{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import the raw data into a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "radiation = read.csv(\"Radiation_KKW_Goesgen.csv\", header = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First lets compute some bare statistics on that data. Lets get the average, the standard deviation and the number of datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu=mean(radiation$Radiation.µSv.h)\n",
    "sigma=sd(radiation$Radiation.µSv.h)\n",
    "n=length(radiation$Radiation.µSv.h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we plot the histogram and compare the distribution to a normal distribution that best fits the data. As we see, the distribution is slightly asymmetric, and the mean is pulled up by some outliers. You can chose to correct this here, but we will continue on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lVUVGR7+cYyBrBPnz7W0YWWNzurU6dO\nOZO8yeBZC+s1DhGZO3eu9RaW+vr6t956y5nNulxbXxmXcMmLExERYT1G+fn5tk81+adDHjsu\nHn61g4ODf/CDH2zdunXPnj1/+9vfYmNjx48ff/nyZRE5evRoXV1da8M2RSQ0NPTJJ5+8evWq\ntQrk5OSsWbPGerMax8tNvx2tsb2CWV5ebv2cjCYaGhp8fHyaF82AgIB58+atWLHC+oZme5+G\nTqf78Y9/vHjxYvn+aqztvE625/OgMlyKhUdZTz4FBQVZPoZ8yZIlBw8etL6H1tTUWK+A2P6J\nsn3Dtbjlllusjzdv3mx7jWbNmjXWxzqdzjIoYdSoUdaF77//vu36bb0/yZbt9ZqJEydaH3/6\n6ae1tbXt3mxHtPWVcZ+2vjhNjpHtJLe2yd2xa3H089YaD7zatbW1rd1YOWTIkFtvvdXy2Gw2\nO3OjqmUSaes/X331VetvJcfL+riDvx2rV6+e/D3bMRlN2A5BsJ2sTkRsv+tz584lJiamp6db\nGryts2fP2r50vXv3tn3Wtr2tWrUqIyPD8nj06NEJCQltTQtvQbGDR/385z+fPn363//+d8uU\nBCJiNpv/+c9/FhcXW9exDmuwPfP/2Wefff755/n5+WfOnLG8u9mOeLh8+fKkSZM++eSTo0eP\n/v73v//1r39tfWrq1Knh4eEiYjs5wuXLlydOnPjRRx99+eWXTz31lO3UAG1l+076xhtvWAbb\nfvHFF04OyHCHtr4y7tPWF8cyi4fFlStXxo8fv2XLls8///yZZ55Zu3atW3ctjn7eWuOBV/vy\n5cs33njj888/v2/fPstVVKvi4mLrbVWhoaEt3lnfhL+/v+3FuzNnzmzcuNHymONlfaqDvx05\nOTmffM96OaK5119//dlnn920adPSpUubDDf529/+ZvvhsLm5uXPmzOnTp8+tt946b968ZcuW\nLV++fO7cuSNHjrS9p8W2toqIXq+3TkZtNBqt12Gb3BvtZFp4DTeMtIV6uPyTJ5577jnrwoCA\ngF69ejW5hHfzzTdbt7Bz584Wf2ife+45ywoOJxoNCwtzZoLiJjcCt2lqhmeffdb2a319fS1b\n8/f3tz2jYDvlmO36ttu0fXEs89BanDt3zvZLnJnnva2vTMenO3HVi/PDH/6wxcABAQG2/3R4\njNqxa4c/b+2e8LbJq23ndbP9KutTtqdkunTpEh8fb+kc/fv3t73Z/4knnrDdlJ1f3qqqKtsi\nNWTIEMv8cBwviw4eL/P1837/6Ec/sv2S5qObWxMREWH5Eieva7f4m/vXv/61yWoBAQGWT250\nJm07vncojjN2UExtbW1JSYntfzf79Onz5ptvWv95++23Dx8+3M4W/vznP1um4G/RjTfe+Omn\nn9qOeF27dm3zjz/q1q3b22+/bbukyd8k+1566SW9Xm/9Z0NDQ1VVla+v75tvvmk7o4qHtfWV\ncZN2vDjr1q0bNGhQk4WBgYHr1q1z964d/ry1xt2vtu39VdeuXcvNzbUMRz179uzVq1cty5OS\nkpYsWeLkBoOCgmznED5+/PiOHTuE4yUiHvztkGZDEJ566qm4uLjmqwUFBUVFRdnf1OjRo61n\nXm098MADTf7zPGXKFNsxW1Afih08av78+X/7298ef/zxUaNG3XjjjV27dvX39w8PD7/11lt/\n97vfZWdn235Gma+v77/+9a+5c+fGxsa2WLb8/PxWrlz5zTffzJ49OzExMTg42LK1tLS0FStW\nZGdnjxw50nb93r1779+/f+HChXFxcQEBAX369Jk5c2ZmZmaTmRqafDqWfb169crIyHj++ef7\n9+/v7+9/ww033HPPPXv27GnrRDCu1dZXxk3a8eLccMMNBw4c+NWvfmU5RuHh4ffff/+hQ4em\nTJni7l07/Hlrjbtf7fDw8PPnz7/xxhvTpk2LjY0NCgqy9DxfX9+ePXvecsstr7322oEDB3r1\n6uX8Np955hnbU0evvvqqcLw8+9shIr/73e9GjRoVEBAQHR39yiuvrFix4oMPPkhNTe3SpUu/\nfv1mzpxpWS08PPzcuXNfffXVokWLfvjDH8bGxgYHB1tOasbGxt53332bNm3at29fkxvsLMLC\nwu6++27bJQybUD2ducOjxwFv94tf/MLyh01E9Hq90WhUNg9g35NPPvn6669/8MEH06ZNUzoL\n2iY4OLiqqsry+LPPPrP9hDfAJThjBw3ZuXPnrFmzdu3aZb2AVVpa+n//93+vvfaadR1lz7QB\nzmjrDGQAtIN57KAhNTU169atW7dunY+Pj+UuE8tNxNYVkpOT//d//1e5gIBTXDizNACV4b99\n0KLGxsbi4uLi4mLbVpeWlvbJJ5/YDjMEOqeUlJQf/ehHDm+oB6BB3GMHDSkpKdm0adPu3btP\nnDhx6dKl8vLy4ODgfv36jR49+sEHH5w0aZLSAQGoHPfYwd0odgAAACrBpVgAAACVoNgBAACo\nBMUOAABAJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUO\nAABAJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABA\nJSh2AAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2\nAAAAKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAA\nKkGxAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGx\nAwAAUAmKHQAAgEpQ7AAAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAA\nUAmKHQAAgEr4KR2gzcxms9FoNBqNZWVlZrM5NDTUYDAYDAadTqd0NAAAACV5U7Grrq5etmxZ\nenp6YWFhk6eio6Nnz549f/78rl27KpINAABAcTqz2ax0BqdUVVVNmjQpIyPDx8dn6NCher0+\nJCREp9OVlpYajcZjx441Njampqbu2rUrKChI6bCAdzh06FBpaanD1UJDQ0eOHOmBPACADvKa\nM3ZLlizJyMiYOXPm0qVLIyMjmzxbWFj4wgsvbNiwYcmSJYsXL1YkIeBd6uvrb7rppm7duvn5\n2XsfqK+vr6qqunbtmr+/v8eyAQDax2vO2MXGxoaFhR04cMDHp+UBH42NjaNGjSovLzeZTB7O\nBnij2trawMDAr776asyYMXZW27dv35gxY65duxYYGOixbACA9vGaUbEFBQXjxo1rrdWJiI+P\nz7hx486dO+fJVAAAAJ2H1xS7kJCQ/Px8++ucPn06NDTUM3kAAAA6G68pdmlpadu2bVu/fn1r\nK6xdu3b79u2TJk3yZCoAAIDOw2vusTt16tSIESPKysqGDx8+efLk+Pj4kJAQESkrK8vNzd25\nc+eRI0dCQ0MPHToUGxurdFjAC3CPHQCoj9eMio2Njd27d+9jjz124MCBzMzM5iuMHj16zZo1\ntDoAAKBZXlPsRCQpKSkjI+Pw4cO7d+/Ozc0tKysTkZCQkPj4+IkTJ6akpCgdEAAAQEneVOws\nUlJS6HAAAADNec3gCQAAANhHsQMAAFAJLy52e/bsufPOO3v37t29e/dhw4YtW7asvr5e6VAA\nAACK8Zpi16dPn+eee876zw0bNkyYMGHnzp0lJSWVlZVHjx5dsGDBfffd5y2ztwAAALic1xS7\noqIiyzBYESkpKXniiSfMZvOLL754+vTpy5cv/+Mf/+jbt+9HH3307rvvKpsTAABAKd43KlZE\n3n///crKyueff/6VV16xLJk+fXpkZGRqauq6detmzpzp/KZqa2vffffd2tpaO+vU19efP39+\n8eLFHQoNAADgZl5Z7I4dOyYijz/+uO3Cm266adiwYUeOHGnTpoqKipYuXVpTU2NnnZqamsLC\nwpdeeikgIKAdaQEAADzDK4tddXW1iAwYMKDJ8oEDB548ebJNm+rXr19WVpb9db7++uuxY8e2\nabMAAACe5zX32NmKi4sTkfLy8ibLr1y5YvkAWQAAAA3ypjN2b7/99saNG0WksbFRRE6cOBER\nEWG7Qn5+fr9+/ZQJBwAAoDSvKXbx8fFNlhw4cGDSpEnWfx4+fPjMmTOTJ0/2bC4AAIDOwmuK\nXU5Ojv0VGhoa/vCHP9hWPQAAAE3xmmLn0KhRo0aNGqV0CgAAAMV45eAJAAAANEexAwAAUAlV\nFbsFCxbExMQonQIAAEAZqip2xcXFZ8+eVToFAACAMlRV7AAAALTMa0bFzpgxw+E6GRkZHkgC\nAADQOXlNsdu0aZPSEQAAADo1ryl23bp1i4qKWrZsmZ11li9fvmvXLo9FAgAA6FS8ptglJyef\nPHnyrrvu0ul0ra3z