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avocado_before_preprocessing.html
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Profile report</title>
<meta name="description" content="Profile report generated by pandas-profiling. See GitHub.">
<meta name="author" content="pandas-profiling">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css"
integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" crossorigin="anonymous">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap-theme.min.css"
integrity="sha384-fLW2N01lMqjakBkx3l/M9EahuwpSfeNvV63J5ezn3uZzapT0u7EYsXMjQV+0En5r" crossorigin="anonymous">
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" crossorigin="anonymous"></script>
<script>
$(function () {
$('[data-toggle="tooltip"]').tooltip()
})
$("a[href^='#'].anchor").on('click', function(e) {
// prevent default anchor click behavior
e.preventDefault();
// store hash
var hash = this.hash;
// animate
$('html, body').animate({
scrollTop: $(hash).offset().top
}, 300, function(){
// when done, add hash to url
// (default click behaviour)
window.location.hash = hash;
});
});
</script>
</head>
<body>
<meta charset="UTF-8">
<style>
.variablerow {
border: 1px solid #e1e1e8;
border-top: hidden;
padding-top: 2em;
padding-bottom: 2em;
padding-left: 1em;
padding-right: 1em;
}
.headerrow {
border: 1px solid #e1e1e8;
background-color: #f5f5f5;
padding: 2em;
}
.namecol {
margin-top: -1em;
overflow-x: auto;
}
.dl-horizontal dt {
text-align: left;
padding-right: 1em;
white-space: normal;
}
.dl-horizontal dd {
margin-left: 0;
}
.ignore {
opacity: 0.4;
}
.container.pandas-profiling {
max-width:975px;
}
.col-md-12 {
padding-left: 2em;
}
.indent {
margin-left: 1em;
}
.center-img {
margin-left: auto !important;
margin-right: auto !important;
display: block;
}
/* Table example_values */
table.example_values {
border: 0;
}
.example_values th {
border: 0;
padding: 0 ;
color: #555;
font-weight: 600;
}
.example_values tr, .example_values td{
border: 0;
padding: 0;
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}
/* STATS */
table.stats {
border: 0;
}
.stats th {
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padding: 0 2em 0 0;
color: #555;
font-weight: 600;
}
.stats tr {
border: 0;
}
.stats td{
color: #555;
padding: 1px;
border: 0;
}
/* Sample table */
table.sample {
border: 0;
margin-bottom: 2em;
margin-left:1em;
}
.sample tr {
border:0;
}
.sample td, .sample th{
padding: 0.5em;
white-space: nowrap;
border: none;
}
.sample thead {
border-top: 0;
border-bottom: 2px solid #ddd;
}
.sample td {
width:100%;
}
/* There is no good solution available to make the divs equal height and then center ... */
.histogram {
margin-top: 3em;
}
/* Freq table */
table.freq {
margin-bottom: 2em;
border: 0;
}
table.freq th, table.freq tr, table.freq td {
border: 0;
padding: 0;
}
.freq thead {
font-weight: 600;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
}
td.fillremaining{
width:auto;
max-width: none;
}
td.number, th.number {
text-align:right ;
}
/* Freq mini */
.freq.mini td{
width: 50%;
padding: 1px;
font-size: 12px;
}
table.freq.mini {
width:100%;
}
.freq.mini th {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
max-width: 5em;
font-weight: 400;
text-align:right;
padding-right: 0.5em;
}
.missing {
color: #a94442;
}
.alert, .alert > th, .alert > td {
color: #a94442;
}
/* Bars in tables */
.freq .bar{
float: left;
width: 0;
height: 100%;
line-height: 20px;
color: #fff;
text-align: center;
background-color: #337ab7;
border-radius: 3px;
margin-right: 4px;
}
.other .bar {
background-color: #999;
}
.missing .bar{
background-color: #a94442;
}
.tooltip-inner {
width: 100%;
white-space: nowrap;
text-align:left;
}
.extrapadding{
padding: 2em;
}
.pp-anchor{
}
</style>
<div class="container pandas-profiling">
<div class="row headerrow highlight">
<h1>Overview</h1>
</div>
<div class="row variablerow">
<div class="col-md-6 namecol">
<p class="h4">Dataset info</p>
<table class="stats" style="margin-left: 1em;">
<tbody>
<tr>
<th>Number of variables</th>
<td>14 </td>
</tr>
<tr>
<th>Number of observations</th>
<td>18249 </td>
</tr>
<tr>
<th>Total Missing (%)</th>
<td>0.0% </td>
</tr>
<tr>
<th>Total size in memory</th>
<td>1.9 MiB </td>
</tr>
<tr>
<th>Average record size in memory</th>
<td>112.0 B </td>
</tr>
</tbody>
</table>
</div>
<div class="col-md-6 namecol">
<p class="h4">Variables types</p>
<table class="stats" style="margin-left: 1em;">
<tbody>
<tr>
<th>Numeric</th>
<td>6 </td>
</tr>
<tr>
<th>Categorical</th>
<td>3 </td>
</tr>
<tr>
<th>Boolean</th>
<td>0 </td>
</tr>
<tr>
<th>Date</th>
<td>0 </td>
</tr>
<tr>
<th>Text (Unique)</th>
<td>0 </td>
</tr>
<tr>
<th>Rejected</th>
<td>5 </td>
</tr>
<tr>
<th>Unsupported</th>
<td>0 </td>
</tr>
</tbody>
</table>
</div>
<div class="col-md-12" style="padding-left: 1em;">
<p class="h4">Warnings</p>
<ul class="list-unstyled"><li><a href="#pp_var_4046"><code>4046</code></a> is highly correlated with <a href="#pp_var_Total Volume"><code>Total Volume</code></a> (ρ = 0.97786) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_4225"><code>4225</code></a> is highly correlated with <a href="#pp_var_4046"><code>4046</code></a> (ρ = 0.92611) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_4770"><code>4770</code></a> has 5497 / 30.1% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_Date"><code>Date</code></a> has a high cardinality: 169 distinct values <span class="label label-warning">Warning</span></li><li><a href="#pp_var_Large Bags"><code>Large Bags</code></a> is highly correlated with <a href="#pp_var_Small Bags"><code>Small Bags</code></a> (ρ = 0.90259) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_Small Bags"><code>Small Bags</code></a> is highly correlated with <a href="#pp_var_Total Bags"><code>Total Bags</code></a> (ρ = 0.99433) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_Total Bags"><code>Total Bags</code></a> is highly correlated with <a href="#pp_var_4225"><code>4225</code></a> (ρ = 0.90579) <span class="label label-primary">Rejected</span></li><li><a href="#pp_var_Unnamed: 0"><code>Unnamed: 0</code></a> has 432 / 2.4% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_XLarge Bags"><code>XLarge Bags</code></a> has 12048 / 66.0% zeros <span class="label label-info">Zeros</span></li><li><a href="#pp_var_region"><code>region</code></a> has a high cardinality: 54 distinct values <span class="label label-warning">Warning</span></li> </ul>
