Chapter15_Hypotheses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up a fancy stylesheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link href='http://fonts.googleapis.com/css?family=Alegreya+Sans:100,300,400,500,700,800,900,100italic,300italic,400italic,500italic,700italic,800italic,900italic' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=Arvo:400,700,400italic' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=PT+Mono' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=Shadows+Into+Light' rel='stylesheet' type='text/css'>\n",
       "<link href='http://fonts.googleapis.com/css?family=Philosopher:400,700,400italic,700italic' rel='stylesheet' type='text/css'>\n",
       "\n",
       "<style>\n",
       "\n",
       "@font-face {\n",
       "    font-family: \"Computer Modern\";\n",
       "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
       "}\n",
       "\n",
       "#notebook_panel { /* main background */\n",
       "    background: #ddd;\n",
       "    color: #000000;\n",
       "}\n",
       "\n",
       "\n",
       "div.text_cell_render{\n",
       "    font-family: 'Arvo' sans-serif;\n",
       "    line-height: 130%;\n",
       "    font-size: 115%;\n",
       "    width:1000px;\n",
       "    margin-left:auto;\n",
       "    margin-right:auto;\n",
       "}\n",
       "\n",
       "\n",
       "/* Formatting for header cells */\n",
       ".text_cell_render h1 {\n",
       "    font-family: 'Philosopher', sans-serif;\n",
       "    font-weight: 400;\n",
       "    font-size: 2.2em;\n",
       "    line-height: 100%;\n",
       "    color: rgb(0, 80, 120);\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.1em;\n",
       "    display: block;\n",
       "}\t\n",
       ".text_cell_render h2 {\n",
       "    font-family: 'Philosopher', serif;\n",
       "    font-weight: 400;\n",
       "    font-size: 1.9em;\n",
       "    line-height: 100%;\n",
       "    color: rgb(200,100,0);\n",
       "    margin-bottom: 0.1em;\n",
       "    margin-top: 0.1em;\n",
       "    display: block;\n",
       "}\t\n",
       "\n",
       ".text_cell_render h3 {\n",
       "    font-family: 'Philosopher', serif;\n",
       "    margin-top:12px;\n",
       "    margin-bottom: 3px;\n",
       "    font-style: italic;\n",
       "    color: rgb(94,127,192);\n",
       "}\n",
       "\n",
       ".text_cell_render h4 {\n",
       "    font-family: 'Philosopher', serif;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 {\n",
       "    font-family: 'Alegreya Sans', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 16pt;\n",
       "    color: grey;\n",
       "    font-style: italic;\n",
       "    margin-bottom: .1em;\n",
       "    margin-top: 0.1em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 {\n",
       "    font-family: 'PT Mono', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 10pt;\n",
       "    color: grey;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       ".CodeMirror{\n",
       "        font-family: \"PT Mono\";\n",
       "        font-size: 100%;\n",
       "}\n",
       "\n",
       "</style>\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML, Image\n",
    "css_file = 'style.css'\n",
    "HTML(open(css_file, 'r').read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>In previous chapter I introduced a lot of theory.  A bit boring, but it does form an important basis for understanding statistical analysis in healthcare (or most any) research.  Subsequent lectures will become more practical and no step is more important than declaring *hypotheses*.  No research should start without it and indeed, excluding it from being the first step, is a sure path towards unethical research.  It should always precede any form of data collection.</p>\n",
    "<p>It is of value to start with formal definitions of the various aspects surrounding hypotheses, although these are somewhat difficult to understand in the absence of examples.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>A *hypothesis* is a statement about a population parameter or a sample statistic subject to verification.  In practical terms is defines the research question to be answered by the collection of data and the statistical analysis thereof.  Two hypotheses should be stated at the commencement of any research, namely the *null hypothesis* and the *alternate hypothesis*.  The null hypothesis is the statement of no departure from an expected result and the alternate hypothesis  is a statement to be accepted when statistical analysis shows a probability so extreme as to force rejection of the null hypothesis, by assumption of the fact the the finding could not have occurred by mere chance.  In healthcare research, this (the alternate hypothesis) would be the statement that is accepted if the p-value is less than what was originally decided on as a cut-off for statistical significance.  In most cases, this would be 0.05 (the alpha-value).  Wow, I hope you got most of that!</p>\n",
    "<p>Following the statement of the research hypotheses, they should undergo a process of hypotheses testing.  This refers to a statistical procedure that uses sample evidence and probability theory to determine whether a statement about the value of a population parameter should be rejected or not.</p>\n",
    "<p>Let's try to clarify the definitions stated above, considering an example.  A statement is made that the mean value of a new laboratory test used in the management of a specific disease is *18*.  A researcher decides to randomly select a group of patients and subject them to this test and calculates and average for her sample subjects of *21*.  It could logically be argued that the original statement was false and should be rejected.  To be sure, this would be incorrect, as statistical analysis should be performed first and more specifically, calculations should be performed to look at the probability of having found a value as extreme as *21*.</p>\n",