/vvvezISAABAp+I1gydSUlLKy8tPnz6tdBAAAIBOymvO2E2cOHH//v0F\nBQWxsbGtrTN16tTo6GhPpgIAAOg8vKbY3Xvvvffee2/H1wEAAFArr7kUCwAAAPsodgAAACpB\nsQMAAFAJr7nHDkBnUVgop09LRITExYkP/zkEgE6EN2UATjt2TO64Q/r1k1tvlfh4MRhkwwal\nMwEA/otiB8A5GzZIaqp07Sr790tNjZw+LfffL48+KnPmSH290uEAACJcigXgDJ+PPpKf/ER+\n/3uZN+8/iwYMkCVL5K67ZNo0aWiQN95QNCAAQIQzdgAcGiziP2uWvPLKf1ud1dixsm2bvPOO\nLF+uRDQAwHUodgDs8amt3SDS+IMfyM9/3vIaqamyapX84heSleXZaACApih2AOyJfPfdG0Tq\n//pXeys9+qjcfrv8v/8nZrOncgEAWkCxA9C6c+ei3n77FyLmnj0drLlqlRw7Jn//u0diAQBa\nRrED0LpFi67GxKx3Zs1+/WT+fPnlL+XaNXeHAgC0hmIHoBX5+fL22+eeeKLRyfV/9jO5elX+\n9je3hgIA2EGxA9CKJUskJaX0ppucXT84WJ57Tv7wB6mrc2csAECrKHYAWnLxorz9tvzyl237\nqmeekcuXZdMm92QCADhAsQPQktdfl8hIueuutn1VWJg88oisXOmeTAAAByh2AJqpq5P0dJk7\nV3x92/y1c+bIgQNy6JAbYgEAHKDYAWhmxw65ckUefbQ9X5uYKBMmSHq6qzMBAByj2AFo5q23\nZPp0CQ1t55c//rhs2iSVlS7NBABwjGIH4HpFRbJzZztP11lMmyZ+fvLhh67LBABwCsUOwPU2\nbJC+fWXixPZvoUsXuf9+WbfOdZkAAE6h2AG43qZNMmOG+HTszeHhh2X3brlwwUWZAABOodgB\nsHH2rGRkyIwZHd3O2LESHS1btrgiEwDAWRQ7ADY2bhS9XoYP7+h2dDqZPl3ee88VmQAAzqLY\nAbCxZYvcd59rNnX//bJ3r5w/75qtAQCcQLED8L2CAjl0SKZNc83Wbr5Z+vSRrVtdszUAgBMo\ndgC+9+GHEhkpI0e6Zms6nUydKtu2uWZrAAAnUOwAfO+jj2T6dNHpXLbBu++WXbukosJlGwQA\n2EWxAyAiIhUV8uWXcvfdrtzmxIni7y+ffebKbQIAWkexAyAiIv/6l/j7y223uXKbXbrI7bfL\njh2u3CYAoHUUOwAiIrJzp0ycKIGBLt7sHXfIzp1iNrt4swCAllDsAIiIyCefyA9/6PrN3nmn\nXLggx4+7fssAgGYodgBETpyQb791S7Hr108GDZJ//tP1WwYANEOxAyDy2Wei10tMjFs2Pnky\nxQ4APINiB0DkX/+S229318bT0uSrr6Sqyl3bBwB8j2IHaF5trXz5paSluWv7t94qIvL11+7a\nPgDgexQ7QPP27ZPqahk/3l3b79ZNbrpJdu1y1/YBAN+j2AGa9/nnMmKEhIW5cReTJlHsAMAD\nKHaA5v373zJhgnt3MWmSHD4sV664dy8AoHkUO0DTfGprJSPDjddhLW66Sbp0ka++cu9eAEDz\n/JQOAEBJ3U+ckPp6GTu2tRWKiopEZMyYMTqdzv6mUlNTV65c2fJz/v6SmipffCFTpnQgLADA\nAYodoGk9MjNlxAjp3r21FS5evCgijzzySJcuXexs5/PPP//yyy/t7em22/jQWEWo+gMAACAA\nSURBVABwN4odoGk9MjOdmcHu0Ucf7d56+RORmpqakydP2tvEbbfJK69IRYWdEgkA6CDusQO0\nK0Cke3a23HKLJ3Z2003i68ttdgDgVhQ7QLtGiPjU1Ni5wc6VunSRkSNl715P7AsAtIpiB2jX\nWJGrAwZIz56e2t9YztgBgFtR7ADtGitSkZzswf2NlYwMqa313B4BQGMYPAGo0FdffeVgKINI\nfV3dfSKXExMjPJNJRMaMkWvX5MgRGT3aY/sEAE2h2AEq9PTTTxcWFvbo0cPOOjfW1Dwl8nlw\ncILHYvXuLfHx8tVXFDsAcBOKHaBCjY2NL7300ty5c+2sc+3NNy8+8URpr14eSyUiMnas7Nsn\n//u/Ht0pAGgG99gBGuWbkbHP83tNTZX9+z2/WwDQCM7YAd7kgw8+2LBhg8PVvv3224KCAvvr\n+B44sE9kgIuCOSs1Vc6dk8JCiYry8J4BQAsodoA32bp169GjR8ePH29/tcrKytOnT9tbo6rK\nJztbgWI3aJCEhMj+/fKjH3l4zwCgBRQ7wMuMGTPm9ddft7/O+vXrHWzl4EER+UbkIVfFcpKP\nj4waRbEDADfhHjtAk/btaxw8uEqRXaemSkaGInsGANWj2AGadOhQw/Dhyux69Gg5dEjq65XZ\nOwCoGsUO0KSMjMZRo5TZ9ejRUl0tJ04os3cAUDWKHaA9Fy5IYWHDiBHK7D0iQvr1s9zkBwBw\nLYodoD3790u3bo3x8YoFGDWKYgcA7kCxA7Tn0CFJSRE/5QbFU+wAwD0odoD2HDqk8Ke1jhol\nJ05IdbWSGQBAjSh2gPYcPiwpKUoGGDlSGhrkyBElMwCAGlHsAI3Jz5fiYlFqSKxFSIjExcnh\nw0pmAAA1otgBGvPNN9Kjh8TGKhxjxAj55huFMwCA6lDsAI355hsZMUJ8lP7dp9gBgBso/eYO\nwMMsxU5xI0bIyZNy9arSOQBAVSh2gMZkZio8csJi+HBpbJTjx5XOAQCqQrEDtOTbb6W4WJT6\nlFhboaESG8vVWABwLYodoCWHD0twsBgMSucQEZGUFMnMVDoEAKgKxQ7QksxMGTZM+ZETFsOH\nU+wAwLU6x/s7AM/IzOwU12Ethg+XEyekrk7pHACgHhQ7QEs6W7GrqZHsbKVzAIB6UOwAzbh0\nSQoKZNgwpXN8LzxcIiO5GgsALkSxAzTjyBEJCJDBg5XOYWPYMIodALgQxQ7QjGPHJDFRAgKU\nzmFj+HA5ckTpEACgHhQ7QDMsQ2I7lWHD5OhRMZuVzgEAKkGxAzTj6FEZOlTpENcbOlRKS+Xc\nOaVzAIBKUOwAbaipkdzcTnfGLjZWgoPl6FGlcwCASlDsAG2wzBg3ZIjSOa7n4yNJSRQ7AHAV\nih2gDceOSb9+0ru30jmaGTqUYgcArkKxA7Th6FFJTlY6REuSkyl2AOAqFDtAG44f73TXYS2G\nDpVTp6SyUukcAKAGFDtAG44d66Rn7JKSxGyWEyeUzgEAakCxAzTg/HkpLu6kxS4kRG68UY4f\nVzoHAKgBxQ7QgGPHJDBQDAalc7QiOZliBwAuQbEDNMDyYWL+/krnaMWQIXLsmNIhAEANKHaA\nBpw40UlHTlgMGcIZOwBwCYodoAHHj0tSktIhWpecLJcvS2Gh0jkAwOtR7AC1q6+XnJxOfcbO\nYJDAQK7GAkDHUewAtTOZ5Nq1Tl3s/PwkIUFOnlQ6BwB4PYodoHYnTkhIiERFKZ3DrqQkprID\ngI6j2AFqZxk5odMpncMuih0AuALFDlC7Tj4k1iIpSbKypKFB6RwA4N0odoDadfIhsRZJSVJd\nLadPK50DALwbxQ5QNUtbGjxY6RyO9O8v3btzNRYAOohiB6hadrY0NHhBsdPpZPBgih0AdBDF\nDlC1rCyJiJDevZXO4YSkJGY8AYAOotgBqnbihBfcYGcxaBDFDgA6iGIHqNrJk15wHdZi8GDJ\nzZW6OqVzAIAXo9gBquZdxa6uTk6dUjoHAHgxih2gXlevytmzXlPsoqIkNJSrsQDQERQ7QL2y\nsqSxUQYNUjqH07jNDgA6hmIHqNfJkxIVJWFhSudwGsUOADqGYgeoV3a2JCYqHaItBg+m2AFA\nR1DsAPXKyvKaG+wsBg0Sk0nq65XOAQDeimIHqFdWlpedsUtMlNpayctTOgcAeCuKHaBOgQ0N\nkp/vTSMnRCQ6WkJCJDtb6RwA4K0odoA6RVVUSGOjl52x0+kkIUGyspTOAQDeimIHqFN0ebnX\nfEqsrUGDOGMHAO1GsQPUKaq83MtO11kw4wkAdADFDlCn6IoKL7vBziIhQXJypKFB6RwA4JUo\ndoA6RVVUeOUZu8GD5do1OXNG6RwA4JUodoAK+Yv0qaz0ymLXv78EBUlOjtI5AMArUewAFYo1\nm329bkishY+PGAyMnwCA9qHYASoU39hY7e8vffsqHaRdEhMpdgDQPhQ7QIUSzOaC7t1Fp1M6\nSLtYxk8AANqOYgeoUILZXNijh9Ip2isxkTmKAaB9KHaACsWbzYXduyudor0SE6W0VL77Tukc\nAOB9KHaA6pjN+sbGAu8tdgaD+PlxNRYA2oFiB6hOQUGwiBefsQsIkIEDuRoLAO1AsQNUJzu7\nVuRicLDSOTogIUFyc5UOAQDeh2IHqE5OTp5O1+ClQ2ItKHYA0C4UO0B1cnKMPl7+qx0fzz12\nANAOXv7uD6C5nJxcrz5dJyLx8fLtt1JZqXQOAPAyFDtAdXJyjN5e7BITxWwWk0npHADgZSh2\ngLqUlcmFC15/xq5nTwkP52osALQVxQ5Ql5wcETF5+z12IhIfz/gJAGgr73/3B2ArN1ciI8uV\nTuECCQmSna10CADwMhQ7QF1ycyUhQekQrsAZOwBoO4odoC45OeopdkajNDYqnQMAvAnFDlCX\n3FyJj1c6hCskJEh1tRQUKJ0DALwJxQ5QkYYGOXVKJcVuwAAJDGRgLAC0CcUOUJH8fLl2TSWX\nYn19JTaW2+wAoE0odoCK5OZK167Sr5/SOVyE8RMA0EYUO0BFjEbR60UFk9hZJCRwKRYA2kQt\nfwAAiIqGxFoYDJyxA4A2odgBKmI0isGgdAjXSUiQwkKprFQ6BwB4DYodoCKqmevEIj5ezGYx\nmZTOAQBeg2IHqEVFhXz3narO2IWFSe/eYjQqnQMAvAbFDlCL3Fwxm1VV7ISBsQDQNhQ7QC2M\nRomIkNBQpXO4FMUOANqCYgeohcpusLMwGLgUCwDOo9gBaqGyIbEWljN2ZrPSOQDAO1DsALXI\nzVVhsTMY/jMoBADgBIodoAqWaUHUV+zi4sTPj9vsAMBJFDtAFc6fl8pKFd5jFxAg/ftzmx0A\nOIliB6iC0Si+vjJwoNI53MBgYI5iAHASxQ5QBaNRBgyQgAClc7gBnxgLAE6j2AGqYDSq8Dqs\nBTOeAIDT/JQO0GZms9loNBqNxrKyMrPZHBoaajAYDAaDTqdTOhqgHFXOdWJhMMjp01JXJ/7+\nSkcBgM7Om4pddXX1smXL0tPTCwsLmzwVHR09e/bs+fPnd+3aVZFsgMJyc+XOO5UO4R4Gg9TV\nyZkzotcrHQUAOjuvKXZVVVWTJk3KyMjw8fEZPny4Xq8PCQnR6XSlpaVGo/HYsWMLFy7csWPH\nrl27goKClA4LeJal96j1jF2/fhIUJEYjxQ4AHPKaYrdkyZKMjIyZM2cuXbo0MjKyybOFhYUv\nvPDChg0blixZsnjxYkUSAorJz5e6OtUWO51O4uLEaJS77lI6CgB0dl4zeGLjxo0jRoxYv359\n81YnIlFRUe+8805KSsqmTZs8nw1QmMkkQUESHa10Drdh/AQAOMdril1BQcG4ceN8fFoN7OPj\nM27cuHPnznkyFdAp5OaKXi8qHj/EVHYA4ByvKXYhISH5+fn21zl9+nRoaKhn8gCdiCo/TMyW\nXs8ZOwBwhtcUu7S0tG3btq1fv761FdauXbt9+/ZJkyZ5MhXQKZhMKh9YoNdLQYFcvap0DgDo\n7Lxm8MQrr7zy8ccfP/LII8uXL588eXJ8fHxISIiIlJWV5ebm7ty588iRI6GhoS+//LLSSQGP\nMxrlxz9WOoQ7GQxiNsupUzJkiNJRAKBT85piFxsbu3fv3scee+zAgQOZmZnNVxg9evSaNWti\nY2M9nw1QUnW1FBaq/FLsDTdIWJgYjRQ7ALDPa4qdiCQlJWVkZBw+fHj37t25ubllZWUiEhIS\nEh8fP3HixJSUFKUDAkowmaSxUeXFTrjNDgCc4k3FziIlJYUOB/yX0ShhYdK7t9I53Eyvl7w8\npUMAQGfnNYMnALRM9SMnLAwGyc1VOgQAdHbed8bObDYbjUaj0VhWVmY2m0NDQw0Gg8Fg0Kl4\nEi/ADtXPdWKh18uqVUqHAIDOzpuKXXV19bJly9LT0wsLC5s8FR0dPXv27Pnz53ft2lWRbIBi\nTCa5/XalQ7ifXi8XL0pZmYSEKB0FADovryl2VVVVkyZNysjI8PHxGT58uF6vDwkJ0el0paWl\nRqPx2LFjCxcu3LFjx65du4KCgpQOC3iQ0ShPPaV0CPeznJU0mWTkSKWjAEDn5TXFbsmSJRkZ\nGTNnzly6dGnzj4stLCx84YUXNmzYsGTJksWLFyuSEFBAeblcvKiJe+x69JDwcIodANjnNcVu\n48aNI0aMWL9+fYsfFxsVFfXOO+/k5uZu2rSpTcXObDbv3bu3pqbGzjonT55sc1zAMywzgGih\n2AmfGAsAjnlNsSsoKJg6dWqLrc7Cx8dn3Lhx6enpbdpsfn5+WlpabW2twzXNZnObtgx4gskk\n4eFaue1Mr6fYAYB9XjPdSUhISH5+vv11Tp8+HRoa2qbNDhw4sKamxmzXV199JSKMukVnpJG5\nTiwMBuYoBgD7vKbYpaWlbdu2bf369a2tsHbt2u3bt0+aNMmTqQCFaWSuE4u4OM7YAYB9XnMp\n9pVXXvn4448feeSR5cuXT548OT4+PiQkRETKyspyc3N37tx55MiR0NDQl19+WemkgAfl5cnd\ndysdwlMMBrlyRYqL1f8xGwDQXl5T7GJjY/fu3fvYY48dOHAgMzOz+QqjR49es2ZNbGys57MB\nijEatXXGTqcTk4liBwCt8ZpiJyJJSUkZGRmHDx/evXt3bm5uWVmZiISEhMTHx0+cOJEPkIXm\nXL4sly9r6B67oCCJjBSTSW6+WekoANBJeVOxs0hJSaHDASIiRqPodKKps9QMjAUAu7xm8ASA\npkwm6dtXgoOVzuFBFDsAsItiB3gtTc11YkGxAwC7VFXsFixYEBMTo3QKwFModgCA66mq2BUX\nF589e1bpFICn5OVpsdhVVMh33ymdAwA6KVUVO0BbNHjGLi5OfHwkL0/pHADQSXnNqNgZM2Y4\nXCcjI8MDSYBO4dIlKSuTuDilc3hWYKD06ycmk9xyi9JRAKAz8ppit2nTJqUjAJ2JySQ6neaK\nnYjExXHGDgBa4zXFrlu3blFRUcuWLbOzzvLly3ft2uWxSICSTCaJjpauXZXO4XGMnwCA1nlN\nsUtOTj558uRdd92l0+laW+f999/3ZCRASSaTFk/XiUhcnOzfr3QIAOikvGbwREpKSnl5+enT\np5UOAnQOGhw5YaHXS16emM1K5wCAzshrzthNnDhx//79BQUFsa1/gNLUqVOjo6M9mQpQjMkk\nDz2kdAglxMVJZaUUFUmfPkpHAYBOx2uK3b333nvvvfd2fB1AJTQ4iZ1FbKz4+orJRLEDgOa8\n5lIsgP8qKpKKCo3eY2ed8QQA0AzFDvBCJpP4+MjAgUrnUAgzngBAKyh2gBfKy9PoXCcWzHgC\nAK2g2AFeSLNDYi04YwcAraDYAV4oL0+jN9hZWIodM54AQDMUO8ALaXZ2Ygu9Xior5bvvlM4B\nAJ0OxQ7wQqdOafpS7MCB4uvL1VgAaI5iB3iZHtXVUl6u6TN2gYESHc34CQBojmIHeJmI8nJN\nz3ViYflgMQDA9Sh2gJeJqKiQqCjtznViwYwnANASih3gZcLLyzV9g50FM54AQEsodoCXidDs\nh4nZslyKZcYTALgexQ7wMhGcsRORuDhmPAGA5ih2gJcJ54ydMOMJALSMYgd4kx7V1V1razlj\nx4wnANAix8XuypUrHsgBwBkR5eVmEa3PdWLBjCcA0IzjYhcVFTVr1qx9+/Z5IA0A+yIqKq50\n66b1uU4sGBgLAM04LnbR0dHr1q0bM2bM0KFD//KXv5SXl3sgFoAWhZeXF/XooXSKziEujkux\nANCE42KXm5u7a9eu+++/Pycn5+mnn46MjPzpT3968OBBD4QD0ERERUVR9+5Kp+gcmPEEAJpx\nXOx0Ot3EiRM3bdp07ty5V199tU+fPmvWrBk9evSIESPeeOONyspKD6QEYBFRXn6RM3YWlhlP\nioqUzgEAnUgbRsWGh4f/7Gc/M5lMn3766Y9+9KPjx4/Pnj07MjJyzpw5J06ccF9EAFbhFDur\n2Fjx8eFqLADYavN0JzqdzmAwJCYmhoWFiUhFRUV6enpycvKDDz5YVlbmhoQAvnfxYte6uu+4\nFGsRGCj9+jF+AgBstaHYNTQ0bN269a677ho4cODixYsDAwNffvnlgoKCjz/++Lbbbtu4cePT\nTz/tvqAAxGQyi1zijJ0VA2MB4Hp+zqx07ty5NWvWrF69urCwUKfTpaWlPfXUU3fffbevr6+I\nREVFTZ48+Z577vn444/dnBbQtry8K9261fr6Kp2j02AqOwC4nuNid/fdd+/cubOhoaFnz57z\n5s2bM2dOXLOPM9LpdKmpqdu2bXNPSAAiIpKXx5DY68TGSkaG0iEAoBNxXOy2b98+atSop556\nasaMGV26dGlttcmTJ/fgChHgViaTt09it2vXrs2bNztczdfX97nnnouPj3ewnl7P4AkAsOW4\n2B06dGjEiBEOV0tJSUlJSXFFJACtyMvz9iGx77zzzr///e9Ro0bZX+3TTz9NTEx0XOysM55E\nRLgsIgB4M8fFzplWB8AT8vIuev/v4/jx49966y376yQnJzu1LeuMJxQ7ABARZ0bFbt68ecKE\nCQUFBU2WFxQUjB8/fsuWLe4JBuB6ly5JWRn32F2nSxeJimL8BABYOS52b775ZkVFRXR0dJPl\n0dHRpaWlb775pnuCAbheXp7odBcpdk0wMBYAbDgudsePHx85cmSLT40cOfL48eOujgSgJSaT\nREXV+jk1RZGGMJUdANhwXOwuX77cq1evFp8KDw8vLi52dSQALcnLk2YzDYFiBwC2HBe7Xr16\nmVqZUCAvLy80NNTVkQC0hGLXorg4ZjwBACvHxe6WW27ZunVrTk5Ok+XZ2dlbt24dO3ase4IB\nuB7FrkV6vZSXy8WLSucAgE7BcbGbN29eXV3d2LFjV6xYkZeXV11dnZeXt2LFiltuuaWurm7B\nggUeSAmAYtcyy4wnXI0FABFxZh67m2++edWqVc8888yzzz5ru9zX13fVqlVjxoxxWzYA3ysu\nlitXRK9XOkfn07WrREaKySS8FwGAM8VORJ588skxY8b85S9/ycjIKC0tDQ0NTU1Nfeqpp4YM\nGeLufABE/jPXicTGKp2jU2LGEwD4nrNTJyQnJ6enp7s1CoBWmUzSt69066Z0jk6JgbEA8D3H\n99gBUN6pU9xg1yq9noGxAGBBsQO8gcnEDXatio3ljB0AWDhV7L744oupU6f26dMnMDDQrxl3\nRwQgJhNn7Fql10tZmVy6pHQOAFCe41q2ffv2e+65p7GxMSQkRK/X0+QABTDXiR1xcaLTSV6e\n3HCD0lEAQGGOW9qiRYt0Ot3f//73Bx98UKfTeSATgOuUlDDXiT3WGU9uvlnpKACgMMfF7sSJ\nE9OnT3/ooYc8kAZACywjAwYOVDpHJ8aMJwAgIs4Uu27duoWHh3sgCqBlH3zwwWOPPdbiU/fX\n1v7Gxyexf38RqaqqGjRokGejeQM+MRYARMSZYpeWlpaRkeGBKICWFRQUhIaGvvHGG82fGrh+\nfeCRI5v/+EcReeSRRyorKz2ertPT6+W995QOAQDKc1zsli5detNNN/3mN7958cUXfX19PZAJ\n0Kbg4OC0tLQWnnjrLRk92vJUUFCQp2N5Bc7YAYCIOFPsfv3rXw8ePHjRokVvvfXWsGHDQkND\nm6ywdu1at0QDYJGXJ9OmKR2ic7POeMLAWADa5rjYrVu3zvLg7NmzZ8+ebb4CxQ5wr7w8hsQ6\nEBvLjCcAIM4Uu8zMTA/kANCykhK5fJli50BQEDOeAIA4U+yGDRvmgRwAWmaZxYO5ThyKi2PG\nEwBow2fFnj17dt++fWVlZe5LA6Apk0n69pXu3ZXO0ekxlR0AOFns9u/fP3To0JiYmDFjxhw8\neNCycOPGjUlJSV988YU74wGaxw12TmJgLAA4U+yys7PT0tJOnz59zz332C6fMmXKmTNn3mPu\nKMCtTCaKnVP0eoodADgudosXL66rq/v6669Xr15tuzw4OHjChAl79+51WzYAInl5EhurdAhv\nEBf3nxlPAEDDHBe7Xbt2TZ8+fciQIc2fSkhIKCgocEMqAN/jUqyT4uL+M+MJAGiY42JXUlIS\nExPT4lO+vr4VFRUuTgTAyjLXSVyc0jm8gXXGEwDQMMfFLiws7FIrVzcyMzP79u3r6kgAvmcy\niU7HpVhnMeMJAM1zPI/d2LFjd+zYUVNT02T57t27P/vss5/85CfuCQZAJC/Pi+Y6aWhouHLl\niv11amtr/f393ZWAGU8AaJ7jYrdgwYJbb711+vTpP//5z0Wkurr64MGDGzduXLFihZ+f37x5\n89wfEtCqvDxvuQ578ODBrKysnj17Olxz0KBB7gqh1wvj9AFom1Nn7FatWjV37tydO3eKyNSp\nUy3L/f39V69enZyc7N6AgJZ5z1wn1dXVAQEBX3/9tf