNxmngRsN7xiSZIkSZI0uHKC6bWAi4GpwKQO86XhFEiSJEmSpEGXU837cGAacAQRWC8MzN9mWKDPZZQkSZIkaaDkZKZfC5xL1TWWJEmSJEnzpJzM9GRsrVuSJEmSpKxg+k9E9W5JkiRJkuZpOcH0gcCbgNeMUFkkSZIkSRoXcu6ZngqcCfwOOInIVD/aMO8pwyyXJEmSJEkDKyeYPono9moS8MFiaO0Ga1IxzmBakiRJkjRh5QTTHxmxUkiSJEmSNI7kBNPHjVgpJEmSJEkaR3IaIJMkSZIkSeRlpksLAW8FNgCWAmYA1xGNkz3dv6JJkiRJkjSYcoPp7YAfA8sRjY2VEvAgsDtwbl9KJkmSJEnSgMoJpl8B/AqYHzgduBC4D1gR2BrYBfgFsDmRqZYkSZIkaULKCaa/QmSjtwQub5n2I+B7wO+BLwLv7EvpJEmSJEkaQDkNkL0GOIO5A+nS5cDPiGBbkiRJkqQJKyeYXhL4d5d57gSWGHpxJEmSJEkafDnB9HRgoy7zbFjMJ0mSJEnShJUTTJ8LvB74bJvlJgGfBLYFzulP0SRJkiRJGkw5wfQ3gP8AhwG3AscDBwPHAX8HjiymH9TnMg7FO4CfE9XOnybKdwiweG2e1YkuvdoNS41iWSVJkiRJ40xOa973Ed1e/RB4HbBGy/SLgL2Ae/tTtGH5LHF/95eAu4ENgAOIcr8aeL427yHAWS3LPz7yRZQkSZIkjVc5wTTAv4BtgBcQAeqSwAzgz0QWeFDsCDxQe30J8DBwIrAV0Ud26V/AlaNWMkmSJEnSuJcbTJfuZLCC51YPtBl3TfG48mgWRJIkSZI08eTcM123IvBG4D3F44p9K9HIKfu/vrll/CHAc0SG/Sxg/dEslCRJkiRp/MnNTL8IOAp4Q5tp5wP7Av8cbqFGwMpEA2q/A64txs0CjiHK/QCwNnGP9R+BTZg76K7bsxgkSZIkSfOgnGB6DSLQXB64A7ic6FN6GtEw2XbF9M2I+5AHxWLAmUT2+YO18fcBe9de/wE4D7gJ+DKwa4f3PLYYIFr/liRJkiTNQ3KC6UOIQHo/4DvA7Nq0ycCngP8B/ht4d78KOEwLEVW31yCqed/dZf67gMuAjUe4XJIkSZKkcSwnmH49cC7wrTbTZgNHFPNs24dy9cP8RF/TmxDlurHH5SZhtlmSJEmS1EFOA2QLEl1gdXIdsMDQi9M38wEnE914vYXeu75ajaiyftUIlUuSJEmSNAHkZKZvIKpLd7IGvWeAR9L3gHcCBwNPApvWpt1dDEcQQfcVRANkLwG+CDxPVFWXJEmSJKmtnMz0ocA7aK7GvR2wM4MRiO5QPH6ZCJbrwx7FtJuALYgWvS8ADiAaVXsV8PdRLKskSZIkaZzJyUwvBpxDtHj9W+BS4H5gKtG41xuAXwNLAO9tWfaUYZc0z+o9zHN8MUiSJEmSlCUnmD6JaJhrErB9MZSvSzsBO9Zel415jXYwLUmSJEnSiMkJpj8yYqWQJEmSJGkcyQmmjxuxUkiSJEmSNI7kNEAmSZIkSZLIC6aXBNZi7n6kdwN+DvwY2KhP5ZIkSZIkaWDlVPM+hAicpwLPFOM+BnyHqhGynYENgVv6VUBJkiRJkgZNTmZ6C+BC4KnauM8B9wFbE91hzQfs17fSSZIkSZI0gHIy0ysRwXRpHWA14IvAxcW4txN9TkuSJEmSNGHlZKYXAZ6uvd6c6EP6/Nq424CV+1AuSZIkSZIGVk4wfS/wktrr7YDHgL/Uxi3FnAG3JEmSJEkTTk4174uBXYG9gZnATsAvgedr87wYuLtfhZMkSZIkaRDlZKb/m2h87HvA8USL3l+vTV+euF/6j30rnSRJkiRJAygnM/0vYD1gl+L1mcDttemrA8cCJ/WlZJIkSZIkDaicYBrivun/bZh2TTFIkiRJkjSh5VTzrlsYWB/YrI9lkSRJkiRpXMgNplcETgceBa4H/lCbtjlwA/Da/hRNkiRJkqTBlBNMTwOuBnYGfgtcBUyqTb+a6GN6l7kXlSRJkiRp4sgJpr9GZKa3J7rF+m3L9GeJTLWZaUmSJEnShJYTTL8JOAv4XYd5/g2sNKwSSZIkSZI04HKC6anArV3mmQUsOvTiSJIkSZI0+HKC6YeBVbrMsyYwfejFkSRJkiRp8OUE038k7pVeoWH6i4AdgIuHWSZJkiRJkgZaTjB9OLAIESxvCyxUjF+weP1rIAFH9rF8kiRJkiQNnCkZ814BfBQ4CjivNv6p4nE28GHgxv4UTZIkSZKkwZQTTAP8kOj+6mPApsCywAzgSuC7wN/6WjpJkiRJkgZQbjANcAvw8X4XRJIkSZKk8SLnnulerTMC7ylJkiRJ0sDoZzC9OnAi8Jc+vqckSZIkSQOn12remwEbA88ClzFnI2PLAwcQjY8tgP1MS5IkSZImuG7B9BTgdOCtLeMPAb4CvBs4GlgCeBA4DPhen8soSZIkSdJA6RZMfxR4G9H91WXAJGAL4IvAk8BBxeNXgG8XzyVJkiRJmtC6BdPvBZ4ANgT+UYxbB7iGCKRvBN4I3DtSBZQkSZIkadB0a4BsHeAXVIE0wM3FOIC9MZCWJEmSJM1jugXTiwN3thl/R/F4XV9LI0mSJEnSONAtmJ4EzG4zvhz3TH+LI0mSJEnS4Oula6wlgJXajANYkQi4W1n1W5IkSZI0YfUSTH+6GNq5u8241OP7SpIkSZI0LnULeu8lgmNJkiRJklToFkyvMiqlkCRJkiRpHOnWAJkkSZIkSWphMC1JkiRJUiaDaUmSJEmSMhlMS5IkSZKUyWBakiRJkqRMBtOSJEmSJGUymJYkSZIkKZPBtCRJkiRJmaYMYZllgbcB6wCLAnvXxr8A+Bswsy+lkyRJkiRpAOUG0x8AjgIWASYBiSqYXhm4BvgIcHy/CihJkiRJ0qDJqea9DREk3w68EzimZfoNwM3AW/tTNEmSJEmSBlNOMP0FYDrwGuAXxfNW1wMv7UO5husdwM+BO4Gngb8DhwCLt8y3NPAj4EHgSeB3wPqjV0xJkiRJ0niUE0xvDJwNzOgwz93AtGGVqD8+C8wGvgRsD/wA+ChwAdV3ngScVUz/OLAzMD9wEbDKKJdXkiRJkjSO5NwzvSDweJd5lgKeH3px+mZH4IHa60uAh4ETga2AC4GdgC2ArYkAGuAKohr754FPjFJZJUmSJEnjTE5m+k5gwy7zbALcOvTi9M0DbcZdUzyuXDzuBNxLFUhDZN1/Dbxl5IomSZIkSRrvcoLps4DXAm9vmP5+4OXE/dSDaMvi8ebicV3gr23muwlYDVhsNAolSZIkSRp/cqp5Hwa8C/gpcDrReBdE11ivAXYBbgO+088C9snKwDeIBsauLcYtA9zRZt6Hi8elgSca3m/PYpAkSZIkzYNygumHifuNTwLeUxv//eLxCuDdNAegY2Ux4EzgOeCDtfFlP9mtJvXwnscWAw3vIUmSJEmawHKCaYhM7hbAK4HNgGWJ+4yvBK7qa8n6YyGievoaRDXvu2vTHiay063KjPsjI1s0SZIkSdJ4lRtMl64rhkE2P9HX9CbA64EbW6bfBLyhzXIvBf7N4GXYJUmSJEkDIqcBsvFkPuBkYBuiZe4r28xzFnEv9Za1cUsQ3WqdNdIFlCRJkiSNXzmZ6S/1MM/zwGNEi9mXAc8OpVB98D3gncDBwJPAprVpdxfDWcR93icBnyOqdX+RuGf6m6NZWEmSJEnS+JITTB/EnI1t1Rvqah2fiL6e9wV+NuTSDd0OxeOXi6Hu68ABROD/ZuBwohG1hYjg+nXAXaNSSkmSJEnSuJQTTG8LfAx4E1GF+jLgfmAq0TXWe4Gzia6zXgnsA5wK3Adc3r8i92T1Hud7GPhQMUiSJEmS1JOcYHoa0WDXq4DrW6YdT/QvfRnR6NcXgFOAa4gq1KMdTEuSJEmSNGJyGiDbj8g6twbSpT8X0/crXv8FOIfoQkuSJEmSpAkjJ5hem6iy3cm9xXylW4GlcgslSZIkSdIgywmmnyCqeHeyGdF6dmkR7K9ZkiRJkjTB5ATT5xItXX8DWLhl2sLAgUSfzefUxq8H3DmcAkqSJEmSNGhyGiDbH9iK6GpqH+Le6bI171cAyxD9N5f9Ua8IrAP8oE9llSRJkiRpIOQE0/cBmwDfBHYBtq5NmwWcBHwemF6bf2ofyihJkiRJ0kDJCaYhMtEfAPYkss5LAo8BfyMCakmSJEmSJrzcYLo0i+YusiRJkiRJmtByGiCTJEmSJEnkZ6YnAW8FtgNWBhZsM08qpkuSJEmSNCHlBNMLAGcD2xBBdSoeS6k2XpIkSZKkCSunmvfngdcDhwLTiMD5G8BqwPuBe4DTmLsPakmSJEmSJpScYPpdwJ+Jfqb/U4x7nuhb+iTgdcCOwMf6WUBJkiRJkgZNTjC9BnB57XUC5q+9/ifwG+BDfSiXJEmSJEkDKyeYfg54qvb6CWD5lnnuIIJuSZIkSZImrJxg+h5gldrrW4FNW+Z5OfDIcAslSZIkSdIgywmmL2fO4PlM4GXAMURXWIcAbwAu7lfhJEmSJEkaRDldY50KrF4MdwDfIvqc/giwB9G69+3A/v0soCRJkiRJgyYnmL6wGEpPApsBbwdeTATYZxL3UkuSJEmSNGHlBNMrAc8CD9TGPQuc3tcSSZIkSZI04HLumb4L+OZIFUSSJEmSpPEiJ5h+FPjPSBVEkiRJkqTxIieYvgrYYKQKIkmSJEnSeJETTH8d2BLYfWSKIkmSJEnS+JDTANk2RGvexwF7A9cA04HUMl8i+pyWJEmSJGlCygmmD6o936QY2jGYliRJkiRNaDnB9LYjVgpJkiRJksaRnGD69yNWCkmSJEmSxpGcBsgkSZIkSRJ5menSusB7gHWARYHti/GrARsRjZQ92pfSSZIkSZI0gHKD6a8WQ5nRrrfkPT9wBvBJ4KjhF02SJEmSpMGUU817F+AAIvO8EXBYy/R/An8CdupLySRJkiRJGlA5wfQniYB5R+A6YGabef4GrNmHckmSJEmSNLBygumXAecBszrMcx8wdVglkiRJkiRpwOUE05OA57vMszydg21JkiRJksa9nGD6NmCzLu+1BVHVW5IkSZKkCSsnmP4psCFx73Q7nyfulz51uIWSJEmSJGmQ5XSN9b9Ei95HFo+TivGHAq8BNgWuAY7pZwElSZIkSRo0OcH0U8BWRB/S76bKan+e6G/6NOCjwLN9LJ8kSZIkSQMnJ5gGeBTYFfg0sAmwLDDj/7V33+GWnQW9x78xoRMgoSRIESSIggpcQEEQItIiXUNAigSFKIhcL1JFAaWDVwQUr0EEIaFI6MKlk4AoTZpEBSIBaSEESCgJJTD+8a7zzJmTfWZmzZyZc2bO5/M869l7r/ruOXmy9m+9rXpfdcbaFg0AAAA2prlheslXqjesZUEAAABgXzFnALITq1u3ta80AAAAbEpzwvSvV2+qPlc9pfqpPVIiAAAA2ODmhOlfrJ5fXaJ6ZPXx6r2NQccOWfuiAQAAwMY0J0y/pzquOry6Z/WWxrzTf1l9sTEP9R2qA9e4jAAAALChzAnTS77bmAbrqOoq1aOq06qjq9dWn1+z0gEAAMAGtCtherkzqmdUP1M9vDq/usLuFgoAAAA2sl2dGmvJEdVvVPeprtoY6fvTu1soAAAA2Mh2JUxfqrp7dd/qJo0A/a3q76fllDUrHQAAAGxAc8L07RoB+s7VRaZ1JzcC9EnVuWtast135cao4zesrltdrLp69ZkV+21Z5fjrVx/ZU4UDAABg3zUnTL9xev2v6kWNEP3fa16itXNEdUz1r9W7q9tsZ98XVn+zYt0n90yxAAAA2NfNCdN/1wid/7RnirLm3lUdNr2/f9sP019ozJkNAAAAOzRnNO/7t+Mg/SONZuAbwQ/XuwAAAADsn3Z3aqwlP1Y9odHs+1VrdM696YGN+bPPrd5R/eL6FgcAAICNbHemxjqwUQt9XHWrRjDfUr1tDcq1N51Q/WP1xcZDgYc3AvWtGwOsLXLctAAAALAJ7UqY/vFGk+9j29on+azGAF7Prz67JiXbe+6z7P27q9dWFtdmMwAAF/5JREFUH6+eWN1slWOOn5ZafTRwAAAA9lM728z7oOpu1Vsbo1w/qjq00aT7gEYAfWz7XpBe5JvVG6obrXdBAAAA2Jh2VDN9zeoBjfmlL9cIzh9qjOr9kupr7Z8DfR2QGmcAAABWsaMw/YlGqDyzemb1gurUPV2odXap6vbV+9a7IAAAAGxMO9Nnekv1xuqk9r0gffT0eoPp9ajqK9NySvWw6lrVO9s6ANnDqsOre+3VkgIAALDP2FGY/uPqN6v7NQYc+0SjifeLqy/tyYKtkVes+Pzc6fWU6sjG97nrtFy6+kb1nuq3qvfvnSICAACwr9nRAGRPqq7RqNF99fT+qY35pN9QHbNHS7f7DlhlOXLa/vrqpo3+4BeqLlvdKUEaAACA7djZ0bzf3GgyfZXqDxujdh9VvbTRDPx6bW1KDQAAAPu1nQ3TS85s1EwfUd260Y/6+9UNG7W5H65+dy0LCAAAABvN3DC93Nuru1dXrh7RmH/6utWz16BcAAAAsGHtTpheclb1Z9VPVbdsNP0GAACA/dbOTI01x8nTAgAAAPuttaiZBgAAgE1FmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZ9ucwfeXqOdW/VOdWW6qrLdjvotUzqi9V503733zvFBEAAIB90f4cpo+ojqm+Xr17O/s9v3pA9djqDo1Q/ebqenu6gAAAAOybDlrvAuxB76oOm97fv7rNgn2uW92z+s3qBdO6U6pTqz+t7rSHywgAAMA+aH+umf7hTuxzp+r71cuXrTu/ell12+oie6BcAAAA7OP25zC9M65Tnd7oU73cqdWFG03FAQAAYBv7czPvnXFoo0/1Sl9btn2R46YFAACATWizh+kDGqN8L1q/PcdPS6scDwAAwH5sszfz/lqLa58PWbYdAAAAtrHZw/Sp1dWri69Yf+3qe9Vpe71EAAAAbHibPUy/rrpQdbdl6w6q7l69pfruehQKAACAjW1/7zN99PR6g+n1qOor03JK9ZHGtFh/0QjVp1cPbNRW32uvlhQAAIB9xv4epl+x4vNzp9dTqiOn9/ernlQ9sbpM9dHqdtWH9kL5AAAA2Aft72F6R6NyV51XPXRaAAAAYIc2e59pAAAAmE2YBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmEaQAAAJhJmAYAAICZhGkAAACYSZgGAACAmYRpAAAAmEmYBgAAgJmE6Tqy2rJgOXsdywQAAMAGdtB6F2ADeUj1gWWfz1+vggAAALCxCdNb/Uf13vUuBAAAABufZt4AAAAwkzC91YnVD6qvVi+prrq+xQEAAGCj0sy7zqn+b3VK9Y3q+tUfVv8yvT9zwTHHTQsAAACbkDBdH56WJadU76re3xiU7I8WHHP8tNQY+RsAAIBNRDPvxT5UfbK60XoXBAAAgI1HmF7dAal1BgAAYAFherEbVj9RvW+9CwIAAMDGo8/0GMX79EbT7rMbg449uvpC9Zx1LBcAAAAblDBdH69+vfq96uLVGdWrqsdVZ61juQAAANighOl6yrQAAADATtFnGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYRoAAABmEqYBAABgJmEaAAAAZhKmAQAAYCZhGgAAAGYSpgEAAGAmYbquUp1UnVN9o3pVddV1LREAAAAb2mYP0xev3lH9ZHXf6j7VNat3VpdYx3IBAACwgR203gVYZw+ofry6VnXatO5j1aeq367+fJ3KBQAAwAa22Wum71S9t61Buur06j3VndelRAAAAGx4mz1MX6f6+IL1p1bX3stlAQAAYB+x2Zt5H1p9fcH6r1WHbOe446al6lvVJ9a4XOwFV7rRcTveiY3sctVZ610IgH3Jbxx1+/UuArvOfQ/Wx4+ttmGzh+mqLQvWHbCDY46fFmD9fLC64XoXAgD2Evc92GA2ezPvrzdqp1c6pMU11gAAALDpw/SpjX7TK127+ve9XBYAAAD2EZs9TL+uunFjeqwlV6tuOm0DNi5dLQDYTNz3YIM5YMuWRV2GN41LVB+tzqv+qNF/+gnVwdXPNgYXAwAAgG1s9prpb1e3rD5Zvbg6sTHP9C0TpAEAAFjFZq+ZBgAAgNk2e800sGfcpHpZ9fnqe9U3qg80ulFccR3LBQArHdvo6nfEGp3v5GlZcuR0/iNnnmfOcVuqx888/xzHTte42h68BuxzzDMNrLU/qJ5RvbMxFsGnq0tWv1Ad15gj86h1Kx0A7F0fajxknjtTzK4eB+wlwjSwln6pEaSfVf2fFdveWD2lutt2jr9QdX7j6TcA7A++Ub13Lx4H7CWaeQNr6ZHVWdPrIt+uXji9v1ojND+oenr1xeq71WWm7T9Xva0xGOC3q7dP65Y7uW2b0i35zLLr1NbmaTevXjOd86vVX1UXW7bfQY2m6P9VfWf6Lv9U3WyV7wPA/ufkxv/7b9WoHT63+nh1lwX73qP6z8b969Tqrgv2ObJtm2s/t/pyF6zUukj19eovVjmu6sDqidWXpnKdXF1nwTVf2LgXrnRy2943L1o9s/H9vlWdUb2++skFx650z+rD03HnVP9W/fZOHAf7DWEaWCsHVbeo3troJ72zHlP9RKMJ+F0bIfZnq1OqQxpB+DeqS03rrrsbZTyhOq361caPhwdUf71s+yMbNerPrm5b3a8R4g/djWsCsO+5RqOV1Z837hlfqk5q237Vt6peUn1q2mepZda1dnDuF1VXqG6zYv0dGg+UX7ydYx9f/WFjBpq7VG+pXrejL7MdF2lMCfvE6vbVAxsB+73V4ds57maNe+opUznuVj2vrQ/EYVPQzBtYK5dt3ID/e8G2lf+vOX/Z+y83QvTypt2PbTzl/+Xq7GndWxtP2R/X+NGyK95YPWx6/5bpmn9aPbkxRd5NpvXPWnbM63fxWgDsuy7XaM30qenzhxqB+pjGPaPqTxq10neufjit+49GEP3Eds793um892ncl5bcZzr+X1c57pDGA9/j2/Ze9oPqqTvxnRY5p7r/ss8HVm9u3Jt/vfHgeZEbN+7Pv79s3Vt2sQywz1IzDayVA1ZZf3j1/RXL8nD9mi7YR/rm1T+2NUjX6Dv2ukbt9676hxWfX9b4/+BS8/EPVL9SPanx1P3Cu3EtAPZdn2prkK46c1quOn0+sLpRo7b6h8v2e1+Lm1evdEIjhB88fT60MTjni7ZzzM9Ul2jxvWx3HNMo99mNh93fbgwcur0a9g80wv0Jba1Rh01HmAbWylmNJtpXXbD+RtPyvAXHfWnBukNXWX9G4+a9q768yucrTa9PbtR836l6d6Nf9QsaNRQAbB5fW7Duu40WWDXuCxfqgveVVlm30ouncx09fb7HdL4Tt3PM0tSSq93LdsUdq5c3asTvWf184379lbZ+10VOaTTtvkr16mn/tzW6acGmIUwDa+X86l3Vrdu2Rvf86oPT8sUFxy0auftrLe6rdXjb/sD5Totrj1fr43zYKp+/ML1+v3pa4+n/FRvN6X6tMVAZACw5q3HPWHlfaZV1K51evae69/T53o2BwT63nWOWHjKvdi9bbrX742VXfL5HYyyRYxtNzt9ffbSdGyvkpEZrsUMa3bWuWL0p+YJNxH/swFp6euNp/dN28zynNAZCOXjZuoMbT9BPWbbus43By5b/YLj5iuOWO2bF53s0mue9f8G+Z1R/23jS/tM7W3AANoUfNJo6H922v6d/vjFbxc54cWOk7iMbY3Zsr4l31ccaTbAX3ctW+mwjZC9vWXWNLth0++JtO45Jjb7bB+6gLMt9q9E1628agXplYIf9lgHIgLX09upRjYFQfrbxw+D0RlOxn2jc8L/djueRfkKjD9bbG8F8S2Ok7Ys3Bgxb8rLGKOB/15gG5OrVQxsDqizyK43RVt/S6Cf9uKmMn5y2v7bxRP5DjelJrl/drvEDAQCWe1zjfvKaxn3i8o1Byc7YyeP/oTF7xAnVedUrd7D/2Y0BwR5TfXO69o2q31qw7ysa99ITGyOSX656dKNGfbk3NUbjfmYjEN+gekjbjlmyyJ82wvo7G63Orjwd95FGk2/YFNRMA2vt6dUvNvobP7lRs3tSdd9Gv6xrNp7ob8/HGk/qv1H9fePp/bcazck+umy/d1a/06gJeH1jKqt7t/qPgHs3Qv2rqz9o9OF+0LLt72pMVfL8xg+MB07f5xE7KC8Am8/bqns1antfVT28Mbr19kbyXu7sxr3rSo1A/s2dOObxjXvrfRqDct6m0WprpdMateZL535E42HzJ1fs97zGoJt3n8py++l8qz2UXvK+Rg38MxuzbTytra3KYNM4YMuWHVUQAezzjm0MJHbNxg8MAADYLWqmAQAAYCZhGgAAAGbSzBsAAABmUjMNAAAAMwnTAAAAMJMwDQAAADMJ0wAAADCTMA3A/mjLzOXYdSnlYic1ynS59S7IGrlk4/v8426c4+Dq96u3VmdU36u+VX2iOqE6ujpo94oJAPO48QCwP/qTBet+v7p09azq7BXbPrLHS8Su+qXqpdVh1Rert1Wfb/yGuVp12+pe1cnTvgCwVwjTAOyPHr9g3bGNMP0X1Wf2YlnYdTeo3lBdqHpo9Zzq/BX7XKi6W3WfvVs0ADY7zbwBYKsPNpoPX6x6YnVao0nxXy7b54BGMH9Xo4b7O9XHq0c2gt1KxzRqVk+rzp3O//7qd6ZzLVlqDv1r0+evtLUZ+scXlPGiUxlPr86rTm1roDyg+t/Vv0/l+2z16BXXW+5m1WuqL0/f97PTd77Cgn2Xrn/hxkOLT1ffnY55Qts+qH9w9c3p/e3btmn9w1Ypy3LPbfwtHls9swsG6arvVy+p7rhi/R2WXedm1Zurr3fBJvQ3qV5bnTV9j083HrhcfsG1lr77Ig+ezn30ivVnNf5+l62Or77U+Jv8W/Xbq5wLgH2AmmkA2NaPNPr3XqsRwL7aCIo1wuhLq7s3ardf0QiLN6ueWt28Eep+uOx8f94Icf/caKZ8merW1V9X160eOO33vUbz9GOqn6qe0QjfVWeuKOMB1aun/d7UCHHHVC9qBOvbVHdp1Oq+tbpr9eTqG9VfrTjXg6tnN0Li66Yy/mT1oEYg/flGyF55/VdV15uu/+3pe//R9P1+b9rv/dVTGkH+U43Qu+Sf277rVj9XndMItzuyKGhX3XIqwzuqv60OX7bvMdWJ1Q8af8vPVzduPIi4c3XTxr/H7rpYoxn6QY0+3hdv1Kb/v+rHGw9iANjHHLBly5b1LgMA7A2fqX6sunqrN/P+YKNp8QcagXRl3+oHN5oan1j9VqMms0a4fHqjFvT+1fOXHXON6r9WnOfA6uWNWuifbtQqLzlpWn/5Rq3mamV8d/Urba0pvU71sUb4PLO6RVtD8BWmMpxdXWXZua43ne8/G6FzeWi/YyNcn9C2TaiXrv/P0/XPmdZfqlETfvlG/+alf7tLNh44vKERznfWgxrB/03VUTOOW3KH6vXT+3s3/mbLHdr47+Ci1S80vteSJzQeDLy6+tVl6z/YeNBwyQXXW/pv426Nv+GSsxq10m9t1M5/f1p/WPWv1Y9WN5reA7AP0cwbAC7o0V0wSNeosTy3Oq6tQbpGzfBjpm33WnHMyiBdoyb02dP72+5iGR/etk2OT20EskMazaKX1yaf2QilV24EuyW/2wj2v9sFa79f3xjs6+jqIguu/wdtDdI1ar1f3mj+fb15X2Whw6fXL6yy/RGNZubLlx9dsN8/dcEgXSP0Hly9sG2DdNWTGqOG37m1G1X9kW0N0jX+Pk9ta7cBAPYxmnkDwAW9f8G6y1VHNMLdI1Y57txG0+vlDpv2v11j9OmLr9h+pV0o3w+rDy9Yv9QkeVEt51IovXKj6XqN/sI1mp0vGgn7Mo2a26s1pqHa0fU/N70esqjQMy31716tCd0j2vbBQI3m+SubZS/6W1b9r+n1HQu2fadR8/6rjebmb99uSXfsmy3+9zp5er3+bp4fgHUgTAPAts5t66BZyy0FtytVj9vO8ctri6/QCLZXqv6lekGjxvv8adsDW1zruyPnNfpYr7TUF/ic7WxbPkja0nd6zA6ut7JZ83ltWzO/8hoH7uB8O+NL0+tqDxuW1xi/plGLvMgZq6y/9IrrrHb9y6yyfY6Vfc6XLJXt0qtsB2ADE6YBYFur1YQuBdR3NwYa2xkPaoTBh1d/tmLbrds6+Nh6OadRc37hVh/Aa728Z3r9hcYAXuft4nl29Pc8fJXtV1yxX40a+dV+O20vdB+2yvqlay96+AHABqfPNADsnDMaA1Zdv8UDUC1yxPT6ygXbbrHKMT+YXteidndH3ttoTn3TPXiNXf0+H20MBHfp6iFrWqJhqdn1kQu2XaTRBH5L9ZFl678+bVs0bdYNt3Otg1vclHvp2ouagAOwwQnTALDzntkI0s9rBKSVLtfoY7vkM9PrkSv2u0n10FWusdSf+aq7VMJ5ntUIu3/ZGOV8pYu2+0H7vGnZle/zoOnYJzYGf1tUK3xgO/9wY7l/aDTJv1/b/s1qDEB3xbbOP71kqf/1A1bsf8fqTju43tPaton9YdWjGoH9hTtbaAA2Ds28AWDnPacxcNV9q19uTHf0340QfY3GfNPPbtSq1pgi6yHV8Y1ppD7dmL/6Do3pk+6+4BpvbzT/flGjL/C3GyNtH78Hvs+HG4H1uY3psf5/Yz7oizXC782r09p+revOeHvjO7+y+rdGk/K3NWrGt+eD03Evbcw1/fDqnY35oC/UCLy3bDSX/mSr949e5GuNUdlf3OjP/orGIG03bgzG9rnGdFfL/U3j7/mkxhzYn6quXd2q8be66yrX+nRjpPGPNQZJu1hjjuvLN6ZUMy0WwD5ImAaAnbelMY3R6xpB7LaN+ZW/Wn22ekojnC05vRFIn9IIaEc15mK+XyPILgrTr2wMCHZso/b6wo1pr/ZEmG467wena92iEfq/2RgV+8RGkN1dv9MIw0dWd2m0jPtOOw7TNUbbvmZjXu87NOb/PqQxANsXG+H6VY0wO7ff90sbD0MeNZ374Omcz2nUhq+cLuzz03d4WiNA/3L1vsa/2w1bPUyfN+3z1Mac14c2gvgfNwI6APugA7ZsWW1cDgAAdtNZjRrzn17vggCwtvSZBgAAgJmEaQAAAJhJmAYAAICZ9JkGAACAmdRMAwAAwEzCNAAAAMwkTAMAAMBMwjQAAADMJEwDAADATP8DpFooIo513fIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Treatment\")[\"Individual Answer Stage 1\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average First Individual Response by Treatment Group\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"Treatment Group\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Response\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.set_xticklabels([\"Groups\", \"Individuals\"])\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "\n",
    "plt.savefig('avg_individual_response_round1.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the difference in means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[df[\"Treatment\"] == \"I\"][\"Individual Answer Stage 1\"].to_list()\n",
    "x = df[df[\"Treatment\"] == \"G\"][\"Individual Answer Stage 1\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-stat: -1.1285209867807153\n",
      "p-value: 0.2625590736752503\n"
     ]
    }
   ],
   "source": [
    "t, p = ttest_ind(x, y)\n",
    "\n",
    "print(f\"t-stat: {t}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the normality approximation of the difference in means is arguable ($N_{\\text{Individuals}} = 20 < 30$), we can also do a Mann-Whitney U test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Treatment\"] == \"G\"][\"Individual Answer Stage 1\"].tolist()\n",