    "<p>In essence the value *18* will be taken as a mark on the x-axis of a bell-shaped curve, which is formed from all the possible means that could have been calculated should all the possible random samples have been analyzed as has been explained in the lecture on the Central Limit Theorem.  The value *21* will fall somewhere on the x-axis.  See the schematci representation below.  Since *21* is larger than *18*, it would naturally fall to the right of *18* and if the area under the curve from *21* towards the positive side could be calculated, this will be the probability of finding a value as extreme as *21*.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABF4AAAGoCAYAAAB/tyFcAAAAAXNSR0IArs4c6QAAAdVpVFh0WE1M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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = 'AUC.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>To make more sense of this example, requires the introduction of the *alpha-value*.  In healthcare research, this would most often be 0.05 and signifies firstly, the risk the researcher is willing to take to falsely reject the null hypothesis, known as a *type 1 error*.  In more practical terms, it is the cut-off for clinical significance.  If the *p-value* (probability) of the statistical analysis is less than the alpha-value, the null hypothesis is rejected (statistically significant difference).</p>  <p>Visually, this *0.05* represents an area under the curve and can be placed in three possible locations depending on how the null hypothesis was stated.  The first possibility is the most common and is shown in the image above.  Here the null hypothesis, as per our example, would be that the mean for a sample should equal *18*.  This means the alternate hypothesis states that the mean is not *18*.  It is clear from this that the alternate hypothesis refers to values both above and below *18*.  In essence, the alpha value of *0.05* must be split in two, with *0.025* on either side. A mean value calculated by the researcher for her sample that falls in these shaded areas would result in that value’s area (probability) being less than *0.05*.</p>\n",
    "<p>The second and third possibilities come from other ways of stating the null hypothesis.  The researcher might have stated as a null hypothesis, that the value that she is researching is larger than *18*.  She would now have to visually entertain, what is referred to as a one-tail right-sided test and all of the shaded area would fall to the right.  This would be akin to the image below.  If her initial alternate hypothesis was that a score must be less than *18*, it would be a one-tail left test, with all of the *0.05* area falling in the left side.</p>\n",
    "<p>This is why the original statement of the research hypothesis is so important.  It makes a difference as to where the shaded areas fall and directly impacts whether the researcher’s calculated mean value will fall within the chosen alpha-value or not.  For this reason it is vitally important to state the hypotheses before initiating any research so as to remain an ethical researcher.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABWsAAAGrCAYAAAC2d8VOAAAAAXNSR0IArs4c6QAAAdVpVFh0WE1M\nOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6\neG1wdGs9IlhNUCBDb3JlIDUuNC4wIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3\nLnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlv\nbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6dGlmZj0iaHR0cDovL25zLmFkb2JlLmNv\nbS90aWZmLzEuMC8iPgogICAgICAgICA8dGlmZjpDb21wcmVzc2lvbj41PC90aWZmOkNvbXByZXNz\naW9uPgogICAgICAgICA8dGlmZjpQaG90b21ldHJpY0ludGVycHJldGF0aW9uPjI8L3RpZmY6UGhv\ndG9tZXRyaWNJbnRlcnByZXRhdGlvbj4KICAgICAgICAgPHRpZmY6T3JpZW50YXRpb24+MTwvdGlm\nZjpPcmllbnRhdGlvbj4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94\nOnhtcG1ldGE+CrDjMt0AAEAASURBVHgB7N15dFzneef5HwGwCijsKIAAQXEnJS6iuGjhYpui3VJs\nx+4ktjOZ6fRJ2sk4Ts+Mk+5kTqZncua4T3d65vTkTP81PeP5I9PdJydzOnEysd1xR7YlWRJtS6Ic\nUaTFVdwBAiSWwr7UgmXue6mCIAokQQD3qVu3vvccCBBQdZ/3+TxvlYzHF8/VbIBHV1dXgGe/c2qL\nGC7Siy++GJlcrMws4ljUxRXeIherOFa5RKk2VmZWcSxqY5VLlOJY1CVq7zNW9beojVUuUYpjURer\n14xVHKv6R6U2/SPXZ79/8t/Mnu18wZUo0CPw2szMzHb/xe/PZl/540DzcCcPPJf3M7CIYxHDpROV\n14zLxcrMKo5FbaxyiVIci7pEbT9b1d+iNla5FDJOmTgQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\nCi5As7bgJWABCCCAAAIIIIAAAggggAACCCCAAAIIIICARLOWXYAAAggggAACCCCAAAIIIIAAAggg\ngAACCIRAgGZtCIrAEhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAZq17AEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQQCAEAjRrQ1AEloAAAggggAACCCCAAAIIIIAAAggggAACCNCsZQ8ggAACCCCAAAIIIIAA\nAggggAACCCCAAAIhEKBZG4IisAQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBmLXsAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBAIgQDN2hAUgSUggAACCCCAAAIIIIAAAggggAACCCCAAAI0a9kDCCCAAAII\nIIAAAggggAACCCCAAAIIIIBACARo1oagCCwBAQQQQAABBBBAAAEEEEAAAQQQQAABBBCgWcseQAAB\nBBBAAAEEEEAAAQQQQAABBBBAAAEEQiBAszYERWAJCCCAAAIIIIAAAggggAACCCCAAAIIIIAAzVr2\nAAIIIIAAAggggAACCCCAAAIIIIAAAgggEAIBmrUhKAJLQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE\naNayBxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgRAI0KwNQRFYAgIIIIAAAggggAACCCCAAAIIIIAA\nAgggQLOWPYAAAggggAACCCCAAAIIIIAAAggggAACCIRAgGZtCIrAEhBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAZq17AEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAhXd3d2BLaOnpyewc+dPbBHDxUql\nUgrSysWwyiVKcSzqErXaWNU/SrWxMrOKY1Ebq1yiFMeiLlbvZ1GqizOzqE3UzCzysaiL1WvGKo5F\nXaxeMxZmI+k+pdNpjYyOFP/vAbOzymWzXj6T6gvw9z+LurgY7rDYzxYxXC5Rej+zMrOKY1Ebq1yi\nFMeiLlbvM1ZxrOpvURurXAoZp6K9vd3tjcCOoM/vFm4RI5lMmsSxyMXKzCKOVV0scnEx3GGxByxi\nRK02FmZW9beqTZTMLGpjVReLXKxiWMWxqg2vGVfRxR9WdXErilJtLHKJSm3iozl1DFeqrrbOZA8E\nWhuvWXsrFlNlZZVqA/79j9fM4t/H8o+Mymsmn0+gezkfxPtsEceqNha5OLqoxLGqS5TMrHKxqk1U\n9rKrizvuzocxCHdc+CcCCCCAAAIIIIAAAggggAACCCCAAAIIIFBQAZq1BeUnOAIIIIAAAggggAAC\nCCCAAAIIIIAAAgggcEeAZi07AQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQCIEAzdoQFIElIIAAAggg\ngAACCCCAAAIIIIAAAggggAACNGvZAwgggAACCCCAAAIIIIAAAggggAACCCCAQAgEaNaGoAgsAQEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQQoFnLHkAAAQQQQAABBBBAAAEEEEAAAQQQQAABBEIgQLM2BEVg\nCQgggAACCCCAAAIIIIAAAggggAACCCCAAM1a9gACCCCAAAIIIIAAAggggAACCCCAAAIIIBACAZq1\nISgCS0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBGjWsgcQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEQ\nCNCsDUERWAICCCCAAAIIIIAAAggggAACCCCAAAIIIECzlj2AAAIIIIAAAggggAACCCCAAAIIIIAA\nAgiEQIBmbQiKwBIQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGatewBBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAgBAI0a0NQBJaAAAIIIIAAAggggAACCCCAAAIIIIAAAgjQrGUPIIAAAggggAACCCCAAAII\nIIAAAggggAACIRCgWRuCIrAEBBBAAAEEEEAAAQQQQAABBBBAAAEEEECAZi17AAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAQQQCIEAzdoQFIElIIAAAggggAACCCCAAAIIIIAAAggggAACNGvZAwgggAACCCCA\nAAIIIIAAAggggAACCCCAQAgEaNaGoAgsAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQoFnLHkAAAQQQ\nQAABBBBAAAEEEEAAAQQQQAABBEIgQLM2BEVgCQgggAACCCCAAAIIIIAAAggggAACCCCAQMVLL70U\nmEIqlVIymQzs/O7EFjFcnJMnT7pPgR5WuUQpjkVdXNGjZGaVS5RqY2VmFceiNla5RCmORV2s3s+i\nVBdnZlGbqJlZ5GNRF6vXjFUci7pYvWYszHKrBjWkfqVHLqn7QoULGdgRdG1WeStfNzykWe/zuQB/\n/3NAQefiYrjDIo5FDJdLlN7PrMys4ljUxiqXKMWxqIvV+4xVHKv6W9TGKpdCxql47rnn3N4I5Oju\n7lZ7e3sg586f1CJGPlaQVi6GVS5RixN0XaJWG6v6O7eo1MbKzCqORW2scolaHF4zbncu/rCqv1tR\n0LWxyiVqcYKui6t9lMyscrF4zVjUJjV6Q29f7tQjLdu165HgfmeyyEWzs7r1ly+oeU2bnjtW5Lk4\nMO+w2M8WMe5kE/x/Z6JmFqXaWOUStTj8b4D8u8fiPlvV360m6NpY5VLIOIxBWNy+5lEIIIAAAggg\ngAACCCCAAAIIIIAAAggggECgAjRrA+Xl5AgggAACCCCAAAIIIIAAAggggAACCCCAwOIEaNYuzolH\nIYAAAggggAACCCCAAAIIIIAAAggggAACgQrQrA2Ul5MjgAACCCCAAAIIIIAAAggggAACCCCAAAKL\nE6BZuzgnHoUAAggggAACCCCAAAIIIIAAAggggAACCAQqQLM2UF5OjgACCCCAAAIIIIAAAggggAAC\nCCCAAAIILE6AZu3inHgUAggggAACCCCAAAIIIIAAAggggAACCCAQqADN2kB5OTkCCCCAAAIIIIAA\nAggggAACCCCAAAIIILA4AZq1i3PiUQgggAACCCCAAAIIIIAAAggggAACCCCAQKACNGsD5eXkCCCA\nAAIIIIAAAggggAACCCCAAAIIIIDA4gRo1i7OiUchgAACCCCAAAIIIIAAAggggAACCCCAAAKBCtCs\nDZSXkyOAAAIIIIAAAggggAACCCCAAAIIIIAAAosToFm7OCcehQACCCCAAAIIIIAAAggggAACCCCA\nAAIIBCpAszZQXk6OAAIIIIAAAggggAACCCCAAAIIIIAAAggsToBm7eKceBQCCCCAAAIIIIAAAggg\ngAACCCCAAAIIIBCoAM3aQHk5OQIIIIAAAggggAACCCCAAAIIIIAAAgggsDgBmrWLc+JRCCCAAAII\nIIAAAggggAACCCCAAAIIIIBAoAI0awPl5eQIIIAAAggggAACCCCAAAIIIIAAAggggMDiBGjWLs6J\nRyGAAAIIIIAAAggggAACCCCAAAIIIIAAAoEK0KwNlJeTI4AAAggggAACCCCAAAIIIIAAAggggAAC\nixOgWbs4Jx6FAAIIIIAAAggggAACCCCAAAIIIIAAAggEKkCzNlBeTo4AAggggAACCCCAAAIIIIAA\nAggggAACCCxOgGbt4px4FAIIIIAAAggggAACCCCAAAIIIIAAAgggEKgAzdpAeTk5AggggAACCCCA\nAAIIIIAAAggggAACCCCwOIGK7u7uxT1yCY/q6elZwrMe7ikWMdyKUqmUgrRyMaxyiVIci7pErTZW\n9Y9SbazMrOJY1MYqlyjFsaiL1ftZlOrizCxqEzUzi3ws6mL1mrGKY1EXq9eMhdlIuk/pdFojoyPF\n/3vA7Kxy2ayXz6T6Avz9z6IuLoY7LPazRQyXS5Tez6zMrOJY1MYqlyjFsaiL1fuMVRyr+lvUxiqX\nQsapaG9vd3sjsCPo87uFW8RIJpMmcSxysTKziGNVF4tcXAx3WOwBixhRq42FmVX9rWoTJTOL2ljV\nxSIXqxhWcaxqw2vGVXTxh1Vd3IqiVBuLXKJSm/hoTh3DlaqrrTPZA4HWxmvW3orFVFlZpdqAf//j\nNbP497H8I6PymsnnE+hezgfxPlvEsaqNRS6OLipxrOoSJTOrXKxqE5W97OrijrvzYQzCHRf+iQAC\nCCCAAAIIIIAAAggggAACCCCAAAIIFFSAZm1B+QmOAAIIIIAAAggggAACCCCAAAIIIIAAAgjcEaBZ\ny05AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRCIECzNgRFYAkIIIAAAggggAACCCCAAAIIIIAAAggg\ngADNWvYAAggggAACCCCAAAIIIIAAAggggAACCCAQAgGatSEoAktAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQRo1rIHEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBEAjQrA1BEVgCAggggAACCCCAAAIIIIAA\nAggggAACCCBAs5Y9gAACCCCAAAIIIIAAAggggAACCCCAAAIIhECAZm0IisASEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABmrXsAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIAQCNGtDUASWgAACCCCAAAII\nIIAAAggggAACCCCAAAII0KxlDyCAAAIIIIAAAggggAACCCCAAAIIIIAAAiEQoFkbgiKwBAQQQAAB\nBBBAAAEEEEAAAQQQQAABBBBAgGYtewABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiBAM3aEBSBJSCA\nAAIIIIAAAggggAACCCCAAAIIIIAAAjRr2QMIIIAAAggggAACCCCAAAIIIIAAAggggEAIBGjWhqAI\nLAEBBBBAAAEEEEAAAQQQQAABBBBAAAEEEKBZyx5AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRCIECz\nNgRFYAkIIIAAAggggAACCCCAAAIIIIAAAggggADNWvYAAggggAACCCCAAAIIIIAAAggggAACCCAQ\nAgGatSEoAktAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRo1rIHEEAAAQQQQAABBBBAAAEEEEAAAQQQ\nQACBEAhUdHd3B7aMnp6ewM6dP7FFDBcrlUopSCsXwyqXKMWxqEvUamNV/yjVxsrMKo5FbaxyiVIc\ni7pYvZ9FqS7OzKI2UTOzyMeiLlavGas4FnWxes1YmI2k+5ROpzUyOlL8vwfMziqXzXr5TKovwN//\nLOriYrjDYj9bxHC5ROn9zMrMKo5FbaxyiVIci7pYvc9YxbGqv0VtrHIpZJyK9vZ2tzcCO4I+v1u4\nRYxkMmkSxyIXKzOLOFZ1scjFxXCHxR6wiBG12liYWdXfqjZRMrOojVVdLHKximEVx6o2vGZcRRd/\nWNXFrShKtbHIJSq1iY/m1DFcqbraOpM9EGhtvGbtrVhMlZVVqg349z9eM4t/H8s/MiqvmXw+ge7l\nfBDvs0Ucq9pY5OLoohLHqi5RMrPKxao2UdnLri7uuDsfxiDcceGfCCCAAAIIIIAAAggggAACCCCA\nAAIIIIBAQQVo1haUn+AIIIAAAggggAACCCCAAAIIIIAAAggggMAdAZq17AQEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQCAEAjRrQ1AEloAAAggggAACCCCAAAIIIIAAAggggAACCNCsZQ8ggAACCCCAAAII\nIIAAAggggAACCCCAAAIhEKBZG4IisAQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQIBmLXsAAQQQQAAB\nBBBAAAEEEEAAAQQQQAABBBAIgQDN2hAUgSUggAACCCCAAAIIIIAAAggggAACCCCAAAI0a9kDCCCA\nAAIIIIAAAggggAACCCCAAAIIIIBACARo1oagCCwBAQQQQAABBBBAAAEEEEAAAQQQQAABBBCgWcse\nQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEQiBAszYERWAJCCCAAAIIIIAAAggggAACCCCAAAIIIIAA\nzVr2AAIIIIAAAggggAACCCCAAAIIIIAAAgggEAIBmrUhKAJLQAABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEaNayBxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgRAI0KwNQRFYAgIIIIAAAggggAACCCCAAAII\nIIAAAgggQLOWPYAAAggggAACCCCAAAIIIIAAAggggAACCIRAgGZtCIrAEhBAAAEEEEAAAQQQQAAB\nBBBAAAEEEEAAAZq17AEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAEAjRrQ1AEloAAAggggAACCCCA\nAAIIIIAAAggggAACCNCsZQ8ggAACCCCAAAIIIIAAAggggAACCCCAAAIhEKBZG4IisAQEEEAAAQQQ\nQAABBBBAAAEEEEAAAQQQQIBmLXsAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAIgQDN2hAUgSUggAAC\nCCCAAAIIIIAAAggggAACCCCAAAIV3d3dgSn09PQEdu78iS1iuFipVEpBWrkYVrlEKY5FXaJWG6v6\nR6k2VmZWcSxqY5VLlOJY1MXq/SxKdXFmFrWJmplFPhZ1sXrNWMWxqIvVa8bCbCTdp3Q6rZHRkeL/\nPWB2Vrls1stnUn0B/v5nURcXwx0W+9kihsslSu9nVmZWcSxqY5VLlOJY1MXqfcYqjlX9LWpjlUsh\n41S0t7e7vRHYEfT53cItYiSTSZM4FrlYmVnEsaqLRS4uhjss9oBFjKjVxsLMqv5WtYmSmUVtrOpi\nkYtVDKs4VrXhNeMquvjDqi5uRVGqjUUuUalNfDSnjuFK1dXWmeyBQGvjNWtvxWKqrKxSbcC///Ga\nWfz7WP6RUXnN5PMJdC/ng3ifLeJY1cYiF0cXlThWdYmSmVUuVrWJyl52dXHH3fkwBuGOC/9EAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQQKKkCztqD8BEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBO4I0Kxl\nJyCAAAIIIIAAAggggAACCCCAAAIIIIAAAiEQoFkbgiKwBAQQQAABBBBAAAEEEEAAAQQQQAABBBBA\ngGYtewABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiBAM3aEBSBJSCAAAIIIIAAAggggAACCCCAAAII\nIIAAAjRr2QMIIIAAAggggAACCCCAAAIIIIAAAggggEAIBGjWhqAILAEBBBBAAAEEEEAAAQQQQAAB\nBBBAAAEEEKBZyx5AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRCIECzNgRFYAkIIIAAAggggAACCCCA\nAAIIIIAAAggggADNWvYAAggggAACCCCAAAIIIIAAAggggAACCCAQAgGatSEoAktAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAAQRo1rIHEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBEAjQrA1BEVgCAggggAAC\nCCCAAAIIIIAAAggggAACCCBAs5Y9gAACCCCAAAIIIIAAAggggAACCCCAAAIIhECAZm0IisASEEAA\nAQQQQAABBBBAAAEEEEAAAQQQQAABmrXsAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIAQCNGtDUASW\ngAACCCCAAAIIIIAAAggggAACCCCAAAII0KxlDyCAAAIIIIAAAggggAACCCCAAAIIIIAAAiEQoFkb\ngiKwBAQQQAABBBBAAAEEEEAAAQQQQAABBBBAgGYtewABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAiB\nAM3aEBSBJSCAAAIIIIAAAggggAACCCCAAAIIIIAAAjRr2QMIIIAAAggggAACCCCAAAIIIIAAAggg\ngEAIBCpCsAaWgAACCCCAAAIIIIDAggLp/j7NZLMf+VnZ8JAmurs+8v3530i0r5v/r3yNAAIIIIAA\nAggggEDoBSpeeumlwBaZSqWUTCYDO787sUUMF+fkyZPuU6CHVS5RimNRF1f0KJlZ5RKl2liZWcWx\nqI1VLlGKY1EXq/ezKNXFmVnUJmpmd+fjGqv5o3zog6/LBgf8b6/KTWnV6Ej+IYv6nPbO887bf7eo\nx+YfNNPUlP9Ss+UVmq2rm/v36YYG/+uZmhrJ+1n+uDuX/PdX+rNFHIsYzsXiNePiBJ1PbtWghtSv\n9MgldV/4YE+42Ct9BJ3LKm/B67zX4az3+VyAv/85l6BzcTHcYRHHIobLJSqvGau6WMaxqI3VPotS\nHIu6WO4zi9pYxHBmFrWxyqWQcSqee+455xnI0d3drfb29kDOnT+pRYx8rCCtXAyrXKIWJ+i6RK02\nVvV3blGpjZWZVRyL2ljlErU4vGbc7lz8YVV/t6Kga2OVS1Bx8le/Tt686Rewt6tTib5ppXt77l/Q\n1avv/Nx9TlQt+NjKNWtUFq/8yM+uX7miDVu3fuT7+W/MZNJe/N78vy78eWz0g+/P/9r77ur6ev9j\nfHpKLXW1/uOqHnlEsdo6VdTe+fcPnrz8r4KqzfyVWcTIxwv6NePiBJ1PavSG3r7cqUdatmvXI8H9\nzmSRi2ZndesvX1DzmjY9d6zIc3l/kwVdf5O6vJ+L+xSF14ylmUX98+UJujZWuUQtTtB1idp+tqq/\ncwu6Nla5FDJORf4NiM8IIIAAAggggAACCNxLYDqbUaa/X5k+99GrnHclbG542P+4+znZyUmVVX3Q\nfJ3fcE14Dc/8UbXug68T6x5uZMFF7+rA9Q9x0YG/fm/t+WPG5dPXl/9XTbzfaHYN5plMZi63jJdL\nv5fv3Uflmlat9q7Mjbe0qLJljcpra1TZ3HL3w/h3BBBAAAEEEEAAAQQeSoBm7UNx8WAEEEAAAQQQ\nQCD6AlOjo35jNu01KSe6bs41Lu+Vecy7CrWirt5rXDar3LsKdmx1TC3eFbEP24C91/lX4vvlsfhH\n1lOzecvcqZPPHJz72n3hXy2cyer2tauqj8U07V2p6xrVUyPDynpNatfUdR+jly996HmuiVtW6WI9\nInclbrzZM/FicyCAAAIIIIAAAgggsBgBmrWLUeIxCCCAAAIIIIBARAVcY9bdqMuNDMh4N/Oa6Oi4\nZ6aJ9Rv8huxq15j1riKNeVeWLjQSIOONwkoEPArrnotcoR/kr5KtXLVKyQVymejq8q8unhoZ8a/K\nzY9dyI9+mO/oRiq4q2+rHlnv+3HjsxUqEqdBAAEEEEAAAQQiKECzNoJFJSUEEEAAAQQQQOBeAu6K\n0cmubn+UwcTNzgXHGOSvlHUjC/w/8/casws1Ze8VoxS+f+eq4TujG+Zflet8/SuTvRELbrTCRGfH\n3JXJ86/CdVfgOtvE+vVeY/vhRkCUgi85IoAAAggggAACpSpAs7ZUK0/eCCCAAAIIIFASAu6qWXfj\nLzfOYOjadY2Wl30o77K49yf7XsPQv/LT+9N9f5QBf7b/IaOH+Rf/ilyvue1GLOSbuK6Be2feb69f\nC3cVc36MwvDZM/7ps+XlKtuxc655S3P8YdR5LAIIIIAAAgggEB0BmrXRqSWZIIAAAggggAAC/qxV\nd+Xs2NXLHxlp4G6qFWtr8/8cPz9PNf/n/tAFJ+CMfWevGZs/3BgFN3bC3eTMXXE7MzQk17jNN2/d\n6ISENzbBNdKrN29m7m0ejs8IIIAAAggggEDEBWjWRrzApIcAAggggAAC0RaY9hqwaa85O3rFa84u\nMNag0rvRl2vMusbf0NS0Htm+PdogRZKdG6OQvwFb23PP64bXqK2bmvJrONF5ZzzFsHcjs3zz1o1N\ncHWs9uromrccCCCAAAIIIIAAAtEUoFkbzbqSFQIIIIAAAghEWCA/d3ahq2fdvFl3IyvX2KvZsuVD\nV2SOeDf+4ginwOrGJjV6NzJr3LvPX+CdGndp7MoVf+5tfmzC4Mm3VR6vVM22bVx1G85SsioEEEAA\nAQQQQGBZAjRrl8XHkxFAAAEEEEAAARuB8WvX7nn1bGL9BtVs3aoq72pNxhrY1CPoKPnRCfnmrRub\n4Jrzbv6wm3k7f2RCYoNX/y3bVOs155l1G3RlOD8CCCCAAAIIIBCsAM3aYH05OwIIIIAAAgggsGSB\nfIN27PJlTWfSc+fJXz2bb9CWc0OwOZuofjF/bMLU6KhGr3pX3HpjL9zemOjo8D96X/2h8uMS6nbu\npHEf1c1AXggggAACCCAQaQGatZEuL8khgAACCCCAQLEJ3K9BW+1dPVu3cxdNuGIr6gqv11096664\ndR9uZvHY1av+FbfuRmXzxyW4m5TVbN3m7RkatytcAk6HAAIIIIAAAggEJkCzNjBaTowAAggggAAC\nCCxOIOvdUOr2ubP+VZLzr6B1NwdzzbZq78/bGW+wOMtSe5S7qrp+x07/w92obOzaVf+K23Fv1m3W\nu0GZm3HrPlzjdnbDJk15jV5GJZTaLiFfBBBAAAEEECgmAZq1xVQt1ooAAggggAACkRFwN5AaOnXK\nb9CODw0qU1Xl5+YatHW7dnvzR7fSVItMte0Sqdm8Re5Dn3hWbo+NnD/nzbc9q5zXuE2/9aauvHva\nH5Xg9ljdzh0fugGd3SqJhAACCCCAAAIIIHAvAZq195Lh+wgggAACCCCAwAoL3Jk1elWD77ztN8/y\np69IJrXmmYM0aPMgfF4RAf8mZV7Tdo334a64vfn22yrzGrj5UQluxm3ttu2q2eZdvb15M43bFVHn\nJAgggAACCCCAwPIEaNYuz49nI4AAAggggAACDxQYuXjBv4LWzRTNH+4mYW4GbdO+A+r1bhjV2N6e\n/xGfEVhxAXe1bV28Uu3ePhu+cF5jVy7P7cn8vqzf/bg/E9ndzIwDAQQQQAABBBBAoDACNGsL405U\nBBBAAAEEEIi4wJ0/QT+vEe9P0OfPoXUNsRqvSev/qXrewGvWciBgJZCfcZu/OdnQOye9q217vXEJ\nZ/wPN9+2cf+TjEmwKghxEEAAAQQQQACBeQI0a+dh8CUCCCCAAAIIILAcAdf8Gjl/QSPezcLcn5rn\nj/wc2rqdO/lT8zwKnwsuMP/mZG5Ex7A339btXXdjMjciwX1wtW3By8QCEEAAAQQQQKDEBGjWlljB\nSRcBBBBAAAEEVl5goqvr/Rs5nZk7eVk87jW63E2cdsnNDuVAIMwCFbW1Snpzk92Hm2877DVtxy5f\n5mrbMBeNtSGAAAIIIIBAJAVo1kayrCSFAAIIIIAAAhYCbhbt4En3J+QfXEXrbtZUv2v3h8ccWCyG\nGAiskIAb0eE+uNp2hUA5DQIIIIAAAggg8BACNGsfAouHIoAAAggggAAC+QaWa9LmZ9G6q2ibDjyp\neu8qWneFIgcCURDgatsoVJEcEEAAAQQQQKDYBGjWFlvFWC8CCCCAAAIIFETAjToYOvWORi9fmovv\nZtE27D8gd8MmDgSiLHC/q21Tb7yuOm/kR9O+/fyfFVHeBOSGAAIIIIAAAiYCNGtNmAmCAAIIIIAA\nAsUo4G4YVtFzW1f/w79TzrvpUv7gpkt5CT6XmsDdV9sOvvOOJjo7vHEgb/sf7rVRNvTBa6XUfMgX\nAQQQQAABBBBYrgDN2uUK8nwEEEAAAQQQiJyAG3Uw4F1FO3L2rFZ33FCutVWx+nrVebNoG/btU3ks\nHrmcSQiBhxXIX22b7u/zrjo/NXczsnhPjzrLV/mzm+se2/Gwp+XxCCCAAAIIIIBASQtUdHd3BwbQ\n4/0PtaAPixguh1QqpSCtXAyrXKIUx6IuUauNVf2jVBsrM6s4FrWxyiVKcSzqYvV+Vsx1yQ0OKH3h\ngtKX3nNc/jEer9TaJ59SfMtWZbzv9PSn3v/Jyn0qZrOFFCzyidJrxhlamAUaw/s/Mqo3btLklctK\ne79fDFy86H+U1dQosetxxbduUdnq2ELbZcnfCzQfb1Uj6T6l02mNjI4U/+8Bs7PKZbNePpPqC/D3\nP1fMoOuS3zAWcSxiuHyi9H5mZWYVx6I2VrlEKY5FXdxrM0pmVrlY1MYql0LGqWhvb3d7MLAj6PO7\nhVvESCaTJnEscrEys4hjVReLXFwMd1jsAYsYUauNhZlV/a1qEyUzi9pY1cUiF6sYKxnHzaNNvfWm\n0h0d7rSqrKqS+3NudxVt36nT2vLxT/jfD/IfvGYeTjdqrxmXvcUeCDzG9u26sWqVNm3cqIE331DW\nGx8y9e5pzb53UTXbtqn54KEVnWsbZD7x0Zw6hitVV1tX/LXxmrW3YjFVVlapNuDf/6z2slWcIPeY\ny8EdUXs/szBzbhZxrGpjkYuVmUUcq7pY5OJiuMNiD1jEsKqNRS5WdVkoDmMQnAoHAggggAACCJSk\nwPi1a964g5OaeL9JWxaPe01ad6OkAyvaUCpJXJIuXYHyCv+me+7Ge/7/EXLiTX+u7fDZM/6oBGY+\nl+7WIHMEEEAAAQQQeLAAzdoHG/EIBBBAAAEEEIiYwMjFC+r37mCfv2mYa9I2HXiSebQRqzPpFF4g\nsW6dEl/8ktwc6H6vaZtv2LrPiQ0blHzmkNxjOBBAAAEEEEAAAQTuCNCsZScggAACCCCAQEkITGcz\nclfSzm/SupuGNew/oLqdO7lpWEnsApIslEBFba3annteLUeP+jcjGzj5tn9Fu7uqnaZtoapCXAQQ\nQAABBBAIowDN2jBWhTUhgAACCCCAwIoJuCatu1P94MmTms6k/fO6Jm3TocP+n2qvWCBOhAACDxQo\nj8W9q2kP+lexu9clTdsHkvEABBBAAAEEECgxAZq1JVZw0kUAAQQQQKBUBNyfXQ+fP/ehJm1i/QY1\n7t+vms1bSoWBPBEIpcD9mraVa1rVeMC74v2xHaFcO4tCAAEEEEAAAQSCFKBZG6Qu50YAAQQQQAAB\nc4H8lbRu3EH+cE3apHcXemZj5kX4jEA4BO5u2o6cO6t0b49ufe8Ff2RJ8+EjNG3DUSpWgQACCCCA\nAAJGAjRrjaAJgwACCCCAAALBCuSbtPPHHbi7zjfs26fK5pZgg3N2BBBYlkC+aetGJAxfOK+BN99Q\ndniYpu2yVHkyAggggAACCBSjAM3aYqwaa0YAAQQQQACBOYGFmrQ127ap9egxuZsacSCAQHEJ1O/Y\n6c+TpmlbXHVjtQgggAACCCCwMgI0a1fGkbMggAACCCCAgLHATC6r1FsnPjKTlnEHxoUgHAIBCTyo\naavauoAic1oEEEAAAQQQQKBwAjRrC2dPZAQQQAABBBBYgsDclbQ//rHGy8v8MzCTdgmQPAWBIhG4\nV9M2W16uhi98iVnURVJHlokAAggggAACixOgWbs4Jx6FAAIIIIAAAiEQmH8l7Uw2o8Sjj3HjsBDU\nhSUgYCFwd9M2ffu2Ov/qm0ps8G4g+Aw3ELSoATEQQAABBBBAIHgBmrXBGxMBAQQQQAABBJYpMHLx\ngn9n+Jx3wyF3uCtpKzdv0fr9+5d5Zp6OAALFJpBv2l754cuaee+iJjo6/A+atsVWSdaLAAIIIIAA\nAgsJ0KxdSIXvIYAAAggggEAoBBZq0uZn0nZ3d4dijSwCAQQKI1Dl3Yis9eMf19CpUxo4+fZc07Z2\n23atOfosNxgsTFmIigACCCCAAALLFKBZu0xAno4AAggggAACKy9wd5M2Vl+vlmePqca7mpYDAQQQ\nyAuUx+LeCISDati3b65pO3r5ktxH/e7H1XzwEE3bPBafEUAAAQQQQKAoBGjWFkWZWCQCCCCAAAKl\nITDR1aXUW2/6V8i5jF2TtunQYbk/e+ZAAAEE7iUwv2mbOvGmBk+e1PDZM/4HTdt7qfF9BBBAAAEE\nEAijAM3aMFaFNSGAAAIIIFBiAlOjo7r10g9o0pZY3UkXgZUWcE3bNZ94Vk37Dqjfa9rmG7buc/Ph\nI/4VuO4xHAgggAACCCCAQFgFaNaGtTKsCwEEEEAAgRIQmM5m1Hf8uN9QcemWxeNqOvAkDZUSqD0p\nIhCkQEVtrdqee94fg5Bv2va/8bp/xW3jgQP+6IQg43NuBBBAAAEEEEBgqQI0a5cqx/MQQAABBBBA\nYMkCrknrbgrk/lR5OpP2z+P+VLnl6FFx1duSWXkiAgjcJZBv2rqZtu7/GJro7JBr2g6fO+tfaVv3\n2I67nsG/IoAAAggggAAChRWgWVtYf6IjgAACCCBQcgLu5mG9r7wy16St2bZNrUePcROgktsJJIyA\nnUBlc4vWf/FLcnOx+46/qnRvr2597wW/aZt85pAS69bZLYZICCCAAAIIIIDAfQRo1t4Hhx8hgAAC\nCCCAwMoJuCbJ7Re/r9zwsH/SxPoNSnp3aqdJsnLGnAkBBO4v4N5vNv6Df6jhC+fV++or/pzsiY4O\ncROy+7vxUwQQQAABBBCwE6BZa2dNJAQQQAABBEpSwDVpU2+9yc3DSrL6JI1AOAXqd+xUzZYt/jiW\ngZNvz92IjJuQhbNerAoBBBBAAIFSEqBZW0rVJlcEEEAAAQQMBaZGR+fuxu7CupuHuUZI4959hqsg\nFAIIILCwgJuPnXzmoOp37pp7r8rfhCzpvVdpS8PCT+S7CCCAAAIIIIBAgAI0awPE5dQIIIAAAgiU\nosBMLutdSXvCv4lPPn+uVstL8BkBBMImsNBNyHpf/aEy58o1s3EibMtlPQgggAACCCAQcQGatREv\nMOkhgAACCCBgKeCatIM//rHGy8v8sMyBtNQnFgIILEdg/k3Ierz52v26rsHp2xq/dlvxiZi2Pvqp\n5Zye5yKAAAIIIIAAAosSqOju7l7UA5fyoJ6enqU87aGeYxHDLSiVSilIKxfDKpcoxbGoS9RqY1X/\nKNXGyswqjkVtrHKJUhyLugT5fpbz/ps/8pMfaWZsTJl0WhWbNivx9NOaaWxSrzcOQe5jhQ+r+lvU\nxiqXKMWxqEuQr5m7Xw4WtbGI4fIq+tqsWqX4z31Gsasva3b6lnJlWb3d9U291/lDbWn/vGrrN9xd\nvmX/e+C1mZ1VLptVOj2pvgB//3MQgefyvrZFHIsYLp2if828XxP3ycrMKo5FbaxyiVIci7pEbT9b\n1d+iNla5FDJORXt7+7y31pX/MujzuxVbxEgmkyZxLHKxMrOIY1UXi1xcDHdY7AGLGFGrjYWZVf2t\nahMlM4vaWNVlpXNxc2l7j7+m9OVLinknj7W1qWzvfm3cv9+FCvyw2GdWtbHIxRUkKnGs6hIlM6tc\nolKbeO1R9V3oUDrbr9yqCY2U9+nd23+qtqFHdWDvryuRaHKkK3YE+tr0mrW3YjFVVlapNuDf/xxI\noLnME7eIYxEjKq+ZfGkszKz2mVVtomRmURuruljkYvm6sdhnVrWxyKWQ9WcMQv6VwWcEEEAAAQQQ\neCgBf+TByZOazqT9m4c1HXjSv1lP0H+J8lCL5MEIIIDAMgTKylerJfmYGla36lr365qcHVV35oJ6\n3/i6djR/Srv2fnEZZ+epCCCAAAIIIIDARwVo1n7UhO8ggAACCCCAwH0Exq5dVe9rryo3POw/ys2l\nbTl6VO7O6hwIIIBAFAUSNa3a/egX1Hv7nLqHTilXkdOZwR/oyss/0b5Nv6z1Ww9HMW1yQgABBBBA\nAIECCNCsLQA6IRFAAAEEEChGATfy4NZLP9BER4e//Mo1a7wm7TEl1q0rxnRYMwIIIPDQAmvadsl9\ndHadUO/Ye5pYNaY3O/9UF7p+oI899TsrPhrhoRfIExBAAAEEEECg6AVo1hZ9CUkAAQQQQACBYAWm\nsxkNnTql/jde9wOVxeNqPnxEjXv3BRuYsyOAAAIhFVi/7qBaM7t14+ZPNJS7rYGZbv3tif9Z66ue\n0MFD/zikq2ZZCCCAAAIIIFAMAjRri6FKrBEBBBBAAIECCTDyoEDwhEUAgdALxOI12r7105oY69Gl\nrteUU1o30qfU9crvau+6X9LWRz8V+hxYIAIIIIAAAgiET4BmbfhqwooQQAABBBAouAAjDwpeAhaA\nAAJFIuDm2e597Ff8ebY3h04qtyqjk91/qfNd39ORx39bTS1biyQTlokAAggggAACYRCgWRuGKrAG\nBBBAAAEEQiLAyIOQFIJlIIBA0Qnk59levvaihjO3NK5hvXz2f9fa+GM6sPfXmWdbdBVlwQgggAAC\nCBRGgGZtYdyJigACCCCAQOgERi5e8OfS5oaH/bU1Hjig5MFDKo/FQ7dWFoQAAgiEVWDb5ueVzYzp\n0o0XNTEzou7MBfW89XVtbTiifft+NazLZl0IIIAAAgggEBIBmrUhKQTLQAABBBBAoFAC6f4+9f3o\nuCY6OvwlJNZvUMvRo6psbinUkoiLAAIIFLWAm2e7+9EvaKD3kjoH3vbm2Wb03tBx3XjtbT2x8Re1\nedPRos6PxSOAAAIIIIBAcAI0a4Oz5cwIIIAAAgiEWsCNPEidOKHBk2/76yyLx7Xm2CdVv2NnqNfN\n4hBAAIFiEWhas13uo6PjdfWPX/FuQTamv7v+H3Wl67iefPzX1Vi/oVhSYZ0IIIAAAgggYCRAs9YI\nmjAIIIAAAgiEScCNPOh95RVNZ9L+shh5EKbqsBYEEIiawIYNR9SWeUI3On6k4alepXKdevnkv1Z7\n9eM68sx/G7V0yQcBBBBAAAEEliFAs3YZeDwVAQQQQACBYhPIDQ6o88QbjDwotsKxXgQQKHoBNxph\n+/bPamKsR1duHlembFI3J36mb736T