3Vpk+fXltb664QTGUHQPMcFzsRefLJ\nJ8eNG5eenr5v376SkpKQkJDU1NS5c+cOHjzY3fkATTOZ5N57lQ7hLB8fnxEjRthfJzAw0I0J\n9Pr/zHhyww1u3AsAdGJOFTsRGTx48IoVK9waBUBTzHXSJnFx4uMjJhPFDoBmteGzYgF4VEmJ\nXLniLffYdQpdu0pUFFdjAWgZxQ7orCxznVDs2oQZTwBom+NLsXGO/q7k8TYKuIPJJH37Srdu\nSufwKnxiLABtc1zsiouLmyypqqqqr68XkR49euh0OrfkAmAyicGgdAhvExcnGzcqHQIAFOO4\n2JWWljZZUldXl5mZ+fzzz/fu3XvLli3uCQZonsnEddg244wdAG1rzz12/v7+o0eP3rFjx6FD\nh5YsWeLyTABEvGkSu05Er5eKCikqUjoHACij/YMnwsLC0tLS1q1b58I0AP6LuU7awTrjCQBo\nUodGxQYGBhYWFroqCoD/unhRysoodm0WGCjR0RQ7AJrV/mL33Xffbdu2LSoqyoVpAPyHySQ+\nPhIbq3QOL2QwUOwAaJbjwROLFi1qsqS+vv7cuXMffvhheXn5yy+/7JZcgMbl5UlUlHTtqnQO\nL8T4CQAa5rjY/eY3v2lxedeuXRcsWPCrX/3K1ZEAiBiNzHXSTnFx8vXXSocAAGU4Lnbbtm1r\nssTHxycsLGzIkCHBwcHuSQVoHnOdtJvBIHl5YjYLs2wC0B7HxW7KlCkeyAHgOiaTPPyw0iG8\nU1ycVFXJhQsSGal0FADwND4rFuh8zGY5dYohse00cKD4+XGbHQBtotgBnc+FC1JRQbFrp4AA\nufFGMRqVzgEACnB8KTYmJsb5zZ05c6bdUQD8h8kkvr4yYIDSObwWA2MBaJXjYldZWdnQ0GD9\nxNhu3bpVVVVZHoeGhvr6+roxHaBNJpP07y+BgUrn8FoUOwBa5fhS7JkzZ5KSklJSUnbs2FFR\nUVFZWVlRUbFjx47hw4cnJSWdOXOm2IYHEgPqx5DYDmKOYgBa5bjYLVy48Pz583v27Lnzzjst\n85sEBwffeeede/fuPX/+/MKFC90fEtAYJrHroLg4ycuTxkalcwCApzkudu+99969994bFBTU\nZHlQUNC99977/vvvuycYoGF5eYyc6BCDQWpq5Nw5pXMAgKc5LnaXLl0ym80tPmU2my9duuTq\nSIC2NTZS7Dqqf38JCOBqLAANclzsYmJitmzZYh0wYVVVVfX+++8PYOAe4Frnzsm1a1yK7RA/\nPxkwgBlPAGiQ42L35JNPnjlzZuzYsR9++OHly5dF5PLlyx9++OHYsWPPnj07e/Zs94cEtMRo\nlIAA6d9f6RxKys/Pf/bZZ3WOBAQE5OTktLwJBsYC0CTH050899xz2dnZb7755vTp00XEz8+v\nvr7e8tQTTzzx7LPPujcgoDUmkwwYIH6OfzdVrL6+fty4cS+99JL9dX74wx9a/rfZAoNBcnPd\nEg4AOjHHfzx8fHzeeOONBx98cN26dZmZmWVlZSEhIcOHD581a9b48ePdnxDQGJOJ67AiEhER\nkZaWZmeF2tpae1+v18u2bS7OBACdnrNnBSZMmDBhwgS3RgEgImI0SkKC0iG8n8Eg+flSVyf+\n/kpHAQDPacNnxZ49e3bfvn1lZWXuSwOAIbGuYTBIfb3k5yudAwA8yqlit3///qFDh8bExIwZ\nM+bgwYOWhRs3bkxKSvriiy/cGQ/QGEsX4VJsx0VFSbdujJ8AoDWOi112dnZaWtrp06fvuece\n2+VTpkw5c+bMe++957ZsgPacPi11dZyxcwGdTuLimPEEgNY4vsdu8eLFdXV1hw4d6tu370cf\nfWRdHhwcPGHChL1797ozHqAxRqMEBUl0tNI5VIEZTwBoj+Mzdrt27Zo+ffqQIUOaP5WQkFBQ\nUOCGVIBWGY2i14tOp3QOVTAYOGMHQGscF7uSkpKYmJgWn/L19a2oqHBxIkDLmOvEhfR6ih0A\nrXFc7MLCwlr7QNjMzMy+ffu6OhKgYUYjxc5lDAYpKJBmH4cIACrmuNiNHTt2x44dNTU1TZbv\n3r37s88+Y45iwJUsl2LhEgaDmM2Sl6d0DgDwHMfFbsGCBZcuXZo+fXpWtB4qdwAAIABJREFU\nVpaIVFdXHzx4cP78+ZMnT/bz85s3b577QwKa0LWxUQoLOWPnMr17S8+eXI0FoCmOR8WOHTt2\n1apVc+fO3blzp4hMnTrVstzf33/16tXJycnuDQhoxo21tWI2c8bOlRg/AUBjnPpIsSeffHLc\nuHHp6en79u0rKSkJCQlJTU2dO3fu4MGD3Z0P0I6Ymhrp2VN691Y6iIoYDMx4AkBTHBe7/fv3\nd+nSZdiwYStWrPBAIECzbqyp4TqsixkMsmOH0iEAwHMc32M3ZsyYxYsXeyAKoHExNTUSH690\nCnXR6yU3V+kQAOA5jotdr169goKCPBAF0LiY2lrO2LmYwSCXL0tJidI5AMBDHBe78ePHHzhw\noKGhwQNpAC3jUqzrWT7Gg5N2ADTDcbFbsmRJcXHx888/f/XqVQ8EArSpa2VlSEMDxc7FunWT\n6GgGxgLQDseDJ377298mJyevXLly48aNw4YNi4yM1F3/QZZr1651VzpAM8IuXmwU8YmLUzqI\n6jAwFoCWOC5269atszwoLi7+17/+1XwFih3QcSEXLxb5+/flflaXi4/nUiwA7XBc7DIzMz2Q\nA9C4sIsXzwQG8tHLrqfXy549SocAAA9xXOyGDRvmgRyAxoVevHgwMPBmpWOoUHy8mEzS2Cg+\njm8pBgBv1+o73caNGzMyMjwZBdAyyxk7pVOokcEg167JuXNK5wAAT2i12D344IN//etfrf9c\ntmzZ5MmTPRIJ0J6GhpCSEoqdW8TESJcu3GYHQCOcvTZx/PjxTz75xK1RAO06c8a3ru5MQIDS\nOdTI11cGDqTYAdAIbjoBOoHc3AZ//+/8/ZXOoVIMjAWgGRQ7oBMwGktvuKHx+hki4TIUOwCa\nQbEDOgGj8Up4uNIh1MtgoNgB0AiKHdAJ5OaWUuzcJyFBCgqkqkrpHADgdvbmsXv33Xc//PBD\ny2PLB8WGhoY2X620tNQdyQANycm5MnGiXLigdA6Vio8Xs1lMJmFWTgBqZ6/Y1dXVlZWV2S5p\n8k8ALlBRIRculEZEKJ1DvXr2lN69JTeXYgdA9VotdtXV1Z7MAWiX0ShmM/fYuRfjJwBoQ6vF\nrkuXLp7MAWhXTo5ERNR07ap0DlWj2AHQBgZPAErLzZWEBKVDqF18vOTkKB0CANyOYgcoLTdX\n4uOVDqF2CQmSmytms9I5AMC9KHaA0ih2HhAfL1VVUliodA4AcC+KHaCoxkYxmSh2bjdwoPj7\nczUWgOpR7ABFffutXL1KsXM7f38ZOJDxEwBUj2IHKCo3VwIDZcAApXNogOU2OwBQNYodoKjs\nbNHrxddX6RwakJDApVgAqkexAxTFXCcew1R2ADSAYgcoKieHYuchCQly7pxUViqdAwDciGIH\nKConh5ETHpKYKGazGI1K5wAAN6LYAcopK5PvvuOMnYeEhkp4OLfZAVA3ih2gnJwc0enEYFA6\nh2YwfgKA2lHsAOXk5EhkpPTooXQOzWDGEwBqR7EDlJOTI4mJSofQkoQEyc5WOgQAuBHFDlBO\ndjY32HlUQoKYTNLQoHQOAHAXih2gHOY68bCEBLl2Tc6cUToHALgLxQ5QSF2dnD7NpViP6t9f\ngoIYPwFAxSh2gEJMJqmr44ydR/n4iMHAbXYAVIxiBygkJ0d69JC+fZXOoTGJiZyxA6BiFDtA\nIZYhsTqd0jk0JjGRM3YAVIxiByiEIbGKYMYTAKpGsQMUkp3NyAkFJCbKlStSVKR0DgBwC4od\noASzWXJzZdAgpXNoj14vfn6ctAOgVhQ7QAnffiuVlZyxU0BgoAwYQLEDoFYUO0AJ2dkSGCgx\nMUrn0CTGTwBQL4odoITsbDEYxM9P6RyaNGiQZGUpHQIA3IJiByiBkRMK4owdAPWi2AFKsExi\nB0UkJsr581JaqnQOAHA9ih2ghKwsip1iLPNCc9IOgBpR7ACPKyqSkhLmOlFMcLD060exA6BK\nFDvA47KyxM9PDAalc2gYt9kBUCmKHeBxWVkSGyuBgUrn0DAGxgJQKYod4HHZ2VyHVVhiIsUO\ngCpR7ACPy8qi2Cls0CA5e1YqK5XOAQAuRrEDPI4hsYobPFjMZsnJUToHALgYxQ7wrJISKSqi\n2CksNFQiI7kaC0B9vO8Tjcxms9FoNBqNZWVlZrM5NDTUYDAYDAadTqd0NMAJJ0+Kj48kJCid\nQ/MGDWJgLAD18aZiV11dvWzZsvT09MLCwiZPRUdHz549e/78+V27dlUkG+CsrCwZMECCgpTO\noXmDBsnJk0qHAAAX85piV1VVNWnSpIyMDB8fn+HDh+v1+pCQEJ1OV1paajQajx07tnDhwh07\nduzatSuIP5nozE6elMGDlQ4BkUGDZPt2pUMAgIt5TbFbsmRJRkbGzJkzly5dGhkZ2eTZwsLC\nF154YcOGDUuWLFm8eLEiCQGnnDwpqalKh4DI4MFy5oxUVUm3bkpHAQCX8ZrBExs3bhwxYsT6\n9eubtzoRiYqKeuedd1JSUjZt2uT5bEAbcMaukxg8WBobGRgLQGW8ptgVFBSMGzfOx6fVwD4+\nPuPGjTt37pwnUwFtU1IiFy9S7DqFsDDp25fb7ACojNcUu5CQkPz8fPvrnD59OjQ01DN5gPaw\nDImNj1c6B0REZPBgih0AlfGaYpeWlrZt27b169e3tsLatWu3b98+adIkT6YC2ubkSRk4UBi7\n3UlQ7ACojtcMnnjllVc+/vjjRx55ZPny5ZMnT46Pjw8JCRGRsrKy3NzcnTt3HjlyJDQ09OWX\nX1Y6KdC6EyckKUnpEPje4MHy0UdKhwAAV/KaYhcbG7t3797HHnvswIEDmZmZzVcYPXr0mjVr\nYmNjPZ8NcNbJk3LLLUqHwPcGD/7PJ8YGBysdBQBcw2uKnYgkJSVlZGQcPnx49+7dubm5ZWVl\nIhISEhIfHz9x4sSUlJT2bfbChQvV1dV2Vjh//nz7tgw0dfKkPPmk0iHwPcsolpMn5aablI4C\nAK7hTcXOIiUlpd0drrlTp07FxcU5s6bZbHbVTqFR330nxcUMie1EQkIkOppiB0BNvK/YuVZs\nbOy3335bV/f/27vzgCjL/e/j32EHWRUXRENwFxUUBfe1cuVk5pLar9JyyfJRs9XqpGWe1JNL\nejq545JZecw0l9xNS01FcA0VNRcwTQRZlHWeP6bDIYQBYYZr5p736y+