    "y = df[df[\"Treatment\"] == \"I\"][\"Individual Answer Stage 1\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 471.5\n",
      "p-value: 0.07738168081380284\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of Experience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to understand if the two treatments are similar in terms of experience reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_nas = df.dropna(subset=[\"Experience\"])\n",
    "experiences = not_nas[\"Experience\"].unique()\n",
    "treatments = not_nas[\"Treatment\"].unique()\n",
    "experience_proportions = {\n",
    "    \"I\": {},\n",
    "    \"G\": {}\n",
    "}\n",
    "experience_totals = {\n",
    "    \"I\": {},\n",
    "    \"G\": {}\n",
    "}\n",
    "\n",
    "for experience in experiences:\n",
    "    for treatment in treatments:\n",
    "        total = not_nas[(not_nas[\"Treatment\"] == treatment) & (not_nas[\"Experience\"] == experience)][\"ID\"].count()\n",
    "        proportion = total / not_nas[(not_nas[\"Treatment\"] == treatment)][\"Experience\"].count()\n",
    "        experience_proportions[treatment][experience] = proportion\n",
    "        experience_totals[treatment][experience] = total\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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KWO/7PXOK9wyaZj94dOrV/bNT97v+Y9YpqdvQNL6W+n3dLMmDh5Qf271unXB+vdtq/iSLnyh3RepV9Z9MWb+lnJ96dWnHwPjvp25Lyejjz6DeueAjSa4ZKLs2bfuA+HzqFfdhV2Vf0r0+KfWqV7+TU6/u7ZvFx4Nk4faiQW9LvVJ3y9TbApbrlVl8Ve/HWWj+fXjq+eySvvKzsnA8u+/AvJa7jD3PS/Llvv+vzcITk+43cgmWdkFqK4ebpe6HrB1xXhviPHFeP3HeYvuntuBKFo5Jg16SGkP1Dy8bMe247+RZqS1u3pqdW8y9NbUl4F5ZOH7MyumpLa37vS+1pfLG1FbJrfxhFm7J7fcrqS3nT05tddffeulHqd0f/CB1/+m/xe3c1H1r8LxwaRbuHpg0hhzm0tT109969u2pMfz+qdvkPwy8p9cif8vA+OUuY8//pR4f++PbL2TheDO4702jdzvqnaZ50+6SlOot5+APlWE+072+MrXZ2/VmWI93ruC9X8rioLrnY6mB8X6pt2KslU2pzQh3ZOFk0u+q1JNwMvre2GGPQ70k9cR6nUwWbCb1hJvu84Y1m//n1APOoZn9D4kvpjblHDV8d8h7PpHadHi/1CaR9+/mM+z2qX6jmgb31v+WvnEP6l7fn+GJgB1ZeDT9PYaUfz7TPwmjl9T49wzf936c2hR5z+zct0tSD7TD6tprCnrLvnEHpB6cr07yLxPUbY/UYKpkcRP+fqd0r8PWxyg3614nafI+yjT7QW9bf2sWB+U9w/bFpWztXo8dGH9Y6vd0URbf8jFK/zFh2G1Vl6T2jTFrG1KbIP9xajPsf05dpt4Pn9tOOJ/eueC5qX3V3Gh2VZzauzI8IXVw6g+RizO6D7Nx2/HNUwOWv0rtsHVrNxzalU+6roYZto30EpOXpd6qMaj3FKP+fXuly5gM36fOTQ3ibpDhfZ5MqndMv/nYqVht4rzVJ86rxHkLxHnTe1jqbaX9wyNGTDvuO+ntA1tHlL+pe90ybQWXMM02uZquzvDjSbKwXQ4m63q+n4V+3gYTJ3ukLsOfZHEM2UtwryQu+mSGP2ChFxsNJk6Tmri9IjvHKitZxqTG38OOncP2vWktKy7aHfqUShY6Ght2ohh0YmpfE49P/SFwbepVl4+n/vAbFkhPalg2d1LnjinbnnrS3TRmmtXWO+l/K6P7azl7YNpB3xwx/oepV1gmvULYm/+odXZF6j2vB3bDBRPOdxLLfVTwn6feh35Eav0em6X7vRm1fNu71/7t4dbd6zOyuIPqYYZ1hLmcbbf3mX/ZDdN+5rjtIVm8PRzc955J+ty4ceoBPlncWmPSuo1yw+51JU/1mWY/6H3HS20L03hrkr9NvXf+xlkIvJ7cvb45wxNgg3p163VKPav6jXPz1B+F41r43GBMWb9TUlvgPDsLHTd+LTWo//fU+/hbGbX/9faxm2fpPswGt+PfTz3uXGfMeyZdV8MMdjiaLPRrMKysv7x/G1/JMib1B8+wFmGl+7zrdJ+33D5levv6WiYtEee1IM6rxHk7f6Y4r/pu6rllQzfPrw+Zpr8l3ZYsPHhgmHHfyVL7wFL74nJNs02upvMy+iJEb7t8QzeM0//db0rdv8cl/2cdFyVLx0Y/Tt0X+mOV5S5jzzT73rSWFRftDkmpDVnIEA67AjVoR2oz0z9P7Zz3nt3wO93wpoy+RWAps75VZdA0HSvPupVcr7nquDpsGFOWTHaFs1VdWjs8CyeqfVKvBv3vCufZv/wbu9fTU4Pvcc4YMm45227vM0/J0smHYSfeWW0Pw/Tqdm1m0xllz/dTD/43SG1RtByzXO7ldLb+g9Qfao9L/dH2d6nHiyd25VtnUrOVGXX8+sfUhNR/p/5o+GLqd3J1ahP+K0e8b5Tnpt5e8fDU1lf3TPLr3fCfqYm7wVv7VsOo/a+3HV+a0VcMe/qDnSNTOyO9MvUWgA+l/mj7Seo285rU891KjpPjtuNptvHlLuNyPms5egHqsE58aUOcN5w4b9ciztvZvMV5V6ceg34+9Ym5p2ZlJvlORu0Dy93+V3rcWO5DfqY1bt30vvuPZnSip6c/YX1iakLq5NQO8L+SGhNfnXp73aVZvbhokvJ+y13G5XzWtJYVF+0OSamHpF59uSaLn8awlK9k4aC+R2ozuX9N8tTU+79X47aTcTZPUHZh37hey4RRtyUcPGL8cvV2iFumXnke9uPvkO51llesxtXl1iPK90l9+k6Lukzieqn38O6T2vT7mNR+B05PfdrWKJtTf3QPG58s3h7O614/lnovfgu9z3x76qOiV1Mv2Dko9d7ppU7k3+mm2Te1L4FZPXnnktRgZdJbEFaqt/1uHlF+yIjxS9mampQ6NjUp9UupV9s+l+HB7Li63TI1ITSstdTmEe9dzvHreqnH6WtTf2h+f6D80J3eMZlzU1uO/W33/71Sn0TywNTzwXL7j5mF3j52WYY/un2Ux3Svr8hCP0/9lruuVsNyl7GV3r6+1JV4Vo84bzhxXpu6TEKct3LrJc57X2pS6olZiBtWwwVJfjp1Hzh7SPmofXGlx43NS4y/cER5S73t8sTU/W0SN01tuXZFagw5uH3tSnFRsrxlbGVZcdG89yn1U0n+pvv7hCw/aNyReoX2Pd3/d+gr6+3cq53gu0PqVZZBR6b+WPxRFnce2TsI3W7Iezak3mc9zHKX5/zUE2t/i4p+e6W2ukimCxqXo3eP+OMyfDmenLoOzsquEay8PvV7enfqj66XpTYP/rfUH/OjPGGJ8dv6xvXuU35E2iWje5/5mLFTzcZFqf1x7J3awedSrkntwDpZWaeFgz7fvd5+hvMcp7etH52Fqyb9Rm0jS/lI6j5959TjTu/Wva1TzOO81ITOHl39Bt00Ndk1zLjj174Z3mfBDbvPuiw7J6SS5a+LQadmYT3cYcx0LXw99bi7OfWq7KT2717PG1K2KfURxcO0Ot/1W+4yrsSky7kptY+Yi7NrnEt2R+I8cZ44rxLnLbZWcd5rUvfVX0h9oMtq6e0Do9bFU7rXbQPjxx03Dk9N+o0zzTa5VpazXfY64u8lMweNiyHXIjZque8l0y1jb9/4/NipBsxrUmqP1I7kPpua2fxaJr9q8KTUH2KDbpyFjvD6m6D2du5Ds7ob44bUk9oN+8bdNMmru7+Pz+Kd6GOpQdaDUq/s92xM8mdJ7jbic1ayPH/dvb40iw92G1P7Zjk4dd2N6nBwVt6e+mPrkNTm+f3b+e2TvLj7e/DpEWvh2NRt7pupV2eT+mSOU1J/gL1yzHsf1Q39np76g/1HSf6pb/znU4OhQ1Ov1g275/sWqU9Lm9V2/O7UAPrI1Fug9h8yza2zdN8Hk+p9r3+Z4U+Ou0sWL/dLUpvkvjo1aTLYJHeP1M5IRwX2w/T6Bpim08yVeEdq/x6Hpt6u1r8M90p9ktNy7MhCR6LPSg1yr0ptITSN13SvL8viK9rXSb2qet0R7/to93pM6pOKevZNvXf+VkPec3FqU+EbZeGHUc9RWXjKzqQemZqcGTxP7pvkAd3fK+k/ZlZe2L2+LcOTdfukPqWlv4+EXkeWT8nip7LcKDXhNqrj59754bBl1HMllrOMKzHpcvb2820z+lwmJ84T54nzxHmDdoU475LU77kkeW2Sl2d43zrXzUJn5cvxmtTE2+NS45V+j0nts+zqLMRhPb346jlZ3D/SQann/6VuT7trdu6g/8GpieprU5d5rZ2U+gS4h6RetLjhkGluk/pkw55eX2WbUs8t/R6Z8fH0WsRGy1nGlZhmGXv7xrj+0nYyD7fvPS8LTfr3ST2B3zkLB4B3p34hk97X+CupV9suSH004vdTA5V7pwbqn0jtb6XnG6mPqL5Tagb/9NQmzV/P0p3+TeO9qRnss1MD4D1TH5t9g9Sg7IUD038z9cfbM1Lvjf1Easdjd07NBr8mC49I77eS5Xl9ar8Mj0ttbvyx1PV+t9ST0vdSD5TT9usyrV4Hkh9M7aT4kanraP/UE/neqc0dV+O2m0dkfBP8z2fhBHFY6g/za1JPlr1t9NrUH9VfSA0ePpb6/Q96TWrgd1pqi5TbpX5vO1L7vPnWwPRP7ubzyNQg9oup3/cNUk9Gh6WeoP8+s+knZ0fq+vhA6j74+O4zz0/tlPZWqU+xuDizafb9ztTHBb849XH3X0691Wy/1MTGoan7TK/Z/+dSA8U3pR7cX5F6gL8s9aR0266er8xkT5tL6rJenRrkbMjq31t/eWog8B+pTwp5dOr+e4vUhMqrs/TTfUbZmvoEu1/v/n9HJutEuN/fpd7m9qDU7+Lk1ED6XqnH63/J8Kt8p6a2Wvjl1H3mE6nb5F1St6t/zsJVwJ5rU3+IvSr1iUzPTO3j4qdTj0Ev75ZnUkemPkr526nr9NupJ/1fTD2WfC07P7p3Lbwl9fj64tRjxZmp/ZTsSN2Ob5e6DxyThVYW/5B6BfeeqS0fPpn6o3JLav8Jb87wlhCnpG4D90k97nwt9Xs5OcOfsDgry1nGlXhX6rb2ztQr7T/oxv9eFt8C0ktOviesJnGeOG+QOE+ctyvHeW9PvZD3z0men+QPU/ezXr1ukeTnUmOKK1IfsDCtL6bGKK9NXS+fTj12HJq6P+5IjYMG+9l7XWpi866p+/ynUo+ld0t9MuknM/5hMa9JjbOOTV33t0o9JiQ10fWFZSzLrF2d+lCBD6TuX09JPdZdkHr+ODh1PZ2dhTjuitTE+nGp55T/Tk18/0zqMXVcDDlpzDBLy1nGlZg0/rtl6nHmf7NwAXQypZT1Omwvi+0opVxWSjmvlPKRUsrLSymHLzGPnv5x9y6l/G0p5TOllItKKVeWUi4opXy8lPLUUsp1hsxncynl37rpr+nmua2vfFs3bsuYuhzXTXPcwPhju/FbSyk3LaW8savPlaWUc0opf1ZKud6Iee5RSnlOKeVr3fTfLqW8rZRy24H5Trs8W7txxw5574ZSyjHd+vp+97nnllJeX0o5aIrvYdh3vXnMNMOGg0spb+g+/8quPqeUUp7Q1XPYcpfu86bdHnvrZCnv7qbft5Ty5W7cc0bM86hSt+vvllJuNWJ9/Gop5bRSyo9KKT8spfxXKeU+Y+q5sdTv58Olbg9Xl1IuKaV8oZTyulLKAwemH7edTLre9imlPKPUdf/dUspVpZRvlVI+V0r5y1LKLw5MP2pfmLRO9yp1O7+w+6xvl7o/v6iUsv+Q6X+6lPLqUspXSyk/7oazu3X0rFLKLccs+7DhpK5+Rw4p2zJmXfUsZz+4QynlPaWU75VSLi/1+/zNJeY7yX51at/7HzJmut72f+yQsr1KKc8tpZxZ6n54SbeODinjv+t9Sil/Xur+e1Wp3+c/lVJuscT7HlVK+VQp5Qel7hOfLHWfn3Zd3LGU8opuHfSOuReXUj5dSvm9Usp+Y9bHUkOv/qO24d7wjm66R08wz7t28zu3lHJFt+xfL6W8s/tebjgw/S1LKf/cLfsV3evrSz3PvKr73GeP+JwPlbov7+ime21f+Xe6cTcZ8t7Du7KvjFiGZw6Z33KX8frdvH40Zp2NqutepZSXlFL+t9TvvQyZbp9S97eLSyl7j/kMw/KH7WUxcd7OgzhPnDc4iPN2nr5lnNc/3KDUeOE/u/VxZakx2jdLKf9RSvndUsrNl7H8/cM9Sz0HXlzqd31xKeXfSyn3GPOeg0opb+7W25WlnuteVOq5bFv32VsG3tM//gGllJNLjbN+XEr571LKI6Zch8OGns1jplkqjugfrtut/1NLPV9fVeqx9TOlxnd3HfKex3flPyz1ePLxUmOwcTHFUjHDUrHNUrHeuLhq2mUcF98tVdel4r+UUp7Xjf/dEfMfOWwopVUn+cCMbE/NgB+SpZ92QntHpF55OjGT9XsArD+PT23B9eIs7/HwAKNsjzhvV7Y7xnnbUluQ3zduWWe4Dakt2Q9IPX79YPzki81rn1IAa+W01Ob2j0u9nxuYL3skeUFqx7u7Qp81ALQjzoOd/Wrq7Y4vzZQJqURSCmA1PDv1fu+XrnVFgJl7Qmpnyn+c1esvAoBdlzgPFmxMbTl+Vmp/slObh47OAXY138joJ8sB69uJ3QDA7kmcBwuuzeKnZTd+QYgAACAASURBVE9Nn1IAAAAANOf2PQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoDlJKQAAAACak5QCAAAAoLn1mpR6U5JLknxlRPmGJK9JclaSLyW5c6N6AQDMK/EXADBT6zUptTXJUWPKH5TkNt3w9CRvaFAnAIB5tjXiLwBghtZrUurjSb47pvzhSf4lSUlyWpIbJblFg3oBAMwr8RcAMFPrNSm1lAOTnNf3//ndOAAAVof4CwCYyp5rXYFVsmHIuDJk3NO7IUl+YfWqAwDsIr6T5KZrXYk5NWn8lYjBAGB3MjL+mtek1PlJDur7f1OSC4dMd3w3JKODJgBgfnxjrSswxyaNvxIxGADsTkbGX/N6+957kzwp9YrdEUl+kORba1ojAID5Jv4CAKayXltKnZRkS5KbpF6Ve1GSvbqyv0/ygSQPTn0k8eVJntK+igAAc0X8BQDM1IZStJjuWBEAMP9OT3KXta4Ei4jBAGC+jYy/5vX2PQAAAAB2YZJSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADS3XpNSRyX5epKzkjxvSPmtknwsyf8k+VKSB7erGgDA3BKDAQAzsx6TUhuTvC7Jg5LcPsnjutd+f5LkbUnulOToJK9vWUEAgDkkBgMAZmo9JqXulnp17pwkVyV5a5KHD0xTktyg+/uGSS5sVjsAgPkkBgMAZmrPta7AMhyY5Ly+/89PcveBaY5L8p9JfifJ9ZI8YMS8nt4NAACMJwYDAGZqPbaU2jBkXBn4/3FJtibZlNqXwYkZvqzHJ7lLNwAAMJoYDACYqfWYlDo/yUF9/2/Kzk3Dn5ban0GSfCrJPklusvpVAwCYW2IwAGCm1mNS6rNJbpPkkCR7p3ai+d6Bab6Z5P7d34elBkTfblVBAIA5JAYDAGZqPSalrknyzCQfTnJm6tW4M5K8JMnDumn+MMmvJ/likpOSHJudm5cDADA5MRgAMFMbShEndKwIAJh/p0c/RrsaMRgAzLeR8dd6bCkFAAAAwDonKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc5JSAAAAADQnKQUAAABAc3uudQUAAGC1nXXaiWtdBdjlHXrEMWtdBWA3o6UUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM2t16TUUUm+nuSsJM8bMc1jk3w1yRlJ/rVRvQAA5pkYDACYmT3XugLLsDHJ65L8UpLzk3w2yXtTg5+e2yR5fpJ7Jvlekps1riMAwLwRgwEAM7UeW0rdLfXq3DlJrkry1iQPH5jm11ODpu91/1/SrHYAAPNJDAYAzNR6TEodmOS8vv/P78b1u203/HeS01Kbmg/z9CSf6wYAAEYTgwEAM7Ueb9/bMGRcGfh/z9Tm41uSbEryiSSHJ/n+wHTHd8OweQAAsEAMBgDM1HpsKXV+koP6/t+U5MIh07wnydVJzk3tkPM2TWoHADCfxGAAwEytx6TUZ1ODm0OS7J3k6NRONvu9O8l9u79vktqM/JxWFQQAmENiMABgptZjUuqaJM9M8uEkZyZ5W+ojh1+S5GHdNB9Ocmnq02A+luSPuv8BAFgeMRgAMFMbSnEbf8eKAID5d3qSu6x1JVikSQx21mkntvgYWNcOPeKYta4CMJ9Gxl/rsaUUAAAAAOvccp++d0CSWyXZZ0jZx5dfHQAAxhCDAQBzY9qk1IFJ3pzkPkPKNqQ2v9640koBALCIGAwAmDvTJqXekOTwJM9J8uUkV868RgAADBKDAQBzZ9qk1L2TPCuJniIBANoRgwEAc2fajs5/kuSS1agIAAAjicEAgLkzbVLqjUk8JxQAoC0xGAAwd6a9fe+C1IDo5CQfSPLdIdO8aaWVAgBgETEYADB3NpRSppl+xxLl6/nJL1OtCABgXTo9yV3WuhLLIAZbobNO0x0XLOXQIzTIBFbFyPhr2pZSh6y8LgAATEkMBgDMnWmTUt9YlVoAADCOGAwAmDvTJqV6Dk9yZJL9k1ya5ONJvjKrSgEAMJQYDACYG9MmpfZMsjXJ45Js6BtfkvxrkmOTXDuLigEA8P+IwQCAubPHlNO/KMljk7wwtW+DfbvXFyb51e4VAIDZEoMBAHNn2pZST0zy0iR/1jfuG93/G5M8JTVoAgBgdsRgAMDcmbal1C2TfGpE2Se7cgAAZksMBgDMnWlbSl2Y5J5JPjKk7Be7csY467QT17oKsC4cesQxa10FgF2JGAwAmDvTJqXekuQFSXZ0f38ryQFJju7Gv3KmtQMAIBGDAQBzaNqk1HFJbp3kxd3fPRuSnNSNBwBgto6LGAwAmDPTJqWuSfL41E4175Nk/yTfTXJKkq/OtmoAAHTEYADA3Jk2KdVzRjcAANCOGAwAmBuTJKVuldpvwdXd30v55opqBABAIgYDAObcJEmpc5PcI8lnkmxPUpaYfuMK6wQAgBgMAJhzkySlnprk7L6/lwqIAABYOTEYADDXJklKndD399ZVqgcAAIuJwQCAubbHlNOfnOR2I8pu25UDADBbYjAAYO5Mm5TakuQGI8r2S3LkimoDAMAwWyIGAwDmzLRJqWR0fwY/neRHK6gLAACjicEAgLkySZ9ST+mGpAZDxye5bGCafZMcnuSjs6saAMBuTQwGAMy1SVpK7UhybTdsGPi/N1ya5A1JnrY61QQA2O2IwQCAuTbp0/d6T3/5WJLfSvK1VasRAACJGAwAmHPT9Cm1d5IbJrnVKtUFAICdicEAgLk0TVLqqiSHJLlmleoCAMDOxGAAwFya9ul7/5XkgatREQAARhKDAQBzZ5I+pfr9XZI3d+97d5JvZefHE58zg3oBALBADAYAzJ1pk1KndK9/kOT3R0yzcfnVAQBgCDEYADB3pk1KPWVVagEAwDhiMABg7kyblDph6UkAAJgxMRgAMHemTUr1bEhy+yT7J7k0yZnZuV8DAABmSwwGAMyNaZ++lyS/ltq55peSbEvy5SQXJnna7KoFAMAAMRgAMFembSn1hCTHJ/lo6hNgLkpyQN/4y5OcNMsKAgAgBgMA5s+0SannJHlLkmMGxp+Q5MQkz42ACABg1sRgAMDcmfb2vZ9JvTo3zJu7cgAAZksMBgDMnWmTUpcl2TSibFNXDgDAbInBAIC5M21S6oNJXp7k3gPj75HkZV05AACzJQYDAObOcvqUOiL1iS8XpD4B5oDUK3RndeUAAMyWGAwAmDvTJqUuSnLHJE9NvVK3f5LtSU5JsjX1yS8AAMyWGAwAmDvTJqWSGvS8thsAAGhDDAYAzJXlJKWS5NAkd0tyYJLzk3w2tek4AACrRwwGAMyNaZNS+yR5fZJjkmzsG39tkhOSPCPJlbOpGgAAHTEYADB3pn363quSPCHJi1Kv1O3XvR6XGiT95SwrBwBAEjEYADCHpm0pdXSSF6c+krjnnCR/1v39+0meNYN6AQCwQAwGAMydaVtKXSfJZ0aUfTrJ3iurDgAAQ4jBAIC5M21S6iNJHjii7IFJTl5ZdQAAGEIMBgDMnWlv3/vrJCcmuV6Stye5OMnNkzw2yYOTPDHJrfumP2cGdQQA2N2JwQCAuTNtUuqU7vW3kvxm3/gNA+U9GwMAwEqJwQCAuTNtUuopq1ILAADGEYMBAHNn2qTUCatSCwAAxhGDAQBzZ9qkVM+GJLdPsn+SS5OcmaTMqlIAAAwlBgMA5sa0T99Lkl9L8q0kX0qyLcmXk1yY5GmzqxYAAAPEYADAXJm2pdQTkhyf5KNJ3pzkoiQH9I2/PMlJs6wgwHr2lve/b62rAOvCE375oWtdhV2dGAwAmDvTJqWek+QtSY4ZGH9C6mOKnxsBEQDArInBAIC5M+3tez+TenVumDd35QAAzJYYDACYO9MmpS5LsmlE2aauHACA2RKDAQBzZ9qk1AeTvDzJvQfG3yPJy7pyAABmSwwGAMyd5fQpdUTqE18uSH0CzAGpV+jO6soBAJgtMRgAMHemTUpdlOSOSZ6aeqVu/yTbk5ySZGvqk18AAJgtMRgAMHemSUrtleTBSb6U5LXdAADA6hKDAQBzaZo+pa5O8rYkm1enKgAADCEGAwDm0rQdnZ+T5GarUREAAEYSgwEAc2fapNRfJHlBkpuuQl0AABhODAYAzJ1pOzq/X2rHmucmOS31yS+lr7wkefJsqgYAQEcMBgDMnWmTUvdO7dfg20l+uhv6lZ3eAQDASonBAIC5M21S6i5JfpTkilWoCwAAw4nBAIC5M0mfUhuTHJfk+0kuTvLDJP+e5EarVy0AgN2eGAwAmGuTtJT6zSQvTLItyWeT3DrJI1MDo6esWs0AAHZvYjAAYK5NkpT69SRvTPIbfeN+I8lru9erVqFeAAC7OzEYADDXJrl979ZJ3j4w7t9Sm5QfPPMaAQCQiMEAgDk3SVLq+qnNxPtd1r3uN9vqTOyoJF9PclaS542Z7tGpT6O5S4tKAQDMkBgMAJhrkz5978DUq3U9G/vGf39g2nNWWqklbEzyuiS/lOT81D4W3pvkqwPT7ZfkWUk+vcr1AQBYLWIwAGBuTZqUeseI8e8eMm7jkHGzdLfUq3O9wOutSR6enQOilyb5iyTPXuX6AACsFjEYADC3JklK7WpPdzkwyXl9/5+f5O4D09wpyUFJ3p/xAdHTuwEAYFcjBgMA5tokSakTVr0W09kwZFzp+3uPJH+T5NgJ5nV8NwzOAwBgrYnBAIC5NklH57ua81OvwPVsSnJh3//7JTk8ybYk25MckdrfgY42AQCWTwwGAMzUekxKfTbJbZIckmTvJEenBjw9P0hykySbu+G0JA9L8rmWlQQAmDNiMABgptZjUuqaJM9M8uEkZyZ5W5IzkrwkNfABAGD2xGAAwExN+vS9Xc0HuqHfC0dMu2V1qwIAsNsQgwEAM7MeW0oBAAAAsM5JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQnKQUAAAAAM1JSgEAAADQ3J5rXQEAAACYpbe8/31rXQVYF57wyw9d08/XUgoAAACA5iSlAAAAAGhOUgoAAACA5iSlAAAAAGhOUgoAAACA5iSlAAAAAGhOUgoAAACA5iSlAAAAAGhOUgoAAACA5iSlAAAAAGhOUgoAAACA5iSlAAAAAGhOUgoAAACA5tZrUuqoJF9PclaS5w0p/4MkX03ypSQfTXJwu6oBAMwtMRgAMDPrMSm1Mcnrkjwoye2TPK577fc/Se6S5OeTvCPJX7SsIADAHBKDAQAztR6TUndLvTp3TpKrkrw1ycMHpvlYksu7v09LsqlZ7QAA5pMYDACYqfWYlDowyXl9/5/fjRvlaUk+OKLs6Uk+1w0AAIwmBgMAZmrPta7AMmwYMq6MmPaJqU3IjxxRfnw3jJsHAABiMABgxtZjUur8JAf1/b8pyYVDpntAkhekBkNXNqgXAMA8E4MBADO1Hm/f+2yS2yQ5JMneSY5O8t6Bae6U5B+SPCzJJU1rBwAwn8RgAMBMrcek1DVJnpnkw0nOTPK2JGckeUlqAJQkf5nk+knenuQL2TlgAgBgOmIwAGCm1uPte0nygW7o98K+vx/QsC4AALsLMRgAMDPrsaUUAAAAAOucpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBQAAAAAzUlKAQAAANCcpBTA/9/e/cfsXtd1HH8eDhPJIS0oVyrGgtxKxUjzx1aubCZTh2g1kpyIRS4dS1fT5hzYD/o1TSjSfoDir4m2WhQmDoqSyJSRU2wjiZ0ETFPRCjCIuPvjc52d61zc9+Gc031/L+5zPR7btfv6fq7vfd2f73aua6/z/ny+nw8AAACTU5QCAAAAYHKKUgAAAABMTlEKAAAAgMkpSgEAAAAwOUUpAAAAACanKAUAAADA5BSlAAAAAJicohQAAAAAk1OUAgAAAGByilIAAAAATE5RCgAAAIDJKUoBAAAAMDlFKQAAAAAmpygFAAAAwOQUpQAAAACYnKIUAAAAAJNTlAIAAABgcopSAAAAAExOUQoAAACAySlKAQAAADC57VqUem51U3Vz9fp1Xj+iumz2+j9U3z5ZzwAADl0yGACwabZjUWpndVF1SvVd1U/Mfs57RfXV6oTqt6vfmLKDAACHIBkMANhU27Eo9X2N0bdbqnur91enLpxzanXp7PkfV8+udkzVQQCAQ5AMBgBsqsOX3YGD8Ojq1rnj26qn7eOc+6r/qI6pvrxw3tmzR9WdjenoW+qEp790q/8EB+7YHvhvAzbFGc9/wbK7wPp87lfX45bdgW1MBmOz+S5my8hgD1k+96tpw/y1HYtS6422rR3EOVV/MHuw2q6vnrLsTgCT8rmHAyeDsdl8F8Pq8blnL9vx9r3bqsfOHT+m+vw+zjm8Orq6Y+u7BgBwyJLBAIBNtR2LUp+oTqyOrx5WnV5dvnDO5dXLZs9/tPqr1h+lAwBg/8hgAMCm2o63791Xvbq6srELzCXVZ6pfakwFvLy6uHp3YzHOOxqhCTbi9gFYPT73cOBkMDab72JYPT737GXH2prBKwAAAACmtR1v3wMAAABgm1OUAgAAAGByilKsgh3VtdUpc20/Xn14Od0BJrRWvXnu+Oer85bTFYCVIn/BapPB2C+KUqyCteqV1Vuqh1ePqH61etUyOwVM4p7qRdWxy+4IwIqRv2C1yWDsF0UpVsWN1Z9Xr6vOrd5V/Utj2+qPV5+sfq/xmTi8sXPQp2e/d84S+gtsjvsau7y8Zp3XHlddXX1q9vO4CfsFsArkL1hdMhj75fBldwAm9Kbqhure6inVE6rTqme250vz9EZYOrZ64uz3vnHyngKb6aJG6PnNhfbfbfwH6dLqrOrC6oXTdg3gkCd/weqSwXhQilKskruqy6o7G9NJf7h6anX97PUjq1urK6vHVxdUH6o+MnlPgc30n43gc0719bn2ZzSmldcYnV8MTAD8/8lfsLpkMB6UohSr5v7Zo8YCnJdUb1znvCc1FuY8p3pxdfYkvQO2ylsbI/Xv2Mc5axP1BWDVyF+wumQw9smaUqyyqxq7wOxefO+Yxv3M39wITB9srH9w8lJ6B2ymO6oPVK+Ya7uucctI1RmNXaIA2FryF6wWGYx9MlOKVfbpxjoHVzUKtP/T2CXmf6uLG8ForbE4J7D9vbl69dzxOY3R+l+ovlS9fBmdAlgx8hesHhmMDe1YWzNTDgAAAIBpuX0PAAAAgMkpSgEAAAAwOUUpAAAAACanKAUAAADA5BSlAAAAAJicohQAAAAAk1OUAgAAAGByilIAAAAATE5RCgAAAIDJKUoBAAAAMDlFKQAAAAAmpygFAAAAwOQUpQAAAACYnKIUAAAAAJNTlAIAAABgcopSAAAAAExOUQoAAACAySlKAQAAADA5RSkAAAAAJqcoBQAAAMDkFKUAAAAAmJyiFAAAAACTU5QCAAAAYHKKUsBmO7Na2+DxteV160GtVectuxMAAAfpzGQwYJs5fNkdAA5ZP1bdttB23zI6sp+e0QP7CwCw3chgwLahKAVslU9WNy+7E/vhiOqe6mPL7ggAwCaQwYBtw+17wNQOq66pdlVHz7U/sfp69Vtzbbuq91Q/3QhX/13dUP3gOu/7rOrq6r+qu6orqycsnHNNdW31guofG0HoZ2evrTd1/KTq8uqrs779XfX9C+e8szG69z3VR6u7q89Wr1ynj8dX766+MPvbt1QXHMR1AAAcKBlMBoOHHEUpYKvsbMzGnH8cVt1f/WR1VPX7s3OPrN5ffaZ6w8L7PKt67az99EaQ+Mvq8XPnPK8RIu6cvfdLZu//0eqxC+/3ndWF1e9UPzL7vfWcXF1XfVMjkL24+kp1VfW9C+c+snpfI7ydWn2ielt7B7fjq49XP1CdW51Svak69iCvAwBgPTKYDAbbxo61tbVl9wE4tJxZvWOD166onj97flr1J9VZjbUEXtIIIf88d/6u6tuqE6rPzdqOqv519l4vnbXdPGt79tzvPrIxCvae6udmbdc0AsnJjant89YaAeW82fHVs799UnXvrG1ndWN1U/XCWds7q5dVP1T99aztiOr22fWdPWt7V/WiRiD7fOvb3+sAAFh0ZjKYDAbbjDWlgK1yWg9ctHJ+55c/bYzSva0RIM5q7zC028faE4ZqTKm+ohGiqk6svqM6v72/0+6u/r4RgObt6oFhaNGRjdHB8xujivPve1V1xsL5d7cnDNUYSfxsddxc23Oqv2jjMHSg1wEAsB4ZTAaDbUNRCtgqN/bgi2xeWv1M9e+Nqdfr+eIGbY+ePf+W2c+LZ49Fn1s4/rcH6VON6eI7qzfOHuvZPQ2+xnoHi+6pHj53fEz73lnmQK8DAGA9MpgMBtuGohSwLN9QXdIITidWv169Zp3zRggrvgAAAdZJREFUHrVB2+2z51+Z/fzFxgjaonsXjvfnnuWvNcLORY0p3+u5f4P2jXy5PSFuPQd6HQAAB0MG25sMBkukKAUsywWNgPDkxhoHb23scvLhhfOe3lhg8tbZ8VGNxSivmB3f1JgO/t2NULUZ7mosbHlSY6eZAw0/6/lIYz2Db239kcKtuA4AgEUy2N5kMFgiRSlgqzy5vXc12e36xu4oP9VYJPOWxk4sz2ksWPmkxlTy3b7YCBPnNaZjv656RPXLs9fXqldVf1Y9rPpAY0TsUdUzG1Ou33IQ/X9t9beNkHZxI8Qc21igc2f1+gN8v3MbQe66xpoFNzcC4XMbu7xs1XUAAKtFBtubDAYPYYpSwFb54Abtx1V/WL23sZvJbi+vPtUIRc9rzxTvv2ns2HJ+9Zjqnxpb+c4vyPmhxiKUb6j+qLFI5hcaC3RedpD9v6F6aiPIXFgdXX1p1v72g3i/XdXTql+pfq0x2nh7IwDtthXXAQCsFhlsb7uSweAha8fa2v7c2guwFLuqaxujWAAATGNXMhgwgcOW3QEAAAAAVo+iFAAAAACTc/seAAAAAJMzUwoAAACAySlKAQAAADA5RSkAAAAAJqcoBQAAAMDkFKUAAAAAmNz/Abz6S9j7+tSrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