7Sx+pja279Q9DmSAAIIIIAAAggsX4Bm7fINOQMCCCCAAAKh\nF8iPPBj6yY9VWVUlRh6EvmQsEAEEIiqQqGnVnh3/hXpuvauu4dPKesMRLo99X33HT+rpnV9WU8vW\niGZOWggggAACCCCwGAGatYtR4jEIIIAAAggUscDdIw+aDx9Rw759Ko/Fizgrlo4AAggUt0Dr2j1y\nH5eufF/D2R4NzfTqh2f+jdord+rI4d8p7uRYPQIIIIAAAggsWYBm7ZLpeCICCCCAAALhFpjo6vKu\n1HpN6d4ef6GJ9RtUvecJJbdvD/fCWR0CCCBQQgLbt35aPbcvq2foHWXdaITMWX37h/9Uu9s+q+27\nPl1CEqSKAAIIIIAAAk6AZi37AAEEEEAAgYgJuJEHfcePa/jsGT+zWH29Wp49pprNW9Td3R2xbEkH\nAQQQKH6BeGVST3ijEXpvn/NGI7yjzKpJner9tq7d+pE+9uTXVN3QVvxJkgECCCCAAAIILEqAZu2i\nmHgQAggggAACxSEwePq0Um+8rulM2l8wIw+Ko26sEgEEEHACa9p2+R+Xr72o4cxtDZb36YWT/1Lr\nE0/oqQO/wfgatgkCCCCAAAIlIECztgSKTIoIIIAAAtEXWGjkwdrnf04VtbXRT54MEUAAgYgJbNv8\nvCYmU7rS+aoyZeO6kT6lWz/+Z4xGiFidSQcBBBBAAIGFBGjWLqTC9xBAAAEEECgSgfuNPCiSFFgm\nAggggMACAomqpPY8+iVGIyxgw7cQQAABBBCIsgDN2ihXl9wQQAABBCItMH/kQVk8rqYDT6ph3z7+\nTDbSVSc5BBAoNYH8aIQr11/WULqb0QiltgHIFwEEEECg5ARo1pZcyUkYAQQQQKDYBe4eeVCzbZta\njx5j5EGxF5b1I4AAAvcR2Lrp7/mjEa7efE3pmTF/NEL38T/Q7jWf1qNPfO4+z+RHCCCAAAIIIFBM\nAjRri6larBUBBBBAoKQFpkZH1Xv8NY1evuQ7xOrr1fr8p5VYt66kXUgeAQQQKBUBNxrh8e1fVF//\nBd1Mva3s6oxOD3xX119+Qwd3f0X1bZtKhYI8EUAAAQQQiKwAzdrIlpbEEEAAAQSiJJB664QGT57U\ndCat/MiD5DMHo5QiuSCAAAIILFKgpXmH3Ic/GiHTpUH16cVzf6x1l3fqmWe+yjicRTryMAQQQAAB\nBMIoQLM2jFVhTQgggAACCLwv4EYe3H7x+8oND/vfYeQBWwMBBBBAIC+QH41wpetVZabH1Tl1VrcZ\njZDn4TMCCCCAAAJFKVDR3d0d2MJ7enoCO3f+xBYxXKxUKqUgrVwMq1yiFMeiLlGrjVX9o1QbKzOr\nOBa1scolSnHursvM+LjGfvqWMh033NuQymtqVPuxT2hVa6t6vXEIch9LOCzMLGK41K3i3F2bJbA/\n8ClWuUQpjkVdLPeZRW0sYjizqNRmJN2nTCajsrJJDQ0NPvB1vJwHDL//f8gt7RxlWt/8KQ2OXfE+\nLihTkdap1N/o0veP69GWX1S1G40wO6tcNqt0elJ9Af7+59Zvtc8s4ljEcGZRec1Erf5WtbHaZ1GK\nE6XXjNXrxqr+FrWxyqWQcSra29vd3gjsCPr8buEWMZLJpEkci1yszCziWNXFIhcXwx0We8AiRtRq\nY2FmVX+r2kTJzKI28+uSH3mwyht5kGhoUNOBJ7WSIw8samMRw6IuLsb82rh/D+qIkpkzCjofq7pY\n5JLfU0GbWeUSldrER3O6PhRXVWWVGhoa82UK7PNyYzQ0PKVHpnero+tNDaU7NbZqSKeH/kztI4/q\n4KF/rN5YTJVeLrUB//5ntc+s4li8LqPymsm/OCzMrOpvVZsomVnUxqouFrlYvm4s9plVbSxyKWT9\nGYOQf2XwGQEEEEAAgQIL3D3yoH7342o+eEgVtbUFXhnhEUAAAQSKQWB1eZW2bvikJiZTutLtjUaY\nGtfNmQvq+fH/oA2ZKTUXQxKsEQEEEEAAgRIXoFlb4huA9BFAAAEECi8w5Y00iJ86qc6z7/qLqVyz\nRi1Hjymxbl3hF8cKEEAAAQSKTiBRldSerV9ST+qcuvvfUXZ1Rlfap3Wr94IOXzyl5GP7ii4nFowA\nAggggECpCNCsLZVKkycCCCCAQOgEprMZDZ06pf43XlfZwIDKNmxQ8+EjatzLL9GhKxYLQgABBIpQ\noDW5S00Nm73RCCc0MHZV48lVevn6v1Xz6aSe/aV/qfJYvAizYskIIIAAAghEW4BmbbTrS3YIIIAA\nAiEVGLt2Vb2vvarc+zeVmVrbri2/+V/zi3NI68WyEEAAgWIVuDMa4ZiqT3TqVjynqdVSX3JA3/nB\n17R55gnt/4XfKdbUWDcCCCCAAAKRFKBZG8mykhQCCCCAQFgF3MiDWy/9QBMdHf4S8yMPOs+fp1Eb\n1qKxLgQQQCACAlVlq7VlJK7+RKUG4/3KJqRLsz9T5zd/Wwcf/bJa9x2OQJakgAACCCCAQPEL0Kwt\n/hqSAQIIIIBAEQjMH3ngllsWj3945IHXrOVAAAEEEEAgaIGWuu1qWrVZ3UPvajyR1mRyRq/1/D9q\n+n//Usd+4V9wU8ugC8D5EUAAAQQQeIAAzdoHAPFjBBBAAAEElitw98iD+t2PezcQO8qVtMuF5fkI\nIIAAAksSKJ+t0Pr6/ZrMDKp75j1vNMKsUm2j+k+v/T6jEZYkypMQQAABBBBYOQGatStnyZkQQAAB\nBBD4kEC6v099Pzo+N/IgsX6D36StbG750OP4FwQQQAABBAohUBVv1FYdVN/IJUYjFKIAxEQAAQQQ\nQGABAZq1C6DwLQQQQAABBJYj4EYepE6c0ODJt/3TuJEHa459UvU7di7ntDwXAQQQQACBQAQ+Mhqh\n+f3RCH/2Fzr2i3/EaIRA1DkpAggggAACCwvQrF3Yhe8igAACCCCwJIGRixfU+8orms6k/ec3Hjig\n5MFDjDxYkiZPQgABBBCwElhwNMLacX3n1d/Tltm9euIzX+W/ZVbFIA4CCCCAQEkL0Kwt6fKTPAII\nIIDASgkw8mClJDkPAggggEAhBfKjEQaGrylV1aNctXRp9mfq/Pbvau/GL2rjwU8XcnnERgABBBBA\nIPICNGsjX2ISRAABBBAIUoCRB0Hqcm4EEEAAgUIJNNVvVpM26+bIKU1UpTXpjUY4MfSXuvjnf6tP\nfe5fMRqhUIUhLgIIIIBA5AVo1ka+xCSIAAIIIBCUACMPgpLlvAgggAACYRF4pG6f0lNj6sqc1VRs\nVoMtE/r2j39fW9K7tfdz/w2jEcJSKNaBAAIIIBAZAZq1kSkliSCAAAIIWAlMdHWp7/hrSvf2+CET\n6zdo7fM/x1VGVgUgDgIIIICAqUBlRY22VhxUfjTCVHxWl2Nn1fE3X9O+tl/Spo99znQ9BEMAAQQQ\nQCDKAjRro1xdckMAAQQQWFEBN/Kg7/hxDZ894583Vl+vlmePqWbzlhWNw8kQQAABBBAIo0B+NELn\nyDuarMoo0yj9dPxbeu/Pf6Ajh/+pajZuDuOyWRMCCCCAAAJFJUCztqjKxWIRQAABBAolMHj6tFJv\nvK7pTNpfQvPhI2rYt48//yxUQYiLAAIIIFAwgfV1+/3RCLcmzytbOeWPRvjeuf9Vm05sV9vBXynY\nugiMAAIIIIBAFARo1kahiuSAAAIIIBCYgBt5kPrW/6fY9LQfg5EHgVFzYgQQQACBIhJwoxE21z59\nZzRCvEduNMLV2CV1nPpfNHWT0QhFVEqWigACCCAQMgGatSErCMtBAAEEEAiHwNToqHq9ubSjly9p\nZnJSsbY2tT7/aSXWrQvHAlkFAggggAACIRDIj0boGjqt8epJ5Wqlt9Lf9kcjPLn/N5R8bF8IVskS\nEEAAAQQQKB4BmrXFUytWigACCCBgJJB664QGT570Rx6UxeNKPLZDmz/zWaPohEEAAQQQQKD4BNY1\n7NXU1KQ6xn+mXGLGH43wytX/U+tOb9Qzv/QHjA0qvpKyYgQQQACBAgnQrC0QPGERQAABBMInMHbt\nqnpfe1W54WF/cfW7H1fzwUPq9a6y5UAAAQQQQACB+wtUVFRp7eo9mpzoUer90QgdsRu6/cLvaG/d\nZ7Xlk1+4/wn4KQIIIIAAAgiIZi2bAAEEEECg5AXcyINbL/1AEx0dvkXlmjVqOXrsg5EHNGtLfo8A\ngAACCCCweIH8aITukTMaqxpTtnZWfzfzt7r8H1/Rkwd+k9EIi6fkkQgggAACJShAs7YEi07KCCCA\nAAJ3BKazGQ2dOqX+N173v+FGHjQfPqLGvczXY48ggAACCCCwXIH2usf90Qidk2eUrZzS4JpJudEI\n7e88ooNf/B8ZjbBcYJ6PAAIIIBBJAZq1kSwrSSGAAAIIPEhg5OIFv0k7f+RBy9Gj/OL4IDh+jgAC\nCCCAwEMIuNEIm2uf1vBYl/rKOjUVn1Vny031eKMRHlt9SLt+/jcf4mw8FAEEEEAAgegLVLz00kuB\nZZlKpZRMJgM7vzuxRQwX56R3o5mgD6tcohTHoi6u7lEys8olSrWxMrOKY1Ebq1yWEmfV2Khily+p\nbGDAf1ufaWxSdvt2dWqVdPxHC77VLyXOgie6zzct6uLCW+RiEcMqFxfHojZRM7PIx6IulvvMwswi\nhtVrxqI2uVWDGtKAxscmNHSr3IUM7Bjz/ttUU1Mb2PndiZsmJ7RqslxD3V2BxslkMop7f6my0FGt\n9cpW3FamMeePRjgz+4Yu/9mbqll9WDPJ9Qs95Z7fs9jPFjFcglF6P7Mys4pjURurXKIUx6Iu7rUZ\nJTOrXCxqY5VLIeNUPPfcc24PBnJ0d3ervb09kHPnT2oRIx8rSCsXwyqXqMUJui5Rq41V/Z1bVGpj\nZWYVx6I2Vrk8TBw38iB14oQGz16XVq9W2YYNWnPsk6rfsdOR3Pd4mDj3PdEDfshr5gFAd/3Yqi4u\nbNC1scolanGCrourfZTMrHKxeM1Y1CY1ekM/vXRN1Yk6tTfudiEDO4aGBtXQ0Bh69ZqaAAA8H0lE\nQVTY+d2Jh3/6rvfPMq1tWxdonMmJCVUlEveJsc4fjXBz4qwyVTlNrpXSUz9RW98aHfn8/6SK2sU1\nrS32s0WMPFRU3s+szKziuPoEXRurXKIWJ+i6uNpHycwqF14zTuDhjoVqwxiEhzPk0QgggAACRSgw\nePq0Ut5c2ulM2l9944EDSh48xMiDIqwlS0YAAQQQKH4BNxphU91TGp3oVo86NL1aurWmT3/zw/+e\n0QjFX14yQAABBBBYpgDN2mUC8nQEEEAAgfAKTHR58/GOv6Z0b4+/yMT6DVr7/M8t+qqd8GbGyhBA\nAAEEECh+gdpEu2rVrttDZzRSPTY3GuH6n/+dDu7/qpKPccPP4q8yGSCAAAIIPKwAzdqHFePxCCCA\nAAKhF3AjD/qOH9fw2TP+WmP19Wp59phqNm8J/dpZIAIIIIAAAqUm0NbwuJqnJtU9dk4T1VmNtuT0\n8vV/q+Q7jXr2c1/n/2QttQ1BvggggECJC9CsLfENQPoIIIBA1ARSb3lzab2bQuZHHjQfPqKGffsY\neRC1QpMPAggggECkBNxohA0NT2piol/duuyPRuhvGdK3f/z72pp7Qk985qv8tzxSFScZBBBAAIF7\nCdCsvZcM30cAAQQQKCqBsWtX1fvaq8oND/vrrtm2Ta1Hj3E1TlFVkcUigAACCJS6QCLRrG1qVt/Y\nFQ2u7tVUXLoU+5muvfA72t/wOW1+9hdLnYj8EUAAAQQiLkCzNuIFJj0EEEAg6gJuLm3qrTc10dHh\np1q5Zo1avCZtYl2wd7uOuiv5IYAAAgggUEiBlpqtatFW3Rx4RxO1GeVqZ/XTqe/qzF99Xx8/9HtS\nWXUhl0dsBBBAAAEEAhOgWRsYLSdGAAEEEAhSYGp0VKNvvK7Rm51+mLJ4XGuOfVL1O3YGGZZzI4AA\nAggggIChwCNN+zXlzbO9OfauMtXTmmjM6cWz/5vqhmrV+oV/zWgEw1oQCgEEEEDARoBmrY0zURBA\nAAEEVkjA3Txs6NQpfy5temhQlVVVYi7tCuFyGgQQQAABBEIo4ObZbmp4RpOTKXXPXtLUamm4eUzf\nevlr2jC9U898/vdDuGqWhAACCCCAwNIEaNYuzY1nIYAAAggUQGDk4gX1vvLK3M3D4tu2a+unP8Nc\n2gLUgpAIIIAAAghYC1RVJb3BCEmlBi9roKrfm2c7q2uz53XzO1/Vow2f0OPP/pr1koiHAAIIIIDA\nigvQrF1xUk6IAAIIILDSAm4ubd/x15Tu7fFPnVi/QcmDhzS0ahWN2pXG5nwIIIAAAgiEXCDZuE2J\niXYNTVzRSM2YN89WOjd1XDe+dUJPPvGbatt6IOQZsDwEEEAAAQTuLUCz9t42/AQBBBBAoMACbi5t\nr9ekHb18yV9JrL5eTYcOz82lHeruLvAKCY8AAggggAAChRJY27RHLdlxdY2eU7p2SmP1GR2/8n+p\n8WdNOvzJ31NNw9pCLY24CCCAAAIILFmAZu2S6XgiAggggEBQAm4uberECW8u7dt+CHfzsKYDTyr5\nzMGgQnJeBBBAAAEEEChCgYpYtTY2Pa2J0R7d0jVNVUoDdYP63htfV3tus478wh8WYVYsGQEEEECg\nlAVo1pZy9ckdAQQQCKHA4OnTSr3x+txc2vrdj6vl6FHu9hzCWrEkBBBAAAEEwiKQqG315tm2qn/w\nkobiKX+ebWfsmv76u7+t7YnD2vOpL4dlqawDAQQQQACB+wrQrL0vDz9EAAEEELAScHNpb7/4feWG\nh/2Qbi6ta9JWNrdYLYE4CCCAAAIIIFDkAs2N29Ws7bo18K7G3DzbxIzOz/5EV79zQgcf+7LadvBX\nOkVeYpaPAAIIRF6AZm3kS0yCCCCAQLgF0