</div>
</div>
<div class="row headerrow highlight">
<h1>Variables</h1>
</div>
<div class="row variablerow ignore">
<div class="col-md-3 namecol">
<p class="h4 pp-anchor" id="pp_var_4046"><s>4046</s><br/>
<small>Highly correlated</small>
</p>
</div><div class="col-md-3">
<p><em>This variable is highly correlated with <a href="#pp_var_Total Volume"><code>Total Volume</code></a> and should be ignored for analysis</em></p>
</div>
<div class="col-md-6">
<table class="stats ">
<tr>
<th>Correlation</th>
<td>0.97786</td>
</tr>
</table>
</div>
</div><div class="row variablerow ignore">
<div class="col-md-3 namecol">
<p class="h4 pp-anchor" id="pp_var_4225"><s>4225</s><br/>
<small>Highly correlated</small>
</p>
</div><div class="col-md-3">
<p><em>This variable is highly correlated with <a href="#pp_var_4046"><code>4046</code></a> and should be ignored for analysis</em></p>
</div>
<div class="col-md-6">
<table class="stats ">
<tr>
<th>Correlation</th>
<td>0.92611</td>
</tr>
</table>
</div>
</div><div class="row variablerow">
<div class="col-md-3 namecol">
<p class="h4 pp-anchor" id="pp_var_4770">4770<br/>
<small>Numeric</small>
</p>
</div><div class="col-md-6">
<div class="row">
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Distinct count</th>
<td>12071</td>
</tr>
<tr>
<th>Unique (%)</th>
<td>66.1%</td>
</tr>
<tr class="ignore">
<th>Missing (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Missing (n)</th>
<td>0</td>
</tr>
<tr class="ignore">
<th>Infinite (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Infinite (n)</th>
<td>0</td>
</tr>
</table>
</div>
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Mean</th>
<td>22840</td>
</tr>
<tr>
<th>Minimum</th>
<td>0</td>
</tr>
<tr>
<th>Maximum</th>
<td>2546400</td>
</tr>
<tr class="alert">
<th>Zeros (%)</th>
<td>30.1%</td>
</tr>
</table>
</div>
</div>
</div>
<div class="col-md-3 collapse in" id="minihistogram-3083320413624879844">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAAaxJREFUeJzt1zGq1FAYhuE/w22T6YfJIgS3ILgguwt2li7F3tbeXcwwC0iwsHCOhY7NhQ9G5MSB52lD8iVw3iJDa61VZ%2BfzueZ57j3LgzudTnU8HrtuPnVd%2B20cx6r69cHTNG3xCjyQZVlqnuc/56anTQIZhqGqqqZpehHI6%2BfPdz/v64e3/%2BS9%2BL/dzk1Pu%2B6L8EAEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKB4GmL0dZaVVUty/Li2o/v3%2B5%2B3qt3n%2B6%2B58v7N3ffwzZu5%2BR2bnraJJB1Xauqap7nLearqmr/cbNp/tK6rrXf77tuDm2DLK/Xa10ulxrHsYZh6D3Pg2mt1bqudTgcarfr%2B1ewSSDwKPykQyAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBD8BOOVQHEmG5VEAAAAAElFTkSuQmCC">
</div>
<div class="col-md-12 text-right">
<a role="button" data-toggle="collapse" data-target="#descriptives-3083320413624879844,#minihistogram-3083320413624879844"
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<div class="row collapse col-md-12" id="descriptives-3083320413624879844">
<ul class="nav nav-tabs" role="tablist">
<li role="presentation" class="active"><a href="#quantiles-3083320413624879844"
aria-controls="quantiles-3083320413624879844" role="tab"
data-toggle="tab">Statistics</a></li>
<li role="presentation"><a href="#histogram-3083320413624879844" aria-controls="histogram-3083320413624879844"
role="tab" data-toggle="tab">Histogram</a></li>
<li role="presentation"><a href="#common-3083320413624879844" aria-controls="common-3083320413624879844"
role="tab" data-toggle="tab">Common Values</a></li>
<li role="presentation"><a href="#extreme-3083320413624879844" aria-controls="extreme-3083320413624879844"
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<div role="tabpanel" class="tab-pane active row" id="quantiles-3083320413624879844">
<div class="col-md-4 col-md-offset-1">
<p class="h4">Quantile statistics</p>
<table class="stats indent">
<tr>
<th>Minimum</th>
<td>0</td>
</tr>
<tr>
<th>5-th percentile</th>
<td>0</td>
</tr>
<tr>
<th>Q1</th>
<td>0</td>
</tr>
<tr>
<th>Median</th>
<td>184.99</td>
</tr>
<tr>
<th>Q3</th>
<td>6243.4</td>
</tr>
<tr>
<th>95-th percentile</th>
<td>106160</td>
</tr>
<tr>
<th>Maximum</th>
<td>2546400</td>
</tr>
<tr>
<th>Range</th>
<td>2546400</td>
</tr>
<tr>
<th>Interquartile range</th>
<td>6243.4</td>
</tr>
</table>
</div>
<div class="col-md-4 col-md-offset-2">
<p class="h4">Descriptive statistics</p>
<table class="stats indent">
<tr>
<th>Standard deviation</th>
<td>107460</td>
</tr>
<tr>
<th>Coef of variation</th>
<td>4.7051</td>
</tr>
<tr>
<th>Kurtosis</th>
<td>132.56</td>
</tr>
<tr>
<th>Mean</th>
<td>22840</td>
</tr>
<tr>
<th>MAD</th>
<td>35859</td>
</tr>
<tr class="">
<th>Skewness</th>
<td>10.159</td>
</tr>
<tr>
<th>Sum</th>
<td>416800000</td>
</tr>
<tr>
<th>Variance</th>
<td>11549000000</td>
</tr>
<tr>
<th>Memory size</th>
<td>142.6 KiB</td>
</tr>
</table>
</div>
</div>
<div role="tabpanel" class="tab-pane col-md-8 col-md-offset-2" id="histogram-3083320413624879844">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xt0VPW9///XCMkQaTINxGQyXKNLEQhSSSwJVgO2JCCX06JVBFPS2lQKiBA4CForcBYXK3CsomIpXlqpUA/CsQcaE1QulgQIECWAiBoEJSFAQwIUkgCf3x/8sr8OCSSe88ll4PlYa6%2BVvfd7PvuzP4TJa332nj0uY4wRAAAArLmmqTsAAABwpSFgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlLZu6A1eD8%2BfP69ChQwoNDZXL5Wrq7gAAcFUwxujEiRPy%2BXy65prGnVMiYDWCQ4cOqUOHDk3dDQAArkoHDx5U%2B/btG/WYBKxGEBoaKunCP3BYWFgT9wYAgKtDeXm5OnTo4PwdbkwErEZQfVkwLCyMgAUAQCNrittzuMkdAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJbxZc8BLv6JzKbuQr3lzRrQ1F0AAKBRMIMFAABgGQELAADAsmYbsDZs2KAhQ4bI5/PJ5XJp1apVfvtdLletyzPPPOPUdO7cucb%2BqVOn%2BrVz4MABDRkyRK1bt1ZERITGjx%2BvyspKv5r169crLi5OrVq10vXXX69FixY13IkDAICA12zvwTp16pR69uypn//857rnnntq7C8qKvJb//vf/66HHnqoRu3MmTOVnp7urH/nO99xfj537pwGDRqk6667Th9%2B%2BKGOHTumUaNGyRij559/XpJUWFiou%2B%2B%2BW%2Bnp6XrjjTf0j3/8Q2PGjNF1111Xa78AAACabcAaOHCgBg4ceMn9Xq/Xb/2///u/1a9fP11//fV%2B20NDQ2vUVsvKytLu3bt18OBB%2BXw%2BSdL8%2BfOVlpamWbNmKSwsTIsWLVLHjh317LPPSpK6du2qvLw8zZs3j4AFAABq1WwvEX4bhw8f1urVq/XQQw/V2Pf000%2Brbdu2%2Bt73vqdZs2b5Xf7LyclRbGysE64kKSUlRRUVFdq2bZtTk5yc7NdmSkqK8vLyVFVVVWt/KioqVF5e7rcAAICrR7Odwfo2Xn/9dYWGhmrYsGF%2B2x999FH16tVL4eHh2rJli6ZNm6bCwkL98Y9/lCQVFxcrKirK7zXh4eEKDg5WcXHxJWuioqJ09uxZHT16VNHR0TX6M2fOHM2YMcPmKQIAgAByRQSsV155RSNHjlSrVq38tk%2BcONH5%2BZZbblF4eLjuvfdeZ1ZLunCz/MWMMX7bL64xxlzytZI0bdo0ZWRkOOvl5eXq0KHDtzwrAAAQqAI%2BYG3cuFF79%2B7V8uXL66xNSEiQJH322Wdq27atvF6vNm/e7FdTWlqqqqoqZ9bK6/U6s1nVSkpK1LJlSyekXcztdsvtdv9vTgcAAFwBAv4erCVLliguLk49e/ass3bHjh2S5FzWS0xMVEFBgd8nErOysuR2uxUXF%2BfUZGdn%2B7WTlZWl%2BPh4BQUF2ToNAABwBWm2M1gnT57UZ5995qwXFhYqPz9fbdq0UceOHSVduPT21ltvaf78%2BTVen5OTo9zcXPXr108ej0dbt27VxIkTNXToUOf1ycnJ6tatm1JTU/XMM8/on//8pyZPnqz09HSFhYVJkkaPHq2FCxcqIyND6enpysnJ0ZIlS/Tmm282wigAAIBA1GwDVl5envr16%2BesV9/TNGrUKL322muSpGXLlskYowceeKDG691ut5YvX64ZM2aooqJCnTp1Unp6uqZMmeLUtGjRQqtXr9aYMWN0%2B%2B23KyQkRCNGjNC8efOcmpiYGK1Zs0YTJ07UCy%2B8IJ/Pp%2Beee45HNAAAgEtymeo7ttFgysvL5fF4VFZW5syM2cKXPQMAULuG/Ptbl4C/BwsAAKC5IWABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAy5ptwNqwYYOGDBkin88nl8ulVatW%2Be1PS0uTy%2BXyWxISEvxqKioq9MgjjygiIkKtW7fW0KFD9dVXX/nVHDhwQEOGDFHr1q0VERGh8ePHq7Ky0q9m/fr1iouLU6tWrXT99ddr0aJFDXPSAADgitBsA9apU6fUs2dPLVy48JI1AwYMUFFRkbOsWbPGb/%2BECRO0cuVKLVu2TB9%2B%2BKFOnjypwYMH69y5c5Kkc%2BfOadCgQTp16pQ%2B/PBDLVu2TCtWrNCkSZOcNgoLC3X33Xfrjjvu0I4dO/T4449r/PjxWrFiRcOcOAAACHgtm7oDlzJw4EANHDjwsjVut1ter7fWfWVlZVqyZIn%2B/Oc/60c/%2BpEk6Y033lCHDh20du1apaSkKCsrS7t379bBgwfl8/kkSfPnz1daWppmzZqlsLAwLVq0SB07dtSzzz4rSeratavy8vI0b9483XPPPRbPGAAAXCma7QxWfaxbt06RkZG66aablJ6erpKSEmfftm3bVFVVpeTkZGebz%2BdTbGysNm3aJEnKyclRbGysE64kKSUlRRUVFdq2bZtT8802qmvy8vJUVVXVkKcHAAACVLOdwarLwIED9dOf/lSdOnVSYWGhnnzySd11113atm2b3G63iouLFRwcrPDwcL/XRUVFqbi4WJJUXFysqKgov/3h4eEKDg6%2BbE1UVJTOnj2ro0ePKjo6ukbfKioqVFFR4ayXl5dbOWcAABAYAjZg3X///c7PsbGxio%2BPV6dOnbR69WoNGzbskq8zxsjlcjnr3/y5vjXGmEu%2BVpLmzJmjGTNm1O9EAADAFSegLxF%2BU3R0tDp16qR9%2B/ZJkrxeryorK1VaWupXV1JS4sxIeb1eZ6aqWmlpqaqqqi5bU1JSopYtW6pt27a19mXatGkqKytzloMHD1o5RwAAEBiumIB17NgxHTx40LlkFxcXp6CgIGVnZzs1RUVFKigoUJ8%2BfSRJiYmJKigoUFFRkVOTlZUlt9utuLg4p%2BabbVTXxMfHKygoqNa%2BuN1uhYWF%2BS0AAODq0WwD1smTJ5Wfn6/8/HxJFx6XkJ%2BfrwMHDujkyZOaPHmycnJytH//fq1bt05DhgxRRESEfvKTn0iSPB6PHnroIU2aNEnvvfeeduzYoQcffFA9evRwPlWYnJysbt26KTU1VTt27NB7772nyZMnKz093QlFo0eP1pdffqmMjAzt2bNHr7zyipYsWaLJkyc3zcAAAIBmr9neg5WXl6d%2B/fo56xkZGZKkUaNG6aWXXtLOnTv1pz/9ScePH1d0dLT69eun5cuXKzQ01HnNf/7nf6ply5a67777dPr0af3whz/Ua6%2B9phYtWkiSWrRoodWrV2vMmDG6/fbbFRISohEjRmjevHlOGzExMVqzZo0mTpyoF154QT6fT8899xyPaAAAAJfkMtV3bKPBlJeXy%2BPxqKyszPrlwvgnMq2215DyZg1o6i4AAK4iDfn3ty7N9hIhAABAoCJgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJY124C1YcMGDRkyRD6fTy6XS6tWrXL2VVVV6bHHHlOPHj3UunVr%2BXw%2B/exnP9OhQ4f82ujcubNcLpffMnXqVL%2BaAwcOaMiQIWrdurUiIiI0fvx4VVZW%2BtWsX79ecXFxatWqla6//notWrSo4U4cAAAEvGYbsE6dOqWePXtq4cKFNfb961//0vbt2/Xkk09q%2B/btevvtt/Xpp59q6NChNWpnzpypoqIiZ/nNb37j7Dt37pwGDRqkU6dO6cMPP9SyZcu0YsUKTZo0yakpLCzU3XffrTvuuEM7duzQ448/rvHjx2vFihUNc%2BIAACDgtWzqDlzKwIEDNXDgwFr3eTweZWdn%2B217/vnn9f3vf18HDhxQx44dne2hoaHyer21tpOVlaXdu3fr4MGD8vl8kqT58%2BcrLS1Ns2bNUlhYmBYtWqSOHTvq2WeflSR17dpVeXl5mjdvnu655x4bpwoAAK4wzXYG69sqKyuTy%2BXSd7/7Xb/tTz/9tNq2bavvfe97mjVrlt/lv5ycHMXGxjrhSpJSUlJUUVGhbdu2OTXJycl%2BbaakpCgvL09VVVW19qWiokLl5eV%2BCwAAuHo02xmsb%2BPMmTOaOnWqRowYobCwMGf7o48%2Bql69eik8PFxbtmzRtGnTVFhYqD/%2B8Y%2BSpOLiYkVFRfm1FR4eruDgYBUXF1%2ByJioqSmfPntXRo0cVHR1doz9z5szRjBkzbJ8mAAAIEAEfsKqqqjR8%2BHCdP39eL774ot%2B%2BiRMnOj/fcsstCg8P17333uvMakmSy%2BWq0aYxxm/7xTXGmEu%2BVpKmTZumjIwMZ728vFwdOnT4lmcGAAACVUAHrKqqKt13330qLCzU%2B%2B%2B/7zd7VZuEhARJ0meffaa2bdvK6/Vq8%2BbNfjWlpaWqqqpyZq28Xq8zm1WtpKRELVu2dELaxdxut9xu9//2tAAAQIAL2HuwqsPVvn37tHbt2kuGnW/asWOHJDmX9RITE1VQUKCioiKnJisrS263W3FxcU7NxTfUZ2VlKT4%2BXkFBQbZOBwAAXEGa7QzWyZMn9dlnnznrhYWFys/PV5s2beTz%2BXTvvfdq%2B/bt%2Bp//%2BR%2BdO3fOmWVq06aNgoODlZOTo9zcXPXr108ej0dbt27VxIkTNXToUOdThsnJyerWrZtSU1P1zDPP6J///KcmT56s9PR0ZzZs9OjRWrhwoTIyMpSenq6cnBwtWbJEb775ZuMPCgAACAguU31DUTOzbt069evXr8b2UaNGafr06YqJian1dR988IH69u2r7du3a8yYMfrkk09UUVGhTp06afjw4ZoyZYquvfZap/7AgQMaM2aM3n//fYWEhGjEiBGaN2%2Be3yW%2B9evXa%2BLEidq1a5d8Pp8ee%2BwxjR49ut7nUl5eLo/Ho7KysjovY35b8U9kWm2vIeXNGtDUXQAAXEUa8u9vXZptwLqSELAuIGABABpTUwasgL0HCwAAoLkiYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgmfWA9cYbb%2BjMmTO2mwUAAAgY1gNWRkaGvF6vHn74YW3ZssV28wAAAM2e9YB16NAhvfLKKyoqKtIPfvADde/eXfPnz9eRI0dsHwoAAKBZsh6wWrZsqWHDhumdd97RgQMHNGrUKL3yyitq3769hg0bptWrV8sYY/uwAAAAzUaD3uTu9Xr1wx/%2BUH379pXL5VJeXp5GjBihG2%2B8URs3bmzIQwMAADSZBglYR48e1bPPPquePXvq9ttvV0lJiVatWqUvv/xSX3/9tQYPHqyf/exnDXFoAACAJtfSdoM/%2BclPtGbNGsXExOiXv/ylRo0apeuuu87Z/53vfEdTpkzRc889Z/vQAAAAzYL1gBUWFqa1a9fqjjvuuGRNdHS09u3bZ/vQAAAAzYL1gPX666/XWeNyuXTDDTfYPjQAAECzYP0erIkTJ2rhwoU1tr/wwguaNGmS7cMBAAA0O9YD1ltvvaWEhIQa2xMTE7V8%2BXLbhwMAAGh2rAeso0ePKjw8vMb2sLAwHT161PbhAAAAmh3rAeuGG27Qu%2B%2B%2BW2P7u%2B%2B%2Bq5iYGNuHAwAAaHas3%2BQ%2BYcIETZgwQceOHdNdd90lSXrvvff0u9/9TvPmzbN9OAAAgGbHesBKT0/XmTNnNHv2bD311FOSpPbt2%2Bu5557TL37xC9uHAwAAaHasByxJeuSRR/TII4%2BoqKhIISEh%2Bu53v9sQhwEAAGiWGiRgVYuOjm7I5gEAAJol6ze5HzlyRD//%2Bc/VsWNHtWrVSsHBwX4LAADAlc76DFZaWpo%2B//xz/fu//7uio6PlcrlsHwIAAKBZsz6DtWHDBr355pt65JFHdO%2B99%2Bqee%2B7xW75NO0OGDJHP55PL5dKqVav89htjNH36dPl8PoWEhKhv377atWuXX01paalSU1Pl8Xjk8XiUmpqq48eP%2B9Xs3LlTSUlJCgkJUbt27TRz5kwZY/xqVqxYoW7dusntdqtbt25auXLltxwVAABwNbEesNq3b29l1urUqVPq2bNnrV%2B7I0m/%2B93vtGDBAi1cuFBbt26V1%2BtV//79deLECadmxIgRys/PV2ZmpjIzM5Wfn6/U1FRnf3l5ufr37y%2Bfz6etW7fq%2Beef17x587RgwQKnJicnR/fff79SU1P10UcfKTU1Vffdd582b978fz5HAABwZXKZi6dr/o8yMzP1%2B9//XosXL1b79u2ttOlyubRy5Ur9%2BMc/lnRh9srn82nChAl67LHHJEkVFRWKiorS008/rYcfflh79uxRt27dlJubq969e0uScnNzlZiYqE8%2B%2BURdunTRSy%2B9pGnTpunw4cNyu92SpLlz5%2Br555/XV199JZfLpfvvv1/l5eX6%2B9//7vRnwIABCg8P15tvvlmv/peXl8vj8aisrExhYWFWxqRa/BOZVttrSHmzBjR1FwAAV5GG/PtbF%2BszWKmpqfrggw/UqVMnhYeHKzIy0m%2BxobCwUMXFxUpOTna2ud1uJSUladOmTZIuzDx5PB4nXElSQkKCPB6PX01SUpITriQpJSVFhw4d0v79%2B52abx6nuqa6DQAAgItZv8l97ty5tpusobi4WJIUFRXltz0qKkpffvmlU1NboIuMjHReX1xcrM6dO9doo3pfTEyMiouLaz1OdRu1qaioUEVFhbNeXl5ezzMDAABXAusB66GHHrLd5CVdfK%2BXMcZvW233gtVVU33FtK6ay91nNmfOHM2YMaMeZwAAAK5E1i8RStL%2B/fs1ffp0paamqqSkRJKUlZWlPXv2WGnf6/VKUo1ZpJKSEme2yev16vDhwzVee%2BTIEb%2Ba2tqQVGfNxbNa3zRt2jSVlZU5y8GDB7/N6QEAgABnPWBt3LhR3bt31/r16/XXv/5VJ0%2BelCRt375dv/3tb60cIyYmRl6vV9nZ2c62yspKrV%2B/Xn369JEkJSYmqqysTFu2bHFqNm/erLKyMr%2BaDRs2qLKy0qnJysqSz%2BdzLh0mJib6Hae6prqN2rjdboWFhfktAADg6mE9YD322GOaPn26PvjgA78nt991113Kzc2tdzsnT55Ufn6%2B8vPzJV24sT0/P18HDhyQy%2BXShAkTNHv2bK1cuVIFBQVKS0vTtddeqxEjRkiSunbtqgEDBig9PV25ubnKzc1Venq6Bg8erC5duki68BgHt9uttLQ0FRQUaOXKlZo9e7YyMjKcS4CPPvqosrKy9PTTT%2BuTTz7R008/rbVr12rChAm2hgwAAFxhrN%2BD9fHHH2vp0qU1tkdGRurIkSP1bicvL0/9%2BvVz1jMyMiRJo0aN0muvvaYpU6bo9OnTGjNmjEpLS9W7d29lZWUpNDTUec3SpUs1fvx451OAQ4cO9XuulsfjUXZ2tsaOHav4%2BHiFh4crIyPDOZYk9enTR8uWLdNvfvMbPfnkk7rhhhu0fPlyv08nAgAAfJP152C1b99eb731lhITExUaGqqPPvpI119/vVatWqVJkybp888/t3m4gMBzsC7gOVgAgMZ0RT0Ha/jw4Zo6daqOHDniXGbbvHmzJk%2BerAcffND24QAAAJod6wFr9uzZ8nq9io6O1smTJ9WtWzf16dNHt912m5588knbhwMAAGh2rN%