    "\n",
    "exp_ind = list(experience_proportions[\"I\"].values())\n",
    "sns.barplot([\"Yes\", \"No\"], exp_ind, ax=ax1, palette=colors)\n",
    "ax1.set_title(\"Distribution of Experience (Individuals Treatment)\\n\", fontsize=22)\n",
    "ax1.set_xlabel(\"\\nExperience\", fontsize=16)\n",
    "ax1.set_ylabel(\"Proportion\\n\", fontsize=16)\n",
    "\n",
    "ax1.spines['bottom'].set_color('white')\n",
    "ax1.spines['top'].set_color('white') \n",
    "ax1.spines['right'].set_color('white')\n",
    "ax1.spines['left'].set_color('white')\n",
    "ax1.tick_params(axis='x', colors='white')\n",
    "ax1.tick_params(axis='y', colors='white')\n",
    "ax1.yaxis.label.set_color('white')\n",
    "ax1.xaxis.label.set_color('white')\n",
    "ax1.title.set_color('white')\n",
    "ax1.set_ylim(0, 1)\n",
    "\n",
    "\n",
    "exp_ind = list(experience_proportions[\"G\"].values())\n",
    "sns.barplot([\"Yes\", \"No\"], exp_ind, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"Distribution of Experience (Group Treatment)\\n\", fontsize=22)\n",
    "ax2.set_xlabel(\"\\nExperience\", fontsize=16)\n",
    "ax2.set_ylabel(\"Proportion\\n\", fontsize=16)\n",
    "\n",
    "ax2.spines['bottom'].set_color('white')\n",
    "ax2.spines['top'].set_color('white') \n",
    "ax2.spines['right'].set_color('white')\n",
    "ax2.spines['left'].set_color('white')\n",
    "ax2.tick_params(axis='x', colors='white')\n",
    "ax2.tick_params(axis='y', colors='white')\n",
    "ax2.yaxis.label.set_color('white')\n",
    "ax2.xaxis.label.set_color('white')\n",
    "ax2.title.set_color('white')\n",
    "ax2.set_ylim(0, 1)\n",
    "\n",
    "plt.savefig('experience_distribution_treatments.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test the difference in proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I': {'Yes': 0.55, 'No': 0.45},\n",
       " 'G': {'Yes': 0.7608695652173914, 'No': 0.2391304347826087}}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experience_proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'I': {'Yes': 11, 'No': 9}, 'G': {'Yes': 35, 'No': 11}}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experience_totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z-stat: -1.7131124461949745\n",
      "p-value: 0.08669185471921562\n"
     ]
    }
   ],
   "source": [
    "yeses_i = experience_totals[\"I\"][\"Yes\"]\n",
    "totals_i = yeses_i + experience_totals[\"I\"][\"No\"]\n",
    "\n",
    "yeses_g = experience_totals[\"G\"][\"Yes\"]\n",
    "totals_g = yeses_g + experience_totals[\"G\"][\"No\"]\n",
    "\n",
    "z, p = proportions_ztest([yeses_i, yeses_g], [totals_i, totals_g])\n",
    "print(f\"z-stat: {z}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of Skills"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to understand if the two treatments are similar in terms of self-reported skills."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "not_nas = df.dropna(subset=[\"Skills\"])\n",
    "skills = not_nas[\"Skills\"].unique()\n",
    "skills.sort()\n",
    "treatments = not_nas[\"Treatment\"].unique()\n",
    "skills_proportions = {\n",
    "    \"I\": {},\n",
    "    \"G\": {}\n",
    "}\n",
    "skills_totals = {\n",
    "    \"I\": {},\n",
    "    \"G\": {}\n",
    "}\n",
    "\n",
    "for skill in skills:\n",
    "    for treatment in treatments:\n",
    "        total = not_nas[(not_nas[\"Treatment\"] == treatment) & (not_nas[\"Skills\"] == skill)][\"ID\"].count()\n",
    "        proportion = total / not_nas[(not_nas[\"Treatment\"] == treatment)][\"Skills\"].count()\n",
    "        skills_proportions[treatment][skill] = proportion\n",
    "        skills_totals[treatment][skill] = total\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors[3] = \"#c57958\"\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
    "\n",
    "skill_ind = list(skills_proportions[\"I\"].values())\n",
    "sns.barplot(skills, skill_ind, ax=ax1, palette=colors)\n",
    "ax1.set_title(\"Distribution of Skills (Individuals Treatment)\\n\", fontsize=22)\n",
    "ax1.set_xlabel(\"\\nSkill Level\", fontsize=16)\n",
    "ax1.set_ylabel(\"Proportion\\n\", fontsize=16)\n",
    "\n",
    "ax1.spines['bottom'].set_color('white')\n",
    "ax1.spines['top'].set_color('white') \n",
    "ax1.spines['right'].set_color('white')\n",
    "ax1.spines['left'].set_color('white')\n",
    "ax1.tick_params(axis='x', colors='white')\n",
    "ax1.tick_params(axis='y', colors='white')\n",
    "ax1.yaxis.label.set_color('white')\n",
    "ax1.xaxis.label.set_color('white')\n",
    "ax1.title.set_color('white')\n",
    "ax1.set_ylim(0, 1)\n",
    "\n",
    "\n",
    "skill_ind = list(skills_proportions[\"G\"].values())\n",
    "sns.barplot(skills, skill_ind, ax=ax2, palette=colors)\n",
    "ax2.set_title(\"Distribution of Skills (Group Treatment)\\n\", fontsize=22)\n",
    "ax2.set_xlabel(\"\\nSkill Level\", fontsize=16)\n",
    "ax2.set_ylabel(\"Proportion\\n\", fontsize=16)\n",
    "\n",
    "ax2.spines['bottom'].set_color('white')\n",
    "ax2.spines['top'].set_color('white') \n",
    "ax2.spines['right'].set_color('white')\n",
    "ax2.spines['left'].set_color('white')\n",
    "ax2.tick_params(axis='x', colors='white')\n",
    "ax2.tick_params(axis='y', colors='white')\n",
    "ax2.yaxis.label.set_color('white')\n",
    "ax2.xaxis.label.set_color('white')\n",
    "ax2.title.set_color('white')\n",
    "ax2.set_ylim(0, 1)\n",
    "\n",
    "plt.savefig('skill_distribution_treatments.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can test the difference in proportions with a 𝜒² contingency table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'I': {1.0: 0.15, 2.0: 0.5, 3.0: 0.3, 4.0: 0.05}, 'G': {1.0: 0.08695652173913043, 2.0: 0.32608695652173914, 3.0: 0.4782608695652174, 4.0: 0.10869565217391304}}\n"
     ]
    }
   ],
   "source": [
    "print(skills_proportions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'I': {1.0: 3, 2.0: 10, 3.0: 6, 4.0: 1}, 'G': {1.0: 4, 2.0: 15, 3.0: 22, 4.0: 5}}\n"
     ]
    }
   ],
   "source": [
    "print(skills_totals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 10, 6, 1], [4, 15, 22, 5]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contingency_table = []\n",
    "for treatment in skills_totals:\n",
    "    counts = []\n",
    "    for level in skills_totals[treatment]:\n",
    "        counts.append(skills_totals[treatment][level])\n",
    "    contingency_table.append(counts)\n",
    "\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "𝜒²-stat: 3.207763975155279\n",
      "p-value: 0.36068785972175865\n",
      "df: 3\n"
     ]
    }
   ],
   "source": [
    "chi2, p, dof, ex = chi2_contingency(contingency_table)\n",
    "print(f\"𝜒²-stat: {chi2}\\np-value: {p}\\ndf: {dof}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram of Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create bins for histogram, get the number of responses for the round in each bin (in groups only get one per group)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = 10\n",
    "\n",
    "results_groups = []\n",
    "results_individuals = []\n",
    "labels = []\n",
    "\n",
    "for i in range(0, 101, bins):\n",
    "    lower_bound = i\n",
    "    upper_bound = i+bins-1\n",
    "    label = f\"{lower_bound} to {upper_bound}\"\n",
    "    labels.append(label)\n",
    "    groups_count = df[(df[\"Treatment\"] == \"G\") & (df[\"Final Answer Stage 1\"] >= lower_bound) & (df[\"Final Answer Stage 1\"] <= upper_bound)][\"ID\"].count()/3\n",
    "    individual_count = df[(df[\"Treatment\"] == \"I\") & (df[\"Final Answer Stage 1\"] >= lower_bound) & (df[\"Final Answer Stage 1\"] <= upper_bound)][\"ID\"].count()\n",
    "    results_groups.append(groups_count)\n",
    "    results_individuals.append(individual_count)\n",
    "\n",
    "labels[-1] = \"100\"\n",
    "\n",
    "merged_data = []\n",
    "for i in range(len(labels)):\n",
    "    data = [labels[i], results_individuals[i], results_groups[i]]\n",
    "    merged_data.append(data)\n",
    "\n",
    "histogram_df = pd.DataFrame(merged_data, columns=[\"Interval\", \"Individuals\", \"Groups\"])\n",
    "histogram_df = histogram_df.melt(value_vars=[\"Individuals\", \"Groups\"], id_vars=[\"Interval\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 8))\n",
    "\n",
    "sns.barplot(x=\"Interval\", y='value', hue='variable', data=histogram_df, ax=ax, palette=[colors[1], colors[0]])\n",
    "\n",
    "plt.legend([\"Groups\", \"Individuals\"])\n",
    "\n",
    "ax.set_title(\"Histogram of Answers (Stage 1)\", fontsize=26)\n",
    "ax.set_xlabel(\"Responses\", fontsize=20)\n",
    "ax.set_ylabel(\"Total Count\", fontsize=20)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "ax.legend(h, [\"Individuals\", \"Groups\"], title=\"Treatments\")\n",
    "\n",
    "plt.savefig('histogram_answers_stage1.png', transparent=\"True\", pad_inches=0.2, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = 10\n",
    "\n",
    "results_groups = []\n",
    "results_individuals = []\n",
    "labels = []\n",
    "\n",
    "for i in range(0, 101, bins):\n",
    "    lower_bound = i\n",
    "    upper_bound = i+bins-1\n",
    "    label = f\"{lower_bound} to {upper_bound}\"\n",
    "    labels.append(label)\n",
    "    groups_count = df[(df[\"Treatment\"] == \"G\") & (df[\"Final Answer Stage 2\"] >= lower_bound) & (df[\"Final Answer Stage 2\"] <= upper_bound)][\"ID\"].count()/3\n",
    "    individual_count = df[(df[\"Treatment\"] == \"I\") & (df[\"Final Answer Stage 2\"] >= lower_bound) & (df[\"Final Answer Stage 2\"] <= upper_bound)][\"ID\"].count()\n",
    "    results_groups.append(groups_count)\n",
    "    results_individuals.append(individual_count)\n",
    "\n",
    "labels[-1] = \"100\"\n",
    "\n",
    "merged_data = []\n",
    "for i in range(len(labels)):\n",
    "    data = [labels[i], results_individuals[i], results_groups[i]]\n",
    "    merged_data.append(data)\n",
    "\n",
    "histogram_df = pd.DataFrame(merged_data, columns=[\"Interval\", \"Individuals\", \"Groups\"])\n",
    "histogram_df = histogram_df.melt(value_vars=[\"Individuals\", \"Groups\"], id_vars=[\"Interval\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/XinrSzEsvn2a3GEQQstDa7aC9EAmpZ7zVwqWapoOhOkkytGO6rdaLSb0ZTyDcDnyFfHze7PpQB8rSPfFG0vj/oOp4LiA0Mq4I6EfYXYLYqZU39HuhMvAW8o6QqvgTwkh+KOkP9/fobzW//Wkg+7WhQqW6EPSl2p2aeI+Un+308gdBiH/3+07pKccKfQd0o7800605PvajhDkU5/Vc4FflbmPbqQH+jIQSi3IQCipHi0n/R/mjjQeOAPSIxwW6meYms9uGIXn8Touvn2Cxr/az4pvDyZczprLQNIjHzZH6jKuQq+nVpfZlSoiDIYDoc9WvteyLem5Gx/JU6Y5DiPdP+nTFO8D+2hc9iRazjoaj1w7KEeZzEtCx8b3Z2aVSf1ocjjpUU6z+w9CGJgkFSRPKbmWlfc66XnxtqP8Vq1U39Ts74OmSvUZzh4RNXPu00J/k8X+bhvI/+NERLicGcJlpfnC8YHkbyFsyff1OtLfld9n03lES/HRjPsL8paSVHEGQkmt1S5F1mf+p29Z1rrU40IjN/6e9PQGPyX39+GXCP3JAH6dY3sI/2m9KMe2qQEpKiE1UMcgcofXfQkTTrd218e3/Qn9IHO5gnDpWZRRvpJSwW4lcFcJ5W+Ob4cCwytYjwEUHmGz0OcbGvcjPCzruZRU+DuH9DnN7j8I4VLY1KV955EeQTeffjS9r1mucJsp9bo/oPxLhh+Ob7On+Mj0SeCLFJ/4/AzSgfCvWeuWkx71s9B3TOrv9hByv9fnUXh6hdR3zCA2nfYGwnvwgwLbt9T7+iPCOYUwSE+u78NSpAbvWUXhkWolVZiBUFJrdR3hEslvEvpJbU/oG7QboV/h9+Nys4FXs7ZNzad3MuGSu66ESwUz+02/BXwvvn8kYSCE/Qh99XYh/EcrNQrkXDYNhI8Qpk8A+BZhAJDBcR1HEfrSHEPxURdLcTvhl/6OcT3HEfpQDiD8Z/8+Ck+d0VrcQRgpFsIgIj+l8Tm7jXRL51WUPjJhqbYAxsf3/0R6BNBC/kC6laWUOQlLdTLhktgrCH1i+xF+XPhovC413+N7hBEbs2X2I+xD4/6DKTPj29SIuM+QO1xCuPT6DUJr9wOkR9fsFS9DCK1MdxIGfmnqgD/3Ev5mv07429wu3v8IQrD4SlzuNvLPZZhPKhAOJP9lo30I03y8TuiDfDThnG9F+I45mDCwTKr/4pukvwdSVpG+tPVMwo8Fm7Ppd0wqjO1C+LztTXitqWkoLqHwZ/xm0t9lvwD+L35tPQmXAP+VcN5ez7l1UO339VzSfbhvI3w355veohuFLwNOBfk5NG6FlVRt1Zz13sXFpU0uE6O0/iWUn5JRPtf6lFOynp8ZFfdiFEU75djn4QW2yaxzuyiKri5yjPlRFG2fp+5bR1E0t8C234+i6Lfx/ek5tk+dy8V59p+5XFTgOG9EUbRbxuPsc0l8jCg+ZrH3amaBMqdkHKcpn58eURT9vcBriaIoujGKoo55tk99LqY04difzzjGuDK2mxFv83oUPjPFPrulvMeZf0f5rCxSzycyyt6ZY31DFEX/zijzsyKvc+coip4poV7rovDZL/ezk/k5LOTxKIp6FdlPrqVzFEXL4n1MyFPm5BKOn/J8FP6ucu3njALbpcq0j6LovgLl/h5F0ZEZj/vnOE6/KIpezrP9+iiKvhRF0YPx41/lqWtz3tdiSynvZ6ZcrzG1vBSX+Z8y6+Di4tLMxRZCSa3VyaRHqVsA/IfQYrCMcNnb/xJ+mX8xx7b3ElrR7ifMNZZvWoANhBaJgwmTKL9B+GV6OaG1ITXKY7555pYTLsP6ZlzHNXE9/wZ8itByWKkRMy8Gjie0TK4ktFI8TxjWfneKz+3XWvyH0EfwVEKLxTLCOX+L0IpyFGGo+mq0EKRa+JZTeDL5bKnLRncgfFYq4aeEAZImE1qB3iR8vt8jDBhzGeFSwbsL7COzP+DMHOuzLxHN1X8w0wuEFqeTCZfTvkm4dHMNYdCbuwiXJvem9Ln+sh1GaLWaRmhlW0F43f8itHidRpiQfWkT9r2G9GWWJ+Qp81tCi+mXCYP4PB3XYT3h7+olQuvs5wjfL/n+rq6NyzxMGABmQ44y6wmf5wvj/ayNj/U44ftrLMWnlHiFdOvpongfbxM+FwcTrqQo9h3TEu9rc+1DuOJhNWFwGkktqCGKouKlJElNNZ/Q9+znwFdrXBeprRtCCHkfUNuA05KWEy55PYfwA1E9+hnh+/EGQtCW1IJsIZSk6ulHaGUAB0mQWsIzhBavTqQHOmnLPk56NNZ6/Y7ZktB6v4HQEiqphRkIJanptqDxIBKZ2hMGrWhHaK2Y1lKVkhLuAsLlmufQ9DkEW4utyT8Qy+akp3d4i+pM09ISvkYItb/F6SakmjAQSlLT7UkYJfB/CZeFbk0YqXAcoX/c0XG5nxL6zkmqvmcJ4WJbQj/kenY0oY/pGcCuhO+YHQn9T+eQnqphEuWPytoadCP0KV0DfLfGdZESyz6EktR0YwkDyBRyO2EqhbVVr42ktuYUwjQYhfyc0Mrmf+gkNYmBUJKarjthRMxDCb/eb0eY72sp8BihleKOmtVOUr3rTfiO+QSwM6HVsyNhVNZZhFFGZ9aqcpLahiQEwjb/AiVJkiSpgKXANrlW2IdQkiRJktq2V/KtMBBKkiRJUkIZCCVJkiQpoQyEkiRJkpRQ+SZUliRJkqSK+fDDD1myZAlr1qypdVXarM6dO9O3b186duxY8jYGQkmSJElVt2TJErbYYgv69+9PQ0NDravT5kRRxLJly1iyZAkDBgwoeTsvGZUkSZJUdWvWrKFnz56GwSppaGigZ8+eZbfAGgglSZIktQjDYHU15fwaCCVJkiS1esuWLWPEiBGMGDGC3r1706dPn42PP/jgg2bt+/rrr+ett96qUE1rf5xy2IdQkiRJUqvXs2dP5s+fD8DEiRPp1q0b55xzTqMyURQRRRHt2pXX7nX99dezxx570Lt374rVt5bHKYcthJIkSZLq1qJFixgyZAhnnHEGe+yxB2+++Sb33nsv++yzD3vssQcTJkxg5cqVAHz3u99l5MiRG8tHUcTUqVOZP38+EyZM2Nja2LdvX771rW8xevRoRo4cybx58zjkkEPYaaed+OUvf7nx2JdccgmjRo1i2LBhTJo0qVF9Tj31VAYPHszhhx/OmjVrch7n3HPPZbfddmPYsGGcf/75NTl/BkJJkiRJde3ZZ5/l1FNP5YknnqBjx45ccsklzJgxg3nz5jFs2DCuvPJKAM466ywee+wxnn76aVasWMF99923MaClAlunTp0A6N+/P3PmzGH06NGceuqp3HHHHcyaNYtvf/vbANxzzz28+uqrPProo8yfP59Zs2Yxa9YsAJ5//nnOPvtsFixYwOabb86dd965yXGWL1/OPffcw4IFC3jqqae48MILa3LuvGRUkiRJUl3baaedGDlyJACzZs3i2WefZcyYMQB88MEH7LfffgDMmDGDyy67jDVr1rB06VL23HNPDj/88Jz7HD9+PABDhw5l3bp1dO3ala5du9KuXTvef/997r//fu6991523313AN5//33++c9/su222zJw4ECGDh0KwJ577snixYs32X+PHj1o164dp512GkceeSTjxo2r6DkplYFQkiRJUl3r2rXrxvtRFHHYYYdxww03NCqzatUqzjzzTObNm0efPn246KKLCk7RsNlmmwHQrl27jfdTj9etW0cURVx00UWceuqpjbZbtGhRo/Lt27dn3bp1m+y/Y8eOPP744/z1r3/llltu4ZprruH+++8v74VXQGu4ZHQgcB3wJLAemFmk/E+BCLi8utWSJEmSVG/GjBnDgw8+yEsvvQTAypUreeGFF1i9ejXt2rWjV69evPfee9x2220bt9liiy147733yjrOoYceyq9//euN/ROXLFnC0qVLC26TeZz33nuPd999l3HjxnHFFVfwxBNPlHX8SmkNLYSDgSOAOUCnImV3A74AvFvtSkmSJEmqP9tttx2//vWvmTBhwsbpKH7wgx9w5JFHcvLJJzNkyBD69evH3nvvvXGbz3/+83zxi19k88035x//+EdJxzniiCNYuHAho0ePBkLYu+mmmwpuk3mcadOmcdxxx7F27Vo2bNjAT37ykya+4uZpiKKoJgfO0A7YEN//I9ALGJun7HRgNvDZuOw5ecplqvkLlCRJkpLuueeeY9ddd611Ndq8POd5LrBXrvKt4ZLRDcWLAHAcsCtwSRXrIkmSJEmJ0RoCYSk2B34MXACsrHFdJEmSJKlNqJdAeCHwJvD7WldEkiRJktqK1jCoTDEDCH0FD6L0/oCnx0urtWjODcULVcnA0Z+t2bElSZIktR71EAgvAe4FFgJbxc+1AzaLH69g06A4OV7IsU6SJEmSRH1cMroL8ClgecayI3BmfL9P7aomSZIkSfWrHgLhF4EDs5Z/AX+I779du6pJkiRJqhfdunUrq/zMmTMZN24cANOmTeOSSwpPePCd73yH6dOnF9xPU/Tv37/opPdN1RouGe1CmJgeQmvfloQpJgDuAR7Psc0a4DVgZrUrJ0mSJKnybrz7roru78RxR1V0f9nGjx/P+PHjC5aZNGlSVetQDa2hhXBb4NZ4GQ3slvF42xrWS5IkSVIbNHPmTMaOHctxxx3HoEGDOPHEE4miMPTIfffdx6BBg9hvv/24/fbbN24zZcoUzjzzTFasWEH//v3ZsCFMp75q1Sp23HFHPvzwQ0455RT++Mc/FtzPxIkTufzyyzc+HjJkCIsXLwbgmGOOYc8992Tw4MFMnjyZbCtXruTII49k+PDhDBkyhKlTpzb7XLSGFsLFQEOZ2/SvfDUkSZIkJcUTTzzBggUL2GGHHdh33335+9//zl577cVpp53GAw88wMCBA5kwYcIm23Xv3p3hw4fz4IMPcuCBB3LXXXdx6KGH0rFjx41l1qxZU3Q/uVx//fX06NGD1atXM3LkSI499lh69uy5cf19993HDjvswJ///GcAVqxY0cyz0DpaCCVJkiSpRY0aNYq+ffvSrl07RowYweLFi1m4cCEDBgxg5513pqGhgZNOOinnthMmTNjYOnfLLbdsEvhK3U+2n/3sZwwfPpzRo0fz2muv8cILLzRaP3ToUKZPn87555/Pww8/TPfu3ZvwyhszEEqSJElKnM0222zj/fbt27Nu3ToAGhqKX7w4fvx47r33Xv7zn/8wd+5cDjrooE3K5NtPhw4dNl5uCqE1EcJlrNOnT2f27Nk8+eST7L777hvXpXzsYx9j7ty5DB06lAsvvLAifRYNhJIkSZIEDBo0iJdffpkXX3wRgJtvvjlnuW7dujFq1CjOOussxo0bR/v27UveT//+/Zk3bx4A8+bN4+WXXwbC5Z9bb701Xbp0YeHChcyZM2eT477xxht06dKFk046iXPOOWfjfpqjNfQhlCRJkqSa69y5M5MnT+bII4+kV69e7LfffjzzzDM5y06YMIHjjz+emTNnlrWfY489lt/97neMGDGCkSNH8rGPfQyAww47jGuvvZZhw4axyy67MHr06E32+/TTT3PuuefSrl07OnbsyDXXXNPs19yQGk2nDWuVL3DRnBtqduyBoz9bs2NLkiQpmZ577jl23XXXWlejzctznucCe+Uq7yWjkiRJkpRQBkJJkiRJSigDoSRJkiQllIFQkiRJkhLKQChJkiRJCWUglCRJkqSEMhBKkiRJSox//etffOYzn+GjH/0oe+65J/vssw933HFHratVM05ML0mSJKnFVXpe7lLm2o6iiGOOOYaTTz6Zm266CYBXXnmFadOmNSq3bt06OnRIRlSyhVCSJElSIjzwwAN06tSJM844Y+Nz/fr146tf/SpTpkzh+OOP56ijjuKQQw4hiiLOPfdchgwZwtChQ5k6dSoAM2fOZNy4cRu3P/PMM5kyZQoA/fv35/zzz2fUqFGMGjWKRYsWAXDrrbcyZMgQhg8fzv77799yL7gEyYi9kiRJkhJvwYIF7LHHHnnXz549m6eeeooePXpw2223MX/+fJ588kmWLl3KyJEjSwpzW265Jf/4xz/43e9+x9lnn83dd9/NpEmT+Mtf/kKfPn145513KvmSmtssM6QAACAASURBVM0WQkmSJEmJ9JWvfIXhw4czcuRIAD7xiU/Qo0cPAB555BFOOOEE2rdvz3bbbccBBxzAY489VnSfJ5xwwsbb2bNnA7Dvvvtyyimn8Mtf/pL169dX6dU0jYFQkiRJUiIMHjyYefPmbXx89dVXM2PGDN5++20AunbtunFdFEU599GhQwc2bNiw8fGaNWsarW9oaNjk/rXXXsvFF1/Ma6+9xogRI1i2bFnzX0yFGAglSZIkJcJBBx3EmjVruOaaazY+t2rVqpxl999/f6ZOncr69et5++23eeihhxg1ahT9+vXj2WefZe3ataxYsYIZM2Y02i7V13Dq1Knss88+ALz44ovsvffeTJo0iV69evHaa69V6RWWzz6EkiRJkhKhoaGBO++8k69//etceumlbLPNNnTt2pUf/ehHrF69ulHZT37yk8yePZvhw4fT0NDApZdeSu/evQH47//+b4YNG8bOO+/M7rvv3mi7tWvXsvfee7NhwwZuvvlmAM4991xeeOEFoiji4IMPZvjw4S3zgkvQkK8ptA1plS+w0sPslqOUIXklSZKkSnruuefYdddda12Nqurfvz+PP/44vXr1qlkd8pznucBeucp7yagkSZIkJZSXjEqSJElSBSxevLjWVSibLYSSJEmSlFAGQkmSJEktIgHjl9RUU86vgVCSJElS1XXu3Jlly5YZCqskiiKWLVtG586dy9rOPoSSJEmSqq5v374sWbJk4yTwqrzOnTvTt2/fsrYxEEqSJEmquo4dOzJgwIBaV0NZvGRUkiRJkhLKQChJkiRJCWUglCRJkqSEMhBKkiRJUkIZCCVJkiQpoQyEkiRJkpRQBkJJkiRJSigDoSRJkiQllIFQkiRJkhLKQChJkiRJCWUglCRJkqSEMhBKkiRJUkIZCCVJkiQpoQyEkiRJkpRQBkJJkiRJSigDoSRJkiQllIFQkiRJkhLKQChJkiRJCWUglCRJkqSEag2BcCBwHfAksB6YmbV+e+CyeP37wGvAb4EdWq6KkiRJktT2dKh1BYDBwBHAHKBTjvV7Ap8EfgU8CmwHTARmAUMIIVGSJEmSVKbWEAjvAv4U3/8j0Ctr/SPAIGBdxnPzgOeBYwmthZIkSZKkMrWGQLihyPp3cjz3T2AVsG3lqyNJkiRJydAa+hA2xTCgC/BsrSsiSZIkSfWqHgNhO+BK4AXg/hrXRZIkSZLqVmu4ZLRcPwT2AQ4APsxT5vR4kSRJkiTlUW+B8H+Ac4ETCCOO5jM5XgCialdKkiRJkupRPV0yeixwFXAeMLXGdZEkSZKkulcvgXAscCPwc+Dy2lZFkiRJktqG1nDJaBfCxPQAfYAtgePix/cA/YA7gYWElsHRGdu+DbzYMtWUJEmSpLalNQTCbYFbs55LPR4A7A10B4YDf88q91vglGpWTpIkSZLaqtYQCBcDDQXWT4kXSZIkSVIF1UsfQkmSJElShRkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEqo1BMKBwHXAk8B6YGaOMg3AN4HXgNXAQ8CIFqqfJEmSJLVJrSEQDgaOAP4ZL7lcAHwb+BFwFPA+MB3o3RIVlCRJkqS2qDUEwruAHYHjgQU51ncmBMIfAj8nBMHjgQg4s4XqKEmSJEltTmsIhBuKrB8DbAn8IeO5lYQgeXi1KiVJkiRJbV1rCITFDCL0LXwh6/nn4nWSJEmSpCaoh0C4NaHP4Pqs55cDXYBOLV4jSZIkSWoDOtS6AiWKcjzXUGDd6fEiSZIkScqjHloIlwNbAO2znt8KWAV8mGObycBe8SJJkiRJyqEeAuFCQhgcmPX8oHidJEmSJKkJ6iEQzgLeJUw1kdKFMB/hvTWpkSRJkiS1Aa2hD2EXwsT0AH0IU0wcFz++h3BZ6CWEiemXE1oFv0EIs1e1aE0lSZIkqQ1pDYFwW+DWrOdSjwcAiwmBsB1wIdATeBz4BPCvlqmiJEmSJLU9rSEQLiY9Ymg+EfD9eJEkSZIkVUA99CGUJEmSJFWBgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFDlBMKngDOKlDktLidJkiRJauXKCYRDgG2LlNkWGNz06kiSJEmSWkqlLxntCnxQ4X1KkiRJkqqgQ5H1PeLbhvh284znMrUHPgIcA7xSmapJkiRJkqqpWCBcCkQZj8+Ll3wagG82t1KSJEmSpOorFghvJwTCBuBTwELg2Rzl1gPLgBnxNpIkSZKkVq5YIDwu4/4GYCowqXrVkSRJkiS1lGKBMNMWOGCMJEmSJLUZ5QTClVWrhSRJkiSpxZUTCAG6AScBo4CtCaOLZouAo5tZL0mSJElSlZUTCHcC/gb0IT0NRS5RgXWSJEmSpFainEB4OdAX+DkwGXgNWFeNSkmSJEmSqq+cQHggcD/wtSrVRZIkSZLUgtqVUbY98GS1KiJJkiRJalnlBML5wMBqVUSSJEmS1LLKCYTfB44C9q5SXSRJkiRJLaicPoRdgDuBmcD1wFzgnTxlb29etSRJkiRJ1VZOIPwjYUqJBuDL5J5eoiF+Ptf8hJIkSZKkVqScQPjVqtVCkiRJktTiygmEV1etFpIkSZKkFlfOoDKSJEmSpDbEQChJkiRJCVXOJaNPlVguAoY3oS6SJEmSpBZUTiDcgdwji3YDNovvvwOsb26lJEmSJEnVV04g7JXn+XbAMOAKYC1h8npJkiRJUitXiT6EG4D5hCA4CPhWBfYpSZIkSaqySg4q8z7wF+BzFdynJEmSJKlKKj3K6BpCX0NJkiRJUitXyUDYHTgaeKOC+5QkSZIkVUk5g8p8o8A+dgSOA7YFJjazTpIkSZKkFlBOILycMO1EQ571a4CfAhc3t1KSJEmSpOorJxDmm05iA7AceBpY2ewa5fdp4DzgY8AKYAZwAV6iKkmSJElNUk4g/HPValHceOBm4GrgXGB7Qkvk3cBehFAqSZIkSSpDOYGwlj4DzAPOzHjuXeBPwC7Ac7WolCRJkiTVs6YEwmHAF4Ddga0Il2/OBaYAT1asZo11jI+T6Z34Nl+fRkmSJElSAeVOO3EBIfx9Dfg4MBTYDzgrfv5bFa1d2vXx8T4HbEnoR3gx8Dfg2SodU5IkSZLatHIC4XjgB8DbwNmElsJt4tuzgX8DkwhzEVban4FTgMmElsLngfbAp6pwLEmSJElKhHIC4dnAMmBP4GfAM/HjZ+LHewH/IbQWVtqBwLXAlfH9TwM9gDsIwTDb6cDj8SJJkiRJyqGcPoR7ADcBb+ZZ/wZwK2EAmEr7MTANOD/jufnAQkKL5O1Z5SfHC4S5EyVJkiRJWcppIexEGNmzkHfjcpU2iBAAMz0PrAZ2qsLxJEmSJKnNKycQvgQcRv5RPRuAQ4GXm1upHF4htFBm2hXYHFhcheNJkiRJUptXTiD8A2EAmVuAHbPW9QFuiNdPrUzVGrkWmEC4dPS/gBOBOwlh8J4qHE+SJEmS2rxy+hBeBhwJHA8cA7xI6E/Ym3DZZifCIC6XVbiOEAat+QD4MnAGYQ7CR4ALgZVVOJ4kSZIktXnlBMLVwAHAt4HPE/r1DYrXvQX8hjA34OpKVjAWAdfEiyRJkiSpAsoJhABrCJPPf4vQMtidMC/gWxWulyRJkiSpysoNhJnewiAoSZIkSXWrlEFlvgP8COhYoEwn4FLgokpUSpIkSZJUfcUC4cHAd4F/Ax8WKPcBobXw/4CxFamZJEmSJKmqigXCkwgjel5dwr6uBpYDJze3UpIkSZKk6isWCMcAMwiDyRSzNi67b3MrJUmSJEmqvmKBsC9hvsFSvRxvI0mSJElq5YoFwgagfRn7a2hGXSRJkiRJLahYIPwX8LEy9rdLvI0kSZIkqZUrFggfJYw02rOEffUC/guY09xKSZIkSZKqr1ggvAHoCkyh8DyEHYDrgc2B31WkZpIkSZKkqioWCP8M/AU4EngM+DSwbcb6beLnHovL/AW4t/LVlCRJkiRVWocSypwA/An4OHBj/Ny6rO0bgIfispIkSZKkOlCshRDCxPQHAV8G5gMR4fLRjvH9J4AzCH0NV1SnmpIkSZKkSiulhRBgPXBdvHQBtiO0Cr4FrKpO1SRJkiRJ1VRqIMy0ijABvSRJkiSpjpVyyagkSZIkqQ0yEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEqrQPITfaMZ+f9KMbSVJkiRJLaBQILwciICGMvcZYSCUJEmSpFavUCA8qsVqIUmSJElqcYUC4Z9brBaSJEmSpBbnoDKSJEmSlFAGQkmSJElKqEKXjObSEzgPOBToA2yWo0wEdG9mvSRJkiRJVVZOINwOmAP0A14mhMM3CQFwh7jMQmBlJSsoSZIkSaqOci4Z/Q7wEeAYYKf4ueuAvsAuwIPAWuCgSlZQkiRJklQd5QTCw4HpwLQc614gBMVtgInNr5YkSZIkqdrKCYQ7AE9lPF4PdM54vAL4C/CpCtRLkiRJklRl5QTC94D2GY/fId13MOU/hL6GkiRJkqRWrpxA+CqwY8bjp4EDaTzS6EHA6xWolyRJkiSpysoJhA8AY0mPTPp7QkCcCXwXmAGMAG6vXPUkSZIkSdVSzrQT1wNrCJeEvg78BtgHOBXYOy5zNzCpkhWUJEmSJFVHOYHwOeDbGY8j4DTgYmAgsBh4sWI1kyRJkiRVVTmBMJ9X4kWSJEmSVEfK6UP4LnB+kTLnEqafkCRJkiS1cuUEwm40HlE0l05xOUmSJElSK1dOICxFd2BthfcpSZIkSaqCYn0I98h6vEOO5yBMWP8R4ATghQrUS5IkSZJUZcUC4eOE0UQhParoaQXKNxDmJJQkSZIktXLFAuFPCEGwAfgGMBuYlaPcemAZYfL6uZWsoCRJkiSpOooFwnMy7p8M3AFcXr3qSJIkSZJaSjmDymxDbcNgB+ACQh/FtcAS4Ioa1keSJEmS6lpTJ6YfAewObEWYd3AeML9SlcrjN8DBwP8BC4Edgd2qfExJkiRJarPKDYSDgSnkHmn0CeAU4JnmVSmnw4BPA8OBZ6uwf0mSJElKnHICYT/gQaAHIfz9DXgT2B4YSwiJfwNGAosrWUngC4QBawyDkiRJklQh5fQh/A4hDJ4K7EkYcObH8e1ehNDWA/h2hesIsDfwT+DnwLvAKuB2wryIkiRJkqQmKKeF8BBgGqEvXy5TgE/G5SqtN+Fy1CcJl45uAVxKGPV0NOm5ElNOjxdV2I1331WT45447qiaHFeSJElqy8oJhNsCC4qUeQY4tOnVyashXo4mzHcI4XLVB4GDgBlZ5SfHC2waFiVJkiRJlHfJ6DJg5yJlBgLLm16dvJYDT5MOgwCPAB/gSKOSJEmS1CTlBMKZhEtCx+VZfxjwKcLAMpX2XJ7nG4ANVTieJEmSJLV55Vwy+j1gPPAn4H7So4z2JowyehiwGri4slUE4G7C/IO9gKXxc/sDHQn9CiVJkiRJZSonED4HHA7cQOgnmDl4TAPwKvA5qjM1xGTga8BdwA8Ig8r8CJhOuHRUkiRJklSmciemfxjYCTiYMO9gd2AFYV7C6cD6itYu7V3C4DE/A24h9B38E/D1Kh1PkiRJktq8YoHwc8B84KmM59YTLhm9v1qVymMRcEQLH1OSJEmS2qxig8pMAY5pgXpIkiRJklpYOaOMSpIkSZLaEAOhJEmSJCWUgVCSJEmSEqqUUUa3Aj5S5n5fbUJdJEmSJEktqJRAeFa8lCoqcb+SJEmSpBoqJbi9C7xT7YpIkiRJklpWKYHwCmBStSsiSZIkSWpZDiojSZIkSQllIJQkSZKkhDIQSpIkSVJCGQglSZIkKaGKDSpjYJQkSZKkNsrAJ0mSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJVa+BsA/wPhAB3WpcF0mSJEmqS/UaCC8jBEJJkiRJUhPVYyD8OHAYcHmtKyJJkiRJ9axDrStQpvbAVcAk4J0a10WSJEmS6lq9tRCeAXQGrq51RSRJkiSp3tVTC2FP4HvAScCHRcqeHi9F3Xj3Xc2sVtPs3asmh5UkSZKkjeopEH4feBS4p4Syk+MFwkikkiRJkqQs9RIIBwNfAPYHtoqf6xLfdgfWA6trUC9JkiRJqlv1Egh3BjoCs3OsWwL8Gvhii9ZIkiRJkupcvQTCR4ADs547DDgfOAJ4qcVrJEmSJEl1rl4C4VJgZtZz/ePbh3GSekmSJEkqW71NOyFJkiRJqpB6DoRTgAZsHZQkSZKkJqnnQChJkiRJagYDoSRJkiQllIFQkiRJkhLKQChJkiRJCWUglCRJkqSEMhBKkiRJUkIZCCVJkiQpoQyEkiRJkpRQBkJJkiRJSigDoSRJkiQllIFQkiRJkhLKQChJkiRJCWUglCRJkqSEMhBKkiRJUkIZCCVJkiQpoQyEkiRJkpRQBkJJkiRJSigDoSRJkiQllIFQkiRJkhLKQChJkiRJCWUglCRJkqSEMhBKkiRJUkJ1qHUFpFIsmnNDzY49cPRnm7ztjXffVcGalOfEcUfV7NiSJEmqD7YQSpIkSVJCGQglSZIkKaEMhJIkSZKUUAZCSZIkSUooA6EkSZIkJZSBUJIkSZISykAoSZIkSQllIJQkSZKkhDIQSpIkSVJCGQglSZIkKaEMhJIkSZKUUAZCSZIkSUooA6EkSZIkJZSBUJIkSZISykAoSZIkSQllIJQkSZKkhDIQSpIkSVJCGQglSZIkKaEMhJIkSZKUUAZCSZIkSUooA6EkSZIkJZSBUJIkSZISykAoSZIkSQlVL4HweGAa8DrwPjAXOKGmNZIkSZKkOteh1hUo0TeAl4GvA0uBI4CbgF7AVTWslyRJkiTVrXoJhEcRgmDKA8AOhKBoIJQkSZKkJqiXS0aX5njuCWDblq6IJEmSJLUV9RIIcxkDPFvrSkiSJElSvaqXS0azHQwcDXyh1hWRJEmSpHpVj4GwP2FAmT8BU/KUOT1epMRaNOeGmhx34OjPNnnbG+++q4I1Kc+J446q2bGbyvNVHs+XJEmbqrdA2AO4F3gVOKlAucnxAhBVu1KSJEmSVI/qqQ9hF+BuoBNwJLCyttWRJEmSpPpWLy2EHYBbgZ2BfYF/17Y6kiRJklT/6iUQ/oIwGf1ZhMtGR2esewJYW4tKSZIkSVI9q5dAeEh8e2WOdQOAxS1XFUmSJElqG+olEPavdQUkSZIkqa2pp0FlJEmSJEkVZCCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKKAOhJEmSJCWUgVCSJEmSEspAKEmSJEkJZSCUJEmSpIQyEEqSJElSQhkIJUmSJCmhDISSJEmSlFAGQkmSJElKqHoKhLsBM4BVwBvAJKB9TWskSZIkSXWsQ60rUKKtgenAs8DRwE7AjwmB9qIa1kuSJEmS6la9BMIzgM2BTwHvAn8FtgQmApfGz0mSJEmSylAvl4weDvyFxsHvFkJIPKAmNZIkSZKkOlcvgXAQsDDruVcJ/QkHtXx1JEmSJKn+1Usg3Bp4J8fzy+N1kiRJkqQyNURRVOs6lOJD4BzgyqznXwemAN/Kev70eAHYBXi+mpVrol7A0lpXoo54vsrnOSuP56s8nq/yeL7K4/kqj+erPJ6v8ni+ytNaz1c/YJtcK+plUJnlwFY5nu9O7pbDyfHSmj0O7FXrStQRz1f5PGfl8XyVx/NVHs9XeTxf5fF8lcfzVR7PV3nq7nzVyyWjC9m0r+COQFc27VsoSZIkSSpBvQTCe4FDgS0ynpsArAYerEmNJEmSJKnOtZ84cWKt61CKBYS5CA8E3gT+C/ghcAUhLNarubWuQJ3xfJXPc1Yez1d5PF/l8XyVx/NVHs9XeTxf5fF8laeuzle9DCoDsBvwc2AfQr/BXxEmpl9fwzpJkiRJUt2qp0AoSZIkSaqgeulD2FrsBswAVgFvAJOA9kW2GUVoyayk/YDZwJq4Ht+n5UaMHQhcBzxJaJ2dmadcA/BN4DVCX8+HgBEl7P88YGxzK5lhAnA74VLjCDglT7lqndPjgWmEKVLeJ1xCcEKesqcBL8R1mAscXML+TweOaX41gfBZPh94GFgWL/cDI3OU3RG4Dfj/9s48yo6ibOO/+cAAARJCQIigBmQJQSMa2UEIO4Igq4AsAQ8fqAioyPIBGlZFWd0Akc2FXVAkYBAh7IsIsgYUSAJB9oQlAcL2fH881ef29PSdexnm3ulh3t85fe5MdXV13edWV9db9VbVq8ArwEXAR3shD9sDt6V7v4m3jDkCGFSIV5XydQZe2Go2Xg35JuzSXqQdz+xSKR8CFiqcq4peeQ7Eeb2s5Fwr9Rqf7ls89i3Eq4pmkynP7/yFeK3UbF7gUFw/zQVm4CkbeaqiF3hp9QuBmfh9fR+wWSFOq/SaTPnvJezhlFEFvUZ2k9fidl2tLF87Affg+utp4LfAxwpxqqAXwBDgVGAaLltTcF3WUYjXSr2+AtyPn8WpwHdL4lRFr/mAk4FnsV43U776Zl+2a5uhmbZvs5r3xJZoPZLiaO4YJum/kq6TtLGkfSXNkXRsg+v2k+mtfCwj6Q1Jf5K0uaRvS5ot6dQ26bC1pKckXSppiqTJdeIdlvK5n6SNJF0t6UVJSzZI/0VJE3oxv5dKukfSWel3GN9mTW+XdIGkHSVtIOnElI9vF+LtJOldSUdKGifptylPn26Q/t2SzuslrRaSNEvSyZK+lLSYKGmupLG5ePNKelDSw5K2k7SDpMck/VPSPB8wD/tIOk7SNkmHQ5IOvyjEq0r5Oj/lYWNJW0i6XNLbktbIxWnXM3uBpGdlFqqoXtnxUUkzJT0v6bLCuVbrNT5pNC79Ttnx0YpqNlnS9YW8riGpo42a/U5+/+0jaT1Ju0o6vqJ6fTzl9Rr5fbWRpIPkOqUdeo0u+a2ulfSCXHdWSa/5SvI6Tq7D8lq0Uq+tZH4haUO5bE2T39v/UzG9kOv4FyX9r/xOP0bSe5K+0ya91k73+42kTeQ2w9uSDizEq4peZ0p6RW4zby4/ly9L+mSb9Oqto5m2bzOa99SWaPnR1wL3p+MwubE8JBd2sKTXC2HFo7cNwjMlPaHOL5b95QphRBt0yFfQl6n8oZhfrgB+kAtbUH4hNir0vV0ZZfldKP0O49us6WIlYRdImloIe1TSOYV8PyDp9w3S702DcB65ssqHDZJfzufmwnaWjdflc2Fjkr7b9+Jvlx3HyS+QrAFcpfJVpuGTkn6WC2vHM7uubGAdlH6HvEFYRb3Olo2MyepqELZar/ElGhWPKmlWplHxaKVmm6V0RvcTvS6SdLM6v6vaqVfxGCQ/m6dXVK/isYPM6m3S6yK5MzEflhmJK1VMr8Hyu6/YoXu5pDvbpNckSTcVwk6Wy9igium1tKR3JO2VC5tP0tPq3Mnb1+3aZo5Gbd9mNe+pLdHyI1xGm2dzYBJ2kcu4CFgAWK/ONeOBn6e/MzeMybnzGwB34iHy54Bf0dXVq8gqKY13cmHX4qH1TRpc2xu810SctbBbxSW5sDnAX7CO9ZgGDAd+SE2v9dO5wcDPsNvBm8A/aO77NpPfVmr6YknYvXR2r1wWWIHOer0HXEr3ek0GxgJ7UNNrfDo3D3ZVfhK7lTwE7NIgr+9it8c8b6Vr8/ldBZiO3ccy7sdleIsG9+gJL9HZZbRK5avIu3jRq3x+W/3MzoPrmaMpL29V02tVYEfsglhGX9dxUD3NGtFKzfYCrgce7iZOVfQaCmyL36Xd1f3tLGObAcOwC2tGVfQqBjMQzgAAExxJREFUY2fshnhnLqyVen0ETzvI83L6zNwwq6LXvHiqVVl+8y6jrdRrFeC6Qti1uIxlLslV0esz+P2Uz+9c7DaabytUoc5vRKO2ZLOa98SWaAthEDbPKDxXKM+T2Ad4VJ1rJgInpb/XTMc30/+jgb/iBtx2+AHchfL5NHnmx430PHPT50oNrm0Xo3DD+D+F8CnU1wpgG1zRnk1Nr3vSubOAPbFf+TbYR3si9jv/oLRb07Xo3LjKNCmWrynAosDiddL5Zrrmamp6TUznjgYOB34NbAXcCvyB+vMX6zEfNjrz+S3TC6xZb+k1D34BrQPsD5yOX05QvfLVgV9cw4HvAMsD5+TOt7p87Zvu8cs656ukVwdeLfoneK5QGe16Hh/HDZBHgX0K56qkGbhR9Ho6JgFjCudbqdnqwL/x7/ZqysPldJ7jVRW9Po8NDOE672083/EwOjfY21nn74TL+s25sKroVWQIbrBeWAhvpV7nAOsCu6f7rwAcC9xA7b1TFb1exQ3+g7ERszCwJe7gyte/rdSrmbSrolc2z7ksv5/E7/gsXtXbtY1oVvOe2BJtoUoTNqvOMGq9VnlmpXNlvIB7XADuKJz7AR5l2Yra1hkzgYvxQ3h7nTQfo+siH6ulz0XrXNNuhuHJ4cUtQWbhCmAQ5QbFvbiBNoPOeq2EDZk9gfNT2CQ8KnUksOkHzG87Nd0Q2Br3umdk5adYvmblzr9QktbDuAfqBTrrtSie5H5sOsB6LY1HDYsv++44PN3/N7mwx7ARMhyP3oEbh0tRq8Q/KHOwMQpeYOD7uXNVK19fpabpnPT/XbnzrSxfw4FjgF1x47eMKum1J7AkcGI3cVr9PD6D83kX7njYGS8ONJjaQilV0uzGdM1juBF1ODYuPkvt/dJKzZbEngf3YeNmYWzQXwGsgY2vqui1ZPo8E3ciHYH3Lz4WN3R/lc63q84fDHwZd8zll3Svil5FvoIb5xcVwlup10Rcvs6mlvfbcNsoo0p67Y47V+9N/wt3OJyfi9NKvZpJuyp6PZY+V8UjZeCOmVXT5yLYEOoP7dpGNKt5T2yJthAjhO+Psj06OuqEN2I1/ELNF