v196vvRcU10dPgLdXNpW5//tBLr1oV74awOAQQQQAAB\nBEIr4ObZTuXn2dZM+TNtj3f9iRre/QsdOvpPVNe6MbRrZ2EIIIAAAqUtQLO2tOtP9ggggEDBBNzN\nw/pPvKnhs2f8Nbi5tM2Hj6hx776CrYnACCCAAAIIIBAdgfw828nJlG5nLyvrXWU72DSq7536I7WM\ntero3/86Y5aiU24yQQABBCIjQLM2MqUkEQQQQKA4BNzNw4ZOnVK/N5c2f7gmbcO+ffzClAfhMwII\nIIAAAgismEBVVVKbvY/BwetKxW5rOib1NvTqWy9/jZuQrZgyJ0IAAQQQWCkBmrUrJcl5EEAAAQTu\nK5Bv0g6ePPmhm4c1Hzykitra+z6XHyKAAAIIIIAAAssVaGzcpEZt8m9CNlDd792ETHI3IfurF35L\n21cf1N7nvrLcEDwfAQQQQACBZQvQrF02ISdAAAEEEHiQwMjFC+p95ZW5Ji03D3uQGD9HAAEEEEAA\ngaAE8jchuz34rkaqx/ym7cXZE7rynbe0vfaI9nzqy0GF5rwIIIAAAgg8UIBm7QOJeAACCCCAwFIF\nXJPWjTvIDQ/7p3BN2qR3JS03D1uqKM9DAAEEEEAAgZUSaGvcozbvZDcH3tF4TVo57w99zs/+RFf+\n5k0d2v4bUt36lQrFeRBAAAEEEFi0AM3aRVPxQAQQQACBxQrkenrUeeINTXR0+E+J1der5dljqtm8\nZbGn4HEIIIAAAggggICJwCNN+zWVHVfX6Dmla6aUqZ7W8a4/UdXZ1Srb9xW1bT1gsg6CIIAAAggg\n4ARo1rIPEEAAAQRWTGCiq0upt97U0MWLqqyqkmvSNh06rPodO1csBidCAAEEEEAAAQRWWqAiVq2N\nTU8rmx5RV/qCsl7DdqIxp+PXv6H6d+t0+PDvqq5140qH5XwIIIAAAgh8RIBm7UdI+AYCCCCAwMMK\nTI2Oqv/Emxo+e8Z/6qpYTM2Hjyj5zMGHPRWPRwABBBBAAAEECiYQq6zT5spnNDHeq9u5a8pVz2io\ndkTfO/VHahxr1MGPf42mbcGqQ2AEEECgNARo1pZGnckSAQQQCETg7iZtWTyupgNParK1VcmNmwKJ\nyUkRQAABBBBAAIGgBRLVa7R2okZTIyn1VHZpOiYNNAz5Tdu23AYd/fzXg14C50cAAQQQKFGBiu7u\n7sBS7/FmFgZ9WMRwOaRSKQVp5WJY5RKlOBZ1iVptrOofpdpYmVnFWYnazOSymjx/Xulz5zSTzbiX\nieLbtqvq6aeVWR1Tn/f+X+Z9DvqwMrOIsxJ1WYy3RS4WMVyuVnEsamOVS5TiWNTFcp9Z1MYihjOL\nSm1G0n3KZDIqK5vU0NCgSy2wY/j9G3EGFsA78fT0lFZNlys3MRFkGE1OTgZ6/vzJLeK4GFVVSa1T\nUiPDNzRaNew3bW9VdOqvXvgtNY+369Ejv51f0pI/R+U14wCs3mes4ljUxiqXKMWxqEvU9rNV/S1q\nY5VLIeNUtLe3L/k/Kot5YtDnd2uwiJFMJk3iWORiZWYRx6ouFrm4GO6w2AMWMaJWGwszq/ovpzbT\nXmN26NQpDZ48qZlMWrHyMtU/9bSaDx5SRa13C+V5R5TMLGqznLrMY1/Ulxa1sYhhURcXw6o2UTKz\nqI1VXSxycTHcYbEHLGJEpTbx0ZyuD8VVVVmlhobGO0UK8J9Bxxgu9/6o0vvvdkUiEWAWd05dZRDD\nRbKIk49RldipVi9mf995DdQPaSruNSZj3eo//c+1fdXT2vuZr95Jfgn/jMprJp+6xfuMi2URx6o2\nFrlYmVnEsaqLRS4uhjss9oBFDKvaWORiVZeF4jAGwalwIIAAAgjcV2B+k3baa9K6I7F+g1qOHlVl\nc8t9n8sPEUAAAQQQQACBqAg0t+xUs5fMrYF3NVoz5jdtL87+VFe+83faGn9qWU3bqBiRBwIIIIDA\n8gRo1i7Pj2cjgAACkRa4V5M26V1Jm1i3LtK5kxwCCCCAAAIIIHAvgbVNe7TW++HNgZMar8ko5/2B\n0VzTtvoZ7X3uK/d6Kt9HAAEEEEDgvgI0a+/Lww8RQACB0hSgSVuadSdrBBBAAAEEEHg4gUeaDmhq\nalLdo+c0WZ19v2l7wrvS9i1trz2iPZ/68sOdkEcjgAACCJS8AM3akt8CACCAAAIfCNCk/cCCrxBA\nAAEEEEAAgcUIVFRUaUPjkx9p2p6f/Ykufed1mraLQeQxCCCAAAJzAjRr5yj4AgEEEChdAZq0pVt7\nMkcAAQQQQACBlRGYa9pmx9XlXWmbrpnyr7SlabsyvpwFAQQQKBUBmrWlUmnyRAABBBYQoEm7AArf\nQgABBBBAAAEEliFQEavWxqanNUXTdhmKPBUBBBAoXQGataVbezJHAIESFqBJW8LFJ3UEEEAAAQQQ\nMBGgaWvCTBAEEEAgcgI0ayNXUhJCAAEE7i3gmrSrb1zXtX93SdOZtP/AxPoNSh48pMS6dfd+Ij9B\nAAEEEEAAAQQQWJLAg5q2NYPt3nmfW9K5eRICCCCAQPQEaNZGr6ZkhAACCHxEYP6VtBUdNzTd2iqa\ntB9h4hsIIIAAAggggEBgAvdq2g7VdOmvv/NVbY0/pb2f+Wpg8TkxAggggEBxCNCsLY46sUoEEEBg\nSQLzm7T5K2lnGpu0/pd/hStplyTKkxBAAAEEEEAAgeUJzDVtpyZ1e+S8Jmqy3o3IZnVx9qe68t2f\nqm1ms478wh8uLwjPRgABBBAoWgGatUVbOhaOAAII3FtganRU/Sfe1PDZM3MPyl9J23n+PI3aORW+\nQAABBBBAAAEECiNQUVGlR5oO6PbNTk3H+jTekFE2IXXOXtNfvfBbWpvZqD2Hf011rRsLs0CiIoAA\nAggURIBmbUHYCYoAAggEI5Du79PQqVMfatLWbNumxn0HPmjQes1aDgQQQAABBBBAAIFwCMyWlWnd\nmgP+Ym4NvKvRmjFNxaWbsRvqOvVHahxr1MGPf42mbTjKxSoQQACBwAVo1gZOTAAEEEAgeIGJri6l\n3npTEx0dc8Hqdz+uZu/GYRW1tXPf4wsEEEAAAQQQQACB8Aqsbdqjtd7yeobParRi1LviVhpoGNL3\nvKZt7Vi19u/7R2rbeqexG94sWBkCCCCAwHIEaNYuR4/nIoAAAgUWGLl4QcPnzs41acvicTUdeFL1\nO3fRpC1wbQiPAAIIIIAAAggsVaC1frdavSf3D17SUCzlN21HGid0/Po3VP2zuJ7c+Wtq23Fwqafn\neQgggAACIRagWRvi4rA0BBBA4F4Crknb/8bryg0P+w/JN2kb9u1Tecz7uzkOBBBAAAEEEEAAgaIX\naG7crmZt1/jIbfXMXFeuelZj9Rkd7/oTxS79e22tPqQ9n/py0edJAggggAACHwjQrP3Agq8QQACB\nUAtMZzP+PNrBkyc1nUn7a43V16vp0GHV79gZ6rWzOAQQQAABBBBAAIGlC1TXtWmL2jQx3qve7HVl\nqqf9j/OzP9Gl77yu9th2HfrsHyw9AM9EAAEEEAiNAM3a0JSChSCAAAILC0yNjmr4/DnNb9JWrlmj\nhv0HaNIuTMZ3EUAAAQQQQACBSAokqtdok/eRTY/o1sRFpWumlK2d1Y3Z93Tzhd/S2sxGHfr5P+Av\nrSJZfZJCAIFSEaBZWyqVJk8EECg6AXfTsBGvSTt89szc2hPrNyjp3TQssW7d3Pf4AgEEEEAAAQQQ\nQKC0BGKVddpY+bSmsuPqG72q0ZoxTXmTsG7Gbuivf/jfqX6sXk/t+UdqeuyJ0oIhWwQQQCACAjRr\nI1BEUkAAgWgJuHm07iradG/PXGL1ux9XnXfTMJq0cyR8gQACCCCAAAIIlLxARaxaa5v2aK0ncXvw\nXY1Wjfs3IxtsHNFLN/8PVZ+La2vdEe34e79a8lYAIIAAAsUiQLO2WCrFOhFAINICbtTB+M9O6/J/\n+s7cPNr8TcPqvSZtRW1tpPMnOQQQQAABBBBAAIHlCbQ17vGm2kqpwcsaKk9pqmrGvxnZz2Zf0bm/\neVVNmbVq/+V/sbwgPBsBBBBAIHABmrWBExMAAQQQuLfA/FEH6clJVVZViXm09/biJwgggAACCCCA\nAAL3F0g2blNS2/ybkfXlOpRO5JStnlVPolvf/P5X1DjRpD1P/Fdq23rg/ifipwgggAACBRGgWVsQ\ndoIigECpCyw06iC+bbvWHz7CqINS3xzkjwACCCCAAAIIrICAuxnZRq2RZmZ0a+isxqrGNLNaGqgb\n1PHr31D1z+LatOZj2v2xf7AC0TgFAggggMBKCdCsXSlJzoMAAgg8QGA6m9HQqVMaPndWueFh/9Hz\nRx30eqMQEu3tDzgLP0YAAQQQQAABBBBA4CEEysr8ubaTExMaH/NuYLtqQLnqOyMSzqZ/qIv/+RW1\nTW/SkV/4w4c4KQ9FAAEEEAhKgGZtULKcFwEEEHhfIN3fd6dJe/bMnMmCow68Zi0HAggggAACCCCA\nAAJBCTQ3blezd/KJ8V71ZW4oXTOlbNWsOmev+SMSGkbr9cS+X1XbtieDWgLnRQABBBB4gEBFd3f3\nAx6y9B/39HxwJ/Oln+X+z7SI4VaQSqUUpJWLYZVLlOJY1CVqtbGqf5RqsxSzmVxWmStXvY/LyqX6\n3Tbyj9iGDUrs3K3Vra0a974zPu89eClx3j/tQ32yqI1VLlGKY1EXt1EszCxiWOXi4ljUJmpmFvlY\n1MVyn1mYWcSwes1Y1GYk3adMJqOyskkNDQ26kIEdw+//xU1gAbwTT09PadV0uXLe1ZVBHpPeDH6L\nwyKORQxn5faZu+o16GN+PqtW1WhN5W4pN6OB9BVNJCb8EQmDjSM6fuP/VuU75Wop266Nhx9uRILV\n+4xVHIv/1ljlEqU4FnVxr8comVnlYlEbq1wKGaeiPeA/uQ36/O4FZBEjmUyaxLHIxcrMIo5VXSxy\ncTHcYbEHLGJErTaLNcvfMGz0/atoy72arm5oUP3u3Wrad0AVtbV+ne/1j8XGudfzF/N9q9pY5OLy\njUocq7pYmUWlLs7LqjZRMrPYZ1Z1scjFxXCHxR6wiBGV2sRHc7o+FFdVZZUaGhrvFCnAfwYdY7jc\n+6PK8jJVJBIBZnHn1FUGMVwkizgWMeJxb58V0Gxd9V6/cKnByxopSymbmNFkclodsxfUdfqfq36i\nQXt2/7Ladhy8U+AH/NPifcYtwSKO1fuZRS5WZhZxrOpikYuL4Q6LPWARw6o2FrlY1WWhOIxBcCoc\nCCCAwDIE3CzakfMXNPjO23OzaN3parZtU83WbarfsXMZZ+epCCCAAAIIIIAAAggEL5Bs3Kaktmly\nMqX+zHVNJrKains3JIsN6XjXnyh+8T9oXWynnvrs7wa/GCIggAACJSxAs7aEi0/qCCCwPIH8VbTD\n82bRxurrVbdrt+p37nrgVbTLi86zEUAAAQQQQAABBBBYeYGqqqTWex/u6B+6pFHvhmTuatt07ZSu\nzL6r6y/8lmrTNdr7xK+pbeuBlV8AZ0QAAQRKXIBmbYlvANJHAIGHE5jybgI2evXqglfR1ntN2prN\nWx7uhDwaAQQQQAABBBBAAIGQCjQ33Lkh2VR2XLfH3tNETdq/2nYoNqbj17+h1WfL1Fa2RYc//89C\nmgHLQgABBIpPgGZt8dWMFSOAQAEExq9d0/C5sxq9fGkuOlfRzlHwBQIIIIAAAggggECEBSpi1Xqk\nab+fYWrgPQ2XDSiXmFW2ZsabbXtZnd//imrHqtXeetSbvfmlCEuQGgIIIBC8AM3a4I2JgAACRSqQ\n7u/zZtGeV+r0KY1OT89l4WbRchXtHAdfIIAAAggggAACCJSQQLLpUW+2reSutu0buayxxIRmVksj\njRMazXxPV777PbVObdSup/5LNT6yvYRkSBUBBBBYGQGatSvjyFkQQCAiAvkxByPeVbTp3h4/q5nJ\nScXa2tSw/4Dqdu5Uecy70wIHAggggAACCCCAAAIlLOCutl2b3OsLDA93aGDVbWUrp735ttLN2Rvq\nuvjHqnqzQm2Vj+rpz/9eCUuROgIIIPBwAjRrH86LRyOAQAQFprMZuTEHY5cvf2jMQVk8rtpt21W5\ndq027n48gpmTEgIIIIAAAggggAACyxeor9+gem3QxMiIRqZvaDw+rqnVs5pomtLV2XO65o1JqJ6o\n1Lamo3rs2V9ZfkDOgAACCERYgGZthItLagggcH8B16AdvXJZw2fPfOiBd4856O7u/tDP+RcEEEAA\nAQQQQAABBBD4qMCqCu9K2ro9/g8mJ1NKTXZosirtj0kYq8/o1NSLeveFF1WbrtGjW39em594/qMn\n4TsIIIBAiQvQrC3xDUD6CJSaQH4O7cjZs5rOpOfST6zfoLpdu1SzZQtjDuZU+AIBBBBAAAEEEEAA\ngaUJVFUl9Yj34Y7RyVtKZW56IxKmNOVNFBuKjemnfd/UO9/9pppyrdqx8++rbcfBpQXiWQgggEDE\nBGjWRqygpIMAAh8VyM+hHXznbeWGh+ceEKuv9+fQ1m7Zqora2rnv8wUCCCCAAAIIIIAAAgisnEBt\n1Vq5D3cMDVzXYHmPslUz/nzbntke9Xb9iWKX/r3WzG7Q4wf/oepaN65ccM6EAAIIFJkAzdoiKxjL\nRQCBxQnkG7TzbxTmnukatNVbt3o3CtulyuaWxZ2MRyGAAAIIIIAAAggggMCKCDQ0bVKDNkkzM+od\nOq/x8lFlq2eVrp5S5+w13TzzrxT3bkzWsmo9jdsVEeckCCBQbAI0a4utYqwXAQTuKZAfcTB586bS\nvT1zj8vfKKzGa9LWbN4y932+QAABBBBAAAEEEEAAgQIJlJVpTdNuP/hUdlx9Y1c1sdq7MVml17it\npXFboKoQFgEEQiBAszYERWAJCCCwdIHc4IB6