2BDFRwcrOXLl%2BvTTz/V9u3bdf78efXq1Us333yz7UMBAAA0S9YDVrWbbrpJN910U0M1DwAA0GxZD1i/%2BtWvLrv/D3/4g%2B1DAgAANCvWA1ZRUZHfelVVlXbt2qUTJ07ozjvvtH04AACAZsd6wPrb3/5WY9vZs2f161//Wl27drV9OAAAgGanQb6L8GItW7bU5MmT9cwzzzTG4QAAAJpUowQsSfriiy9UVVXVWIcDAABoMtYvEU6ZMsVv3RijoqIivfPOOxo5cqTtwwEAADQ71gNWTk6O3/o111yj6667TnPnzlV6errtwwEAADQ71gPWxo0bbTcJAAAQUBrtHiwAAICrhfUZrNtuu835kue6bNmyxfbhAQAAmpz1gNWvXz%2B9/PLLuummm5SYmChJys3N1d69e/Xwww/L7XbbPiQAAECzYj1gHT9%2BXGPHjtXs2bP9tj/xxBM6fPiw/vjHP9o%2BJAAAQLNi/R6sv/71r/r5z39eY3taWpreeust24cDAABodqwHLLfbrU2bNtXYvmnTJi4PAgCAq4L1S4Tjx4/X6NGjtWPHDiUkJEi6cA/W4sWL9fjjj9s%2BHAAAQLNjPWA98cQTiomJ0e9//3u98sorkqSuXbtq8eLFGjFihO3DAQAANDvWA5YkjRgxgjAFAACuWg3yoNHy8nK99tpr%2Bu1vf6vS0lJJ0kcffaSioqKGOBwAAECzYn0Gq6CgQD/60Y907bXX6uDBg0pLS1N4eLj%2B%2Bte/6quvvtLrr79u%2B5AAAADNivUZrIkTJ2rEiBH6/PPP1apVK2f7oEGDtGHDBtuHAwAAaHasz2Bt3bpVL730Uo2vy2nXrh2XCAEAwFXB%2BgxWcHCwTp48WWP7vn37FBERYftwAAAAzY71gDV06FD9x3/8h86ePStJcrlc%2BvrrrzV16lQNGzbM9uEAAACaHesBa/78%2BTp06JC8Xq9Onz6tu%2B66S9dff71atWpV4/sJAQAArkTW78HyeDzatGmTsrOztX37dp0/f169evVSSkpKjfuyAAAArkRWA1ZVVZXuvvtuvfjii0pOTlZycrLN5gEAAAKC1UuEQUFB2rFjBzNVAADgqmb9HqwHH3xQr776qu1mAQAAAkaDfFXOwoUL1bt3b40dO1ZTpkzxW2zp3LmzXC5XjWXs2LGSpL59%2B9bYN3z4cL82SktLlZqaKo/HI4/Ho9TUVB0/ftyvZufOnUpKSlJISIjatWunmTNnyhhj7TwAAMCVx/pN7tu2bdMtt9wiSfr444/99tm8dLh161adO3fOWS8oKFD//v3105/%2B1NmWnp6umTNnOushISF%2BbYwYMUJfffWVMjMzJUm/%2BtWvlJqaqr/97W%2BSLnynYv/%2B/dWvXz9t3bpVn376qdLS0tS6dWtNmjTJ2rkAAIAri7WA9cUXXygmJkYbN2601eRlXXfddX7rc%2BfO1Q033KCkpCRn27XXXiuv11vr6/fs2aPMzEzl5uaqd%2B/ekqTFixcrMTFRe/fuVZcuXbR06VKdOXNGr732mtxut2JjY/Xpp59qwYIFysjI4F4zAABQK2uXCG%2B88UYdOXLEWb///vt1%2BPBhW81fVmVlpd544w394he/8As9S5cuVUREhLp3767JkyfrxIkTzr6cnBx5PB4nXElSQkKC85iJ6pqkpCS53W6nJiUlRYcOHdL%2B/fsv2Z%2BKigqVl5f7LQAA4OphLWBdfF/SmjVrdOrUKVvNX9aqVat0/PhxpaWlOdtGjhypN998U%2BvWrdOTTz6pFStW%2BD1Jvri4WJGRkTXaioyMVHFxsVMTFRXlt796vbqmNnPmzHHu6/J4POrQocP/5fQAAECAsX4PVlNYsmSJBg4cKJ/P52xLT093fo6NjdWNN96o%2BPh4bd%2B%2BXb169ZJU%2Bz1hxhi/7RfXVAfJy10enDZtmjIyMpz18vJyQhYAAFcRawGr%2BpN6F29raF9%2B%2BaXWrl2rt99%2B%2B7J1vXr1UlBQkPbt26devXrJ6/XWegnzyJEjziyV1%2ButMVNVUlIiSTVmtr7J7Xb7XVYEAABXF2sByxijtLQ0J1icOXNGo0ePVuvWrf3q6gpC39arr76qyMhIDRo06LJ1u3btUlVVlaKjoyVJiYmJKisr05YtW/T9739fkrR582aVlZWpT58%2BTs3jjz%2BuyspKBQcHS5KysrLk8/nUuXNnq%2BcBAACuHNYC1qhRo/zWH3zwQVtNX9L58%2Bf16quvatSoUWrZ8v%2Bdyueff66lS5fq7rvvVkREhHbv3q1Jkybp1ltv1e233y5J6tq1qwYMGKD09HS9/PLLki48pmHw4MHq0qWLpAuPcZgxY4bS0tL0%2BOOPa9%2B%2BfZo9e7Z%2B%2B9vf8glCAABwSdYCVlM8vX3t2rU6cOCAfvGLX/htDw4O1nvvvaff//73OnnypDp06KBBgwbpqaeeUosWLZy6pUuXavz48c53Jg4dOlQLFy509ns8HmVnZ2vs2LGKj49XeHi4MjIy/O6vAgAAuJjL8FjyBldeXi6Px6OysjKFhYVZbTv%2BiUyr7TWkvFkDmroLAICrSEP%2B/a1Lg3xVDgAAwNWMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDKLKNsgAAAWWElEQVQCFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsC9iANX36dLlcLr/F6/U6%2B40xmj59unw%2Bn0JCQtS3b1/t2rXLr43S0lKlpqbK4/HI4/EoNTVVx48f96vZuXOnkpKSFBISonbt2mnmzJkyxjTKOQIAgMAUsAFLkrp3766ioiJn2blzp7Pvd7/7nRYsWKCFCxdq69at8nq96t%2B/v06cOOHUjBgxQvn5%2BcrMzFRmZqby8/OVmprq7C8vL1f//v3l8/m0detWPf/885o3b54WLFjQqOcJAAACS8um7sD/RcuWLf1mraoZY/Tss8/qiSee0LBhwyRJr7/%2BuqKiovSXv/xFDz/8sPbs2aPMzEzl5uaqd%2B/ekqTFixcrMTFRe/fuVZcuXbR06VKdOXNGr732mtxut2JjY/Xpp59qwYIFysjIkMvlatTzBQAAgSGgZ7D27dsnn8%2BnmJgYDR8%2BXF988YUkqbCwUMXFxUpOTnZq3W63kpKStGnTJklSTk6OPB6PE64kKSEhQR6Px68mKSlJbrfbqUlJSdGhQ4e0f//%2BRjhDAAAQiAI2YPXu3Vt/%2BtOf9O6772rx4sUqLi5Wnz59dOzYMRUXF0uSoqKi/F4TFRXl7CsuLlZkZGSNdiMjI/1qamujet%2BlVFRUqLy83G8BAABXj4C9RDhw4EDn5x49eigxMVE33HCDXn/9dSUkJEhSjUt4xhi/bbVd4qurpvoG98tdHpwzZ45mzJjxLc4GAABcSQJ2ButirVu3Vo8ePbRv3z7nvqyLZ5lKSkqcGSiv16vDhw/XaOfIkSN%2BNbW1IdWcHfumadOmqayszFkOHjz4vz8xAAAQcK6YgFVRUaE9e/YoOjpaMTEx8nq9ys7OdvZXVlZq/fr16tOnjyQpMTFRZWVl2rJli1OzefNmlZWV%2BdVs2LBBlZWVTk1WVpZ8Pp86d%2B58yb643W6FhYX5LQAA4OoRsAFr8uTJWr9%2BvQoLC7V582bde%2B%2B9Ki8v16hRo%2BRyuTRhwgTNnj1bK1euVEFBgdLS0nTttddqxIgRkqSuXbtqwIABSk9PV25urnJzc5Wenq7BgwerS5cuki48xsHtdistLU0FBQVauXKlZs%2BezScIAQDAZQXsPVhfffWVHnjgAR09elTXXXedEhISlJubq06dOkmSpkyZotOnT2vMmDEqLS1V7969lZWVpdDQUKeNpUuXavz48c6nDYcOHaqFCxc6%2Bz0ej7KzszV27FjFx8crPDxcGRkZysjIaNyTBQAAAcVleCx5gysvL5fH41FZWZn1y4XxT2Raba8h5c0a0NRdAABcRRry729dAvYSIQAAQHNFwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsC9iANWfOHN12220KDQ1VZGSkfvzjH2vv3r1%2BNX379pXL5fJbhg8f7ldTWlqq1NRUeTweeTwepaam6vjx4341O3fuVFJSkkJCQtSuXTvNnDlTxpgGP0cAABCYAjZgrV%2B/XmPHjlVubq6ys7N19uxZJScn69SpU3516enpKioqcpaXX37Zb/%2BIESOUn5%2BvzMxMZWZmKj8/X6mpqc7%2B8vJy9e/fXz6fT1u3btXzzz%2BvefPmacGCBY1yngAAIPC0bOoO/G9lZmb6rb/66quKjIzUtm3bdOeddzrbr732Wnm93lrb2LNnjzIzM5Wbm6vevXtLkhYvXqzExETt3btXXbp00dKlS3XmzBm99tprcrvdio2N1aeffqoFCxYoIyNDLper4U4SAAAEpICdwbpYWVmZJKlNmzZ%2B25cuXaqIiAh1795dkydP1okTJ5x9OTk58ng8TriSpISEBHk8Hm3atMmpSUpKktvtdmpSUlJ06NAh7d%2B/v9a%2BVFRUqLy83G8BAABXj4CdwfomY4wyMjL0gx/8QLGxsc72kSNHKiYmRl6vVwUFBZo2bZo%2B%2BugjZWdnS5KKi4sVGRlZo73IyEgVFxc7NZ07d/bbHxUV5eyLiYmp8fo5c%2BZoxowZtk4PAAAEmCsiYI0bN04ff/yxPvzwQ7/t6enpzs%2BxsbG68cYbFR8fr%2B3bt6tXr16SVOslPmOM3/aLa6pvcL/U5cFp06YpIyPDWS8vL1eHDh2%2B5VkBAIBAFfAB65FHHtE777yjDRs2qH379pet7dWrl4KCgrRv3z716tVLXq9Xhw8frlF35MgRZ5bK6/U6s1nVSkpKJP2/mayLud1uv0uKAADg6hKw92AZYzRu3Di9/fbbev/992u9VHexXbt2qaqqStHR0ZKkxMRElZWVacuWLU7N5s2bVVZWpj59%2Bjg1GzZsUGVlpVOTlZUln89X49IhAACAFMABa%2BzYsXrjjTf0l7/8RaGhoSouLlZxcbFOnz4tSfr88881c%2BZM5eXlaf/%2B/VqzZo1%2B%2BtOf6tZbb9Xtt98uSeratasGDBig9PR05ebmKjc3V%2Bnp6Ro8eLC6dOki6cJjHNxut9LS0lRQUKCVK1dq9uzZfIIQAABcUsAGrJdeekllZWXq27evoqOjnWX58uWSpODgYL333ntKSUlRly5dNH78eCUnJ2vt2rVq0aKF087SpUvVo0cPJScnKzk5Wbfccov%2B/Oc/O/s9Ho%2Bys7P11VdfKT4%2BXmPGjFFGRobfPVYAAADf5DI8krzBlZeXy%2BPxqKysTGFhYVbbjn8is%2B6iZiJv1oCm7gIA4CrSkH9/6xKwM1gAAADNFQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyAVU8vvviiYmJi1KpVK8XFxWnjxo1N3SUAANBMtWzqDgSC5cuXa8KECXrxxRd1%2B%2B236%2BWXX9bAgQO1e/dudezYsam7FzDin8hs6i58K3mzBjR1FwAAAYoZrHpYsGCBHnroIf3yl79U165d9eyzz6pDhw566aWXmrprAACgGWIGqw6VlZXatm2bpk6d6rc9OTlZmzZtqvU1FRUVqqiocNbLysokSeXl5db7d67ilPU2ccGtk1c0dReuWOt/27%2BpuwDgKlD9d9cY0%2BjHJmDV4ejRozp37pyioqL8tkdFRam4uLjW18yZM0czZsyosb1Dhw4N0kcg0HjmN3UPAFxNTpw4IY/H06jHJGDVk8vl8ls3xtTYVm3atGnKyMhw1s%2BfP69//vOfatu27SVf879RXl6uDh066ODBgwoLC7PW7tWMMW0YjGvDYFwbBuNqX1ONqTFGJ06ckM/na7RjViNg1SEiIkItWrSoMVtVUlJSY1armtvtltvt9tv23e9%2Bt8H6GBYWxpuAZYxpw2BcGwbj2jAYV/uaYkwbe%2BaqGje51yE4OFhxcXHKzs72256dna0%2Bffo0Ua8AAEBzxgxWPWRkZCg1NVXx8fFKTEzUH/7wBx04cECjR49u6q4BAIBmqMX06dOnN3UnmrvY2Fi1bdtWs2fP1rx583T69Gn9%2Bc9/Vs%2BePZu6a2rRooX69u2rli3JyrYwpg2DcW0YjGvDYFztu