54/4oexux6X03Ev6JHAYviF/OOUTpX2ZKynVb1z3ZFVHpfmwjKXyt4YIWyXpiOBC4A/A+eVnC/q0lO9Po0roEsL4Rfj3tdmVwPdAjc+D6HzinMXYJeRs4FP4O91bjrXW3qthXuNv4cN6F8UzlepfE1KaWyOn+mL6LxKWyvL13HYtevqBvGqoNdQ4Hhs3L/RTbxWP4+TsIFwLXANbuBdgo2H/DuxCpqBvUfOxUbg77GBI9zpk9FKzTrSsTUuZxcDu+F32Aa5eFXQK/v9rsEuyTfgztfzcaM9o111/pfxNJCyTrgq6FVkZzxF4IFCeCv1Goc7ZE5Lf++EjYAr6LzyYlX0OgWPmu+JXfyOwB2tX8/FaaVeZ+BncW9sQGyK35MU0q6CXg/gkfoTsafR4vgdsEIhv/2lXduIZjXvTVui1wiDsHlm4d6MIkMpt/YbMQLPucrzLh5x6a5H5DpcAR2OR4Zuwg3zmSXp9RWzcC9ycRndrDeo3khGPUbgnpfXC+HPYcNnvi5XvD/aoemiuJHyJB7NyZONBBbLV/b/+y1fI9JnMe/Z/830Qq2KG35n4iW287yE3ZvXwKPcU/Fvc3XJPXvKPcAteLnq/YFvAJ9K56pWvmYBd2MX8N3w6P7RufOtKl8r45Hmo/B3X4SaC85QPCchy18V9MqW4742l995sYvfIrn89UUddxl+Rkem/6uiWRnP4kbW53NhrdRsFm7YvZQLuwX3do/OxamCXjPT5w2F8Ouxh8SQ9H+7ythOePTj7kJ4VfTKMxxvmVNmvLZSr5Pw1kyH4HlkF+ORyvWx4QPV0WsMfhfthTt1b8IGzqnY6Mna1K3U6xxsFJ6e0ruc2vsmS7sqeoFHf1/Hz8DzuJPktJSH7HntD+3aRjSreW/bEr1GGITN8whd/Xs/DixIV3/gZniGriM18+BKeWbX6J04DveijAGWwJXD4nR1S+0rHsHfZblCeJnvdDM8g3tZBxfCl8APWm+4KbZS08HAVdhlYAvsVpgn06RYvkbhslDmLtodz6TPYvlaIn02Kl8rYDeevwPfrhNnIm5grYyfg23w4jitKIPZHIZl0mfVy9e9WIs8rShfy2Nj6nb8kplFbR7LDGoLWlVFrxXx/lOzcsfa2DVsFp33aOurOi7roa2KZt1R7E1ulWZT6oR3UFtooSp6dZdX6LwwRKvL2FDK5+NBdfTKsz3uoCm6i2a0Sq9RwL8KYY9iL4KsE7AqemXv6GJ+78WN/OG5sFbp9S6wX0orSztLM/usil7gDpHP4d9yFF5oZj78Xs8bplVv1zaiWc1725boNcIgbJ5r8ND8wrmwr+JK68Zursv8tIubCN+JG9H53oRtcYV8SxP5mY17bWcB38IjNcWVp/qK2/Dk6x1yYdlcimsaXPsWXbX6B278bJ8L60j/N6NVs7RC03mxW8XyuHHwfEmcJ/CiDXm9/if93xO9HsSV9A6F8B3TfbozMEdgl7rHsftQd+4a7+B5jDOw68woyl1hPyhrp8+p6bPK5asDGzZTS871dvm6BbtY5Y8T0rkvAT9Nf1dFr2w+V/64D/cGj6Orm1o767jt8AJf09P/VdGsjCXwM/HPknOt0Owq3EhbLBf2RdwZcV/6vyp6TcMujxsWwjfEddrsQngry9g2uOFbZhBWRa88O+N5tY93E6cVek2n82g3eM7aAtTmyFZFr6x+KOZ3LO7oLa7y3MrylY3cz8YLzN1GzaCoil55nsCG/nDcFjm7JE6V27WNaFbzntoSLWeeCRMm9OX9+xMP4YU0xuHeko2AH2F/8u4esAWxr/ls/KAtjF1vHsFzHFbFk93H4SV9b8S+0/VYDvcOLYAb4N8FDqDmmtJqBmM3jtG4UC+KjZzRuPJ+GxsKHbgB+DJ20zkF94LsQdcRsjxb4NW7puAGyGt4hbYV8AqOr6V7noAbRXthN8x6jMbGyqexwf10+g6LU6vcW6npGSmdw/DI3NK54wVqBtdLeHGQ93AnwQ9xo2sPyo3IjLXxPJ6HsCvo3HSfBdM9303f6/v4hb8fNhjLWAC7Wo3A85OG5PK6OHZVy/hpusfS6fudictto1VyG/FXvDDEUDzKtjuen3EFXuUMqlO+1sXP7CCs/ViswQb4BZ2tNtaq8vU6fubyx9LY3WpfaiPFVdHr+ZL87oQbUT+h1svc6jruj3i0eUhK/0jsAn0otaX2q6LZGOwilpWx9XBDajB2xcqWv2+lZg/id9iW+Df8Al6c5S7cqw/V0Qtc7o+itoXT13Hj8gBqnQ7teI/+GH/vY0vOVUkv8KJgp2L3zbIRmVbqNS+eAzcEv//WxO6Qr+HvU6U2xbO4s21X/OwNw9MEDsbz3P+W4rVSrzVSOoPT9zoae1lsR829sip6gad8LIcNwY3xInGPprQyL4e+btc2Q6O27xs0p3lPbYnWU4HNHvvTMVrS9ZLekPSMpGPkjai7u6ZD0k8k/VfSe+q8meWG8mamb0p6XtKv1P1myUj6hLwp6SuS5qT01m2jBiNVn5GF7324pBlJr5slfa6J9MdKuiN9N0laP4UPlvRzSc9JmitvyL5pE+lNqJPX/O/QSk2nNakXkvaW9Fj6fvfI5aNR+stKui7lXfKm28jl8ihJT0l6S9LDkr7WIK3ufttphbiXyGV2rqQHUt57Q69jJD0oaba8Gf098ibAHynEq0L5GilvUDsjXTND0lWS1izEa+czOz59r2I9UgW9yo7J6rrpeqv1Ol7So/JGwG/Im2LvVhKvCpotJelq+X3zlqSXJP1R0qg2a7ZcyscceVPl8yQNq6Be2bGrpClJs8ck7dtmvRaTN9U+tJs4VdLrQHnD9Y/VOd9KvTokfUPS/SntpyVdLL/bqqjXkpJ+I2m6XIdMkTcbH5SL00q9xkr6h/yOfFXSREmfqaNrFfQ6RNLUdM1Tkk5IabWrfPXWMVL1Gfk+Ne+JLdHyo0Pq00VtgiAIgiAIgiAIgj4i5hAGQRAEQRAEQRAMUMIgDIIgCIIgCIIgGKCEQRgEQRAEQRAEQTBACYMwCIIgCIIgCIJggBIGYRAEQRAEQRAEwQAlDMIgCIIgCIIgCIIBShiEQRAEQRAEQRAEA5QwCIMgCIIqosLxLjATmAyMBzr6KmNBEARB8GFi3r7OQBAEQRB0w1Hp8yPAcsA2wHrAF4D9+ipTQRAEQfBhoUNSX+chCIIgCIpkL6fiSODawE0p/FPA1HZmKgiCIAg+bITLaBAEQdCfuBV4BBuEY+vE2RS4GngRmAs8DvwUWKQk7hjgQmBaivsCcA9wKh6VzJiAjdT1gT2Ae4E3gOeBc4Al6+RleeC3wNPAW8B/0//Ll8TN32N74C7gdewqexGwVMk1ywK/Bh5L+ZkJPACcAQwvib8zcAMwC3gTmAIcAcxXEndd4C/ADKzNs8AdwA/rfNcgCIKgHxIjhEEQBEEVqTdCCPAwsBLwFeDPhXM/wG6mM4GrsME2BtgkXbcm8GqKOwa4M93rSjzaOAS7po4DFgVmp7gTsCF0ZUrrYuAZYJ10TAVWxwZlxqrAdcDC6bqHgVHA1indDYG7c/Gze1wKbJWumZ7SXRcbwqtg4wxgBPBgyvPV6fz8wDIp7dXT+Yyzgb2wgXct8DKwBrAWnpu5MfBOirsZMDFpdSU2aBfFuo8CliAIgiD4UBBzCIMgCIL+xBeBFfFo212Fc+OwMXg78CVs8GSMB85N57+TwvbABlSZYTkMj84V2RwbWvfmwk4BDgR+DHw9hXXgkcAhwK7AH3Lxv4pH/H4PjAbeK9xjM2xMPpALuwCP7m0NXJLCtsdG2oHAaYU0FiykOx4bg1cAX8OjiRkTsCH6rVw6e2MvovWB+wppL0YQBEHwoSFcRoMgCIIqMyEdx+FRueuwsXUQHqHLs3/63JvOxiDAecC/sDFU5I2SsFl0NdQAfkdnYzDL4yvALtRcL9fCI2m309kYBH+PW7Bhu07JPX5GZ2MQ4Kz0uVpJ/LL8zymEH4BH//YqiX8M8BLNa/NiSVgQBEHQT4kRwiAIgqDKFOerCY/CnVsSd03gbWCHdBQZBCyO59a9hA2zA4A/AZdhY/NWPOewHjeWhL2Cjc31sEvlv4DPp3PX10nnemwMfg4vkpPn7q7ReSp9DsuFXQkcD/wSz5uclPL/MDWXW4DBwGexIXdgnfzMTXnP+AOwLXapvRjPO7wVu5sGQRAEHyLCIAyCIAiqTDaHcEFs8J2NF0yZTldjazh+rzVa9GQhbBDehefmHY7dL3dL5x/FrqUXllz7XJ00n02fQwufxVFMCuFlC90URzehNrdvnlzYdDxiOAG7mW6bwp8CTsQjjWAjsgMbw80uCHM5sCXwPTyquE8K/ydwGPC3JtMJgiAIKk64jAZBEAT9gTl4BO/L2Cg6H4985XkFu3p2NDim5665HRs+w/CWFsfgBVMuADYqyUe9xVSyVUZfKXzWW310RCFeT5mC5yQOx3szHorf7adRm8+Y3eNeGmuTZyKwAdZmQzxXcmW8WM/oD5jvIAiCoCKEQRgEQRD0J+7H8+mWprY4TMYd2HhZuQfpzgVuw6uUZnMRty6Jt15J2FC8+me2jQPU5hmuX+d+Wfg97zOf9XgHj96dgBefAS+WA17R9CGsy6I9SHsOHo39LnZRHYQX1wmCIAg+BIRBGARBEPQ3jsXG10F0nlN3Svo8C/hYyXUL4m0WMtal5tqZJxsFLFtldDc87y/PhJTOhdS2hLgVu56ug91R82yPV0v9N15cpqesRvmIZVn+T8aG3DmUu6kOozbvETwiuECTaQdBEAT9mJhDGARBEPQ3ngbOxAvCHIzntAH8HbtM/gj4D96bbyqeM/hJPLp3C55vB54ftwneg+8JPJK2Mh79moU3fC9yDTb2LqHzPoTT0r0zhLe1+BtelOXPeJ/AFfHI3WvA7pSvZNosu+CtIm7EG9PPAj6F3WrnAqfm4p4DjAW+iRfNmQQ8iUcMl8EG6rnAvin+ScBIrM00vM3HWOxCOh1vmxEEQRB8CIiN6YMgCIIq0t3G9OCRqifS38vSebGXdbDb5zp4z7xXsBF5PZ4bmK3iuQl2r1wdWAp3ks7AxtJJdJ5rOAEvyDIOG0oHYuNuNp5T93+ULyCzInAEno+4GF7p8+94ruKjhbj5e0wunBuJjdvz8Z6CpHyPx1tcfByP6D0N3Jzy/yBd2RIbfavhkcKZ2DC8Fu+L+EiKtyOwDZ6XOAIbrk9iw/ZU4IWStIMgCIJ+SBiEQRAEQdCYCdQ31oIgCIKg3xJzCIMgCIIgCIIgCAYoYRAGQRAEQRAEQRAMUMIgDIIgCIIgCIIgGKDEHMIgCIIgCIIgCIIBSowQBkEQBEEQBEEQDFDCIAyCIAiCIAiCIBighEEYBEEQBEEQBEEwQAmDMAiCIAiCIAiCYIASBmEQBEEQBEEQBMEAJQzCIAiCIAiCIAiCAcr/A4ZeRvWoJtKWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 8))\n",
    "\n",
    "sns.barplot(x=\"Interval\", y='value', hue='variable', data=histogram_df, ax=ax, palette=[colors[1], colors[0]])\n",
    "\n",
    "plt.legend([\"Groups\", \"Individuals\"])\n",
    "\n",
    "ax.set_title(\"Histogram of Answers (Stage 2)\", fontsize=26)\n",
    "ax.set_xlabel(\"Responses\", fontsize=20)\n",
    "ax.set_ylabel(\"Total Count\", fontsize=20)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "ax.legend(h, [\"Individuals\", \"Groups\"], title=\"Treatments\")\n",
    "\n",
    "plt.savefig('histogram_answers_stage2.png', transparent=\"True\", pad_inches=0.2, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = 10\n",
    "\n",
    "results_groups = []\n",
    "results_individuals = []\n",
    "labels = []\n",
    "\n",
    "for i in range(0, 101, bins):\n",
    "    lower_bound = i\n",
    "    upper_bound = i+bins-1\n",
    "    label = f\"{lower_bound} to {upper_bound}\"\n",
    "    labels.append(label)\n",
    "    groups_count = df[(df[\"Treatment\"] == \"G\") & (df[\"Final Answer Stage 3\"] >= lower_bound) & (df[\"Final Answer Stage 3\"] <= upper_bound)][\"ID\"].count()/3\n",
    "    individual_count = df[(df[\"Treatment\"] == \"I\") & (df[\"Final Answer Stage 3\"] >= lower_bound) & (df[\"Final Answer Stage 3\"] <= upper_bound)][\"ID\"].count()\n",
    "    results_groups.append(groups_count)\n",
    "    results_individuals.append(individual_count)\n",
    "\n",
    "labels[-1] = \"100\"\n",
    "\n",
    "merged_data = []\n",
    "for i in range(len(labels)):\n",
    "    data = [labels[i], results_individuals[i], results_groups[i]]\n",
    "    merged_data.append(data)\n",
    "\n",
    "histogram_df = pd.DataFrame(merged_data, columns=[\"Interval\", \"Individuals\", \"Groups\"])\n",
    "histogram_df = histogram_df.melt(value_vars=[\"Individuals\", \"Groups\"], id_vars=[\"Interval\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 8))\n",
    "\n",
    "sns.barplot(x=\"Interval\", y='value', hue='variable', data=histogram_df, ax=ax, palette=[colors[1], colors[0]])\n",
    "\n",
    "plt.legend([\"Groups\", \"Individuals\"])\n",
    "\n",
    "ax.set_title(\"Histogram of Answers (Stage 3)\", fontsize=26)\n",
    "ax.set_xlabel(\"Responses\", fontsize=20)\n",
    "ax.set_ylabel(\"Total Count\", fontsize=20)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "ax.legend(h, [\"Individuals\", \"Groups\"], title=\"Treatments\")\n",
    "\n",
    "plt.savefig('histogram_answers_stage3.png', transparent=\"True\", pad_inches=0.2, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 1**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if responses for each round were different for each treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[df[\"Treatment\"] == \"G\"]\n",
    "seats = sub_df[\"Seat\"].unique().tolist()\n",
    "\n",
    "x = []\n",
    "\n",
    "for seat in seats:\n",
    "    group_ans = sub_df[sub_df[\"Seat\"] == seat][\"Final Answer Stage 1\"].to_list()[0]\n",
    "    x.append(group_ans)\n",
    "\n",
    "y = df[df[\"Treatment\"] == \"I\"][\"Final Answer Stage 1\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 90.5\n",
      "p-value: 0.0015818476253053298\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if responses for each treatment where different from zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 90.5\n",
      "p-value: 0.0015818476253053298\n"
     ]
    }
   ],
   "source": [
    "zeros = np.zeros(20).tolist()\n",
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W-stat: 10.0\n",
      "p-value: 1.4908061610982427e-08\n"
     ]
    }
   ],
   "source": [
    "zeros = np.zeros(20).tolist()\n",
    "u, p = mannwhitneyu(y, zeros)\n",
    "print(f\"W-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-stat: 5.09022425154778\n",
      "p-value: 6.50177057442766e-05\n"
     ]
    }
   ],
   "source": [
    "t, p = ttest_1samp(x, 0)\n",
    "print(f\"t-stat: {t}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-stat: 7.662755609813169\n",
      "p-value: 3.1583896201885087e-07\n"
     ]
    }
   ],
   "source": [
    "t, p = ttest_1samp(y, 0)\n",
    "print(f\"t-stat: {t}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[df[\"Treatment\"] == \"G\"]\n",
    "seats = sub_df[\"Seat\"].unique().tolist()\n",
    "\n",
    "x = []\n",
    "\n",
    "for seat in seats:\n",
    "    group_ans = sub_df[sub_df[\"Seat\"] == seat][\"Final Answer Stage 2\"].to_list()[0]\n",
    "    x.append(group_ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[df[\"Treatment\"] == \"I\"][\"Final Answer Stage 2\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 76.0\n",
      "p-value: 0.0004031811106233999\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if responses for each treatment where different from zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 50.0\n",
      "p-value: 1.6515114075603853e-06\n"
     ]
    }
   ],
   "source": [
    "zeros = np.zeros(20).tolist()\n",
    "u, p = mannwhitneyu(x, zeros)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 20.0\n",
      "p-value: 5.259344238138618e-08\n"
     ]
    }
   ],
   "source": [
    "zeros = np.zeros(20).tolist()\n",
    "u, p = mannwhitneyu(y, zeros)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-stat: 4.015973489349137\n",
      "p-value: 0.0007387400418842687\n"
     ]
    }
   ],
   "source": [
    "t, p = ttest_1samp(x, 0)\n",
    "print(f\"t-stat: {t}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-stat: 6.446034751724916\n",
      "p-value: 3.528341799294264e-06\n"
     ]
    }
   ],
   "source": [
    "t, p = ttest_1samp(y, 0)\n",
    "print(f\"t-stat: {t}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[df[\"Treatment\"] == \"G\"]\n",
    "seats = sub_df[\"Seat\"].unique().tolist()\n",
    "\n",
    "x = []\n",
    "\n",
    "for seat in seats:\n",
    "    group_ans = sub_df[sub_df[\"Seat\"] == seat][\"Final Answer Stage 3\"].to_list()[0]\n",
    "    x.append(group_ans)\n",
    "\n",
    "y = df[df[\"Treatment\"] == \"I\"][\"Final Answer Stage 3\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 59.0\n",
      "p-value: 6.35211685174059e-05\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if responses for each treatment where different from zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 70.0\n",
      "p-value: 1.2764813288999263e-05\n"
     ]
    }
   ],
   "source": [
    "zeros = np.zeros(20).tolist()\n",
    "u, p = mannwhitneyu(x, zeros)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 30.0\n",
      "p-value: 1.739148920693556e-07\n"
     ]
    }
   ],
   "source": [
    "zeros = np.zeros(20).tolist()\n",
    "u, p = mannwhitneyu(y, zeros)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using t-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-stat: 3.6497748991723467\n",
      "p-value: 0.0017040073798565384\n"
     ]
    }
   ],
   "source": [
    "t, p = ttest_1samp(x, 0)\n",
    "print(f\"t-stat: {t}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-stat: 3.2343043454825717\n",
      "p-value: 0.004364951935981083\n"
     ]
    }
   ],
   "source": [
    "t, p = ttest_1samp(y, 0)\n",
    "print(f\"t-stat: {t}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution of the Average Response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to understand how the average response changed over time (over each round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "means_groups = []\n",
    "means_individuals = []\n",
    "labels = []\n",
    "\n",
    "for i in range(1, 4):\n",
    "    label = f\"Round {i}\"\n",
    "    labels.append(label)\n",
    "    means = df.groupby(\"Treatment\")[f\"Final Answer Stage {i}\"].mean()\n",
    "    means_groups.append(means[\"G\"])\n",
    "    means_individuals.append(means[\"I\"])\n",
    "\n",
    "\n",
    "merged_means = []\n",
    "for i in range(len(labels)):\n",
    "    data = [labels[i], means_individuals[i], means_groups[i]]\n",
    "    merged_means.append(data)\n",
    "\n",
    "means_df = pd.DataFrame(merged_means, columns=[\"Round\", \"Individuals\", \"Groups\"])\n",
    "means_df = means_df.melt(value_vars=[\"Individuals\", \"Groups\"], id_vars=[\"Round\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Round</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Round 1</td>\n",
       "      <td>Individuals</td>\n",
       "      <td>41.28315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Round 2</td>\n",
       "      <td>Individuals</td>\n",
       "      <td>22.74000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Round 3</td>\n",
       "      <td>Individuals</td>\n",
       "      <td>14.95070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Round 1</td>\n",
       "      <td>Groups</td>\n",
       "      <td>24.58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Round 2</td>\n",
       "      <td>Groups</td>\n",
       "      <td>7.40750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Round 3</td>\n",
       "      <td>Groups</td>\n",
       "      <td>1.06950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Round     variable     value\n",