vZuEjXkf80cc0KBduinPRAABBBBAAAEEEEDAUqAi\nVq21TR/cmGxgslMTCe/GZOULN26b1x+V2tstl0gsBBBAwEyAZq0ZNYEQQGClBPJX0LoG7ejt20pX\nVfmnpkG7UsKcBwEEEEAAAQQQQACBwgi4G5Ote//GZOnsiAbHbmgyNqFc5Ywma3N3RiUMXNOZ//yn\nap5aq0cf/0W1bT1QmMUSFQEEEAhAgGZtAKicEgEEVl5gfoN2/hW05TU1aty7V4lH1jPiYOXZOSMC\nCCCAAAIIIIAAAgUTqIzVzV1xmx+VkG/cZqtmdWu2W7evf0MV56WmXKu2b/u01u35RMHWS2AEEEBg\nJQRo1q6EIudAAIEVF5jOZpTu6taod/XsxM3OD404mH+TsIFsTmv4E6gV9+eECCCAAAIIIIAAAgiE\nSWD+qISxkX6NTt3SZNm4colZZRNSz2yPenv/VOXeFbfV2RptajqsHc/+SphSYC0IIIDAogRo1i6K\niQchgICFwNToqEavXtXYVa9B29HxoZDzG7SVzS0f/Ky7+4Ov+QoBBBBAAAEEEEDg/2/vzprjuK4D\njp9ZMBsw2BcSBHdSlmxZju1UlLjsShxZTspVcfbX5DHP+Q55Sb5C/AESpSqqchw7VTYfYquiWLJN\nRWQsKZZMc4OJHZgBZl9ybg96MDMEyCGBPhg0/u0CZuvp0/d3ekDj6OJcBBAIvUAsnpGzo60et26w\ny2s/l514XirphjS0Q9pWalver31fbn/v+5IuJWU2elk+9dt/IaNzF0NvwwARQODkC1CsPfk5ZAQI\nnGiBnTt3ZEdnzvYuEOYGlTl/QUauXpX0uXPSVaA90SPm5BFAAAEEEEAAAQQQQOAoBWanPt0+3Mrm\nh7IdzWmP27rUkiLbybJsNz+UO7f/TuL/HZHJxqycv/AlufrFb7Tfwx0EEEBgkAQo1g5SNjgXBE6B\ngJs9W1h8KNsf6+JgH/+ia8T+AmHphQUZuXJFYgn9f1dsCCCAAAIIIIAAAggggECfAjPjL4r/d3i5\n/APJVVekpAXbelLbJWSbrXYJm2/Kzf94U9LFpMylXpAXf+cvZWT8bJ8R2A0BBBAIVoBibbC+HB0B\nBFTAK85+8okUHzyQ0vJSl0lqdlZnz16TYS3OMnu2i4YHCCCAAAIIIIAAAgggcAiB0eyCjMqCdwS3\nQNn69q9kJ7YtNW2XUEvorNuEm3V7S37501sytBOVifqMXLr6u3LpldcPEZW3IoAAAocToFh7OD/e\njQAC+wiUVlckroXZxX//jhTu35d6udTey82ezZw/3yrQnluQeDbbfo07CCCAAAIIIIAAAggggEAQ\nAm6BstnJz7QPvbGh7dga61JKV6WeaEp5pN6adbvyhvzke29IppCU6PaobDy4KBML19vv4w4CCCAQ\ntADF2qCFOT4Cp0DAXxisqL1n/eLs0NKS5LfmvNG72bOt1gbXJKP9Z9kQQAABBBBAAAEEEEAAgeMU\nmJi4LBNy2TsFN+t2bfuOFGI7rUXKtBtbXlsnyPiKfP+jf5DYTV1Po5KRmcxVeeELf8xCZceZOGIj\ncAoEKNaegiQzRASOWsDvO+sKswUt0Fa3trpCJMbGpBaNyZnXX5dhZs922fAAAQQQQAABBBBAAAEE\nBkvAzbqdm3y5fVKu1+1mfVnK8bI0UiKNYZHccEFyrmXC7VsSf1eLt9URmRm+Lp/56l9Jamik/V7u\nIIAAAocViC8uLh72GAe+f0ln1gW9WcRwY1hbW5MgrVwMq7GEKY5FXsKWm+fJf6NakdqjJaksPZLy\nvbvS2N52LO0tkkhI4sJFSZw5I4m5MxIdHpZHP/qR7IyOyY4uKCbuK6DtecbzrKdiEcOdk1Uci8+N\n1VjCFMciL1bXWZjy4swschM2M4vxWOTF6jNjFcciL1afGQuzXGlFyuWyRKNF2dzccCED27Z6/sN3\nEIHq9ZpE6jGpFgpBHL59zGKx2L4f5B2LOBYxnJG7zooB58XFsRiPRQyLsQzFJmVGv5b09585/f0m\nV7grxWheaslGa6GyjEhVtmWreVM++eFNieuvR5nSiIxr8fbCF/7EnWLfm9XP5jDF4f8D9H15tXe0\nyr9FbqzGcpxx4vPz8+3kBXEn6OO7c7aIMTU1ZRLHYixWZhZxrPJiMRYXw20W18DTYvgzZ0vLy48t\nCqZ9+CU6Pu71nc0snJe0tjXYb2GwsOXmaWZe8o7gm0Ucq9xYjMWRhyWOVV6szMKSF+dllZswmVlc\nZ1Z5sRiLi+E2i2vAIkZYcpPMV+VXm0lJp9IyPj7RSlKA34OOsRXTP6qMRSWe0SpTwFvaIIYbgkUc\nixhJXdPBIk6YzKzG4ucmnXnJhWxtjYasbX7sSrVSTdW9frcVXYajmm0Vb+99cFOGtqMyrDNvz4x/\nRj71+W9Kcmraf/e+txY/m13gsMQJy78znReDRW4sYljlxmIsx/mZoQ1C56eD+wicUoHC4kMpr6xq\nYXav52wvReb8BcksLMjwlSv7Fmd79+cxAggggAACCCCAAAIIIBA6gWhUpiZfkKndgbl+t+v5u16/\n22qyJo0hnS2drUulmZMNeVs+fO9tieuE83QpLWOJM3LlhdfkzIuvho6FASGAwNEJUKw9OkuOhMCJ\nEHAtDXbu3JHSyrIUHj6Qwr17j513VP/rfub8eS3Onpfk9AyLgj0mxBMIIIAAAggggAACCCCAgIjr\ndzs79ek2Ra1WlPVtLd5GcjrztiGNeFN0bTKpZora8/aOPHj4LZG7/yjJYlyGZVyyycs64/Vv2u/n\nDgIIIECxlmsAgZALlFZXpKI9l/3FwPKPHslOOt016tTsrCRnZiXtZs6yIFiXDQ8QQAABBBBAAAEE\nEEAAgX4F4vG0zE682LX76sYvvOJtLV6TaqIhorNvi0M1KTXXxP3v7o2faN/biKRraZnJXJWFy68y\n+7ZLkAcInC4BirWnK9+MNuQCfmHW9Zota5G2vLQs9XLpsVH7LQ3SWphNzkxLLJF8bB+eQAABBBBA\nAAEEEEAAAQQQOLzA9MT1roMU8kuyVXkkpVhZFy2re60TKtmmLlpW0Nm3t+SXD29J9M63ZKgck0w9\nK3NjL8mlz74mo3MXu47DAwQQCKcAxdpw5pVRnQKB3sLsfu0MHENibEwSM9rKYHcxsPVK1ayx/ClI\nA0NEAAEEEEAAAQQQQAABBJ5JIJOdk4zMee8pFgqSasRlbeeu7DQ3pTZU1wJuUxo6n6auhdySPrfu\net/efluiP9NJuZW4ZGq6eNnky3Lx079HAfeZ5NkZgZMhQLH2ZOSJszzlAs9amE25lgYHzZpdXDzl\nmgwfAQQQQAABBBBAAAEEEBgcgUgiIdOJ6zLdcUq5vK4vUtuQUqSoBdyGFm5bBdxasiol0QJu4y35\n4PZbIu81JaEF3OFKViazl+XchS/SQqHDkbsInEQBirUnMWucc2gF3OJfhcWHUl5ZlWpuy2tl8LQZ\ns08szIZWioEhgAACCCCAAAIIIIAAAuEVGM0uyKgsdA1wa+ueFOpbUooWpe4KuENNr/9tSfvfloe1\ngNu8KZ88vOktYBYvRWWkmpWx9LxMTFyRkUuvdh2LBwggMLgCFGsHNzecWcgFXFG2ls9LdWtLCg8f\neLf7Lf7lGPxWBhRmQ35RMDwEEEAAAQQQQAABBBBA4ACBsbELMtbzmut/u11dlaL2u61q2wS/gFvR\nYu6G5GSjmZO7xQ9FPviuxN/V2m41LqnIiExkzsvE7DW5+sVv9ByRhwggcNwCFGuPOwPED73AfkVZ\nV6A9aHOLfw2NjnpfB7YyOOjNPI8AAggggAACCCCAAAIIIHBqBDr73/qDLuwsS6G0ITuRnNcD17VQ\naEZFtFOCLmJW07Kum4W7KZHNW/LTG29KvCgSq+liZs1RmRy+JONTlyji+pjcInAMAhRrjwGdkOET\nqFfK2rJg9bGZss9SlE1ogXZZZ9rOz8+HD4gRIYAAAggggAACCCCAAAIImAhkhmfFfXX2wHULmRXK\ni1JpFqWiXW/dLNyGtlHwirgZkYjoYmY6F3e9uaFF3JutIm4pIrGqFnHrOhN35JKMZuflhS/9qckY\nCILAaRagWHuas8/Yn0mgtyBbXlkR99xBPWX9g6dmZyWuhVjXwiA5MyPxbFZS0zP+y923WqxlQwAB\nBBBAAAEEEEAAAQQQQOCoBaYmrj1+yEZDVrc+kWqjIJVIxSviNrWI23AzcdPaEzfdaC1o1nxPpPie\nvHfjuxKtiH5FJF1NSTySlDOTL0s6MyHXXv3m48fnGQQQeGYBirXPTMYbwixwUEF2/aOPJJ9OP3Ho\nrn1BNJnoryj7xCPxIgIIIIAAAggggAACCCCAAAIGAtGoTE9c3zfQ6sYvpNosdc3EdUXcRlJ319YK\nVe2UqxVcWW+8JbIt8rMb/ybRqhZy343oy0lJ1FMymb0siaFh+exX/3rfGDyJAAKPC1CsfdyEZ0Iu\n4HrIuq344IF32+8MWbdzZ0HWzZYdyo7qbNlpiSXcv1ZsCCCAAAIIIIAAAggggAACCIRD4KAi7tb2\nQ6lWta1CJC/1iC5qFq9rOwWdjTukhdyEjj3hCrklvVPStgo39Uld3+yGFnRrTYmXohJrtlorZGJj\nMjp2XkaHz8rF3/p6ONAYBQJHIECx9ggQOcTgCJRWV6RRqbR7x9bLrpesPlcqS2l56aknGk0mJTU7\n117gyy/IrteqsnDx0lPfzw4IIIAAAggggAACCCCAAAIIhFlgbOTcgcPbWLsnjUhJ59tuaxfcutdW\nQRviSl0LuKLF3MpQQx82d1srbGoh965ozVd+fONf2sXcSCQiI9VRnaIbkbnsixJNJOTSK6/JyPjZ\nA+PyAgJhEqBYG6ZshnwsfouCZlnboa8se6NNvvczuZ/fkvLSstTL7r/cPX1zs2Pd5s2ITaa8PrJR\nnRmbOXfwPzibi4tPPzB7IIAAAggggAACCCCAAAIIIHCKBVLpae1fqyuW7bOVCpuyXV5p98etayuF\nhvvf7kJnfjHXvbUiWsjVbb35tug0Xfn5T//Te+zaLERKTfn2t9/UnrkZicRiMjf2kvfaS7/5Z94a\nMd4DviFwggUo1p7g5IXl1P22BDVdXKu6teUNy29N4B48aQGv6Pq6FIb0P8/tbomxMV3Ma6w9M9bN\nlE3uLub1pGKs/35uEUAAAQQQQAABBBBAAAEEEEDg6AVSmXFxXwdtpUpOtneWpNHQbriRHW+3arLu\n3da1oCuuX67XZsHVb8s6e1dXOtOn11zPXN0+eKd1G9OnI7oAmtv8RdDcfbcQmttGpubl8iuve/f5\nhsAgClCsHcSshOCcXAG2urwsO9qGwJ8FW83lpJrPeaN7lpmwPoffosA9ziwseE8v3rkj57/8FfrG\n+kjcIoAAAggggAACCCCAAAIIIHACBVKJUXFfT9rya/dls7wu8VTUW/isqQXc2tBuQVdn6rrNXwDN\n3fcXQXP3vYXQ3J0VkXdvvOHuSVQLu9Hdwu5wMyvRerQ9W3dnZ0dKjy7LxZd/n3VqPC2+WQlQrLWS\nPqFx/B6w7vQ72w+4x52zX93j3hmwpWJRSum0e+mJm9+WIJpMSGpm1tvX7xXrHjxpRmw9l3/i608M\nzIsIIIAAAggggAACCCCAAAIIIHBiBLJT52V7MSpnJw9uY1iorElhZ90bU1Hy0mjqAmg60baWaBV1\nG3FtvxBrDbm7sNuaXOZe8Wbrajnj7sZN+cmP/tXbOVpuSqwa08m8raJw56zdZDwjE6NXvP1cawZ6\n7HoUfHtOAYq1zwl3kt7m93r1z7ly/4GsPbjvP+wquva7EFf7zQfcSc3OSkN7y47MTO9bgPX6xWqf\nWDYEEEAAAQQQQAABBBBAAAEEEEDgqAQyiSlxX0/dGg3Jbd6XSqQikWZTCs2cLo7WKsT67ReaUS3s\n7nZebKS04JtqFXzdsTtn7UpzU37d2F3rprHXY9ft19RjD+3oFODdLdlMSqKuB9PNFXZH5KysjbXa\nQ8S018OFK1+WkYuXd/fm5jQKUKwd0Kx3zmj1T7G8sqoF0O5FtAoPH/gve7f9FFvdjNdyHzNe3QFd\n0TWqi3C5rXPmq3vcOfvVPe6dAbuoi3LNz8+7l9gQQAABBBBAAAEEEEAAAQQQQACBwRGIRmV08mL7\nfHrLu8VCQdLpvcXSSrVt2ck/as+sLUTy0mzsztbVom59SKu0btNZvHWdvet67Hqb3q1kd1/TJ6ri\n6jp7tZ215pr2bmjt6r7f/viHIh/vPY7UdUZv0T9Y6/nOgq97JhKLS3ZkTjKxib036r2rr/6RZDKT\nXc/xYPAFKNY+R472K6T6h+lcJMt/zr+ta//W8qo2R+nZ3KJa7ssVUfN9FlF7DvHEh529Xt2OjVJR\npq9db78nOTMj0d1Zrl5BdndBrvYO3EEAAQQQQAABBBBAAAEEEEAAAQROsUAqPiKpiWvPLFAubEq+\nvFcLKje3pab/8zd/Fq973NT2DK7Q21oebXcPV/ztKPa6Z3sLvu65teZqV9HXPffB22+5m9b2gd5o\n4The0APqbe82vJ2QaGqfVpbRiMxmrmvdyK3utrfFdEF399ylz31dUkMjey9w79ACA1+s7f0T/t4R\nu0WsCh1XWW9f1d79Ox/3zkrteu3evc6Hkl5ako/+91bXc0E+6JzR6sfxWgfsznL1n0ufay205T92\nt70zXDtfc/fdjNcpZrz2svAYAQQQQAABBBBAAAEEEEAAAQQQOFKBZGZc3NdBmzeDN7M3g7d3v1qj\nLJtb3TWqSmNHqpFqe1fXaqGpRV5vRm/7WS3yDnXM8N19vjKyT6VWX6tmy/rdfT2+rTffeawQLEXd\nT7/e/+H39t6gdeA3bvxz67ErDJf1ib269N5+ei9ZiMlQJCGRoe4icNdOuw+mkgsyNNwydAu/rX00\n3LWbaycR3zWcufiKTM08e1G964DH/CC+9s6PD3UK1VxOqvm9JsydB9vW1+qj3Sv5+bNIO/c7zP1+\nF7E6TIze9+5XSPX3GdLxuq/9NjfDNbnPrNWE7h/PZr0iKm0D9pPjOQQQQAABBBBAAAEEEEAAAQQQ\nQOD0CcSjSZme2Pvr6OcR8AvCzWpNtoqLUqs/XpQtxXekXt0rAHfGqSZdxVULr52be6hF4rqrtfa8\n5O9WSe9fGHavV7PuNXfcgnv4xG1dewK3W0W4yb8dbSO8N7rHW61DRN7vKB63njrwe0S7WMRc0Xnf\nAbjzO2Bgu0eMVyOSqmckEn/+ubD1Wk0+6Pgr+8Sw1ghX3/6v3RBHf1PRP+svbCroIbbeP+HvPVRD\nC8KZjuJob1/V3v07H+83K9V/vXcBrPs/+IF86mtf81/mFgEEEEAAAQQQQAABBEIsUKvr0jHVDakV\ntqVeP2Ba0BGNv6RtyvK1ff709IiOL5GIFCIlaegv1JFcsH8t2NAFe6K17t6KRzWMzuNYxLGI4cZU\nSVZkMddaub5zjEd932I8FjGci1Uci9xYjSVMcSzyYnmdWeTmsRjabqF3i2oPhmh8nxd0x6G9ddV6\n3+bNrvWfrFQrkvBnymohtxwtea0d/Nc7b93iba4c6mYFP21z+7p/y560+a823FH7/WdI31TPuqMe\ndA4HPd86k4reFGW79eBQ3zuPsSKRv/3DP3hy5KcEy2sFeLux/yGqlYoM9fS0yNXr4r6OaitrH9ik\nzlgNetvY2JCJie5GzUcd02osYYpjkReX5zCZWY0lTLmxMrOKY5Ebq7GEKY5FXqx+noUpL87MIjdh\nM7MYj0VerD4zVnEs8mL1mbEwu/pKVr78zTmpleuS3wy2WOt+UY085ZdQN+bDbFeHdTlz/bWttP+k\nqcMcuuu97nfugIfixbOIYxHDDcYi/604wefGziz4sVjlJnxmwecmTJ+Z8H02g//37Mg+M1qo9f69\nikefMn/WZemALRaRiBaG/ULxAXs99Wk9yt4+7rz0It+/0rq323Pfc71Rg/6zfosYDuAHOrP2awHP\nrLUaS5jiWOTF5T9MZlZjCVNurMys4ljkxmosYYpjkRern2dhyoszs8hN2MwsxmORF6vPjFUci7xY\nfWYszNbyd+Wd//snmZu8LFfmvuJCBrYtLy/J7OxcYMePaJV26Tt/LyPpUYm+/OeBxXEH3lhfk4nJ\n3jXVjz6kRRyLGE7m/f+5Ka987vNHj9RzRIvxWMRww7KKY5Ebq7GEKY5FXiyvM4vcWMRwZha5sRrL\nccZ5/qYKLgtsCCCAAAIIIIAAAggggEBIBWLRIV3helTGM2cDHWEh0Qw2hs7PKUhcprO6vsbl3wh0\nLI3kopwxWMzYIo5FDJeM25+sypmA8+LiWIzHIobVWFwci9yEzcxiPBZ5sbzOLMwsYvCZcQLPvu2X\nm367ODx7NN6BAAIIIIAAAggggAACCCCAAAIIIIAAAggg0LcAxdq+qdgRAQQQQAABBBBAAAEEEEAA\nAQQQQAABBBAIToBibXC2HBkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEOhbgGJt31TsiAACCCCAAAII\nIIAAAggggAACCCCAAAIIBCdAsTY4W46MAAIIIIAAAggggAACCCCAAAIIIIAAAgj0LUCxtm8qdkQA\nAQQQQAABBBBAAAEEEEAAAQQQQAABBIIToFgbnC1HRgABBBBAAAEEEEAAAQQQQAABBBBAAAEE+hag\nWNs3FTsigAACCCCAAAIIIIAAAggggAACCCCAAALBCVCsDc6WIyOAAAIIIIAAAggggAACCCCAAAII\nIIAAAn0LUKztm4odEUAAAQQQQAABBBBAAAEEEEAAAQQQQACB4AQo1gZny5ERQAABBBBAAAEEEEAA\nAQQQQAABBBBAAIG+BSjW9k3FjggggAACCCCAAAIIIIAAAggggAACCCCAQHACFGuDs+XICCCAAAII\nIIAAAggggAACCCCAAAIIIIBA3wIUa/umYkcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCA4AYq1wdly\nZAQQQAABBBBAAAEEEEAAAQQQQAABBBBAoG8BirV9U7EjAggggAACCCCAAAIIIIAAAggggAACCCAQ\nnADF2uBsOTICCCCAAAIIIIAAAggggAACCCCAAAIIINC3AMXavqnYEQEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQQCE6AYm1wthwZAQQQQAABBBBAAAEEEEAAAQQQQAABBBDoW4Bibd9U7IgAAggggAACCCCA\nAAIIIIAAAggggAACCAQnQLE2OFuOjAACCCCAAAIIIIAAAggggAACCCCAAAII9C1AsbZvKnZEAAEE\nEEAAAQQQQAABBBBAAAEEEEAAAQSCE4gvLi4GdvSlpaXAju0f2CKGi7W2tiZBWrkYVmMJUxyLvIQt\nN1b5D1NurMys4ljkxmosYYpjkRern2dhyoszs8hN2MwsxmORF6vPjFUci7xYfWYszHKlFSmVSpLL\n507+7wHNplQrFR1PUVYC/P3PIi8uhtssrmeLGG4sYfp5ZmVmFcciN1ZjCVMci7xY/ZyximOVf4vc\nWI3lOOPE5+fn3bUR2Bb08d2JW8SYmpoyiWMxFiszizhWebEYi4vhNotrwCJG2HJjYWaVf6vchMnM\nIjdWebEYi1UMqzhWueEz4zLa/2aVF3dGYcqNxVjCkptkvir3tlIymh01uQYCzY0Wa3+dSEgqlZZs\nwL//8Znp/+eYv2dYPjP+eAK9lv0gemsRxyo3FmNxdGGJY5WXMJlZjcUqN2G5ll1e3NY7HtogtFz4\njgACCCCAAAIIIIAAAggggAACCCCAAAIIHKsAxdpj5Sc4AggggAACCCCAAAIIIIAAAggggAACCCDQ\nEqBYy5WAAAIIIIAAAggggAACCCCAAAIIIIAAAggMgADF2gFIAqeAAAIIIIAAAggggAACCCCAAAII\nIIAAAghQrOUaQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEBkCAYu0AJIFTQAABBBBAAAEEEEAAAQQQ\nQAABBBBAAAEEKNZyDSCAAAIIIIAAAggggAACCCCAAAIIIIAAAgMgQLF2AJLAKSCAAAIIIIAAAggg\ngAACCCCAAAIIIIAAAhRruQYQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEBEKBYOwBJ4BQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAAGKtVwDCCCAAAIIIIAAAggggAACCCCAAAIIIIDAAAhQrB2AJHAKCCCA\nAAIIIIAAAggggAACCCCAAAIIIIAAxVquAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYAAEKNYOQBI4\nBQQQQAABBBBAAAEEEEAAAQQQQAABBBBAgGIt1wACCCCAAAIIIIAAAggggAACCCCAAAIIIDAAAhRr\nByAJnAICCCCAAAIIIIAAAggggAACCCCAAAIIIECxlmsAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAY\nAAGKtQOQBE4BAQQQQAABBBBAAAEEEEAAAQQQQAABBBCgWMs1gAACCCCAAAIIIIAAAggggAACCCCA\nAAIIDIAAxdoBSAKngAACCCCAAAIIIIAAAggggAACCCCAAAIIUKzlGkAAAQQQQAABBBBAAAEEEEAA\nAQQQQAABBAZAgGLtACSBU0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBCjWcg0ggAACCCCAAAIIIIAA\nAggggAACCCCAAAIDIECxdgCSwCkggAACCCCAAAIIIIAAAggggAACCCCAAALxxcXFwBSWlpYCO7Z/\nYIsYLtba2poEaeViWI0lTHEs8hK23FjlP0y5sTKzimORG6uxhCmORV6sfp6FKS/OzCI3YTOzGI9F\nXqw+M1ZxLPJi9ZmxMMuVVqRUKkkunzv5vwc0m1KtVHQ8RVkJ8Pc/i7y4GG6zuJ4tYrixhOnnmZWZ\nVRyL3FiNJUxxLPJi9XPGKo5V/i1yYzWW44wTn5+fd9dGYFvQx3cnbhFjamrKJI7FWKzMLOJY5cVi\nLC6G2yyuAYsYYcuNhZlV/q1yEyYzi9xY5cViLFYxrOJY5YbPjMto/5tVXtwZhSk3FmMJS26S+arc\n20rJaHbU5BoINDdarP11IiGpVFqyAf/+x2em/59j/p5h+cz44wn0WvaD6K1FHKvcWIzF0YUljlVe\nwmRmNRar3ITlWnZ5cVvveGiD0HLhOwIIIIAAAggggAACCCCAAAIIIIAAAgggcKwCFGuPlZ/gCCCA\nAAIIIIAAAggggAACCCCAAAIIIIBAS4BiLVcCAggggAACCCCAAAIIIIAAAggggAACCCAwAAIUawcg\nCZwCAggggAACCCCAAAIIIIAAAggggAACCCBAsZZrAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQGAAB\nirUDkAROAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQoFjLNYAAAggggAACCCCAAAIIIIAAAggggAAC\nCAyAAMXaAUgCp4AAAggggAACCCCAAAIIIIAAAggggAACCFCs5RpAAAEEEEAAAQQQQAABBBBAAAEE\nEEAAAQQGQIBi7QAkgVNAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQo1nINIIAAAggggAACCCCAAAII\nIIAAAggggAACAyBAsXYAksApIIAAAggggAACCCCAAAIIIIAAAggggAACFGu5BhBAAAEEEEAAAQQQ\nQAABBBBAAAEEEEAAgQEQoFg7AEngFBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAYq1XAMIIIAAAggg\ngAACCCCAAAIIIIAAAggggMAACFCsHYAkcAoIIIAAAggggAACCCCAAAIIIIAAAggggADFWq4BBBBA\nAAEEEEAAAQQQQAABBBBAAAEEEEBgAAQo1g5AEjgFBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDg/wHR\nqaWou1eVFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = 'p_graph.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some important points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The result summary should always reflect the initial hypotheses statements as I have shown in the example above.  It is also important to note that the null hypothesis cannot be accepted.  Only two possible outcomes are allowed: either fail to reject the null hypothesis or reject the null hypothesis (thereby accepting the alternate hypothesis).  Furthermore, although in common medical use, the terms *statistically significant or not* are actually frowned upon.  It is more correct to suggest a likelihood of having found a value as extreme as the research has.  Alas, these terms are imbedded in the medical literature and we accept them as such.</p>\n",
    "<p>You ,may now start to realize that a *p-value* is not some magical genie, granting our wish for a clear-cut answer from our research!  It depends on so many things, not least of which is the important notion that in order to use it, we have to make assumptions.  The most important one is probably the fact that we assume the null hypothesis to be true.  Only if it is true can we infer that our specific outcome was so rare to occur that it represent a significant finding.  If the null hypothesis was never true, our house of cards actually comes tumbling down.  Fortunately, or unfortunately, we never talk about this, though.  It is the deep dark secret of statistics.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}