9rG1GWa4rOLAAAAVzDuwQIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBKwA9eKLLyomJkatWrVSXFycNm7c2NRdahTTp0%2BXy%2BXyW7xer7PfGKPp06fL5/MpJCREffv21a5du/zaKC0tVWpqqjwejzwej1JTU3X8%2BHG/mp07dyopKUkhISFq166dZs6cWeO7rFasWKFu3brJ7XarW7duWrlypd/%2B%2BvSlqWzYsEFDhgyRz%2BeTy%2BXSqlWr/PYH2jjWpy%2BNoa5xTUtLq/H7m5CQ4FdTUVGhRx55RBEREWrdurWGDh2qr776yq/mwIEDGjJkiFq3bq2IiAiNHz9elZWVfjXr169XXFycWrVqpeuvv16LFi2q0d%2B63kfq05eGNmfOHN12220KDQ1VZGSkfvzjH2vv3r3fup/Naczq05eGVp9x7du3b43f1%2BHDh/vV8D5wGQYBZ9myZSYoKMgsXrzY7N692zz66KOmdevW5ssvv2zqrjW4p556ynTv3t0UFRU5S0lJibN/7ty5JjQ01KxYscLs3LnT3H///SY6OtqUl5c7NQMGDDCxsbFm06ZNZtOmTSY2NtYMHjzY2V9WVmaioqLM8OHDzc6dO82KFStMaGiomTdvnlOzadMm06JFCzN79myzZ88eM3v2bNOyZUuTm5v7rfrSVNasWWOeeOIJs2LFCiPJrFy50m9/oI1jXX1pLHWN66hRo8yAAQP8fn%2BPHTvmVzN69GjTrl07k52dbbZv32769etnevbsac6ePWuMMebs2bMmNjbW9OvXz2zfvt1kZ2cbn89nxo0b57TxxRdfmGuvvdY8%2BuijZvfu3Wbx4sUmKCjI/Nd//ZdTU5/3kbr60hhSUlLMq6%2B%2BagoKCkx%2Bfr4ZNGiQ6dixozl58mS9%2B9mcxqw%2BfWkM9RnXpKQkk56e7vf7evz4cb92eB%2B4NAJWAPr%2B979vRo8e7bft5ptvNlOnTm2iHjWep556yvTs2bPWfefPnzder9fMnTvX2XbmzBnj8XjMokWLjDHG7N6920jy%2B4%2Bbk5NjJJlPPvnEGGPMiy%2B%2BaDwejzlz5oxTM2fOHOPz%2Bcz58%2BeNMcbcd999ZsCAAX7HT0lJMcOHD693X5qLi4NAoI1jffrSFC4VsP7t3/7tkq85fvy4CQoKMsuWLXO2ff311%2Baaa64xmZmZxpgLIe6aa64xX3/9tVPz5ptvGrfbbcrKyowxxkyZMsXcfPPNfm0//PDDJiEhwVmv632kPn1pCiUlJUaSWb9%2BvTEm8MasPn1pChePqzEXAtajjz56ydfwPnB5XCIMMJWVldq2bZuSk5P9ticnJ2vTpk1N1KvGtW/fPvl8PsXExGj48OH64osvJEmFhYUqLi72Gxu3262kpCRnbHJycuTxeNS7d2%2BnJiEhQR6Px68mKSnJ7/skU1JSdOjQIe3fv9%2BpufjfICUlxWmjPn1prgJtHOvTl%2BZk3bp1ioyM1E033aT09HSVlJQ4%2B7Zt26aqqiq/8/X5fIqNjfU739jYWL8vr01JSVFFRYW2bdvm1NQ2rnl5eaqqqqrX%2B0h9%2BtIUysrKJElt2rSRFHhjVp%2B%2BNIWLx7Xa0qVLFRERoe7du2vy5Mk6ceKEs4/3gcsjYAWYo0eP6ty5czW%2BaDoqKqrGF1JfiXr37q0//elPevfdd7V48WIVFxerT58%2BOnbsmHP%2Blxub4uJiRUZG1mg3MjLSr6a2Nqr3Xa7mm/vr6ktzFWjjWJ%2B%2BNBcDBw7U0qVL9f7772v%2B/PnaunWr7rrrLlVUVEi6cC7BwcEKDw/3e93F53vxeISHhys4OLjOcT179qyOHj1ar/eR%2BvSlsRljlJGRoR/84AeKjY2VFHhjVp%2B%2BNLbaxlWSRo4cqTfffFPr1q3Tk08%2BqRUrVmjYsGHOft4HLu/qeF79FcjlcvmtG2NqbLsSDRw40Pm5R48eSkxM1A033KDXX3/duVm4rrGpbZzqqjH//w2ZddVcvC2Q/50CaRzr05fm4P7773d%2Bjo2NVXx8vDp16qTVq1f7/eG6mO2xr%2B3fobY26tOXxjRu3Dh9/PHH%2BvDDD%2Busbc5j1tx%2BXy81runp6c7PsbGxuvHGGxUfH6/t27erV69ekngfuBxmsAJMRESEWrRoUSORl5SU1Ej3V4PWrVurR48e2rdvn/NpwsuNjdfr1eHDh2u0c%2BTIEb%2Ba2tqQVGfNN/fX1ZfmKtDGsT59aa6io6PVqVMn7du3T9KFc6msrFRpaalf3cXne/F4lJaWqqqqqs5xbdmypdq2bVuv95H69KUxPfLII3rnnXf0wQcfqH379s72QBuz%2BvSlMV1qXGvTq1cvBQUF%2Bf2%2B8j5waQSsABMcHKy4uDhlZ2f7bc/OzlafPn2aqFdNp6KiQnv27FF0dLRiYmLk9Xr9xqayslLr1693xiYxMVFlZWXasmWLU7N582aVlZX51WzYsMHvY9NZWVny%2BXzq3LmzU3Pxv0FWVpbTRn360lwF2jjWpy/N1bFjx3Tw4EFFR0dLkuLi4hQUFOR3vkVFRSooKPA734KCAhUVFTk1WVlZcrvdiouLc2pqG9f4%2BHgFBQXV632kPn1pDMYYjRs3Tm%2B//bbef/99xcTE%2BO0PtDGrT18aQ13jWptdu3apqqrK%2BX3lfaAODX0XPeyr/qjwkiVLzO7du82ECRNM69atzf79%2B5u6aw1u0qRJZt26deaLL74wubm5ZvDgwSY0NNQ597lz5xqPx2Pefvtts3PnTvPAAw/U%2BlHeW265xeTk5JicnBzTo0cPv4/yHj9%2B3ERFRZkHHnjA7Ny507z99tsmLCzM72PF//jHP0yLFi3M3LlzzZ49e8zcuXNr/VhxXX1pKidOnDA7duwwO3bsMJLMggULzI4dO5yPmwfaONbVl8ZyuXE9ceKEmTRpktm0aZMpLCw0H3zwgUlMTDTt2rXzO5fRo0eb9u3bm7Vr15rt27ebu%2B66q9aP%2Bf/whz8027dvN2vXrjXt27ev9ZEDEydONLt37zZLliy55CMHLvc%2BUldfGsOvf/1r4/F4zLp16/weF/Cvf/2r3v1sTmNWn740hrrG9bPPPjMzZswwW7duNYWFhWb16tXm5ptvNrfeeqvfvz/vA5dGwApQL7zwgunUqZMJDg42vXr18vto7ZWs%2BtknQUFBxufzmWHDhpldu3Y5%2B8%2BfP2%2Beeuop4/V6jdvtNnfeeafZuXOnXxvHjh0zI0eONKGhoSY0NNSMHDnSlJaW%2BtV8/PHH5o477jBut9t4vV4zffp05yPF1d566y3TpUsXExQUZG6%2B%2BWazYsUKv/316UtT%2BeCDD4ykGsuoUaOMMYE3jvXpS2O43Lj%2B61//MsnJyea6664zQUFBpmPHjmbUqFHmwIEDfm2cPn3ajBs3zrRp08aEhISYwYMH16j58ssvzaBBg0xISIhp06aNGTdunN/H4I0xZt26debWW281wcHBpnPnzuall16q0d%2B63kfq05eGVtt4SjKvvvrqt%2Bpncxqz%2BvSlodU1rgcOHDB33nmnadOmjQkODjY33HCDGT9%2BfI3ntvE%2BcGkuYy56nCoAAAD%2BT7gHCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZf8fcspOBh5nT%2B0AAAAASUVORK5CYII%3D"/>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="common-3083320413624879844">
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">0.0</td>
<td class="number">5497</td>
<td class="number">30.1%</td>
<td>
<div class="bar" style="width:43%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">2.66</td>
<td class="number">7</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">3.32</td>
<td class="number">7</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.64</td>
<td class="number">6</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">10.97</td>
<td class="number">6</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.6</td>
<td class="number">6</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.59</td>
<td class="number">6</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">2.74</td>
<td class="number">5</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.65</td>
<td class="number">5</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.63</td>
<td class="number">5</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="other">
<td class="fillremaining">Other values (12061)</td>
<td class="number">12699</td>
<td class="number">69.6%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr>
</table>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="extreme-3083320413624879844">
<p class="h4">Minimum 5 values</p>
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">0.0</td>
<td class="number">5497</td>
<td class="number">30.1%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.83</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.0</td>
<td class="number">3</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.01</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.09</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:1%"> </div>
</td>
</tr>
</table>
<p class="h4">Maximum 5 values</p>
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">1811090.71</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1880231.38</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1896149.5</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1993645.36</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">2546439.11</td>
<td class="number">1</td>
<td class="number">0.0%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr>
</table>
</div>
</div>
</div>
</div><div class="row variablerow">
<div class="col-md-3 namecol">
<p class="h4 pp-anchor" id="pp_var_AveragePrice">AveragePrice<br/>
<small>Numeric</small>
</p>
</div><div class="col-md-6">
