left-handers-vs-right-hander-death-age-difference.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/shreshthvashisht/left-handers-vs-right-hander-death-age-difference?scriptVersionId=135439894\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"01a29c4b","metadata":{"dc":{"key":"4"},"deletable":false,"papermill":{"duration":0.007989,"end_time":"2023-07-01T16:09:27.886546","exception":false,"start_time":"2023-07-01T16:09:27.878557","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 1. Where are the old left-handed people?\n","\n","<p>In this notebook, we will explore this phenomenon using age distribution data to see if we can reproduce a difference in average age at death purely from the changing rates of left-handedness over time, refuting the claim of early death for left-handers. This notebook uses <code>pandas</code> and Bayesian statistics to analyze the probability of being a certain age at death given that you are reported as left-handed or right-handed.</p>\n","<p>A National Geographic survey in 1986 resulted in over a million responses that included age, sex, and hand preference for throwing and writing. Researchers Avery Gilbert and Charles Wysocki analyzed this data and noticed that rates of left-handedness were around 13% for people younger than 40 but decreased with age to about 5% by the age of 80. They concluded based on analysis of a subgroup of people who throw left-handed but write right-handed that this age-dependence was primarily due to changing social acceptability of left-handedness. This means that the rates aren't a factor of <em>age</em> specifically but rather of the <em>year you were born</em>, and if the same study was done today, we should expect a shifted version of the same distribution as a function of age. Ultimately, we'll see what effect this changing rate has on the apparent mean age of death of left-handed people, but let's start by plotting the rates of left-handedness as a function of age.</p>\n","<p>This notebook uses two datasets: <a href=\"https://www.cdc.gov/nchs/data/statab/vs00199_table310.pdf\">death distribution data</a> for the United States from the year 1999 (source website <a href=\"https://www.cdc.gov/nchs/nvss/mortality_tables.htm\">here</a>) and rates of left-handedness digitized from a figure in this <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/1528408\">1992 paper by Gilbert and Wysocki</a>. </p>"]},{"cell_type":"code","execution_count":1,"id":"c9949b7d","metadata":{"dc":{"key":"4"},"execution":{"iopub.execute_input":"2023-07-01T16:09:27.903243Z","iopub.status.busy":"2023-07-01T16:09:27.902891Z","iopub.status.idle":"2023-07-01T16:09:29.606319Z","shell.execute_reply":"2023-07-01T16:09:29.605389Z"},"papermill":{"duration":1.714815,"end_time":"2023-07-01T16:09:29.60897","exception":false,"start_time":"2023-07-01T16:09:27.894155","status":"completed"},"tags":["sample_code"]},"outputs":[{"data":{"text/plain":["Text(0.5, 