       "0  Round 1  Individuals  41.28315\n",
       "1  Round 2  Individuals  22.74000\n",
       "2  Round 3  Individuals  14.95070\n",
       "3  Round 1       Groups  24.58000\n",
       "4  Round 2       Groups   7.40750\n",
       "5  Round 3       Groups   1.06950"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 8))\n",
    "\n",
    "sns.pointplot(x=\"Round\", y='value', hue='variable', data=means_df, ax=ax, palette=[colors[1], colors[0]])\n",
    "\n",
    "plt.legend([\"Groups\", \"Individuals\"])\n",
    "\n",
    "ax.set_title(\"Evolution of Mean Response\", fontsize=26)\n",
    "ax.set_xlabel(\"\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Response\", fontsize=20)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "ax.legend(h, [\"Individuals\", \"Groups\"], title=\"Treatments\")\n",
    "\n",
    "plt.savefig('means_progression.png', transparent=\"True\", pad_inches=0.2, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to Target\n",
    "\n",
    "In the following lines, we calculate the individual's (for both treatments) distance to the target. To do so, we first calculate the target for each treatment, for each market, for each round. This target is, as defined for this experiment, the average of the submissions for the market multiplied by $p=\\frac{2}{3}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "rounds = 3\n",
    "\n",
    "treatments = df[\"Treatment\"].unique().tolist()\n",
    "for treatment in treatments:\n",
    "    results[treatment] = {}\n",
    "    treat_df = df[df[\"Treatment\"] == treatment]\n",
    "    markets = treat_df[\"Market\"].unique().tolist()\n",
    "    for market in markets:\n",
    "        for i in range(1, rounds+1):\n",
    "            target = treat_df[treat_df[\"Market\"] == market][f\"Final Answer Stage {i}\"].mean() * 2/3\n",
    "            results[treatment][market] = target\n",
    "            df.loc[(df[\"Treatment\"] == treatment) & (df[\"Market\"] == market), f\"Target Stage {i}\"] = target\n",
    "\n",
    "for i in range(1, rounds+1):\n",
    "    df[f\"Individual Distance Target Stage {i}\"] = abs(df[f\"Individual Answer Stage {i}\"] - df[f\"Target Stage {i}\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to Target vs. Experience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Experience\")[\"Individual Distance Target Stage 1\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average Distance to Target by Experience (Stage 1)\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"Experience\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.set_ylim(0, 25)\n",
    "\n",
    "plt.savefig('avg_distance_experience_stage1.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Experience\"] == \"Yes\"][\"Individual Distance Target Stage 1\"].to_list()\n",
    "y = df[df[\"Experience\"] == \"No\"][\"Individual Distance Target Stage 1\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 412.5\n",
      "p-value: 0.25597672273758715\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Experience\")[\"Individual Distance Target Stage 2\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average Distance to Target by Experience (Stage 2)\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"Experience\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.set_ylim(0, 25)\n",
    "\n",
    "plt.savefig('avg_distance_experience_stage2.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Experience\"] == \"Yes\"][\"Individual Distance Target Stage 2\"].to_list()\n",
    "y = df[df[\"Experience\"] == \"No\"][\"Individual Distance Target Stage 2\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 415.5\n",
      "p-value: 0.2695407875572563\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Experience\")[\"Individual Distance Target Stage 3\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average Distance to Target by Experience (Stage 3)\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"Experience\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.set_ylim(0, 25)\n",
    "\n",
    "plt.savefig('avg_distance_experience_stage3.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Experience\"] == \"Yes\"][\"Individual Distance Target Stage 3\"].to_list()\n",
    "y = df[df[\"Experience\"] == \"No\"][\"Individual Distance Target Stage 3\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 427.0\n",
      "p-value: 0.32479648840939274\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to Target vs. Skill Level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Skills\")[\"Individual Distance Target Stage 1\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average Distance to Target by Skill Level (Stage 1)\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"Skill Level\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.set_ylim(0, 30)\n",
    "\n",
    "plt.savefig('avg_distance_skills_stage1.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Skills\")[\"Individual Distance Target Stage 2\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average Distance to Target by Skill Level (Stage 2)\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"Skill Level\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.set_ylim(0, 30)\n",
    "\n",
    "plt.savefig('avg_distance_skills_stage2.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Skills\")[\"Individual Distance Target Stage 3\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average Distance to Target by Skill Level (Stage 3)\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"Skill Level\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.set_ylim(0, 30)\n",
    "\n",
    "plt.savefig('avg_distance_skills_stage3.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Skill 1 vs. Skill 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Skills\"] == 1][\"Individual Distance Target Stage 1\"]\n",
    "y = df[df[\"Skills\"] == 4][\"Individual Distance Target Stage 1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 10.0\n",
      "p-value: 0.06680720126885807\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Skill 3 vs. Skill 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Skills\"] == 3][\"Individual Distance Target Stage 1\"]\n",
    "y = df[df[\"Skills\"] == 4][\"Individual Distance Target Stage 1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 63.0\n",
      "p-value: 0.1771797667256096\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Skill 1 vs. Skill 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Skills\"] == 1][\"Individual Distance Target Stage 2\"]\n",
    "y = df[df[\"Skills\"] == 4][\"Individual Distance Target Stage 2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 7.0\n",
      "p-value: 0.02689204443119914\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Skill 3 vs. Skill 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Skills\"] == 3][\"Individual Distance Target Stage 2\"]\n",
    "y = df[df[\"Skills\"] == 4][\"Individual Distance Target Stage 2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 41.0\n",
      "p-value: 0.02729292840289088\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Round 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Skill 1 vs. Skill 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Skills\"] == 1][\"Individual Distance Target Stage 3\"]\n",
    "y = df[df[\"Skills\"] == 4][\"Individual Distance Target Stage 3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 13.0\n",
      "p-value: 0.14132525106865657\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Skill 3 vs. Skill 4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df[\"Skills\"] == 3][\"Individual Distance Target Stage 3\"]\n",
    "y = df[df[\"Skills\"] == 4][\"Individual Distance Target Stage 3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 64.5\n",
      "p-value: 0.19464620647497732\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to Target (Final Answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously, we took a look at individual answer and its distance to the target. Here we will calculate the distance to target using the final answer (group decision for the groups treatment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Final Answer Stage 1</th>\n",
       "      <th>Target Stage 1</th>\n",
       "      <th>Final Distance Target Stage 1</th>\n",
       "      <th>Final Answer Stage 2</th>\n",
       "      <th>Target Stage 2</th>\n",
       "      <th>Final Distance Target Stage 2</th>\n",
       "      <th>Final Answer Stage 3</th>\n",
       "      <th>Target Stage 3</th>\n",
       "      <th>Final Distance Target Stage 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.0</td>\n",
       "      <td>13.146667</td>\n",
       "      <td>23.853333</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.146667</td>\n",
       "      <td>19.853333</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.350667</td>\n",
       "      <td>0.649333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.0</td>\n",
       "      <td>13.146667</td>\n",
       "      <td>23.853333</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.146667</td>\n",
       "      <td>19.853333</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.350667</td>\n",
       "      <td>0.649333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.0</td>\n",
       "      <td>13.146667</td>\n",
       "      <td>23.853333</td>\n",
       "      <td>26.0</td>\n",
       "      <td>6.146667</td>\n",
       "      <td>19.853333</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.350667</td>\n",
       "      <td>0.649333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>11.906667</td>\n",
       "      <td>10.093333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.493333</td>\n",
       "      <td>0.506667</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.236000</td>\n",
       "      <td>0.434000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>11.906667</td>\n",
       "      <td>10.093333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.493333</td>\n",
       "      <td>0.506667</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.236000</td>\n",
       "      <td>0.434000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Final Answer Stage 1  Target Stage 1  Final Distance Target Stage 1  \\\n",
       "0                  37.0       13.146667                      23.853333   \n",
       "1                  37.0       13.146667                      23.853333   \n",
       "2                  37.0       13.146667                      23.853333   \n",
       "3                  22.0       11.906667                      10.093333   \n",
       "4                  22.0       11.906667                      10.093333   \n",
       "\n",
       "   Final Answer Stage 2  Target Stage 2  Final Distance Target Stage 2  \\\n",
       "0                  26.0        6.146667                      19.853333   \n",
       "1                  26.0        6.146667                      19.853333   \n",
       "2                  26.0        6.146667                      19.853333   \n",
       "3                   3.0        2.493333                       0.506667   \n",
       "4                   3.0        2.493333                       0.506667   \n",
       "\n",
       "   Final Answer Stage 3  Target Stage 3  Final Distance Target Stage 3  \n",
       "0                  2.00        1.350667                       0.649333  \n",
       "1                  2.00        1.350667                       0.649333  \n",
       "2                  2.00        1.350667                       0.649333  \n",
       "3                  0.67        0.236000                       0.434000  \n",
       "4                  0.67        0.236000                       0.434000  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rounds = 3\n",
    "for i in range(1, rounds+1):\n",
    "    df[f\"Final Distance Target Stage {i}\"] = abs(df[f\"Final Answer Stage {i}\"] - df[f\"Target Stage {i}\"])\n",
    "\n",
    "\n",
    "cols = []\n",
    "rounds = 3\n",
    "for i in range(1, rounds+1):\n",
    "    cols.append(f\"Final Answer Stage {i}\")\n",
    "    cols.append(f\"Target Stage {i}\")\n",
    "    cols.append(f\"Final Distance Target Stage {i}\")\n",
    "    \n",
    "df[cols].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Distance to Target by Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "means_groups = []\n",
    "means_individuals = []\n",
    "labels = []\n",
    "\n",
    "for i in range(1, 4):\n",
    "    label = f\"Round {i}\"\n",
    "    labels.append(label)\n",
    "    means = df.groupby(\"Treatment\")[f\"Final Distance Target Stage {i}\"].mean()\n",
    "    means_groups.append(means[\"G\"])\n",
    "    means_individuals.append(means[\"I\"])\n",
    "\n",
    "\n",
    "merged_means = []\n",
    "for i in range(len(labels)):\n",
    "    data = [labels[i], means_individuals[i], means_groups[i]]\n",
    "    merged_means.append(data)\n",
    "\n",
    "distance_df = pd.DataFrame(merged_means, columns=[\"Round\", \"Individuals\", \"Groups\"])\n",
    "distance_df = distance_df.melt(value_vars=[\"Individuals\", \"Groups\"], id_vars=[\"Round\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 8))\n",
    "\n",
    "sns.pointplot(x=\"Round\", y='value', hue='variable', data=distance_df, ax=ax, palette=[colors[1], colors[0]])\n",
    "\n",
    "plt.legend([\"Groups\", \"Individuals\"])\n",
    "\n",
    "ax.set_title(\"Evolution of Mean Distance to Target\", fontsize=26)\n",
    "ax.set_xlabel(\"\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\", fontsize=20)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "ax.legend(h, [\"Individuals\", \"Groups\"], title=\"Treatments\")\n",
    "\n",
    "plt.savefig('mean_distacne_target_progression.png', transparent=\"True\", pad_inches=0.2, bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 1**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if the distances from the target for each round were different for each treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[df[\"Treatment\"] == \"G\"]\n",
    "seats = sub_df[\"Seat\"].unique().tolist()\n",
    "\n",
    "x = []\n",
    "\n",
    "for seat in seats:\n",
    "    group_ans = sub_df[sub_df[\"Seat\"] == seat][\"Final Distance Target Stage 1\"].to_list()[0]\n",
    "    x.append(group_ans)\n",
    "\n",
    "y = df[df[\"Treatment\"] == \"I\"][\"Final Distance Target Stage 1\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 167.0\n",