<div class="row">
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Distinct count</th>
<td>259</td>
</tr>
<tr>
<th>Unique (%)</th>
<td>1.4%</td>
</tr>
<tr class="ignore">
<th>Missing (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Missing (n)</th>
<td>0</td>
</tr>
<tr class="ignore">
<th>Infinite (%)</th>
<td>0.0%</td>
</tr>
<tr class="ignore">
<th>Infinite (n)</th>
<td>0</td>
</tr>
</table>
</div>
<div class="col-sm-6">
<table class="stats ">
<tr>
<th>Mean</th>
<td>1.406</td>
</tr>
<tr>
<th>Minimum</th>
<td>0.44</td>
</tr>
<tr>
<th>Maximum</th>
<td>3.25</td>
</tr>
<tr class="ignore">
<th>Zeros (%)</th>
<td>0.0%</td>
</tr>
</table>
</div>
</div>
</div>
<div class="col-md-3 collapse in" id="minihistogram503603685371590132">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAAd5JREFUeJzt3TEO0lAcx/F/iWvLTughTLyCiQdyM3Fz9Cjuru7eAsIBaBwcpA6Ki8lPMNBS8vmshL6SvG/eS1ugGcdxrInt9/vq%2B37qYVm43W5X2%2B120jFfTDrab23bVtWvD9x13RynwIIcj8fq%2B/7PvJnSLIE0TVNVVV3XzRbIq3efr37P1w9v7nAmXOo8b6a0mnxEWBCBQDDLFmuprt2W2ZItnxUEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFA4FGTO/LE8PJZQSAQCAQCgUAgEAgEAoFA8BSXef/ncipcwgoCgUAgEAgEAoFAIBAIBAKBQCAQCAQCwVPcSX8mvmT1WKwgEAgEAoFAIBAIBAKBQCAQCAQCgUAgELiT/gT8%2B%2B79WEEgEAgED7fF8hM%2BPJKHC4T788Tw5WYJZBzHqqo6Ho9/vfbj%2B7epT4cLvHz76er3fHn/%2BiZjn%2BfJed5MaZZAhmGoqqq%2B7%2BcYnomsP972eMMw1Hq9vu1B/6EZZ8jydDrV4XCotm2raZqph2dhxnGsYRhqs9nUajXtdaVZAoGlcJkXAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgeAnzlVSTbQC6goAAAAASUVORK5CYII%3D">
</div>
<div class="col-md-12 text-right">
<a role="button" data-toggle="collapse" data-target="#descriptives503603685371590132,#minihistogram503603685371590132"
aria-expanded="false" aria-controls="collapseExample">
Toggle details
</a>
</div>
<div class="row collapse col-md-12" id="descriptives503603685371590132">
<ul class="nav nav-tabs" role="tablist">
<li role="presentation" class="active"><a href="#quantiles503603685371590132"
aria-controls="quantiles503603685371590132" role="tab"
data-toggle="tab">Statistics</a></li>
<li role="presentation"><a href="#histogram503603685371590132" aria-controls="histogram503603685371590132"
role="tab" data-toggle="tab">Histogram</a></li>
<li role="presentation"><a href="#common503603685371590132" aria-controls="common503603685371590132"
role="tab" data-toggle="tab">Common Values</a></li>
<li role="presentation"><a href="#extreme503603685371590132" aria-controls="extreme503603685371590132"
role="tab" data-toggle="tab">Extreme Values</a></li>
</ul>
<div class="tab-content">
<div role="tabpanel" class="tab-pane active row" id="quantiles503603685371590132">
<div class="col-md-4 col-md-offset-1">
<p class="h4">Quantile statistics</p>
<table class="stats indent">
<tr>
<th>Minimum</th>
<td>0.44</td>
</tr>
<tr>
<th>5-th percentile</th>
<td>0.83</td>
</tr>
<tr>
<th>Q1</th>
<td>1.1</td>
</tr>
<tr>
<th>Median</th>
<td>1.37</td>
</tr>
<tr>
<th>Q3</th>
<td>1.66</td>
</tr>
<tr>
<th>95-th percentile</th>
<td>2.11</td>
</tr>
<tr>
<th>Maximum</th>
<td>3.25</td>
</tr>
<tr>
<th>Range</th>
<td>2.81</td>
</tr>
<tr>
<th>Interquartile range</th>
<td>0.56</td>
</tr>
</table>
</div>
<div class="col-md-4 col-md-offset-2">
<p class="h4">Descriptive statistics</p>
<table class="stats indent">
<tr>
<th>Standard deviation</th>
<td>0.40268</td>
</tr>
<tr>
<th>Coef of variation</th>
<td>0.2864</td>
</tr>
<tr>
<th>Kurtosis</th>
<td>0.3252</td>
</tr>
<tr>
<th>Mean</th>
<td>1.406</td>
</tr>
<tr>
<th>MAD</th>
<td>0.32428</td>
</tr>
<tr class="">
<th>Skewness</th>
<td>0.5803</td>
</tr>
<tr>
<th>Sum</th>
<td>25658</td>
</tr>
<tr>
<th>Variance</th>
<td>0.16215</td>
</tr>
<tr>
<th>Memory size</th>
<td>142.6 KiB</td>
</tr>
</table>
</div>
</div>
<div role="tabpanel" class="tab-pane col-md-8 col-md-offset-2" id="histogram503603685371590132">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3X9UVXW%2B//EX8eNgJCdE%2BZVU2JQ/Bu0WeBWtrFTQMqdrs6xLMVANI%2BWPi8gtlXUn50fiLVMzy26OE92yqLlG15ZG0J0CHcCEICMdc6WNOEJoEqhXAXV//%2Bhyvh1BRf3I9sDzsdZey7P3Z3/2e3/WOYeXn7PPPl6WZVkCAACAMZfZXQAAAEB3Q8ACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMN87C6gJzh58qT27dun3r17y8vLy%2B5yAADoESzL0qFDhxQREaHLLuviOSXLQz311FOWJLclNDTUtf3kyZPWU089ZYWHh1v%2B/v7WmDFjrOrqarc%2BDh48aD300ENWYGCgFRgYaD300ENWQ0ODW5utW7dat912m%2BXv729FRERYv/nNb6yTJ0%2BeU601NTXtamVhYWFhYWHpmqWmpub8A8d58ugZrJ/%2B9Kf66KOPXI%2B9vb1d/37mmWe0ZMkS5eTk6IYbbtDvf/97jR8/Xjt27FDv3r0lSYmJidq7d6/y8/MlSb/61a%2BUlJSk999/X5LU1NSk8ePH64477tCWLVv01VdfKSUlRQEBAZozZ06n62w7Xk1NjQIDAy/4vAEAwNk1NTUpMjLS9Xe4K3l0wPLx8VFYWFi79ZZladmyZcrKytKUKVMkSa%2B99ppCQ0P15ptvatq0adq%2Bfbvy8/NVVlamESNGSJJWrVqluLg47dixQwMHDtSaNWt07Ngx5eTkyOFwKDo6Wl999ZWWLFmijIyMTn/c19YuMDCQgAUAQBez4/Icj77IfefOnYqIiFBUVJQeeOAB7dq1S5K0e/du1dXVKT4%2B3tXW4XBozJgxKikpkSSVlpbK6XS6wpUkjRw5Uk6n063NmDFj5HA4XG0SEhK0b98%2BffPNN6etq7m5WU1NTW4LAADoOTw2YI0YMUL/%2BZ//qQ8//FCrVq1SXV2dRo0ape%2B%2B%2B051dXWSpNDQULd9QkNDXdvq6uoUEhLSrt%2BQkBC3Nh310bbtdLKzs%2BV0Ol1LZGTk%2BZ8oAADwOB4bsCZOnKj77rtPQ4cO1bhx47R%2B/XpJP3wU2ObUKUHLstzWdTRleLY2lmWddt828%2BbNU2Njo2upqak5hzMDAACezmMD1qkCAgI0dOhQ7dy503Vd1qmzTPX19a4ZqLCwMH377bft%2Btm/f79bm476kNrPjv2Yw%2BFwXW/FdVcAAPQ83SZgNTc3a/v27QoPD1dUVJTCwsJUWFjo2t7S0qKioiKNGjVKkhQXF6fGxkZ9%2BumnrjabN29WY2OjW5vi4mK1tLS42hQUFCgiIkLXXntt15wYAADwOB4bsDIzM1VUVKTdu3dr8%2BbN%2BvnPf66mpiYlJyfLy8tL6enpWrhwofLy8lRdXa2UlBRdfvnlSkxMlCQNHjxYEyZMUGpqqsrKylRWVqbU1FRNmjRJAwcOlPTDbRwcDodSUlJUXV2tvLw8LVy48Jy%2BQQgAAHoej71Nw969e/XP//zPOnDggPr166eRI0eqrKxM11xzjSTpiSee0NGjR/X444%2BroaFBI0aMUEFBgdu9MNasWaNZs2a5vm04efJkrVixwrXd6XSqsLBQ06dPV2xsrIKCgpSRkaGMjIyuPVkAAOBRvKy2q7Zx0TQ1NcnpdKqxsZHrsQAA6CJ2/v312I8IAQAALlUELAAAAMMIWAAAAIYRsAAAAAzz2G8RAhdbbFa%2B3SWck/KnJ9hdAgDg/zCDBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYT52FwDAjNisfLtL6LTypyfYXQIAXFTMYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYtwlY2dnZ8vLyUnp6umtdc3OzZs6cqb59%2ByogIECTJ0/W3r173fbbs2eP7rnnHgUEBKhv376aNWuWWlpa3NoUFRUpJiZG/v7%2BGjBggF5%2B%2BeUuOScAAOCZukXA2rJli1555RUNGzbMbX16erry8vKUm5urTZs26fDhw5o0aZJOnDghSTpx4oTuvvtuHTlyRJs2bVJubq7Wrl2rOXPmuPrYvXu37rrrLt16662qrKzU/PnzNWvWLK1du7ZLzxEAAHgOH7sLuFCHDx/Wgw8%2BqFWrVun3v/%2B9a31jY6NWr16t119/XePGjZMkvfHGG4qMjNRHH32khIQEFRQUaNu2baqpqVFERIQk6bnnnlNKSoqefvppBQYG6uWXX9bVV1%2BtZcuWSZIGDx6s8vJyLV68WPfdd1/XnzAAALjkefwM1vTp03X33Xe7QlSbiooKtba2Kj4%2B3rUuIiJC0dHRKikpkSSVlpYqOjraFa4kKSEhQc3NzaqoqHC1%2BXEfbW3Ky8vV2traYU3Nzc1qampyWwAAQM/h0QErNzdXFRUVys7Obretrq5Ofn5%2BCgoKclsfGhqquro