5ueaaz31l8uHeJtvI\nmOjo6IEDB/JZtCiv06fFwUEaNFCdAwU0bcr9EwC0xNaLnYjUrl3b+IAbN25UTBJo3KlT0qCB\nODurzoECmjaV2FjVIQDAZKzmcScP2r9/f+/evX19fT08PEJDQz/55JOcnBzVoYDi8ZkTFqhp\nUzl5UnUIADAZqyl2NWrUGD9+fP6XX375ZdeuXbdu3Xr79u20tLTY2NjXXnttwIABXAkHy8Wz\nTixQcLAkJsrt26pzAIBpWE2x+/333w23wYrI7du3R40apdfr33333YsXLyYlJa1fv97Pz++7\n775bs2aN2pxA0fR6OX2aYmdxmjQRe3s5dUp1DgAwDaspdgWtW7cuLS3t//2///fhhx8GBgb6\n+Pg8+eST3377rYisWLFCdTqgKL/9JnfvSrNmqnPgr1xdJSiIYgdAM6yy2J04cUJERo4cWXBj\nREREaGhoTEyMolCAUSdP/tkhYGmaNaPYAdAMqyx2hucJBwYGFtoeFBSUnJysIhFQklOnJDhY\n7O1V58ADmjXj/gkAmmGVxc7wSOG7d+8W2n7nzh3DB8gCFufkSS6ws1BNm8qpU8J9VwA0wZqK\n3apVq1xcXFxcXKZMmSIipx44e3Lp0qUSH0oHqHHqFBfYWahmzSQlRa5eVZ0DAEzAah5Q3LBh\nw0Jbfvnll+7du+d/GR0dffny5Z49e1ZsLqAUsrIkLo4jdhaqXj1xdZUTJ+SRR1RHAYDysppi\n9+uvvxofkJubO2vWrIJVD7AUcXGSlcUROwtlby9NmsipU9K3r+ooAFBeVlPsStS6devWrVur\nTgEU5cQJqVpV/PxU50Axmjfn/gkA2mBN19gB1urECWneXHUIFI8bYwFoBcUOML+TJyl2Fq15\nc/n1V8nKUp0DAMqLYgeY34kTXGBn0UJCJDtbzp5VnQMAyotiB5hZUpJcv84RO4vm6ys1anA2\nFoAGUOwAMzt58s/7LmHJmjeXEydUhwCA8qLYAWYWGysNGoirq+ocMIobYwFoAsUOMDPunLAK\nzZpJbKzqEABQXhQ7wMxiYiQkRHUIlCQkRBIT5eZN1TkAoFwodoA55ebKmTMUOyvQpIk4OXGZ\nHQBrR7EDzOncOcnIoNhZAUdHadyYs7EArB3FDjCnEyekcmXx91edA6UQEkKxA2DttPNZsYAl\nOnFCmjdPT0/PKulTDTIyMvR6fcWEQtFCQmTFCtUhAKBcKHaAOcXE3H7kkWqennl5eSWOrVSp\nUgUkQrFCQuTsWcnKEicn1VEAoIwodoA5xcbeGT48Ly/v4MGDjo6ORga++eabP/30U4XlQhGa\nN5fsbDlzRkJDVUcBgDKi2AFmc+uWXL+eXr++iLRs2dLJ6HEgHx+fioqFYlStKrVqyfHjFDsA\n1oubJwCzOX5cnJwyAgJU50CphYZy/wQAq0axA8wmJkaCg/VGz8DCsoSGyvHjqkMAQNlR7ACz\niY3lpJ6VCQ2VmBjh9mQAVotiB5gNHyZmdUJD5e5duXRJdQ4AKCNungDMIyND4uKkRQvVOTRu\n5syZ1atXNz6matWq06ZNK9V0QUHi6SkxMRIUZIJwAFDhOGIHmEdsrOTlccTOfAzPfL527dod\noy5cuPDRRx9lZmaWalKdjsvsAFg1jtgB5nH8uNSrJ15eqnNo3MiRI0ePHm1kwMGDB9u1a/cQ\nM7ZoQbEDYL04YgeYx/HjnIe1Si1aSHS06hAAUEYUO8A8KHZWqkULSUyUGzdU5wCAsqDYAWaQ\nnS2nT/OsE6vUuLG4uHA2FoCVotgBZnDmjNy/zxE7q+ToKM2aUewAWCmKHWAG0dFSq5aU9BgO\nWCguswNgtSh2gBkcPSphYapDoKxatpRjx1SHAICyoNgBZhAdLS1bqg6BsgoLk8uX5fZt1TkA\n4KFR7ABTy82VEycodlaseXNxcuJsLABrRLEDTO3sWcnI4FSsFXNykuBgzsYCsEYUO8DUoqOl\nRg3x81OdA+UQFsYROwDWiGIHmBp3TmhAWBhH7ABYI4odYGpHj0qrVqpDoHzCwuTSJe6fAGB1\nKHaASeXkSEyMtG6tOgfKJyREnJw4aAfA6lDsAJM6dUru3eNUrNVzcpJmzeTIEdU5AODhUOwA\nkzp6VGrXlho1VOdAubVqxRE7AFaHYgeYVHQ0h+s0olUrjtgBsDoUO8CkDh/mAjuNaNVKrl2T\nxETVOQDgIVDsANO5f19OnaLYaURwsLi5ydGjqnMAwEOg2AGmExMj2dk860QjHBykRQvOxgKw\nLhQ7wHR++UXq1xcfH9U5YCLh4fLLL6pDAMBDoNgBpnPkiISHqw4B02ndWo4cEb1edQ4AKC2K\nHWA6fOaExoSHS1KSxMerzgEApUWxA0wkOVnOnZM2bVTngOkEBUmVKpyNBWBFKHaAiRw5Ig4O\nEhqqOgdMR6eT8HDunwBgRSh2gIkcOiQtWoizs+ocMKmICDl0SHUIACgtih1gIocPS0SE6hAw\ntTZt5PhxycxUnQMASoViB5jIkSMUOw2KiJDsbImJUZ0DAEqFYgeYwsWLcvMmnzmhQd7e0qCB\nHD6sOgcAlArFDjCFgwfF11fq1VOdA2YQEUGxA2AtKHaAKRw6JG3aiE6nOgfMoE0bOXhQdQgA\nKBWKHWAKP/8s7dqpDgHzaNtWLl2ShATVOQCgZBQ7oNwyMuTECWnbVnUOmEfTpuLpyUNPAFgF\nih1QboaPE+XDxLTK3l5at+ZsLACrQLEDyu3gQWnWTNzdVeeA2bRrR7EDYBUodkC5/fQTF9hp\nXNu2cuwYjykGYPkodkD56PVy8KC0b686B8ypTRvJypLoaNU5AKAEFDugfH79VW7flg4dVOeA\nOfn4SOPG8tNPqnMAQAkodkD5/PST+PvLI4+ozgEz69CBYgfA8jmoDgBYuZ9+4nCdJUtJSRGR\nYcOG2dmV8Htsu3btJkyYUOy327eXV18VvZ7HUAOwZByxA8qHOycs25UrV0TE09PTx6iEhIRl\ny5YZm6hdO/njDzl/voJyA0CZcMQOKIcbN+T8eY7YWb558+Z5eHgYGTB//vzFixcbm6JuXfHz\nkwMHpEEDE4cDANPhiB1QDgcOiKenhISozoEK0aGDHDigOgQAGEOxA8ph/35p107s7VXnQIXo\n2FH271cdAgCModgB5bB/P+dhbUjHjnLhgiQkqM4BAMWi2AFldfeunDghHTuqzoGK0ry5eHtz\n0A6AJaPYAWX100/i6Cjh4apzoKLY2Un79hQ7AJaMYgeU1b59EhEhLi6qc6ACdeokP/6oOgQA\nFItiB5TVvn3SubPqEKhYXbrIqVNy86bqHABQNIodUCYZGRIdTbGzOS1biocHDz0BYLEodkCZ\nGD42tE0b1TlQsRwcpH172btXdQ4AKBrFDiiTPXskPFzc3FTnQIXr3Fn27VMdAgCKRrEDymTP\nHunaVXUIqNCli5w8Kbduqc4BAEWg2AEPLzVVjh2Tbt1U54AKYWHi4cFBOwCWiWIHPLwffxR7\ne4mIUJ0DKjg4SMeOsmeP6hwAUASKHfDw9uyRdu3E1VV1DijSrZvs2qU6BAAUgWIHPLxduzgP\na9O6dZO4OD40FoAFotgBD+nWLTlxQrp3V50D6oSEiK+v7NypOgcAFEaxAx7S7t1SqZK0aqU6\nB9TR6TgbC8AyUeyAh2Q4D+vgoDoHlOreXXbuFL1edQ4A+AuKHfCQdu3iPCzkscckIUHOnFGd\nAwD+gmIHPIwLF+TiRXn8cdU5oFpgoNSty2V2ACwNxQ54GNu3S0CANGyoOgcswOOPy/btqkMA\nwF9Q7ICHsX279OihOgQsQ48esnevZGaqzgEA/0OxA0otJ0f27pXHHlOdA5ahWzfJzpYDB1Tn\nAID/odgBpfbzz5KWxp0T+JOHh7RtKz/8oDoHAPwPxQ4ota1bJSJCfHxU54DF6NFDtm1THQIA\n/odiB5Tali3Su7fqELAkvXrJyZNy5YrqHADwJ4odUDqJiXLypPTqpToHLEloqNSsydlYAJaD\nYgeUztatUq2atGihOgcsiU4nPXvKli2qcwDAnyh2QOls3iy9e4tOpzoHLEyvXrJrl2Rlqc4B\nACIUO6BUMjNlxw7p21d1Dliexx+XzEz58UfVOQBAhGIHlMq+fZKZyRPsUARPT+nUSTZtUp0D\nAEQodkCpbN4snTqJh4fqHLBIffvK99+rDgEAIhQ7oFQ2buQ8LIoVGSkXL8rp06pzAADFDihR\nbKxcvixPPKE6ByxVUJAEB8t336nOAQDioDoAYPE2bJCQEKlTR3UOmFFqampSUtKiRYtKHNm+\nffvg4ODCW594Qr77TiZPNks4ACg1ih1Qku++k379VIeAecXExCQkJMyYMcP4sFu3bg0YMGDZ\nsmWFv/HEE/KPf8i1a1KrlrkiAkApUOwAo65ckZgYWbJEdQ6Yl16vd3Z2jo+PNz5s+PDher2+\niG+0bi01a8rGjTJ2rFnyAUDpcI0dYNT69VKnjrRsqToHLJtOJ/36yYYNqnMAsHUcsQOM+s9/\n5KmnVIeApbh8+fLly5cHDRr04LeCb95898cfRz3xRKqzs4i8+eabYWFhFR4QgK2j2AHFu3FD\nfv5ZPv5YdQ5YimvXrqWkpAQFBT34rczAwPtHjvTKyTnauPGyZcs6duxIsQNQ8Sh2QPHWr5ca\nNaRtW9U5YEGqVKnycXFdPylpQGLigI8/3rJlS8WGAoA/cY0dULxvvpGnnhI7/jdB6Tz1lOzY\nIXfuqM4BwHZxxA4oxo0bsn+/TJtW3Pfz8vJq166dkJBQmslyc3NNlwyW6tFHxcuLWygAKESx\nA4qxbp3x87A5OTkJCQnz5s1r0qSJkWk2b948d+7cnJwcM0SEhXFwkH795OuvVecAYLsodkAx\n1q6VQYNKPA/bqlWrdu3aGRlw8eJFk8aCZRs8WHr18q5XT3UOADaKi4eAovz2m/z8swwZojoH\nrE2XLlKlyuMpKapzALBRFDugKGvWSFCQtG6tOgesjb29DB7ch2IHQBGKHVCUtWtl2DDVIWCd\nhg0LTU/3TEpSnQOALeIaO+ABsbFy8qSsW6c6B6xT69ZXnJ0bHD2qOgcAW8QRO+ABK1dKmzZS\nv77qHLBW33t7N/7lF9HrVQcBYHModsBf5eTImjXyf/+nOges2CYfH69bt+TwYdVBANgcih3w\nVz/8IMnJMniw6hywYgmOjtfr1ZOVK1UHAWBzKHbAXy1bJv36SeXKqnPAup2NiJAvv5R791QH\nAWBbuHkCKOD2bdm8WTZtUp0D1i0xMfHlO3eOpabODA7eWvwvCU5OTl9//XWtWrUqMhsAbaPY\nAQUsXy7Vq0v37qpzwLqlpaX5+fld8Pcf+ccf7gMHFjkmNzf3nXfeuXLlCsUOgAlR7ID/0utl\n8WIZMaLEjxEDStSwYcMWb74p4eFv9u9f5B3WWVlZ77zzTsUHA6Bt/AAD/mvPHomPl+HDVeeA\nVrRqJS1byqJFqnMAsCEUO+C/Fi2SPn3kkUdU54CGjBkjy5fL/fuqcwCwFRQ7QEREEhPl229l\n7FjVOaAtQ4ZITo58/bXqHABsBcUOEBGRhQslIEAee0x1DmhLpUoyfLjMm6c6BwBbQbEDRLKy\nZPFiefllbpuA6Y0dKzExcvCg6hwAbAI/xgCRr7+WtDRum4BZ1K8vPXvKp5+qzgHAJlDsAJFP\nP5UXXhBPT9U5oFETJ8q6dXLliuocALSPYgebt3evREfLuHGqc0C7uneXJk1k/nzVOQBoH8UO\nNm/mTHnqKQkMVJ0D2qXTycSJsmiRpKSojgJA4yh2sG0nT8q2bfLGG6pzQOuGDRNvb/nsM9U5\nAGgcxQ627R//kG7dJCxMdQ5onaOjTJwo8+bJvXuqowDQMoodbNi5c/L118LndaJijBwpeXmy\neLHqHAC0jGIHGzZ9urRpI127qs4B21CpkkyaJDNnSmam6igANItiB1t1/rx88YW8/77qHLAl\nY8fK/fsctANgPhQ72KopUyQigs8QQ4Xy8JDXXpPp0yUjQ3UUANpEsYNNOnlS1q6Vjz5SnQO2\nZ9w40evlX/9SnQOANlHsYJPeflt69JDOnVXngO2pVEneeUc+/lju3FEdBYAGUexge/btk61b\n5R//UJ0Dtmr0aKlSxX7GDNU5AGiQg+oAQMXKy5NXX5X/+z8JCVEdBbbK0VGmT7d/5pm6qoMA\n0B6O2MHGrFgh587J9Omqc8C2PfWUvnXrWapTANAeih1sSXKyvP22vPWW1KypOgpsm06X889/\n/k3E+8gR1VEAaArFDrbk738XT0957TXVOQDRt2ixSCRw9myeVwzAhCh2sBlHj8pnn8n8+eLs\nrDoKICLyjohDaqrM4pQsAJOh2ME25OTIyJEycKD06KE6CvCnOyKXx42Tjz6SX39VnQWARlDs\nYBtmzJCrV2XuXNU5gL+41aOHdOkiI0dKXp7qLAC0gGIHG3DypHzwgXz6qVSvrjoK8ICFC+Xk\nSZkzR3UOAFpAsYPWZWbKM89I374ydKjqKEBRHnlE5syRd9+VU6dURwFg9Sh20Lq33pI//pCF\nC1XnAIo3fLj07i1Dh8r9+6qjALBuFDto2saNMn++rFghvr6qowBGLV4sd+7IxImqcwCwbhQ7\naNfFizJ8uEyeLI8+qjoKUJLKlWXtWlm6VL74QnUUAFaMYgeNysiQ/v0lLEzef191FKB02reX\njz+WUaMkOlp1FADWimIHLcrLk2efldRU+fJLsbdXnQYotVdflf795ckn5cYN1VEAWCWKHbRo\n8mTZuVM2bZIqVVRHAR7SokVSs6b87W+SkaE6CgDrQ7GD5ixYILNnyzffSJMmqqMAD8/VVTZs\nkD/+kEGDJCdHdRoAVsZBdQDApFatkgkTJCpKHnusPNMsX7780KFDxsfk5eWJSHp6enneCChC\n9eryww/SoYM8/7ysXCl2/AYOoLQodtCQL7+UESPk00/lmWfKOdOcOXMcHBzq1atnZExOTo6I\nXLx4sZzvBRShfn354Qfp3l1efFGWLKHbASglih20YuVKeeEF+ec/ZexYk8w3fPjwcePGGRmQ\nlpbm4eFhkvcCihAaKtu3y2OPSU6OLFsmDvxzDaBk/BYITZg3T0aMkHnzZPx41VEA0wkLk127\nZNs2GTBA7t1TnQaAFeBXQFi53Fx57TX57DNZuZJPg4UVMVyjOXDgQBcXFyPDcnJyfFJTN2/e\nfNPHZ3SNGreLf3xPt27dFi9ebPqgAKwKxQ7W7M4dGTZMDh+Wbduka1fVaYCHYLhGs2vXrp06\ndTIybP/+/atXr9764YdPLF26++7d7S+99Mcjjzw4bM+ePYcPHzZXVgDWg2IHq3X0qAweLK6u\ncviwGL3LAbBYHTt2HDVqlPExq1evHjx+vMekSTJqVP9PPpF58+SBl2RmZp4+fdpsMQFYDYod\nrFBurnzyibz3ngweLP/+t1SqpDoQYH6urrJqlbRrJ+PHy/btsnCh+Z6/fffu3T/++KPEYc7O\nzv7+/mbKAKBsKHawNmfOyIsvytmzsnRp+R9rAliZl16S9u3lmWckOFgWLJABA8zxJj169Cjx\nOY4GsbGxzZs3N0cGAGVDsYP1SEuTadNkzhx5/HE5eVJq1VIdCFCheXM5ckSmTZOhQ2X5cpkz\nRxo0MO07pKenv//++88++6yRMdnZ2Y0aNUpLSzPtWwMoJ4odrEFWlixdKh98IM7OsmaNPPWU\n6kCAUs7O8uGHMmSIvPKKNGsmY8a4Vq9u2neoUqVKUFCQkQFZWVmmfUcAJkGxg2W7f1+iomTG\nDElOljfflPHjxdVVdSbAMjRpIrt3y4YNMnnyc/HxOU5Oq2fOzPAIeqqHAAAfOElEQVT2Nv6i\njh07Nm7c2FQRvvvuu1OnThkf4+vr279/f1O9YwXLyspas2ZNaVps48aNO3bsWAGRAOModrBU\n16/LwoWycKHk5Mi4cTJhgpT0EwuwRf36SWTk5+3bdzt8uMFbb31fqdJqL68YZ+cix966dWvA\ngAHLli0r/9tmZ2eLyLJlyzw9PY0Mu3//fkJCQmZmppOTU/nftOJFR0cPHz48MDBQp9MZGXb3\n7t2aNWvGxsZWWDCgOBQ7WJh792TjRlm9WrZulfr15e9/l+eeE3d31bEAC2Zv/2Pt2m/Gxmas\nW/fkp58+uXOnNG0qzz0ngwfLX+9aHT58uF6vN8l7GuaZNm3a6NGjjQw7ePBgu3btTPWmFc/w\nHOlff/3VeDGdP38+T4eGhbC+YqfX68+dO3fu3LmUlBS9Xu/t7d2gQYMGDRoY/3UKlu7WLdm2\nTTZulK1bxc5O+veXXbukUyfhPytQOnoR6dNH+vSR+HiJipJPP5XXX5d27aRfP+nTRxo1Uh0Q\nQEWwpmJ37969Tz755PPPP79+/Xqhb9WqVWv06NGTJk1y5QIsK3Lrlhw8KPv2yZ49Ehsr3t7S\nu7esXCm9enEhHVB2devKhx/KBx/IoUOyfr0sXCivvSaPPCJduzY6fPiH1NRBgwYZn+DKlStX\nr16tmLA2Zd26dV9//XWJw+Li4qpVq+bj42N8mKOj49y5c6tWrWqidNAIqyl26enp3bt3P3z4\nsJ2dXYsWLerXr+/l5aXT6ZKTk8+dO3fixIn33ntv8+bNu3btcnNzUx0WRcnJkYsX5fRpOX1a\nYmLk2DG5fFkqVZKICHnySfnsM2ndWor/HEwAD0enk7ZtpW1bmTVL4uNlxw7ZvXvE+fNv5uZm\nbNz4m6/vVV/f65UrJ/j4/O7tnenwl58FaWlply5dUhVcwzZv3hwbG9ulSxfjw06fPp2Wlvbo\no48aGZOXl7dkyZKXX36ZYodCrKbYTZ8+/fDhw8OGDZs5c2bNmjULfff69euvv/76l19+OX36\n9GnTpilJCBERvV5u3ZJbtyQxURIS5OpVuXpVLl+Wixfl8mXJzhYPD2nSREJD5a23pHVrad5c\nHKzmLyFgrerWlbp1ZcyYdvXr18rO3jNjRuNjxxrHxMjJk3LjhoiIv78EBkpgoNSpI7Vq/REV\nFXTnjly/LlWrinXe9GCx2rVrt3DhQuNjVq5c2bJlS+PDsrKylixZYtJo0Air+Zm6du3asLCw\nlStX2tnZPfhdf3//1atXx8XFffXVVxQ7Y/R6SU4uYntKiuTl/fnn7GwxPHQ0L09SUkRE7t+X\ne/ckK0vS0//88927kpEhaWmSkiIpKZKcLHfuSFKSJCWJ4SppR0epXl1q15batSUkRPr1k/r1\npX59KerzywFUmGuOjjJ4sAwe/OfXd+5IXJycPy/x8XL5shw4INevr8rKctu1689ngHt4SOXK\nUqWKeHuLt7d4eIi7u7i7O7m4TBJpvH+/uLiIm5s4OIiHh4hIpUp/dkFHR3F3d0lICBLRXbok\nVavKg/96l3S2EcDD0lnLzUrOzs5jx46dM2eOkTETJkz4/PPP79+/X/ppL126FBERkZOTY2RM\nTk5OampqVlaWo6Nj6Wcuzt5HHw3dvbvEYXq9/sHbQexFPCr8v5deJEWnE5FUnS5XJEUkWyTd\n3j5NJEOnS9Pp7up0yTpdsk6XpNMl2dnd1ulu29ldzshwcnIqccXu3r3r7u5eZFnPl5eXl5aW\nZvyRCiKSnZ2dlZVVqaTPjTX89XBxcSkxmIODQ4mn9e/cuePm5uZczKMlDDIzMzMyMkq8XCY9\nPT07O9u7pEe6pKSkiIiXl5fxYcnJyY6OjiWuBvnzkb+g5ORkH0fHIBcXX72+il7vo9d75+V5\n6fWeer27Xu8u4qXXu+j1Djk53jqdvU7npdfrRNz0+oo/uJcrkvrAP5VF/vv5IBMOM/wkrfhh\nFbybpaQkWCmHxXTr1mXnzhKHWTWrOWLn5eVV4jUfFy9eLPHftUICAgK+/vpr48VOr9ffvHnT\nJK1ORBr/858Xt28vcditW7e8vLxKfPJTQkKCn5+f8b/Ner0+MTGxer16+qKuYNPb2+e4uorI\n/fv379y5U6NWrdz/tpkcV9cHX5KUlCQilStXdhZxFinup83Vq1d9fX1LvJclPj4+KCioxPzx\n8fH16tUzPtW9e/f++OOP2rVrGx+Wn9/4MPLnI39B5DfIFskuPr9DWpphky4ry+7+/StXrgQE\nBIiIXWamfXb2g7PpcnNzk5NTUlJKvFwsNTVVRDwMhwaLZ/J/Px+8/qeQrKws8uez2Pwi0vjx\nx0scY+2s5ojd0KFDv/rqq+XLlxf38YVRUVEjRowYMmTIF198UcHZAAAALIHVFLv4+PiwsLCU\nlJQWLVr07NmzYcOGhrMJKSkpcXFxW7dujYmJ8fb2Pnr0aN26dVWHBQAAUMBqip2InDp16oUX\nXvjll1+K/G54ePjSpUubNm1awakAAAAshDUVO4Po6Ojdu3fHxcXlXwXcsGHDbt26tWzZUnU0\nAAAAlayv2AEAAKBIxh4zAQAAACtCsQMAANAIih0AAIBGUOwAAAA0gmIHAACgERQ7AAAAjaDY\nAQAAaATFDgAAQCModgAAABpBsQMAANAIih0AAIBGUOwAAAA0gmIHAACgERQ7AAAAjaDYAQAA\naATFDgAAQCMcVAewOW3btj106JDqFAAA2Jw2bdocPHhQdQrzothVtKCgoKpVq77//vuqg9io\nqVOnigjrrwrrrxbrrxbrr9bUqVM9PDxUpzA7il1Fc3JyqlKlSlhYmOogNqpKlSoiwvqrwvqr\nxfqrxfqrZVh/zeMaOwAAAI2g2AEAAGgExQ4AAEAjKHYAAAAaQbEDAADQCIodAACARlDsAAAA\nNIJiBwAAoBEUOwAAAI3gkycqmpOTk+oINo31V4v1V4v1V4v1V8tG1l+n1+tVZ7Atd+7cEREf\nHx/VQWwU668W668W668W66+Wjaw/xQ4AAEAjuMYOAABAIyh2AAAAGkGxAwAA0AiKHQAAgEZQ\n7AAAADSCYgcAAKARFDsAAACNoNgBAABoBMUOAABAIyh2AAAAGkGxAwAA0AiKHQAAgEZQ7AAA\nADSCYgcAAKARFDsAAACNoNiVXXx8/LBhw2rUqOHi4lK/fv133303IyPDVC/U6/Xffvtt9+7d\na9Wq5erqGhQUNHDgwIMHD5phP6yVWddfRKKiotq2bevh4eHm5hYaGjp37tycnBxT74QVK9v6\nr1+/fty4ce3bt3d3d9fpdE8//bRp57cdZl3/0v9nslnmW/+0tLSvvvpqyJAhjRs3dnNz8/Ly\n6tChw5IlS/Ly8sywH9bKfOufm5v7wQcf9OrVKyAgwM3NrXLlyi1atJg6dWpSUpIZ9sM89CiT\nkydPent763S6yMjI8ePHt2zZUkTatGmTkZFhkheOHTtWRLy8vJ555pnx48f36tXLzs5Op9NF\nRUWZc7eshrnX//nnnxeRypUrDxs2bNSoUfXq1RORfv365ebmmnO3rEaZ1z8sLExEPD09GzRo\nICKDBw827fw2wtzrX8phNsus6z9nzhwRcXJyatOmzcCBAzt16uTg4CAif/vb3/j3x8Cs63/v\n3j0RqVGjRqdOnQYMGNCzZ8+qVauKSM2aNS9fvmyeHTIxil0ZhYeHi8jy5csNX+bm5g4ZMkRE\nPvzww/K/MD4+XkR8fX2vX7+ev3HDhg0iUrt2bdPuiJUy6/pv2rRJRAICAhITEw1b7t+/37t3\nbxFZunSpyffFGpV5/ffs2XP+/Pm8vDzDIhfXGMo8v40w9/qXcpjNMuv6r1u37rPPPktOTs7f\ncvr06WrVqonImjVrTLcTVsys65+Xl1eowGVmZg4bNkxERo4caaI9MC+KXVkcO3ZMREJDQwtu\nvHbtmp2dXa1atfLy8sr5wp07d4pI7969Cw7Lzc11cHBwdXU13X5YK3Ov//Dhw0Vk/vz5BYfF\nxsaKSIsWLUy3H9aqzOtfkJF/WE0yv4aZe/3LMMymVOT65/vHP/4hIqNHj37ouJqjZP337t0r\nIl26dHnouCpwjV1Z7N69W0R69epVcKO/v3/z5s2vXbt27ty5cr6wUaNG9vb2R44cuXHjRv6w\nLVu25OTk9OjRw4Q7YqXMvf6GZa9bt27BYYazscePH79z545pdsNqlXn9LWR+a8f6qKVk/b28\nvETE2dnZHJNbFyXr/5///EdEQkJCzDG5yVHsyiIuLk5EGjZsWGi74bS9kb9YpXyhv7//1KlT\nb9261bhx42effXbixIl9+/Z98skn+/Tps3jxYtPth7Uy9/r7+vqKyKVLlwqOyf/SMIktK/P6\nW8j81o71Uavi11+v169cuVJEIiMjTT651amw9Z8wYcKYMWOGDBlSv379+fPnN2/e/J133jHV\n5GbloDqAVUpJSZH//gpVkLe3t4gkJyeX/4XvvPNOUFDQmDFjVq1aZdjSsGHDYcOGGTqHjTP3\n+vft23fVqlWzZ89++umnK1euLCI5OTl///vfDd/liF2Z199C5rd2rI9aFb/+U6dOPXToUP/+\n/R999FGTT251Kmz9lyxZkp6ebvhzz549o6KiDHdRWD6O2JmSXq8XEZ1OV/4XTp06ddiwYWPG\njLl06VJ6evqxY8cCAgKGDh06efJkEwbWGFOt/4ABAyIjI+Pj45s0aTJq1KgJEyaEhoZu2bLF\ncDbW3t7epKm1o8zrbyHzWzvWRy0zrf+CBQumTp3asmXL5cuXm3ZmjTH5+qelpeXl5SUmJq5d\nu/bs2bOhoaHR0dGmmtysKHZlYfhdwfB7Q0HF/SbxsC/cvn37lClTnn766RkzZtSpU8fNza1l\ny5YbNmyoXbv2zJkzf/vtN9PtilUy9/rb2dmtX79+9uzZfn5+q1atWrp0aa1atX788UfD0TvD\n7Wm2rMzrbyHzWzvWR62KXP9PPvlk3LhxYWFhO3fu9PT0NOHM1qsi11+n09WoUWPw4MGbN2++\nceOG4b46y0exKwvD2f0Hr7U6f/68/PdMf3leuHnzZhHp2rVrwTGurq5t2rTJzc2NiYkpZ35r\nZ+71FxEHB4eJEyceP3783r17qamp27Zta9SoUUxMjKura3BwsIn2w1qVef0tZH5rx/qoVWHr\nP2XKlNdee61t27a7du3y8fEx1bTWTsnf/+DgYD8/vxMnTljFpTgUu7Lo1q2biGzbtq3gxoSE\nhNjYWH9/fyN/sUr5wqysLBG5efNmoZf//vvvwo1R5l//Ii1atCgrK2vQoEGOjo7lSm/9yrOM\nljC/tWN91KqY9X/11VenTp3apUuX7du3cxS2ICV//1NTUw0/kQ0Pi7ZwFLuyaNmyZXh4+PHj\nxw13KolIXl7eG2+8kZeXN2bMmILn+KOioubOnZtf0Ur5wo4dO4rIggULrl27lj/Vpk2b9u/f\n7+bm1rZt2wrYR0tm7vUXkXPnzhmu2DDYsGHDe++95+7unn8LhS0r8/qbfH7bZO71h3HmXv+8\nvLxRo0bNmTOnR48eW7ZscXd3N2V662fu9T906JDhqaX5bt++/eyzz+bm5nbq1MnDw6P8u2Bu\nuoI/vVB6p06d6tChQ2pqamRkZGBg4P79+48dOxYREbFnzx5XV9f8YfXq1YuPjz9y5EirVq1K\n/8Lc3NzHHntsz549lSpV6tu3b/Xq1c+ePbtjxw4R+fe//z1mzJiK319LY9b1F5FWrVr9/vvv\nwcHBHh4eZ8+ePX36tJub2/r163mOoEGZ13/9+vUbN24UkWvXru3atatOnTqdO3cWEV9f33/+\n858PO7/NMvf6l3KYzTLr+s+aNeuNN96ws7MbPHiwk5NTwfdt1qzZpEmTKmgnLZhZ1//jjz9+\n++23g4KCAgMDfXx8bty4cezYsXv37vn5+e3evbtRo0YVvrsPT81zkTXhwoULQ4YMqVq1qpOT\nU1BQ0OTJk9PS0gqNMTzk9siRIw/7wszMzNmzZ4eHh7u7u9vb21etWjUyMnLXrl3m3SWrYtb1\nnz9/fps2bXx8fJycnOrUqTN69OhLly6ZdXesTtnWv7gHQQUEBJRhfltm1vUv/X8mm2W+9X/z\nzTeL+3ndo0ePitk7y2e+9T9z5sykSZPCwsJ8fX3t7e29vLzCw8OnTJmSlJRUMbtWfhyxAwAA\n0AiusQMAANAIih0AAIBGUOwAAAA0gmIHAACgERQ7AAAAjaDYAQAAaATFDgAAQCModgAAABpB\nsQMAANAIih0AAIBGUOwAAAA0gmIHAACgERQ7AAAAjaDYAQAAaATFDgAAQCModgAAABpBsQMA\nANAIih0AAIBGUOwAAAA0gmIHAACgERQ7AAAAjaDYAQAAaATFDgAAQCModgAAABpBsQMAANAI\nih0AAIBGUOwAAAA0gmIHAACgERQ7AAAAjaDYAQAAaATFDgAAQCModgAAABpBsQNsna+vb506\ndfK/vHbtmk6n69evn0kmN+1sRhw6dKhDhw7mfhcjtLGMliAmJkan0z3//POqgwBWiWIHwAQu\nXLig0+mefvppVQFyc3Nzc3Pz8vIe/Nb9+/d1Bdjb21euXLlLly5RUVF6vb7ioxqhfBkfVnZ2\n9oIFC9q3b+/t7e3k5OTn59e6devx48fv27dPdTTARjmoDgDAslSrVm3//v1VqlSxwNkedPfu\n3Y8++mjNmjXXr1/X6/WOjo5Vq1YNCwtbsmSJn59fwZFOTk7Dhw8Xkezs7IsXL+7bt2/fvn1H\njx5dsGCBOYJZ1zKWTWZm5qOPPnrgwAE3N7euXbv6+fndunXr3Llzn376aXx8fOfOnVUHBGwR\nxQ7AXzg5OZnwnKZpZytEr9f36dPnwIEDzz77bHBw8MqVKydPnnzmzJnVq1ffvn27ULFzdXX9\n/PPP87/cvXv3448//tlnn02aNCkwMNDk2axoGcts0aJFBw4cCAsL2759e+XKlfO3X7hw4ezZ\nswqDAbaMU7GANcm//Cg+Pv7pp5+uVq2anZ3doUOHRGTx4sX9+vULDAx0dXX19vbu3LnzN998\nU+jleXl5c+fObdy4sYuLS+3atSdOnJiWllZoTJGXcxmf/OOPP65fv76IfPXVV/lnPFevXl3c\nbCKydu3ajh07enp6urq6NmvW7OOPP87MzHxwN69evTp06FBfX19XV9fWrVtv2bKl4CRHjhw5\ncODAgAEDVqxY0alTJ29v76FDh06bNu3ixYsNGzY0vpLdunVr2bKlXq8/duxY6fdUk8v4oO+/\n/16n002ZMqXQdm9v73r16uV/+fPPP4vIuHHjCrY6EalXr15kZKThzwcPHtTpdP3793/wXRo3\nbuzs7JyUlFRcjIeNDUA4YgdYo6tXr0ZERPj6+vbs2TM9Pd3FxUVERo8eHR4e3rVr1+rVq9+8\nefP7778fNGjQjBkz3njjjfwXvvTSS4sWLQoICHjllVd0Ot369euPHj2am5tb4jsanzwyMtLR\n0fG1115r06bNyy+/bHhJ+/bti5vtjTfemDVrVrVq1Z555plKlSpt3rz57bff3rZt244dOxwd\nHQvuZuvWrf39/QcNGnTz5s0NGzZERkbu3bu3Y8eOhgGJiYkiEhwcXGh+Ozs7O7uSf2s1XGBX\n8B1L3FMDjS1jmVWrVs0wv5Exbdu2bdiw4ffff3/79u2Cp5J/+eWXX3/99amnnipUCisgNqBx\negDW4/jx44b/c1955ZWcnJyC37py5UrBL9PT01u1auXq6pqUlGTYsmfPHhEJCQlJS0vLH9Oi\nRQsRCQgIyH+h4ef0E0888VCTnz9/XkQGDx5cKPCDs/34448iEhgYePPmTcOW7OzsXr16ichH\nH31UaDfffffdvLw8w8ZVq1aJSGRkZP5UcXFxItKgQYPr168fPHiwffv2RS7avXv3RMTLy6vg\nxl27dtnb2zs5OSUkJNj4Mj5o06ZNIvL+++8X2u7l5VW3bt38L3/++WfDGk6YMGHXrl137twp\ncrbp06eLyPz58wtuHDt2rIhs3LjxwfFljg1Ar9dT7ABrYviZ5+vrm56eXuSAvLy85OTkGzdu\nJCYmfvTRRyLy3XffGb713