      "p-value: 0.18965478936246422\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 2**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if the distances from the target for each round were different for each treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[df[\"Treatment\"] == \"G\"]\n",
    "seats = sub_df[\"Seat\"].unique().tolist()\n",
    "\n",
    "x = []\n",
    "\n",
    "for seat in seats:\n",
    "    group_ans = sub_df[sub_df[\"Seat\"] == seat][\"Final Distance Target Stage 2\"].to_list()[0]\n",
    "    x.append(group_ans)\n",
    "\n",
    "y = df[df[\"Treatment\"] == \"I\"][\"Final Distance Target Stage 2\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 120.0\n",
      "p-value: 0.015750581331594186\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Round 3**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test if the distances from the target for each round were different for each treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = df[df[\"Treatment\"] == \"G\"]\n",
    "seats = sub_df[\"Seat\"].unique().tolist()\n",
    "\n",
    "x = []\n",
    "\n",
    "for seat in seats:\n",
    "    group_ans = sub_df[sub_df[\"Seat\"] == seat][\"Final Distance Target Stage 3\"].to_list()[0]\n",
    "    x.append(group_ans)\n",
    "\n",
    "y = df[df[\"Treatment\"] == \"I\"][\"Final Distance Target Stage 3\"].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U-stat: 62.0\n",
      "p-value: 9.930275706783055e-05\n"
     ]
    }
   ],
   "source": [
    "u, p = mannwhitneyu(x, y)\n",
    "print(f\"U-stat: {u}\\np-value: {p}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender Leadership in Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_groups = []\n",
    "groups = df[df[\"Treatment\"] == \"G\"][\"Seat\"].unique()\n",
    "\n",
    "for group in groups:\n",
    "    genders = df[df[\"Seat\"] == group][\"Gender\"].unique()\n",
    "    if len(genders) > 1:\n",
    "        multi_groups.append(group)\n",
    "\n",
    "df_groups = df[(df[\"Seat\"].isin(multi_groups)) & (df[\"Treatment\"] == \"G\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "stage = 1\n",
    "decision_genders = []\n",
    "groups_gender = {}\n",
    "\n",
    "for group in multi_groups:\n",
    "    g = df_groups[df_groups[\"Seat\"] == group]\n",
    "    decision_genders_group = []\n",
    "    selected_index = []\n",
    "    for i in range(3):\n",
    "        if g.iloc[i][f\"Individual Answer Stage {stage}\"] == g.iloc[i][f\"Final Answer Stage {stage}\"]:\n",
    "            selected_index.append(i)\n",
    "    for i in selected_index:\n",
    "        decision_genders_group.append(g.iloc[i][\"Gender\"])\n",
    "    if len(decision_genders_group) == 1:\n",
    "        groups_gender[group] = decision_genders_group[0]\n",
    "    decision_genders.append(decision_genders_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "male = 0\n",
    "female = 0\n",
    "\n",
    "for grouping in decision_genders:\n",
    "    if len(grouping) == 1:\n",
    "        if grouping[0] == \"Male\":\n",
    "            male += 1\n",
    "        if grouping[0] == \"Female\":\n",
    "            female += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Males made the final decision: 5 times (62.5%)\n",
      "Females made the final decision: 3 times (37.5%)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Males made the final decision: {male} times ({round(((male/(male+female))*100), 2)}%)\")\n",
    "print(f\"Females made the final decision: {female} times ({round(((female/(male+female))*100), 2)}%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 8))\n",
    "\n",
    "sns.barplot([\"Female\", \"Male\"], [female, male], ax=ax1, palette=colors)\n",
    "ax1.set_title(\"Gender Leadership in Groups\\n\", fontsize=22)\n",
    "ax1.set_xlabel(\"\\nGender\", fontsize=16)\n",
    "ax1.set_ylabel(\"Total Count\\n\", fontsize=16)\n",
    "\n",
    "ax1.spines['bottom'].set_color('white')\n",
    "ax1.spines['top'].set_color('white') \n",
    "ax1.spines['right'].set_color('white')\n",
    "ax1.spines['left'].set_color('white')\n",
    "ax1.tick_params(axis='x', colors='white')\n",
    "ax1.tick_params(axis='y', colors='white')\n",
    "ax1.yaxis.label.set_color('white')\n",
    "ax1.xaxis.label.set_color('white')\n",
    "ax1.title.set_color('white')\n",
    "\n",
    "\n",
    "sns.barplot([\"Female\", \"Male\"], [(female/(male+female)), (male/(male+female))], ax=ax2, palette=colors)\n",
    "ax2.set_title(\"Gender Leadership in Groups\\n\", fontsize=22)\n",
    "ax2.set_xlabel(\"\\nGender\", fontsize=16)\n",
    "ax2.set_ylabel(\"Proportion\\n\", fontsize=16)\n",
    "\n",
    "ax2.spines['bottom'].set_color('white')\n",
    "ax2.spines['top'].set_color('white') \n",
    "ax2.spines['right'].set_color('white')\n",
    "ax2.spines['left'].set_color('white')\n",
    "ax2.tick_params(axis='x', colors='white')\n",
    "ax2.tick_params(axis='y', colors='white')\n",
    "ax2.yaxis.label.set_color('white')\n",
    "ax2.xaxis.label.set_color('white')\n",
    "ax2.title.set_color('white')\n",
    "ax2.set_ylim(0, 1)\n",
    "\n",
    "plt.savefig('gender_leadership_groups.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to Target by Gender Leadership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_groups = df[(df[\"Seat\"].isin(list(groups_gender.keys()))) & (df[\"Treatment\"] == \"G\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juangesino/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n"
     ]
    }
   ],
   "source": [
    "df_groups.loc[:, \"gender_decision\"] = None\n",
    "\n",
    "for group, gender in groups_gender.items():\n",
    "    df_groups.loc[df_groups[\"Seat\"] == group, \"gender_decision\"] = gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 8))\n",
    "\n",
    "male_avg_distance = df_groups[df_groups[\"gender_decision\"] == \"Male\"][\"Final Distance Target Stage 1\"].mean()\n",
    "female_avg_distance = df_groups[df_groups[\"gender_decision\"] == \"Female\"][\"Final Distance Target Stage 1\"].mean()\n",
    "\n",
    "sns.barplot([\"Female\", \"Male\"], [female_avg_distance, male_avg_distance], ax=ax, palette=colors)\n",
    "ax.set_title(\"Distance to Target by Gender Leadership\\n\", fontsize=22)\n",
    "ax.set_xlabel(\"\\nGender\", fontsize=16)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=16)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "\n",
    "plt.savefig('leadership_distance.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance to Target by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m0XzLr6ryBM7yTe+/kkZnRD/JiHcqjY7RLqD9cr6+KDday7nsiOwh2t/e7mfF4wrUb+OjbWniNi8XER39zKKxjGfTuC1mu47eLqL9ibRy+/01rRO8y4hkq9X0ZZJ3Ca2X6UJE54YwNtvv2W3G/ZJG0rZF07gfEr/vSkQneVXz0zgBehLtE+I63e5PHqLRGWB1OKmp3KuLxzuJfW+r3/7WSqytfvs/MfSEcVW5L1iGof9jaxL/Y9C+V/JftBnXy+/QaV3vxrLF4yNdlD2BOCFzII3OFKteBPy4KDdSzyNOdlxOdKRX/X++s1Jurabpyu37Qob+j1fdA1zVYtzaxLYA7Y9FFqPzschYKZdd84kFqS3vMy31XtlL8x3U15aeAnyeRg+XR9SUOZnoFXh5InE+vzJuX2LbfZZ574lY7bn00i7jXbbF+/+h/a07IA5ozqRxu492lmh6vVrxeEuH6W5m3oNTGPpdT6sZX6fVd+218rt2c2BV+hFxJn99opfWLxInKC4kaoIvIf8gvNn+xAFau1rnUvPyqmp18gMa68zCTe9XWyZcR/dWoXFi4YvF0MloLedynb2R9onNDZXnq9K+J/pe24To9bebA8J2y/j2NuOmESezoPP2ewuNxKmquv3+o8M8SmOx/d7UZlzZ8/0LaKwLpVuJbXVroha6Wvu8I3FiBaJn5VwPF4+LEUnpSPcD5W+/GpFsd6PVb9/NvgBin1MmrKtX3m/3e7cb18vv0G5d71a5vXW7z3+SOMlRnuhYllh3DqRxF4y3Eon+icOMaXfieKObuwuM5P+5ueUBDN2+z+vi82Hs/p9bKbez8Y5DE4w101JvrUCj1vgiIjHaoGlYlkZNaavbI/29Uqa5yWr5+ldEzWJVu4PjVlrd87quCWvVDKKJ1pLEAdUHiT/VFYs4pjO0mXPzybvyD75TjcATLd7v5XftpQWIBAraH2g2e444mPoiUZu4MLEufZqoBb2bSLaHexuRtYiDsoWIGqNDifVxeeKWWdOL16V2J1u7OZhvjnN68fgsrWuy6vTTcl6seGy1TpaqB/fTW5bqvUWAnxMH9g8Rzac3JWqIym1yOo0kp90ybrf9Vw/OB2377fb7LFYzrmwevidDv1/ZxPtKuj9xUHVH8TiFeWsQqxYqypRDq1rDXv723Sb21f1Bt+vPbGJ/UWcs/+vGwgPE/+keDL085J3DnN/KRAu3RYkTQO8han5XoLG/r7Y+miz/z1LPWTMt9db+NJqzHUjn655eAGxGfS3yyUQN9l5E4vMMsAZxcFyOb1b945tO54P+kdiHuJZvNrAN9Wew29WOlbF2Omted9BanR5gPRpNfMfby2nc77jb1gGlR4kWCR8jrmfdHHgV8BriJMW3iIOkw4cR19uIGsVHiHXuoZoyne4JOxJlgrkg8ft0m1BXl/NuRK3reGmXSFVVx3dba9YLu9NoWrkDcd/dZlMZ+SUE1WUy0u33OeLEUatrL8dat9+nbt96BtGqZHGiBdHxxMnGsqbxhzXTdOMS4r7sEM3L29XYdqP87f8AbDfCeY3k86H97z2V1gnWeH+HZg8SJ8pz+slo5QdE3yebMPQEZ44Die38KeLylLo+CVaoea/0JJEQ9+L/+XktPr/flMvugXGNQhOONdNSbw2n05BW05xKNCVdnLiOFhq10o8SNdPNqp2fbDiMWHKUnRddS+umYC9pM315vVa76zbbjR/L75rjLZXn3VxzXmcu8bseC7yeOBgpLxn4IJ2TuTrl8voD9Yk0tF9eI1Vt6rxexnT30Ei8x3s531E8rk37FgLVDmzubFmq98pl/C/qE2nozTKeRaPVxUi33wXorw5/1m4zbiEazV/rlutTNDrBK2uj9y2me5buL0dp9hsaNbRvaVewS+Vvvz7jcxx4R+V5u9+73bjx/g7NygRsqbaluldeKjKN4Z3kLPcFf6F1IjsZ/5/bKZedybSy9MMOSBoU69I4i/xRhja3qxvKg659qP+zvItG51tl5zVlMn0G9c3f/kLjjPCbh/EdcpQ1BnU915ba9ar8l+JxI1qfIV+SRudTzW6j8Yf95jafM5Y2ohHLjXR/rVgnD9PoMXkBhjbPKzsQg/bLYqTLa6QuIpIwyDvp9DSN3n5b9ZTcC2Vs7eZfxrEMrddLaPSQfx9xLe1YGctlXG6/O9P6WGIT6q+XhugHoPzN3zzMGLpZZrn2aDNuVxot+lrdPaBs6r050SS7TKrPJq8Phar/0Oi0bwtgv2HOp1Se5FsG2GWE8xqOf9HomG7PNuXajRvv79CsbL7ffNeK4VqleHyQxnpe6ma979X/81bECf06K9G607BrafRY/uY2n9MvFqLRqueGdgWlZibTUu+UCUKiuxqIssfupYlmvHXKptw7E819X9z0frNngO8Wz99M+4MRiAPd4VzbBI1OW9Ylmp8324roQKWVU4ga2PmJ5ux1PkP7zrK+Wjy+Gnhvm3IQBwTtmrWN1CuJJsjTiAT3f8nrzftFHcZXD9KqNcuJRscpK9Jauby2pL7DuP2p7+itVx6hsd6+k6G3FmrWfAlSuZxfSOcOyBaicc16jvI3bfcbnk3jt/4S9evmLjRaktTdYmk0lct4depr/19G5+2kW2XHSau2mOdU4Attpn+Y6LEY4nrObduUhUgumpuclstscXrX+/1Lqd9vTQeOLp7fQeve3C+jkVgdTVxSAcPvRKp0OI1b95xA4/aLw/FrGq2JvkUjcWul074pV6JxcuB1NHrJr1qNaIXTynh/h2blie91aL0uvoxYjp36UdiaxvZQ17qpm31VuS9odbJ6F9rfFrPcvhcGjmxR5ou0TtbnAl8vnr8WOKjNZ0E0sR7PXrQ3oFGp0eoOIlItk2mpN6bQOMt7Kd017TyPRgdirWrqfkY0cV2QxvV299D+tjxHEAdz8xE9bX+XOFhZjvjDWpuo2TiliLPdLZDaOZM4KFqAaHK+C9GZ1RrAhxh6sFPnNhqJ/5uIROtlRFOr9YkDxnfS/r6d36Vxv+KvE7X92xMHD0sRt2DZC/ge0YHXpjXz6NZUht7OYwXiwPttxHe9kPj+s4mOwnKbeJ9A3JrqY8TyWpFYXusCH6dxQHMR896LumzS+2ZgYyLpmMrQpLS8XdOyxLq3TfF8beLWKT8iatNH04eJ2KcRTVc/RxzEzCC+73bEcjy9abpzaRx8f4D4bXcnrh9fkkgedymmvZM4eMtV/oZ7ESeupjPvb/g0cW9ViM72/lTEvDSxHX2Exu98B931PN5LZxO1VlOK53sR6+lqRML7B2L/0apTp9zPKmvqjyGW5QuJZbkFsY5tyby3/an6CPE7TSvKf41IPpchftN1iX3jGcR+oLmX3XKZzUdsHysV85rK8I9v7iD2K0cQJ7CWJq4/v4BGQtbu/rrQ2FfvUzzey9A7MgzHA8TyfIhIcH5O3KLuIGIbXroY1iZ+s98R+1NqYp1D7CueI06GXE3ss9cl9pvLE+v3e4n9Te59sbvxeaLmcj5i+34XkRAvTzSNv5DY3lr1rdAP36GqTMDmp/G7N1ucOEF8H3FyZT9inZpBrNubESfpflPM51kaJ3CqyvV+P2JbW4x591VnFI/TiW1re+IYYC3ifuln0P7a+2uJYwSIE8PHE/93M4ja6FOJFnPtekL/EtHpHsX3PZH431meWEYvJC5j+hGxnxjOJSgziP/1cqjWlK/RNK7dSdayR/L7iR7Kpe6llBwcHEY+vCo1vDtjuhOKaZ5JKS3ZosxZaagvdTHfFVJKF6fO5qaU1m2a9uhi3L+6+Jwj2sx7ZjHv0gE10y+UUjq/zTxOTCl9pnh+U4sYpqeUftHpixZe08V3qg5Tu5xv6a8ppU27nF/z79HN8vpnSun5NfPdrc00q1TKndim3A0ppc0rr7fMiD1n/XlhSumWtt8ypd/XTDctpfTdDtOV3tEmvlbDhimlWS3m1/xbfDLFttPK7SmlFw0jhrphu8p82/3u5fD+NnHdX3zPJ4rXdfuq64txx3TxWSumlG5u8VlzU0ofSCn9qnh9Wot5rJZSuqpNzKU5aei6XA6/b1H+jC7iL4dlKtPtl1K6pk0cn+pifsuloevS5zNi6TS8IKX05zbxVT2XUjo+pbRsi3m9OsU60cnNNdN2s57sXZnHYjXjN0spPdriM59IKW2T2q+rY/EdcoYrivkd1WL8xqn1PqbZ/SmlHVrMZ6vUev/zkkq5r7WZ/60ppZdVXu9a8znTU0p/aTOPr6eUvl08v6xFrDNS+//4qs1bzKPdsHftnOq1W86/LMocO4wYHCb5YM201BtlzfIcGjVT3Sibei9InKGt09yk+5TaUkP9m2h2/DriDPTdxFnuZ4nOSH5LnLFfhZFdH3QEUYtwCXGt9lPEWd1jiE5HOp3hfYZowv5u4mz7U0TnapcSTS3fTKOzrVa9Ij9O1NhsRzRNu52o0ZhF/A5/Jq5hX4uoQe6FOUSz5duJWoSjiOtDNyKaeQ7HAUSv7WcQy+Rhopb7IaJW7P1EzUBdTcAviZra3xXlW92u5i1Ebf/VxG//BHELtk8RNdr3t5iul24hmiC/h1g25TWB9xK/3aeA/6mZbhbx+2xEtDS4mYh/djGPvxC1k+sD3xlGXNcQNdLnENeozm5T9iji9/oJUaPyLLHeXkmsay9h/Go3vkqsCxcQ28bTxDWqXye2yWt6+Fn3ETVBRxPf91liWfwG2An4Cp233zuJ33J/4CwaHc49S/y2vyKW+4rUd6S0F9Gc/B/05hZHjxO1fZ8u5vkU0YLofOI7fbqLedzP0A4iT+xBXKVbidq9bYBvEK1ZHiTW18eK8T8H3ke03DiE1h0q/ZFovfMhYn0p5/MksTxPJpoCj1YHUpcS+4Lv01ju9xD78Y2J/UMn4/0dqr5XPLa6pv0KoqXIQURLpKto7OefJtbv3xA1wS+kdWuGC4nWEucRy7bV/v59xH/oFcX8nyDW6c8Q2227ViMQ28LWREuM64p5/Lf4/P2KODtt3w8Xse5KXP52J/Hf8xyxz/8D0Zx/dRrXaY+1GcS94CHWRSnLlJRyLumTpDF3LtGE9yxGdp2gpLE3k0jqjqC7RHRQnEacaLyckV1eooljMeIE02LEydUrxjecMXExcfLpRHrT0/x4OAQ4jjjR2KqJvtSSNdOS+tl0Gh1VXTWegUjKtgGRSMPk2n6XpNEr+HDvLa2J5wniPuMQ14APuuWIFgQwcbfvKTSW1WfHMxBNXCbTksbTQkRnOq18gUYzsjPalJM09pak9XHEgjRu5/Zf4PdjElF/eDuxb3uSxqU8mhy+QFxq80Z6d5us8bIo0cFonSnEJSXl3St+MVZB9dgexOVTV+AxhobJZFrSeFqFuJ7zk8S1sEsTPX1uS1y7+o6i3Cm073lU0tjbjrhm9900elFemehN/WIarUo+S1wnOcjmJ26JtDPwieK944nrmDV5PEpckzw/cRusiezFxHXnHyGuOZ9BXPO9E3E9d3kHk+OIa90nonJb/ci4RqEJzWumJY2nNYF/dihzIdGh0qOjH46kDHvTucPFHxDXJLbqJGlQPMHQ+2DfS3Su9XB9canvbUTj1latnEfsB54a/XCk/mQyLWk8LUSc3d6R6IV5OaJZ98PEfS5PJXp2HfQDcWkiWpZozroD0fvwckSz0AeIntm/T9w5YDIok+kHiV6lP0zcG1uaqBYjtu8diZYnyxOtLx4C/kr8N58OmEhoUjOZliRJkiQpk9dMS5IkSZKUyWRakiRJkqRMJtOSJEmSJGUymZYkSZIkKZPJtCRJkiRJmUymJUmSJEnKZDItSZIkSVImk2lJkiRJkjKZTEuSJEmSlMlkWpIkSZKkTCbTkiRJkiRlMpmWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIymUxLkiRJkpTJZFqSJEmSpEwm05IkSZIkZTKZliRJkiQpk8m0JEmSJEmZTKYlSZIkScpkMi1JkiRJUqZBTab3Bs4E7gSeBm4GPgdMr5RZHUgthiXHMFZJkiRJ0gQzJaU03jGMhsuAu4CzgZnAhsARwE3A5sBcIpm+nUiyz2ma/kpgztiEKkmSJEmaaKaOdwCjZDfggcrrC4CHgR8B21G2VWMAACAASURBVAB/rIy7jUi+JUmSJEnqyqA2836g5r0ri8eVxzIQSZIkSdLgGdRkus7WxeONTe9/DpgNPEo0915vLIOSJEmSJE08g3rNdLOVgWuAvwHbF++tCHwKOJ+oyV4bOBxYBtiYeZPuqkOKAeDloxCvJEmSJGn8PQgsWzdiMiTTiwF/BlYikuSZbco+D7iBqKE+oMv5D/wPKEmSJEmT1FXARnUjBrUDstJCRGK8BtHMu10iDXA3cDHwilGOS5IkSZI0gQ1yMj2NuNf0xsB2wN+7nG4K1jZLkiRJktoY1A7I5gNOBrYF9qD7W1+tCmwBXD5KcUmSJEmSBsCg1kx/G3g98BngSWDTyriZxfBlIum+lOiA7EXAx4C5wGfHMlhJkiRJ0sQyqB2Q3QGs1mLcp4EjgLcC7wDWBKYTvbT9sRh/c8ZnDeQPKEmSJElq3QHZoCbTY8kfUJIkSZIGU8tkelCvmZYkSZIkadSYTEuSJEmSlMlkWpIkSZKkTCbTkiRJkiRlMpmWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIymUxLkiRJkpTJZFqSJEmSpEwm05IkSZIkZTKZliRJkiQpk8m0JEmSJEmZTKYlSZIkScpkMi1JkiRJUiaTaUmSJEmSMplMS5IkSZKUyWRakiRJkqRMJtOSJEmSJGUymZYkSZIkKZPJtCRJkiRJmUymJUmSJEnKZDItSZIkSVImk2lJkiRJkjKZTEuSJEmSlMlkWpIkSZKkTCbTkiRJkiRlMpmWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIymUxLkiRJkpTJZFqSJEmSpEwm05IkSZIkZTKZliRJkiQp09TxDkAaL/dcefx4hyBNSiu/4pDxDkGSJGnErJmWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIymUxLkiRJkpTJZFqSJEmSpEwm05IkSZIkZTKZliRJkiQpk8m0JEmSJEmZTKYlSZIkScpkMi1JkiRJUiaTaUmSJEmSMplMS5IkSZKUyWRakiRJkqRMJtOSJEmSJGUymZYkSZIkKZPJtCRJkiRJmUymJUmSJEnKZDItSZIkSVImk2lJkiRJkjKZTEuSJEmSlMlkWpIkSZKkTCbTkiRJkiRlMpmWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIymUxLkiRJkpTJZFqSJEmSpEwm05IkSZIkZTKZliRJkiQpk8m0JEmSJEmZTKYlSZIkScpkMi1JkiRJUiaTaUmSJEmSMplMS5IkSZKUyWRakiRJkqRMJtOSJEmSJGUymZYkSZIkKZPJtCRJkiRJmUymJUmSJEnKZDItSZIkSVImk2lJkiRJkjKZTEuSJEmSlMlkWpIkSZKkTCbTkiRJkiRlMpmWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIymUxLkiRJkpTJZFqSJEmSpEwm05IkSZIkZTKZliRJkiQpk8m0JEmSJEmZBjWZ3hs4E7gTeBq4GfgcML2p3FLA94EHgSeB3wPrjV2YkiRJkqSJaFCT6cOAOcDhwE7Ad4B3AL+j8Z2nAOcU498DvA6YBvwJWGWM45UkSZIkTSBTxzuAUbIb8EDl9QXAw8CPgG2APwK7A1sCryYSaIBLgduBDwPvHaNYJUmSJEkTzKDWTD9Q896VxePKxePuwL00EmmAR4FfAnuMXmiSJEmSpIluUJPpOlsXjzcWj+sC19eUuwFYFVhsLIKSJEmSJE08kyWZXhk4kuhg7K/FezOAR2rKPlw8LtVmfocU8/lrmzKSJEmSpAE1qNdMVy0GnA3MBt5SeX8KkGrKT+linscXAy3mIUmSJEkaYIOeTC