6V5vQ0FC37UFBQfLz8ztjm9DQUB0/flwHDhzosK7s7Gw5nU7XEhkZed7nCAAAPI/HBqyamhr9y7/8i9asWSN/f/9O72dZlry8vFyPf/zvzraxLOu0%2B0rSvHnz1NjY6Fpqamo6XR8AAPB8HhuwKioqVF9fr5iYGPn4%2BMjHx0dFRUVavny5fHx8FBoaqpaWFjU0NLjtV19f75qRCgsLc81UtWloaFBra%2BsZ29TX18vHx0fBwcEd1uZwOBQYGOi2AACAnsNjA9bYsWP1xRdfqKqqyrXExsbqwQcfdP3b19dXhYWFrn1qa2tVXV2tUaNGSZLi4uJUXV2t2tpaV5uCggI5HA7FxMS42vy4j7Y2bf0DAACcymO/Rdi7d29FR0e7rQsICFBwcLBr/aOPPqo5c%2BYoODhYffr0UWZmpoYOHeq6ID4%2BPl5DhgxRUlKSnn32WR08eFCZmZlKTU11zTqlpaVpxYoVysjIUGpqqkpLS7V69Wq99dZbXXvCAADAY3hswOqMpUuXysfHR1OnTtXRo0c1duxY5eTkyNvbW5Lk7e2t9evX6/HHH9fo0aPVq1cvJSYmavHixa4%2BoqKitGHDBs2ePVsvvviiIiIitHz5cm7RAAAATsvLartiGxdNU1OTnE6nGhsbuR7Lg8Rm5dtdQrdV/vQEu0sA0APY%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%2B%2BCggI0OTJk7V37163Pvbs2aN77rlHAQEB6tu3r2bNmqWWlha3NkVFRYqJiZG/v78GDBigl19%2B%2BYLqBgAA3Z8tASsjI0NhYWGaNm2aPv300/Pqo3///lq0aJHKy8tVXl6uO%2B%2B8Uz/72c/05ZdfSpLS09OVl5en3Nxcbdq0SYcPH9akSZN04sQJSdKJEyd0991368iRI9q0aZNyc3O1du1azZkzx3WM3bt366677tKtt96qyspKzZ8/X7NmzdLatWsvfBAAAEC35WVZltXVBz1%2B/LjWrVunnJwc5efn6/rrr9cjjzyiX/ziF%2BrXr99599unTx89%2B%2Byz%2BvnPf65%2B/frp9ddf1/333y9J2rdvnyIjI7VhwwYlJCTogw8%2B0KRJk1RTU6OIiAhJUm5urlJSUlRfX6/AwEA9%2BeSTWrdunbZv3%2B46Rlpamj7//HOVlpZ2uq6mpiY5nU41NjYqMDDwvM8PXSs2K9/uEnCJKH96gt0lADgPdv79tWUGy8fHR1OmTNG6deu0Z88eJScn649//KP69%2B%2BvKVOmaP369TqX3HfixAnl5ubqyJEjiouLU0VFhVpbWxUfH%2B9qExERoejoaJWUlEiSSktLFR0d7QpXkpSQkKDm5mZVVFS42vy4j7Y25eXlam1tPW09zc3NampqclsAAEDPYftF7mFhYRo7dqxuv/12eXl5qby8XImJibr%2B%2Buu1cePGM%2B77xRdf6IorrpDD4VBaWpry8vI0ZMgQ1dXVyc/PT0FBQW7tQ0NDVVdXJ0mqq6tTaGio2/agoCD5%2BfmdsU1oaKiOHz%2BuAwcOnLau7OxsOZ1O1xIZGdnp8QAAAJ7PtoB14MABLVu2TDfeeKNGjx6t%2Bvp6vffee/rb3/6mv//975o0aZJ%2B8YtfnLGPgQMHqqqqSmVlZXrssceUnJysbdu2nba9ZVny8vJyPf7xvzvbpm1mraN928ybN0%2BNjY2upaam5oznAQAAuhcfOw76T//0T9qwYYOioqL0y1/%2BUsnJyW7XXl1xxRV64okntHz58jP24%2Bfnp5/85CeSpNjYWG3ZskXPP/%2B87r//frW0tKihocFtFqu%2Bvl6jRo2S9MPM2ebNm936a2hoUGtrq2vWKiwszDWb9eM%2BfHx8FBwcfNq6HA6HHA5HJ0YCAAB0R7bMYAUGBuqjjz7SX//6V2VmZnZ4YXt4eLh27tx5Tv1alqXm5mbFxMTI19dXhYWFrm21tbWqrq52Bay4uDhVV1ertrbW1aagoEAOh0MxMTGuNj/uo61NbGysfH19z6k2AADQc9gyg/Xaa6%2BdtY2Xl5euu%2B66026fP3%2B%2BJk6cqMjISB06dEi5ubn65JNPlJ%2BfL6fTqUcffVRz5sxRcHCw%2BvTpo8zMTA0dOlTjxo2TJMXHx2vIkCFKSkrSs88%2Bq4MHDyozM1OpqamubxqkpaVpxYoVysjIUGpqqkpLS7V69Wq99dZbZgYCAAB0S7YErNmzZ%2Bu6667TjBkz3Na/%2BOKL2rVrl5577rmz9vHtt98qKSlJtbW1cjqdGjZsmPLz8zV%2B/HhJ0tKlS%2BXj46OpU6fq6NGjGjt2rHJycuTt7S1J8vb21vr16/X4449r9OjR6tWrlxITE7V48WLXMaKiorRhwwbNnj1bL774oiIiIrR8%2BXLdd999BkcDAAB0N7bcB6t///567733FBsb67b%2Bs88%2B6/CO656O%2B2B5Ju6DhTbcBwvwTD3uPlgHDhxodwsF6Ydrs850%2BwMAAABPYEvAuu666/Thhx%2B2W//hhx8qKirKhooAAADMseUarPT0dKWnp%2Bu7777TnXfeKUn6n//5Hz3zzDNu10ABAAB4IlsCVmpqqo4dO6aFCxfqqaeekvTDdVnLly/XI488YkdJAAAAxtgSsCRp5syZmjlzpmpra9WrVy9deeWVdpUCAABglG0Bq014eLjdJQAAABhly0Xu%2B/fv18MPP6yrr75a/v7%2B8vPzc1sAAAA8mS0zWCkpKfr666/1r//6rwoPDz/jDycDAAB4GlsCVnFxsYqLi3XTTTfZcXgAAICLypaPCPv378%2BsFQAA6LZsCVhLly7VvHnzut1P4gAAAEg2fUSYlJSkQ4cO6ZprrlFgYKB8fX3dttfX19tRFgAAgBG2BKxFixbZcVgAAIAuYUvAevTRR%2B04LAAAQJew5RosSfrmm2%2B0YMECJSUluT4SLCgo0Pbt2%2B0qCQAAwAhbAtbGjRv105/%2BVEVFRXrnnXd0%2BPBhSdJnn32mX//613aUBAAAYIwtAevJJ5/UggUL9PHHH7vduf3OO%2B9UWVmZHSUBAAAYY8s1WFu3btWaNWvarQ8JCdH%2B/fttqAhdITYr3%2B4SAADoErbMYF155ZWqq6trt76qqkpXXXWVDRUBAACYY0vAeuCBBzR37lzt37/fdUf3zZs3KzMzUw899JAdJQEAABhjS8BauHChwsLCFB4ersOHD2vIkCEaNWqUhg8frn/7t3%2BzoyQAAABjbLkGy8/PT2%2B//ba%2B%2BuorffbZZzp58qRuvvlmDRo0yI5yAAAAjLIlYLW54YYbdMMNN9hZAgAAgHG2BKxf/epXZ9z%2ByiuvdFElAAAA5tkSsGpra90et7a26ssvv9ShQ4d022232VESAACAMbYErPfff7/duuPHj%2Buxxx7T4MGDbagIAADAHNt%2Bi/BUPj4%2ByszM1LPPPmt3KQAAABfkkglYkrRr1y61trbaXQYAAMAFseUjwieeeMLtsWVZqq2t1bp16/Tggw/aURIAAIAxtgSs0tJSt8eXXXaZ%2BvXrp0WLFik1NdWOkgAAAIyxJWBt3LjRjsMCAAB0iUvqGiwAAIDuwJYZrOHDh7t%2B5PlsPv3004tcDQAAgFm2BKw77rhD//Ef/6EbbrhBcXFxkqSysjLt2LFD06ZNk8PhsKMsAAAAI2wJWN9//72mT5%2BuhQsXuq3PysrSt99%2Bqz/84Q92lAUAAGCELddgvfPOO3r44YfbrU9JSdGf/vQnGyoCAAAwx5aA5XA4VFJS0m59SUkJHw8CAACPZ8tHhLNmzVJaWpoqKys1cuRIST9cg7Vq1SrNnz/fjpIAAACMsSVgZWVlKSoqSs8//7z%2B%2BMc/SpIGDx6sVatWKTEx0Y6SAAAAjLElYElSYmIiYQoAAHRLtt1otKmpSTk5Ofr1r3%2BthoYGSdLnn3%2Bu2tpau0oCAAAwwpYZrOrqao0bN06XX365ampqlJKSoqCgIL3zzjvau3evXnvtNTvKAgAAMMKWGazZs2crMTFRX3/9tfz9/V3r7777bhUXF9tREgAAgDG2zGBt2bJFK1eubPdzOVdddRUfEQIAAI9nywyWn5%2BfDh8%2B3G79zp071bdvXxsqAgAAMMeWgDV58mT97ne/0/HjxyVJXl5e%2Bvvf/665c%2BdqypQpdpQEAABgjC0B67nnntO%2BffsUFhamo0eP6s4779SAAQPk7%2B/f7vcJAQAAPI0t12A5nU6VlJSosLBQn332mU6ePKmbb75ZCQkJ7a7LAgAA8DRdHrBaW1t111136aWXXlJ8fLzi4%2BO7ugQAAICLqss/IvT19VVlZSUzVQAAoNuy5Rqshx56SK%2B%2B%2BqodhwYAALjobPstwhUrVuijjz5SbGysAgIC3LY988wzNlUFAABw4WyZwaqoqNCwYcPk5%2BenrVu3qrS01LWUlZV1qo/s7GwNHz5cvXv3VkhIiO69917t2LHDrU1zc7Nmzpypvn37KiAgQJMnT9bevXvd2uzZs0f33HOPAgIC1LdvX82aNUstLS1ubYqKihQTEyN/f38NGDBAL7/88oUNAAAA6Na6dAZr165dioqK0saNGy%2B4r6KiIk2fPl3Dhw/X8ePHlZWVpfj4eG3bts01I5aenq73339fubm5Cg4O1pw5czRp0iRVVFTI29tbJ06c0N13361%2B/fpp06ZN%2Bu6775ScnCzLsvTCCy9Iknbv3q277rpLqampeuONN/SXv/xFjz/%2BuPr166f77rvvgs8DAAB0P16WZVlddTBvb2/V1tYqJCREknT//fdr%2BfLlCg0NveC%2B9%2B/fr5CQEBUVFem2225TY2Oj%2BvXrp9dff13333%2B/JGnfvn2KjIzUhg0blJCQoA8%2B%2BECTJk1STU2NIiIiJEm5ublKSUlRfX29AgMD9eSTT2rdunXavn2761hpaWn6/PPPVVpa2qnampqa5HQ61djYqMDAwAs%2BV08Vm5VvdwnAeSl/eoLdJQA4D3b%2B/e3SjwhPzXIbNmzQkSNHjPTd2NgoSerTp4%2BkHz6GbG1tdbsNREREhKKjo1VSUiJJKi0tVXR0tCtcSVJCQoKam5tVUVHhanPqrSQSEhJUXl6u1tZWI7UDAIDuxbaL3E2yLEsZGRm65ZZbFB0dLUmqq6uTn5%2BfgoKC3NqGhoaqrq7O1ebU2bOgoCD5%2BfmdsU1oaKiOHz%2BuAwcOKDw8vF09zc3Nam5udj1uamq68JMEAAAeo0tnsLy8vNrd/8rE/bBmzJihrVu36q233jprW8uy3I7Z0fHP1qZtJu50tWdnZ8vpdLqWyMjITp0HAADoHrp0BsuyLKWkpMjhcEiSjh07prS0tHa3aXj33Xc73efMmTO1bt06FRcXq3///q71YWFhamlpUUNDg9ssVn19vUaNGuVqs3nzZrf%2BGhoa1Nra6pq1CgsLc81m/bgPHx8fBQcHd1jTvHnzlJGR4Xrc1NREyAIAoAfp0hms5ORkhYSEuGZ2HnroIUVERLjN9jidzk71ZVmWZsyYoXfffVd//vOfFRUV5bY9JiZGvr6%2BKiwsdK2rra1VdXW1K2DFxcWpurpatbW1rjYFBQVyOByKiYlxtflxH21tYmNj5evr22FtDodDgYGBbgsAAOg5unQGy%2BTd26dPn64333xT//3f/63evXu7ZpmcTqd69eolp9OpRx99VHPmzFFwcLD69OmjzMxMDR06VOPGjZMkxcfHa8iQIUpKStKzzz6rgwcPKjMzU6mpqa5QlJaWphUrVigjI0OpqakqLS3V6tWrO/VxJAAA6JlsudGoCStXrlRjY6Nuv/12hYeHu5a3337b1Wbp0qW69957NXXqVI0ePVqXX3653n//fXl7e0v64bYR69evl7%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%2B999y2W5alBQsWKCIiQr169dLtt9%2BuL7/80q1NQ0ODkpKS5HQ65XQ6lZSUpO%2B//96tzRdffKExY8aoV69euuqqq/Tb3/5WlmVd9PMDAACeyaMD1pEjR3TjjTdqxYoVHW5/5plntGTJEq1YsUJbtmxRWFiYxo8fr0OHDrnaJCYmqqqqSvn5%2BcrPz1dVVZWSkpJc25uamjR%2B/HhFRERoy5YteuGFF7R48WItWbLkop8fAADwTD52F3AhJk6cqIkTJ3a4zbIsLVu2TFlZWZoyZYok6bXXXlNoaKjefPNNTZs2Tdu3b1d%2Bfr7Kyso0YsQISdKqVasUFxenHTt2aODAgVqzZo2OHTumnJwcORwORUdH66uvvtKSJUuUkZEhLy%2BvLjtfAADgGTx6ButMdu/erbq6OsXHx7vWORwOjRkzRiUlJZKk0tJSOZ1OV7iSpJEjR8rpdLq1GTNmjBwOh6tNQkKC9u3bp2%2B%2B%2BaZrTgYAAHiUbhuw6urqJEmhoaFu60NDQ13b6urqFBIS0m7fkJAQtzYd9fHjY5yqublZTU1NbgsAAOg5um3AanPqR3iWZbmt6%2BgjvrO1abvA/XQfD2ZnZ7sumnc6nYqMjDzv%2BgEAgOfptgErLCxMUvtZpvr6etcMVFhYmL799tt2%2B%2B7fv9%2BtTUd9SO1nx9rMmzdPjY2NrqWmpubCTgYAAHiUbhuwoqKiFBYWpsLCQte6lpYWFRUVadSoUZKkuLg4NTY26tNPP3W12bx5sxobG93aFBcXq6WlxdWmoKBAERERuvbaazs8tsPhUGBgoNsCAAB6Do8OWIcPH1ZVVZWqqqok/XBhe1VVlfbs2SMvLy%2Blp6dr4cKFysvLU3V1tVJSUnT55ZcrMTFRkjR48GBNmDBBqampKisrU1lZmVJTUzVp0iQNHDhQ0g%2B3cXA4HEpJSVF1dbXy8vK0cOFCvkEIAABOy6Nv01BeXq477rjD9TgjI0OSlJycrJycHD3xxBM6evSoHn/8cTU0NGjEiBEqKChQ7969XfusWbNGs2bNcn3bcPLkyW731XI6nSosLNT06dMVGxuroKAgZWRkuI4FAABwKi%2BLW5JfdE1NTXI6nWpsbOzRHxfGZuXbXQLQI5Q/PcHuEoBLgp1/fz36I0IAAIBLEQELAADAMAKIw9QBAAAKf0lEQVQWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhPnYXAAAwKzYr3%2B4SOq386Ql2lwBcFMxgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM87G7AFyY2Kx8u0sAAACnYAYLAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDDu5A4AsI2n/RpF%2BdMT7C4BHoIZLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMbxECANBJfOsRncUMVie99NJLioqKkr%2B/v2JiYrRx40a7SwIAAJcoAlYnvP3220pPT1dWVpYqKyt16623auLEidqzZ4/dpQEAgEsQAasTlixZokcffVS//OUvNXjwYC1btkyRkZFauXKl3aUBAIBLENdgnUVLS4sqKio0d%2B5ct/Xx8fEqKSnpcJ/m5mY1Nze7Hjc2NkqSmpqajNd3ovmI8T4BAN3DTZlr7S6h04p%2BPd54n21/dy3LMt732RCwzuLAgQM6ceKEQkND3daHhoaqrq6uw32ys7P1m9/8pt36yMjIi1IjAACezvncxev70KFDcjqdF%2B8AHSBgdZKXl5fbY8uy2q1rM2/ePGVkZLgenzx5UgcPHlRwcPBp97lYmpqaFBkZqZqaGgUGBnbpsT0Z43buGLPzw7idH8bt3PXEMbMsS4cOHVJERESXH5uAdRZ9%2B/aVt7d3u9mq%2Bvr6drNabRwOhxwOh9u6K6%2B88qLV2BmBgYE95gVlEuN27hiz88O4nR/G7dz1tDHr6pmrNlzkfhZ%2Bfn6KiYlRYWGh2/rCwkKNGjXKpqoAAMCljBmsTsjIyFBSUpJiY2MVFxenV155RXv27FFaWprdpQEAgEuQ94IFCxbYXcSlLjo6WsHBwVq4cKEWL16so0eP6vXXX9eNN95od2md4u3trdtvv10%2BPuTpc8G4nTvG7PwwbueHcTt3jFnX8bLs%2BO4iAABAN8Y1WAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgNUNvPTSS4qKipK/v79iYmK0cePG07bNycmRl5dXu%2BXYsWNdWLG9iouLdc899ygiIkJeXl567733zrpPUVGRYmJi5O/vrwEDBujll1/ugkovLec6bp988kmHz7W//vWvXVSx/bKzszV8%2BHD17t1bISEhuvfee7Vjx46z7rd27VoNGTJEDodDQ4YMUV5eXhdUe%2Bk4n3Hr6e9tK1eu1LBhw1w3EY2Li9MHH3xwxn16%2BvPsYiNgebi3335b6enpysrKUmVlpW699VZNnDhRe/bsOe0%2BgYGBqq2tdVv8/f27sGp7HTlyRDfeeKNWrFjRqfa7d%2B/WXXfdpVtvvVWVlZWaP3%2B%2BZs2apbVrPedHVE0413Frs2PHDrfn2vXXX3%2BRKrz0FBUVafr06SorK1NhYaGOHz%2Bu%2BPh4HTly%2Bh9pLy0t1f3336%2BkpCR9/vnnSkpK0tSpU7V58%2BYurNxe5zNuUs9%2Bb%2Bvfv78WLVqk8vJylZeX684779TPfvYzffnllx2253nWBSx4tH/8x3%2B00tLS3NYNGjTImjt3boftX331VcvpdHZFaR5BkpWXl3fGNk888YQ1aNAgt3XTpk2zRo4ceTFLu6R1Ztw%2B/vhjS5LV0NDQRVVd%2Burr6y1JVlFR0WnbTJ061ZowYYLbuoSEBOuBBx642OVdsjozbry3tRcUFGT94Q9/6HAbz7OLjxksD9bS0qKKigrFx8e7rY%2BPj1dJSclp9zt8%2BLCuueYa9e/fX5MmTVJlZeXFLtWjlZaWthvjhIQElZeXq7W11aaqPMdNN92k8PBwjR07Vh9//LHd5diqsbFRktSnT5/Ttjnd8%2B1Mr%2BnurjPjJvHe1ubEiRPKzc3VkSNHFBcX12EbnmcXHwHLgx04cEAnTpxo96PToaGh7X6cus2gQYOUk5OjdevW6a233pK/v79Gjx6tnTt3dkXJHqmurq7DMT5%2B/LgOHDhgU1WXvvDwcL3yyitau3at3n33XQ0cOFBjx45VcXGx3aXZwrIsZWRk6JZbblF0dPRp253u%2BXa613R319lx471N%2BuKLL3TFFVfI4XAoLS1NeXl5GjJkSIdteZ5dfNwrvxvw8vJye2xZVrt1bUaOHKmRI0e6Ho8ePVo333yzXnjhBS1fvvyi1unJOhrjjtbj/xs4cKAGDhzoehwXF6eamhotXrxYt912m42V2WPGjBnaunWrNm3adNa25/Ka7u46O268t/3wmquqqtL333%2BvtWvXKjk5WUVFRacNWTzPLi5msDxY37595e3t3e5/HPX19e3%2BZ3I6l112mYYPH96j/pd3rsLCwjocYx8fHwUHB9tUlWcaOXJkj3yuzZw5U%2BvWrdPHH3%2Bs/v37n7Ht6Z5vnX1NdyfnMm6n6onvbX5%2BfvrJT36i2NhYZWdn68Ybb9Tzzz/fYVueZxcfAcuD%2Bfn5KSYmRoWFhW7rCwsLNWrUqE71YVmWqqqqFB4efjFK7Bbi4uLajXFBQYFiY2Pl6%2BtrU1WeqbKyskc91yzL0owZM/Tuu%2B/qz3/%2Bs6Kios66z%2Bmeb519TXcH5zNuHfXR09/bLMtSc3Nzh9t4nnUBmy6uhyG5ubmWr6%2BvtXr1amvbtm1Wenq6FRAQYH3zzTeWZVlWUlKS2zcKFyxYYOXn51tff/21VVlZaT388MOWj4%2BPtXnzZrtOocsdOnTIqqystCorKy1J1pIlS6zKykrrb3/7m2VZljV37lwrKSnJ1X7Xrl3W5Zdfbs2ePdvatm2btXr1asvX19f6r//6L7tOwRbnOm5Lly618vLyrK%2B%2B%2Bsqqrq625s6da0my1q5da9cpdLnHHnvMcjqd1ieffGLV1ta6lv/93/91tTn1NfqXv/zF8vb2thYtWmRt377dWrRokeXj42OVlZXZcQq2OJ9x6%2BnvbfPmzbOKi4ut3bt3W1u3brXmz59vXXbZZVZBQYFlWTzP7EDA6gZefPFF65prrrH8/Pysm2%2B%2B2e2rzGPGjLGSk5Ndj9PT062rr77a8vPzs/r162fFx8dbJSUlNlRtn7bbB5y6tI1TcnKyNWbMGLd9PvnkE%2Bumm26y/Pz8rGuvvdZauXJl1xdus3Mdt3//93%2B3rrvuOsvf398KCgqybrnlFmv9%2BvX2FG%2BTjsZLkvXqq6%2B62pz6GrUsy/rTn/5kDRw40PL19bUGDRrUo0KpZZ3fuPX097ZHHnnE9XegX79%2B1tixY13hyrJ4ntnBy7L%2B72pdAAAAGME1WAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPt/0jzpjnp3gGcAAAAASUVORK5CYII%3D"/>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="common503603685371590132">
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>
<td class="number">Frequency (%)</td>
<td style="min-width:200px"> </td>
</tr>
</thead>
<tr class="">
<td class="fillremaining">1.15</td>
<td class="number">202</td>
<td class="number">1.1%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.18</td>
<td class="number">199</td>
<td class="number">1.1%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.08</td>
<td class="number">194</td>
<td class="number">1.1%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.26</td>
<td class="number">193</td>
<td class="number">1.1%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.13</td>
<td class="number">192</td>
<td class="number">1.1%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.98</td>
<td class="number">189</td>
<td class="number">1.0%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.19</td>
<td class="number">188</td>
<td class="number">1.0%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.36</td>
<td class="number">187</td>
<td class="number">1.0%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">1.59</td>
<td class="number">186</td>
<td class="number">1.0%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="">
<td class="fillremaining">0.99</td>
<td class="number">185</td>
<td class="number">1.0%</td>
<td>
<div class="bar" style="width:2%"> </div>
</td>
</tr><tr class="other">
<td class="fillremaining">Other values (249)</td>
<td class="number">16334</td>
<td class="number">89.5%</td>
<td>
<div class="bar" style="width:100%"> </div>
</td>
</tr>
</table>
</div>
<div role="tabpanel" class="tab-pane col-md-12" id="extreme503603685371590132">
<p class="h4">Minimum 5 values</p>
<table class="freq table table-hover">
<thead>
<tr>
<td class="fillremaining">Value</td>
<td class="number">Count</td>