HPPici3335bcPzmzZtL00hKnLz0jcTwGIvly5cXHHbmzBmdThcY\nGFhwNx955JHs7OyC7+7l5VW9evWCL3zxxRdFxMXFJTQ0tE6dOt98801+ScpnKHZOTk6jR48e\nPXr0iBEjunTpYjjR+emnn7KMDyplsdPr9WvXrvX3988/UlCnTp3nn39+//79hcLb2dm1atUq\nf0tmZmblypWrVatWMFi+MscGoNfrORULWJ/Q0FA3N7dCG48fPz5lypQ9e/akpqYW3H79+vX8\nASLSqVOngt8t9GVxSpy89KKjo0Wka9euBTc2btzYz8/v0qVLycnJ3t7eho0tWrRwcPjfv1E6\nna5WrVqG6pNv4cKFERERS5cuPXr0aE5OzsCBA52cnMaOHTtjxgwnJ6eCI7OyshYuXFhwtqVL\nlxruk32oPdXkMpbZ4MGDBwwYcODAgQMHDsTExOzbty8qKioqKur111+fOXOmYUytWrW6d+++\nY8eOM2fONGnSREQ2bdqUlJQ0ceLEgsEKMWtsQMModoD1qVmzZqEt0dHRHTp0cHFxeemll0JC\nQry8vOzt7Xfu3PnJJ5/kX02fkpLi4OBQ6JImd3f3SpUqGX+70kxeeikpKSJSo0aNQtv9/PwS\nEhJSUlLyG0n+H/I5ODgUupTNzs7uxRdffPHFF3/66aeRI0c+88wzCxYsmDt3ruH5agVHenl5\nJScni0haWtr+/ftfeOGFMWPGBAQEdOvW7aH2VJPLWB729vadO3c2PNxEr9d/+eWXw4cPnzVr\nVu/evbt06WIY8/zzz+/YsWPFihUzZswQkRUrVoiI4dhnccwdG9Aqih1gfXQ6XaEts2fPvnfv\n3saNGx999NH8jYXu9/Ty8vrtt9+SkpIKlpK0tLT09HRfX18jb1eayUvPy8tLRG7cuBEQEFBw\nu+FOCMN3y8Dw2OHJkyePGDGiUaNGUVFR8+fPf3ChRMTd3b1Xr16bNm2KiIh47rnn4uLi8g9/\nsowFFXras16vv3//vvGX6HS6oUOH7t27d/HixTt27Mgvdk8++aSnp+fq1aunT5+elJS0devW\nkJCQkJCQ8ocEUAiPOwG04PLlyyLSpk2bght3795d8EvDBf6Gi+7zFfqyzJPb29uLSGkOqBhi\n7N27t+DGuLi4xMTEwMDAB4/TPKwaNWo0atQoPT3deAsJCwsbOXLktWvX5syZk7+RZSzo2rVr\nBb/87bffSnlk0XBPbsG9cHV1HTRoUEJCws6dO7/44oucnBzjh+sAlBnFDtCCoKAgEdmxY0f+\nljVr1hQqDYYfpVOmTElPTzdsycjIeO+990wyueFJFleuXClxthEjRojIhx9+ePv2bcOWnJyc\nSZMm6fX6F154ocSXF3T8+PH8OyjzxcfHnzx5sk6dOq6ursZf/u6777q4uMyaNSv/UWq2uYwi\nEhUVNXfu3Js3bxbcuH79+vyL//Ly8qZOnSoi2dnZ+QP+9a9/ffvtt1lZWQVfdfTo0TVr1ohI\noeeSGO72WLly5cqVKx0cHIYNG2b83QGUDadiAS145ZVX1qxZM2TIkMGDBwcEBMTExGzZsmXg\nwIEFn3/btWvXkSNHLl68uGnTpk899ZThAWw1a9Ys8ehOaSb39PSMiIg4fPjwkCFDGjVqZG9v\n369fv6ZNmz44W6dOnV599dXZs2cHBwcPGDDAzc1t8+bNZ86c6dix4+uvv/5Qex0XFzdkyJDO\nnTs/9thjmZmZN27cmDRp0rJlyzIyMh58uO6D/P39R48ePW/evBkzZhiu/bLNZRSRadOmxcfH\nd+jQwfBoOgNnZ+dmzZpFRkZWqlTp559/vnDhgr+//5UrV0aMGDFhwoTmzZsfOXJkxYoVHh4e\n4eHhderUyc7OvnDhwsGDB/V6/aBBg/r06VPwLdq3b1+vXr1vvvkmOzs7MjKy4BsV+e4Aykjp\nPbkAHo7hANVzzz334Lf27Nlj+BACT0/Pbt267dq1y/Dorzlz5uSPyc3NnT17doMGDZycnPz9\n/SdMmJCamlqlSpUSn9NRmsnPnz/ft29fHx8fw5Vtq1atKm42vV6/evXqdu3aubu7Ozs7BwcH\nT5s27d69eyXuZkhIiL29ff6XycnJixYt6tOnT2BgoLOzs4jUqFGje/fuO3fuLPiqIp9jZ3Dj\nxg03NzdXV1fDR83a5jLq9fq6deuKyJEjRwxfGh538u67706ePLlGjRrOzs7h4eH79+//8ssv\nPT0969evf/78eb1ef/369YULF/bv379Ro0YeHh6Ojo41a9bs3bv3mjVr8p8/V9CHH35o+Lmz\nbt06I+9e+tgAHqTT6/UVWCMBwCwOHDjw1ltvHThwQHUQLfj+++8jIyPff//90hz4BGBRuMYO\ngBaU5jPEAEDz+KcQgBYU+WQTALA1FDsAWtC2bVvOwwIA19gBAABoBEfsAAAANIJiBwAAoBEU\nOwAAAI2g2AEAAGgExQ4AAEAjKHYAAAAaQbEDAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAA\nAI2g2AEAAGgExQ4AAEAjKHYAAAAaQbEDAADQCIodAACARlDsAAAANIJiBwAAoBEUOwAAAI2g\n2AEAAGgExQ4AAEAjKHYAAAAaQbEDAADQCIodAACARlDsAAAANIJiBwAAoBH/HysTH4sMp12b\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Histogram of radiation$Radiation.µSv.h”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(radiation$Radiation.µSv.h, breaks=seq(0.08,0.13,by=0.001))\n",
    "lines(seq(0.08,0.13,by=0.0001),1.3*dnorm(seq(0.08,0.13,by=0.0001),mean=mu, sd=sigma),col=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot summarizes the same picture. Its a symmetric, approximately normal distribution for the center of the data, but with an asymmetric distribution towards higher values. High radiation, worrying!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2r3T0h+qb2QcqR2/4Tkl9oLKUdq909Ifqm9kHKkdv+E5JfaCylHavdPSH6pvZBy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\nrs8AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(radiation$Radiation.µSv.h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The legal requirement is a mean radiation below 0.1muSh, and we will perform a T-test to demonstrate this. The boxplot already hints that this may quite likely. Our research hypothesis is that the true mean is below 0.1, hence the null hypothesis H0: mu >= 0.1 and we need to perform a one sided T-test. Small values of the T statistic disprove H0, so our critical region will be in the left tail of the T-distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "T= ( mu-0.1)/sigma*sqrt(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we find the critical region for the one-sided T-test, using the specified level of significance alpha=0.001. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "-3.09777463598355"
      ],
      "text/latex": [
       "-3.09777463598355"
      ],
      "text/markdown": [
       "-3.09777463598355"
      ],
      "text/plain": [
       "[1] -3.097775"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qt(0.001,df=n-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "-13.801065865729"
      ],
      "text/latex": [
       "-13.801065865729"
      ],
      "text/markdown": [
       "-13.801065865729"
      ],
      "text/plain": [
       "[1] -13.80107"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our T-distribution is well within the critical region, we can safely reject the null hypothesis and are safe from radiation. Note that many actual measurement values exceed 0.1 and it is very close to the average of the data. But the large number of datapoints gives us enough statistical confidence for this test. Note also that the distribution is slightly asymmetric, which would allow us to use even stricter criteria when using asymmetric distribution functions. The true mean is most certainly below 0.1!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}