9E9Ni9BtHMe2Zl3MNE7XSzska6rtZakiRJkqSBbuY9jbjX9MbAa4C/N42/gbhuutk6wF3AE6ManSRJkiRpwhrUZHo+4GRgW6Jn7stqypxDXEu9deW9xYnbap0z2gFKkiRJkiauQW3m/W3g9cBngCeBTSvjZhbDOcR9pU8CPkQ06/4Ycc30F8cyWEmSJEnSxDKoNdM7F48fJxLm6nBwMW4usCvwO+BY4BfAHOBVwN1jGawkSZIkaWIZ1Jrp1bss9zDw1mKQJEmSJKkrg1ozLUmSJEnSqDGZliRJkiQpk8m0JEmSJEmZTKYlSZIkScpkMi1JkiRJUiaTaUmSJEmSMplMS5IkSZKUyWRakiRJkqRMJtOSJEmSJGWaOt4BSJIkaWz8+LxfjXcI0qR00M67jHcIGgXWTEuSJEmSlMlkWpIkSZKkTCbTkiRJkiRlMpmWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIy5STTLwWW61Bm2aKcJEmSJEkDKyeZvgY4tEOZQ4pykiRJkiQNrJxkekqPykiSJEmSNKH1+prplYAnejxPSZIkSZL6ytQO49/b9HrTmvcA5gdWBQ4Eru5BXJIkSZIk9a1OyfTXgEQ0307ATsXQyiPAx3sTmiRJkiRJ/alTMr1X8TgF+DlwGvDTmnJzgIeIzsee6Vl0kiRJkiT1oU7J9NlNz89qek+SJEmSpEmnUzJdtVfnIpIkSZIkDb6cZLr0fGBf4MXAosDexfsrAS8BLgUe70l0kiRJkiT1odxk+gPA5yvTpcq46cB5wLuA7448NEmSJEmS+lPOfaZ3B44BLge2Ab7aNP5m4Fpgj55EJkmSJElSn8pJpj8I3AnsAFwIPFZT5gbgRT2IS5IkSZKkvpWTTG9INON+uk2Ze4AVRhSRJEmSJEl9LieZnh94tkOZpYFZww9HkiRJkqT+l5NM3wZs0mb8FGBz4B8jikiSJEmSpD6Xk0yfSSTTB7cY/x7idlmnjzQoSZIkSZL6Wc6tsb5M3F/6OOD1wILF+58AXglsB1wHHNvLACVJkiRJ6jc5yfTjwNbA8cRtskpHFo+/BN5K5+uqJUmSJEma0HKSaYD7gT2B1YBNiQ7HHgUuA27tbWiSJEmSJPWn3GS6dGcxSJIkSZI06eR0QCZJkiRJksirmf5KF2XmAo8BNwK/LZ5LkiRJkjRQcpLp9wGpeD6lZnyqvJ+Ap4APEh2WSZIkSZI0MHKaeW8InA08CRxDdES2WfH45eL9XwDbAx8FngG+A+zQw3glSZIkSRp3OTXTmwGvJpLq5p67zyFqoK8kmnd/CTiLuO/0+4DzRxypJEmSJEl9Iqdm+l3A6bS+BdY/i/Hvqbw+F9h42NFJkiRJktSHcpLpNYEHO5R5EHhB5fVtwPTcoCRJkiRJ6mc5yfQjwNYdymwD/LfyenHs0VuSJEmSNGBykulzgE2BbwPLNI1bGjgW2ITopKy0PlE7LUmSJEnSwMjpgOzjRM30O4C3ALcA/wGWB14ILFS894mi/IrAksD3exWsJEmSJEn9ICeZfojoTOwI4EDgpU3jvluMK5t13wesM+IIJUmSJEnqMznJNMDjwAeLYRVgCSJ5vrvHcUmSJEmS1LdykumvADcDxxWvZxaDJEmSJEmTSu59pp8/WoFIkiRJkjRR5CTTdxO9dkuSJEmSNKnlJNOnAzsAi41SLJIkSZIkTQg5yfRRxD2jfwtsSdwKS5IkSZKkSSenA7J7iOR7ceCC4r3HgNRULmFzcEmSJEnSAMtJpu9i3sRZkiRJkqRJJyeZ3mDUopAkSZIkaQLJuWZakiRJkiRhMi1JkiRJUracZt6lJYBXAisDC7Yo841hRyRJkiRJUp/LTabfDxwJLFJ5bwqNjsnK5ybTkiRJkqSBldPM+7XAl4EbgIOJxPmnwDuBnxVlflqUkyRJkiRpYOUk0+8B/gNsA/yweO8m4LvAG4B9gdcDT/YwPkmSJEmS+k5OMr0BcC7wTOW9+SvPzwD+CBzeg7gkSZIkSepbOcn0QsD9ldfPAIs3lbkW2HCkQUmSJEmS1M9ykun7gOUrr2cC6zSVWZZGZ2SSJEmSJA2knGT67wxNni8AXgXsVrzegrhm+u+9CU2SJEmSpP6Uk0z/GtgUWK14/XmiqfdZwLPAhcR9pz/VywAlSZIkSeo3Ocn08cDCwF3F61uBzYBTgauB04GtgD/3MD5JkiRJkvrO1IyyiaiBrroBOKB34UiSJEmS1P861Uw/B3x8LAKRJEmSJGmi6JRMT2XovaQlSZIkSZr0cq6ZliRJkiRJmExLkiRJkpTNZFqSJEmSpEzd9OZ9ELBlxjwTsOPwwpEkSZIkqf91k0yvUQzdSsOMRZIkSZKkCaGbZPrHwE9GOxBJkiRJkiaKbpLp24A/jHYgkiRJkiRNFHZAJkmSJElSJpNpSZIkSZIymUxLkiRJkpSp0zXTbweuHotAJEmSJEmaKDol0yeMSRSSJEmSJE0gNvOWJEmSJCmTybQkSZIkSZlMpiVJkiRJymQyLUmSJElSJpNpSZIkSZIyjSSZXgRYsXiUJEmSJGnSyE2m5wMOA24CHgdmFo83Fe/P39PoJEmSJEnqQ53uM101Dfg18Ori9X3FsCKwFvAFYGdgJ2BWD2OUJEmSJKmv5NRMvx/YFvgNsC6wCvCK4nEd4Dxgm6KcJEmSJEkDKyeZfiPwD2A3oll31c3AHsCNwAG9CU2SJEmSpP6Uk0yvBfwKmNti/Jxi/JojDapHVgG+CVwKPAUkYPWacqnFsMGYRClJkiRJmnByrpmeBSzaocwi9M/10msC+wBXARcBO7QpeyJwXNN7t4xOWJIkSZKkiS4nmb4O2Bv4FPBQzfgZxfjrehBXL1wILF88P5j2yfQ9wGWjHpEkSZIkaSDkNPP+NrAccAXwJmBVoofv5wEHEsnocsCxPY5xuFo1R5ckSZIkaURykunTgGOA5wM/AG4HngHuIJpJrwl8FTi1pxGOjXcAzxLXVv8ReOX4hiNJkiRJ6mc5zbwBPgycA7wN2BBYAngUuIZIsC/qaXRj4yTgXOBeYDXgQ0RCvT3w5xbTHFIMkiRJkqRJKDeZBri4GAbFgZXnFwFnA9cDRwNbtpjm+GKA6PlbkiRJkjSJ5DTzPpzWyWVpi6LcRPY4cYuvV4x3IJIkSZKk/pSTTB8NvLpDmW2Ao4YdTf+YgjXOkiRJkqQWcpLpbkxl4veivTiwC3D5eAciSZIkSepPw7lmup0Nqb8H9XjZu3h8efG4M/BAMVwAHAa8CPgTjQ7IDgNWAN44ppFKkiRJkiaMTsn0+U2vD6L+uun5iftOr0HcQqtf/KzpdXkP7AuIJuk3A3sVwxLAY8AlRG/lV4xNiJIkSZKkiaZTMr1d5XkikuU1WpR9BDgTeH8P4uqVKR3G/7IYJEmSJEnqWqdkelrxOAV4DjiS+g7G5mKHXZIkSZKkSaJTMj2n8vztwFVN70mSJEmSNOnkdEB2wqhFIUmSJEnSBNLrW2NJkiRJkjTwTKYlSZIkScpkMi1JkiRJUiaTaUmSJEmSMplMS5IkSZKUyWRakiRJkqRMObfGKi0N7AW8GFgUOLTy/mrAP4BnehKdJEmSJEl9KDeZfhPwLWARYAqQaCTTKwNXAm8HftCrACVJkiRJ6jc5zby3JZLk24HXA8c1jb8OuBHYszehSZIkSZLUn3Jqpj8C/Bt4JfAosF5NmWuBTXsQlyRJkiRJfSunZvoVwLlEIt3KTGCFEUUkSZIkSVKfy0mmFwQe71BmSWDu8MORJEmSJKn/5STTdwIv71BmY+CW4YcjSZIkSVL/y0mmzwG2Al7bYvxBwPrAz0calCRJkiRJ/SynA7IvAPsCpwM/BZYq3j+U6JRsH+BfwDd6GaAkSZIkSf0mJ5l+GNgGOAnYr/L+scXjpcAbgCd6EpkkSZIkSX0qJ5kGuAPYEngZsBmwNNG792XA5T2NTJIkSZKkPpWbTJeuLgZJkiRJkiad3FtjrQRMazF+gWL8giMNSpIkSZKkfpaTTP8fcCuweIvx04kOyA4faVCSJEmSJPWznGT6NcAfgIdajH8I+D2w60iDkiRJkiSpn+Uk088Hbu5Q5hZg9WFHI0mSJEnSBJCTTE8D5nQoMxdYePjhSJIkSZLU/3KS6duBrTuU2Rq4a/jhSJIkSZLU/3KS6XOAjYAPtBh/WDH+7JEGJUmSJElSP8u5z/QxwAHAl4B9gPOBe4CVgR2JRHom8MUexyhJkiRJUl/JSaYfBrYBTgU2LoYETCnGXwHsT+veviVJkiRJGgg5yTTAbcAmRCK9KbAk8F/gMiKZliRJkiRp4OUm06UrMHmWJEmSJE1SOR2QSZIkSZIk8mumpwK7Es28lwLmrymTgP8ZYVySJEmSJPWtnGR6BeB3wDo0Oh2rYzItSZIkSRpoOcn0l4F1gZ8B3wPuBmaPRlCSJEmSJPWznGR6R+BiYN9RikWSJEmSpAkhpwOyhYFLRysQSZIkSZImipxk+gZg1dEKRJIkSZKkiSInmf4ysDuw9ijFIkmSJEnShJBzzfTdwLlEU++vAFcB/21R9i8jjEuSJEmSpL6Vk0xfTNz2agpwRIeydfefliRJkiRpIOQk058lkmlJkiRJkia1nGT6E6MWhSRJkiRJE0hOB2SSJEmSJAmTaUmSJEmSsuU084bofGxPYEdgZWDBmjKpGC9JkiRJ0kDKSaYXIG6NtS2RVJc9e5dS5X1JkiRJkgZWTjPvDwPbAZ8HViAS5yOBVYGDgHuA04CFexyjJEmSJEl9JSeZ3he4Bvg4cH/x3lxgJnAS8CpgN+BdvQxQkiRJkqR+k5NMrwFcUnmdgGmV17cCvwLe2oO4JEmSJEnqWznJ9GzgqcrrJ4Blm8rcQSTdkiRJkiQNrJxk+h5glcrrW4BNm8qsDzwy0qAkSZIkSepnOcn0JQxNns8GXgocR9wK63PADsCfexWcJEmSJEn9KOfWWKcCqxfDHcBXiXtOvx04mOjd+3bgo70MUJIkSZKkfpOTTP+xGEpPApsBrwXWJBLss4lrqSVJkiRJGlg5yXSdWcBPexGIJEmSJEkTRc410+cDB3Qos39RTpIkSZKkgZWTTG9H59tePR/YdvjhSJIkSZLU/3KS6W4sTNyPWpIkSZKkgZWbTKc241YGdgJmDj8cSZIkSZL6X6dkehbwXDEAfKryujrMAu4CXoYdkkmSJEmSBlyn3rwvp1EbvTlwD5E0N5sDPAT8ATiuZ9FJkiRJktSHOiXTW1aezwVOAI4cvXAkSZIkSep/OfeZXgt4eLQCkSRJkiRposhJpm9t8f5SRA32U8CfiBpsSZIkSZIGVk5v3v8DXALMqLy3IXATcBZwPnAxsEjPopMkSZIkqQ/lJNNvIGqyq029vwQsA/yESKY3AQ7tWXSSJEmSJPWhnGR6LeBvlddLA68CfgC8GdgZuArYv1fBSZIkSZLUj3KS6WWA+yuvtygef1557yJg9RHGJEmSJElSX8tJph8hEurS1sQ9qP9SeW8OsFAP4pIkSZIkqW/l9OZ9I7Ar0Xv3HGBf4Erg0UqZ1YF/9yo4SZIkSZL6UU7N9DeAlYCZwF3AisB3KuPnJ26RdV3PopMkSZIkqQ/lJNNnAe8G/gncAXwU+HFl/HbAdKJXb0mSJEmSBlZOM2+AY4uhzm+JZFqSJEmSpIGWUzMtSZIkSZJoXzO9UvH4b2Bu5XU37h12RJIkSZIk9bl2yfRMIoleB7ileJ26mGfqMF9JkiRJkia0dknvKURi/GjTa0mSJEmSJrV2yfQBHV5LkiRJkjQp2QGZJEmSJEmZcq9tXgTYEFiWaPL9AHAN8HSP45IkSZIkqW91m0y/APgcsDswrWncLOAXwMeB23oXmiRJkiRJ/ambZPrlwG+BGcXr+4hbX00hbpe1ArAvsC2wPfC33ocpSZIkSVL/6HTN9DTgZCKRPgVYG1gZeAWwEZFMvxg4DVimKDv/aAUrSZIkSVI/6JRM7wa8EPg20Zv3LTVlbgb2B75DJNa79TJASZIkSZL6Tadkeg/gSeJ66E4+BjwF7DnSoCRJkiRJ6medkukNgIuAx7qY12NF2Q1HGpQkSZIkSf2sUzK9MnBTxvxuKqaRJEmSJGlgdUqmp9NdrXTp8WIaSZIkSZIGVje9ec/NmN9cur93tSRJkiRJE1I3ie/ixC2wurH4CGKRJEmSJGlC6CaZfn8xSJIkSZIkOifT9wJpLAKRJEmSJGmi6JRMrzImUUiSJEmSNIF06oBsIlsF+CZwKfAUUcO+ek25hYAvAfcBTxfltxqbECVJkiRJE9EgJ9NrAvsAjwAXtSl3AvB24P+AXYmk+rfABqMdoCRJkiRpYhrk21hdCCxfPD8Y2KGmzPrA/sBbgR8W710A3AAcCew+yjFKkiRJkiagQa6Z7ub+2LsDs4CfVt6bDZwG7AgsOApxSZIkSZImuEFOpruxLnA7cU111Q3AAkRTcUmSJEmShhjkZt7dmEFcU93s4cr4OocUgyRJkiRpEprsyfQU6u+jPaXDdMcXAy2mlyRJkiQNsMnezPth6mufl6qMlyRJkiRpiOHUTC8N7AW8GFgUOLTy/mrAP4BnehLd6LuB+C6LMPS66XWA54B/jUdQkiRJkqT+llsz/SbgDuA44P3E/ZlLKwNXEreamijOAaYBr6+8NxXYFzgfeHY8gpIkSZIk9becmultgR8QtblHANsztBOu64AbgT2Lcv1g7+Lx5cXjzsADxXABcC1xW6yvEUn17cA7gOcDbxzTSCVJkiRJE0ZOMv0R4N/AK4FHgfVqylwLbNqDuHrlZ02vjy0eLwC2KZ6/BfgMcDSwJPA3YCfg6jGIT5IkSZI0AeUk068ATicS6VZmAiuMKKLe6tQrN8DTwAeKQZIkSZKkjnKumV4QeLxDmSWBucMPR5IkSZKk/peTTN9J49rjVjYGbhl+OJIkSZIk9b+cZPocYCvgtS3GHwSsD/x8pEFJkiRJktTPcq6Z/gJxy6jTiR6wlyreP5TolGwf4r7M3+hlgJIkSZIk9ZucZPphogfsk4D9Ku+XPWRfCrwBeKInkUmSJEmS1KdykmmAO4AtgZcBmwFLE717XwZc3tPIJEmSJEnqU7nJdOlqvA+zJEmSJGmSyumATJIkSZIkkVczfXgXZeYCjwE3AhcDs4YTlCRJkiRJ/SwnmT4aSJXXUyrPm99PwAPAu4Ezhh2dJEmSJEl9KKeZ9/bA2cBs4EfAwcBuxeOPi/fPInr6PgZYFDgV2KKH8UqSJEmSNO5yaqZXAHYANgGubRr3A+L+0hcDZwIfAU4BrgQ+BFwy4kglSZIkSeoTOTXTHwROZ95EunRNMf6Dxeu/Ab8mbqElSZIkSdLAyEmm1wbu61Dm3qJc6RZgydygJEmSJEnqZznJ9BNEE+92NgOerLxepJhOkiRJkqSBkZNMnwe8CjgSWLhp3MLAUcDWRNPu0kuAO0cSoCRJkiRJ/SanA7KPAtsAHwfeSVw7/R9geWADYAYwk8b9qFcEXgx8p0exSpIkSZLUF3KS6fuAjYEvAvsAr66MexY4Cfgw8O9K+eV7EKMkSZIkSX0lJ5mGqIl+E3AIUeu8BPAY8A8ioZYkSZIkaeDlJtOlZ2l9iyxJkiRJkgZaTgdkkiRJkiSJ/JrpKcCewI7AysCCNWVSMV6SJEmSpIGUk0wvAJwLbEsk1al4LKXK+5IkSfr/9u492vd7vvP48whlZCgNcY/UbVzqtiQWbS2XoklbVGk1SISpaMcQ5uI2hDHu02JKV6sd6k5VtekaGm2ZoJkmKGbGLcjEtSKCxF2SOvPH57ebne13cs7nnH3O/p2cx2Ot7/rt3+f3/X73+/xzvvv1+9wAuNyaGeb9pOre1Quq6zaC87Orw6rjqi9Vb+5H96AGAACAy5WZMP2Q6sONfabPXbT9sLG39Oure1b3qx67mQUCAADAqpkJ0zepTlv3fnt1pXXvz6reXj1qE+oCAACAlTUTpi+uvrvu/bera28457ON0A0AAACXWzNh+kvVDde9/1R1lw3n3L76xp4WBQAAAKtsJkyf1qXD88nV7apXNLbCen513+rUzSoOAAAAVtHM1lhvqg5fHJ+tXtLYc/rR1W80Vvc+u3rKZhYIAAAAq2YmTL97caz5TnXX6leqmzUC9smNudQAAABwuTUTpq9fXVR9dV3bRdWfbGpFAAAAsOJm5kx/oXrR3ioEAAAA9hczYfr86ty9VQgAAADsL2bC9BnVHfdWIQAAALC/mAnT/7m6e3X83ikFAAAA9g8zC5D9XGM171dWv1l9oDqn2r7hvO2NPacBAADgcmkmTD9n3c93XhzLCNMAAABcrs2E6fvstSoAAABgPzITpt+116oAAACA/cjMAmQAAABAcz3Ta25THVPdqjq4OmrRflh1RGORsvM3pToAAABYQbNh+qTFsdajvX4l7ytVf1qdWL18z0sDAACA1TQzzPvXqmc1ep6PqF644fOzqn+o7r8plQEAAMCKmgnTJzYC8/2qD1XfX3LOx6ubb0JdAAAAsLJmwvTtqlOqH1zGOV+urrNHFQEAAMCKmwnT26of7uSca3fZYRsAAAD2ezNh+jPVXXdyr59tDPUGAACAy62ZMP2W6k6NudPLPKkxX/pNe1oUAAAArLKZrbFe2ljR+8WL122L9hdUd6vuUn2gesVmFggAAACrZiZMf7e6R2MP6V/vkl7tJzX2m35z9VvVRZtYHwAAAKycmTBddX718OqJ1Z2rQ6oLqjOqcza3NAAAAFhNs2F6zVert29mIQAAALC/mFmA7A3VfbpkrjQAAAAckGbC9DHVKdUXqudXt9orFQEAAMCKmwnTd6teWR1cPbn6aHV6Y9Gxa25+aQAAALCaZsL0adUJ1XWrh1Z/3dh3+uXVPzb2of6l6qBNrhEAAABWykyYXvODxjZYR1c3qp5SfaZ6cHVy9cVNqw4AAABW0O6E6fXOqf5rddvqP1YXV4fuaVEAAACwynZ3a6w1N6uOyTBqJwAAEFZJREFUq46tDmus9P3/9rQoAAAAWGW7E6avXj2kekR110aA/nb1msXxnk2rDgAAAFbQTJg+qhGgH1BdedF2aiNAv7X67qZWBgAAACtqJky/Y/F6VvXaRoj+/KZXBAAAACtuJky/qnp19Xd7pxQAAADYP8ys5v0b7TxIX6ExDBwAAAAut/Z0Ne81N26E7UdW16sO2qT7AgAAwMrZkzB9UKMX+oTq3o1e6e3V325CXQAAALCydidM36TRC318dZ1F23nVK6pXVp/blMoAAABgRe1qmL5i9cBGL/Q9G73QF1Zvqx5UnVydtDcKBAAAgFWzszB98+rRjf2lr1Vtqz7UWNX7jdXXqx/uxfoAAABg5ewsTJ/ZmAd9bvWS6o+rj+3togAAAGCV7crWWNurd1RvTZAGAACAnYbpZzQWFHtkdVr18epJje2vAAAA4IC0szD93Oqm1dHVny9+fkH1+ert1a/t1eoAAABgBe3KMO+qd1YPrm5UPa3RW3109abGMPA7VHfaGwUCAADAqtnVML3m3EbP9M2q+zTmUV9UHVG9v/pw9djNLBAAAABWzWyYXu9d1UOqGzbmUX+qun31u5tQFwAAAKysPQnTa86rfru6VXWvxtBvAAAAuNza2T7Ts05dHAAAAHC5tRk90wAAAHBAEaYBAABgkjANAAAAk4RpAAAAmCRMAwAAwCRhGgAAACYJ0wAAADBJmAYAAIBJwjQAAABMEqYBAABgkjANAAAAk4RpAAAAmCRMAwAAwCRhGgAAACYJ0wAAADBJmAYAAIBJwjQAAABMEqbrHtX2Jcf5W1gTAAAAK+yKW13ACnl89YF17y/eqkIAAABYbcL0JT5Rnb7VRQAAALD6DPMGAACAScL0Jd5Q/VP1teqN1WFbWw4AAACryjDvuqD6neo91TerO1ZPq/5+8fO5S645YXEAAABwABKm68OLY817qvdW728sSvb0Jdf84eKosfI3AAAABxDDvJf7UPWp6sitLgQAAIDVI0zv2Lb0OgMAALCEML3cEdUtqjO2uhAAAABWjznTYxXvsxtDu89vLDr21OpL1cu2sC4AAABWlDBdH62OqR5XXbU6p3pb9czqvC2sCwAAgBUlTNfzFwcAAADsEnOmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKm60bVW6sLqm9Wb6sO29KKAAAAWGkHepi+avXu6pbVI6pjq5tX/7M6eAvrAgAAYIVdcasL2GKPrm5S/avqM4u2/1N9unpM9eItqgsAAIAVdqD3TN+/Or1LgnTV2dVp1QO2pCIAAABW3oEepm9TfXRJ+8eqW+/jWgAAANhPHOjDvH+i+saS9q9X17yM605YHFXfrs7c5LrYB25w5Ak7P4lVdq3qvK0uAmB/ctzRv7jVJbD7PPdga9x4Rx8c6GG6avuStm07ueYPFwewdT5YHbHVRQDAPuK5ByvmQB/m/Y1G7/RG12x5jzUAAAAc8GH6Y4150xvduvr4Pq4FAACA/cSBHqb/srpLY3usNYdXP7P4DFhdploAcCDx3IMVs2379mVThg8YB1f/u/pe9fTG/On/Ul2tul1jcTEAAAC4lAO9Z/o71b2qT1Wvq97Q2Gf6XgnSAAAA7MCB3jMNAAAA0w70nmlg8xzfmCqx7Lj31pW1Q5+tXr3FNQBwYDi+S56Jt1jy+T3a/WfmqYsD2MfsMw1stl+tvrihzer4AFDfqo6tnrGh/bjFZ1fb5xUBu03PNLDZPlKdvuH45pZWBACr4W3Vw6tt69r+RfWg6s+2pCJgtwnTwL50rer3qy9VP6g+WZ2w4ZzjG8Pcfrp6S+Ob+q9UT118flT14cYCgh+o7rTh+vtW76i+XH23+mj176uDdqG+n2wsRPjVRX0fqR64i/82ANiZ11U3rn52XdsDG8+ojWH6yOqtjdFe36vOrJ7XCN87syvPW2APGeYNbLaDuvT/Ldurf6quXp3W+CPgWY2V83++8bC/cvWyDfd5TfXaxr6av9r4A+Ia1S9Uz22suP+i6i+qm1YXLq67SfWuxf2+Xx2x+H3Xrp5yGXXfqDqjOrd6YiNQP6Txx80vZ+95APbc56r3NoZ6v2/Rdlz15/3oTjKHNb7UfXXji+XbVCc1nnO/fhm/Y/Z5C+wmYRrYbJ/c8P60xjfwJza+jb9t9enFZ3/bCMjPbDzkL1533esa+77XWFjlgdW/ayzccvai/QrVydVdq/cs2v5g3T22Nf5Y+bHqP1RPq364g7qftTj/7tXXFm3vbITsZydMA7A5Xlv9TvX46pqNBceOXnLe+p7qbY3n6TcX1z+2S55VG80+b4HdZJg3sNke2Biatnb860X7UY2e37MbX+StHe+sDqluveE+f7Xu54urzzT2hD97XftacL/RurbrVa9ofPt/YXVR9ZzGHxGHXkbdRzWGh1+wpL7bN77pB4A99aeNHuL7VQ+rzmmMqNro6tULq7MaQ7UvanzRvK26+WXcf/Z5C+wmPdPAZvtoI/hudGh1s8YfA8scsuH9Nza8v3AHbVVXWbxeodGDfP1GT/MnG/PMfrn6T+vOW+bQxlC74y6jPgupAbCnvtWYonRsdXhjrY5lo6b+uNFrfVJjuPd3qjtXv9fOn2czz1tgNwnTwL7ytcZ85BN38PmZm/A7btqYI31s9fp17ffbhWu/1hgS/sIdfP6Pe1YaAPyz11Zvb3wJfMySz69SPaDxxfB/W9d+212497543gIJ08C+c0r1uOrzjYf83nDVxev6b+Ov1BhGtzOnNOZef6zRmw0Ae8vfNHasOL/x3Nnoyo0FPTf2Lh+/C/feF89bIGEa2Hde0lgd+32Ln8+sDq5uWd2t8Q38nvpEY670cxsriF/UWJl7V5xUvb+xyurLq882Fob5qcbKqY/ahPoAoMYzalmP9JoLqtMbWzt+uTqv8Ry6wS7ce188b4GEaWDfuaCxd/RJ1ZMbfxCc33jIb9xbc3dd2Jgf/fLGELqvV69qfDv/Rzu59vNdso3W8xpbaX2tMQf8NZtUHwDsqmMaK2//XmPE1FsaQ7f/x06u2xfPW6Datn379q2uAQAAAPYrtsYCAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQAAAJgkTAMAAMAkYRoAAAAmCdMAAAAwSZgGAACAScI0AAAATBKmAQAAYJIwDQAAAJOEaQBgb7lHtb161taWAQCbT5gGgP3HLaoXVx+qvl5dtHg9o/rt6k5bVxoAHFiEaQBYfduqZ1afqJ7Y6O39k+pF1eur71WPqz5YPXaLagSAA8oVt7oAAGCnTmoMlf5CdUx12pJzDq2eUP34visLAA5ceqYBYLXdpHp6dWF1dMuDdNW51dMavdXrXbV6avWR6jvVt6u/b4Tyje7RJXOc71C9vTq/+m71nuqnd/C7r1O9svpKo5f8I9UjdvLv+onq+Y3e9u9VF1Tvqu675NzjF3UdXx1Vnbo4f/tOfgcA7DV6pgFgtT2y8bx+Y/WxXTj/4nU/X6N6d3XHxjzrVzW+SP/5xf1u0wjqGx1RPakRuv97dVj1oEbYvUN15rpzD6n+VyP0/93iuF71B9Vf76DGGzcC8eHV+6pTqoOrX1r8/Jjqj5Zc9+BGmP6rxf0P38H9AWCvE6YBYLX9zOL13btx7UsbQfrJXbrH+irVXzR6st/a6Ele7xcbIf7V69oe0wiwJ1b/Zl378xtB+qWN+dxrXt4I48u8phGoj6nevK79Go2Q/bvVXzZ6utf7hcVxyg7uCwD7jGHeALDarrt4/dKSzw5vDMlefzxh8dkh1cMbi5JtHPr9/UbA3lY9dMl9T+vSQbpGr/bF1Z3XtV2pelj1rX50+6sPVm9Ycu/bV3ev/qxLB+kaQ8qf2Qj7D1py7ckJ0gCsCD3TALDati1el80PPrwRPtf7XKOX+MjqoHa8z/OVFq+3WvLZB5e0XdToKb7murZbNuZkv68xh3mjU/vRudN3Xbz++A7quvZl1PX+JW0AsCWEaQBYbV9uhNYbLPns1C4J21dsBN41hyxej1wcO/Ivl7Sdv4NzL24E9DVrK4dvHI695pwlbWt13WdxzNS17H4AsCUM8waA1ba2evfPTV631lP8kkbg3tFxzz2obe13XGcHn193SdvaNSfupK5HLrnW6t0ArAxhGgBW26sbPcIPbvnQ5x15f/XD6m57oaY1n2xsm3WHlu9vfY8lbacvXvdmXQCw1wnTALDazqqeU/1YY0uoHe31fI0N789tLAB2RPWMlk/tumn1k3tQ20WL33G1fnT+8xGNxck2+mBjjvWvVI/awX1vWx26B3UBwF5nzjQArL5nN4Y+P6Mx7PsfGj3PX2+E6MOrey/Ofe+66/5tdfPF9cc29oD+SnX9Ri/3kY3tqc7eg9qe1hiC/oRGgF7bZ/oh1Tuq+y+55qGNrb5eWT2+OqMxT/uG1e2qn2osVHbuHtQFAHuVMA0Aq29tRe43Vb/ZmOf80OrgxrZUZ1W/X72u+tC6677Z2IbqhMX5D2psO/WV6tONfaH/Zg9rO6+xF/bzqvs1AvWZ1W9Vn215mP5idafqcYuaHtZY2Oyc6uPVy6r/u4d1AcBetW37dmt5AAAAwAxzpgEAAGCSMA0AAACThGkAAACYJEwDAADAJGEaAAAAJgnTAAAAMEmYBgAAgEnCNAAAAEwSpgEAAGDS/wfZBa/ko6tLKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 10))\n",
    "\n",
    "df.groupby(\"Gender\")[\"Individual Distance Target Stage 1\"].mean().plot(kind=\"bar\", ax=ax, color=colors)\n",
    "\n",
    "ax.set_title(\"Average Distance to Target by Gender (Stage 1)\\n\", fontsize=26)\n",
    "ax.set_xlabel(\"\\nGender\", fontsize=20)\n",
    "ax.set_ylabel(\"Average Distance to Target\\n\", fontsize=20)\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white') \n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "ax.tick_params(axis='y', colors='white')\n",
    "ax.yaxis.label.set_color('white')\n",
    "ax.xaxis.label.set_color('white')\n",
    "ax.title.set_color('white')\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.set_ylim(0, 25)\n",
    "\n",
    "plt.savefig('avg_distance_gender_stage1.png', transparent=\"True\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}