0, 'Age')"]},"execution_count":1,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjMAAAGzCAYAAADaCpaHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABfvElEQVR4nO3de3xT9f0/8FegkCKUFNRCS1tKh6gwLnWA2vZLC3NYL1OHFwRWES9z001QUVRwjonipl9lTqdTBLQKXsGxqZ0INPzaDpBLBQVBLKUNtGYCTQvYYsn5/ZHvCbmcpOck5yTnnLyej0cf2CRNPqdF8u7n875YBEEQQERERGRQXeK9ACIiIqJoMJghIiIiQ2MwQ0RERIbGYIaIiIgMjcEMERERGRqDGSIiIjI0BjNERERkaAxmiIiIyNAYzBAREZGhMZghIiIiQ0uK54tv2LABTz31FLZu3YrGxkasWrUK11xzjd9jdu/ejTlz5sBut8PtdmPYsGF45513kJ2dLes13G43Dh06hJSUFFgsFg2ugoiIiNQmCAJaW1uRkZGBLl3C773ENZg5fvw4Ro4ciRkzZuDaa68Nuv+bb75BYWEhbr31VsyfPx82mw27d+9GcnKy7Nc4dOgQsrKy1Fw2ERERxUhDQwMyMzPDPsail0GTFoslaGfmxhtvRLdu3VBWVhbx87pcLqSmpqKhoQG9e/dWYaVERESktZaWFmRlZaG5uRk2my3sY+O6MxOO2+3Ghx9+iAceeACXXnoptm/fjkGDBuGhhx4KOory1d7ejvb2du/nra2tAIDevXszmCEiIjIYOSkiuk0AdjqdOHbsGJ588kmUlJTgk08+wS9+8QtMmjQJdrs95NctXLgQNpvN+8EjJiIiInPT7THToUOHMGDAAEyZMgXLly/3Pu6qq65Cz549sWLFCsnnCdyZEbepXC4Xd2aIiIgMoqWlBTabTdb7t26Pmc466ywkJSVh6NChfreff/75qKysDPl1VqsVVqtV6+URERGRTug2mOnevTvGjBmDPXv2+N2+d+9eDBw4ME6rIiIi0tapU6fwww8/xHsZmuvWrRu6du2qynPFNZg5duwY9u3b5/18//79qKmpQd++fZGdnY37778fkydPxrhx4zB+/HiUl5fjn//8JyoqKuK3aCIiIg0IgoCmpiY0NzfHeykxk5qaiv79+0fdBy6uOTMVFRUYP3580O3Tp0/HsmXLAABLlizBwoUL4XA4cO6552L+/Pm4+uqrZb+GkjM3IiKieGlsbERzczPS0tJwxhlnmLrRqyAIOHHiBJxOJ1JTU5Genh70GCXv37pJANYKgxkiItK7U6dOYe/evUhLS8OZZ54Z7+XEzOHDh+F0OjFkyJCgIycl79+6Lc0mIiJKFGKOzBlnnBHnlcSWeL3R5ggxmCEiItIJMx8tSVHrehnMUEgulwsOh0PyPofDAZfLFeMVERERBWMwQ5JcLhdKSkpQVFSEhoYGv/saGhpQVFSEkpISBjRERBR3DGZIUmtrK5xOJ2pra1FcXOwNaBoaGlBcXIza2lo4nU7v7CsiIkpMN998MywWS9CHb+sVrTGYIUmZmZmoqKhAbm6uN6Cprq72BjK5ubmoqKjodCw7ERHFmFsA9h4GPjvo+dOtfdFySUkJGhsb/T4GDRqk+euKdNsBmOIvKysLFRUV3gCmoKAAALyBDId4EhHpzPZG4N1dQHPb6dtSk4HrhwJ5wb1c1GK1WtG/f3/Nnr8z3JmhsLKyslBWVuZ3W1lZGQMZIiK92d4IvLLNP5ABPJ+/ss1zv0kxmKGwGhoaUFpa6nfbtGnTsHnzZsnHs8qJiCgO3IJnRyac93ZpduT0r3/9C7169fJ+XH/99Zq8TigMZlRixjJm32Tf3NxcVFVVIScnB3V1dSgoKMCmTZuCHs8qJyKiONh3JHhHJtDRNs/jNDB+/HjU1NR4P5577jlNXicUBjMqMGMZs8PhCEr2zc/Px9tvv42kpCR0dHSgsLDQu0PDKiciojhydRLIKH2cQj179sTgwYO9H1KzlrTEYEYFZixjTklJQVpaWlCy79ixY1FZWekNaCZPnswqJyKieLMlq/s4g2EwowIzljHbbDaUl5fDbrcHJfteeOGFQUdOvtfJ5GAiohgb3NdTtRROn2TP40yIwYxKxDJmMaAxwxu8zWYLGYCNHTsWb775pt9trHIiIoqTLhZP+XU41w31PM6EGMyoKNoyZiMlEUtVOZWWlgblDBERUYzkpQO3XxC8Q9Mn2XO7Rn1mli1bhg8++ECT55aLwYyKonmDN1ISsVSVk+8RGwMaIqI4yUsHFkwAZl0EzBjl+fOxCZo2zNMDBjMqifYN3ihJxKGqnAJzhkLtMBERkca6WIAhZwJjBnj+NOnRki8GMypQ4w0+nknESo63QlU5+eYMpaWlISUlRfV1EhERSeFsJhWIb/AAJN/gi4uLZb3Bx2MWkni85XQ6g15D3BVKS0tDeXk5bDabt8qptbU1KLDKysqC3W5HSkoKbDab6mslIiKSwmBGBWq+wYtJxGIgA2hbJRR4vCUGNL7HW+LjxPWLQY0UI5WfExGROfCYSSXhypgzMzNl71TEukrIjD1yiIgosTCY0ZF4VQmZsUeO0RmpTJ+IKN4YzOhEvKuEou2RQ+oxUpk+EZEeMJjRiXhXCbEJnn4YpUyfiEgvGMzoRLhZSGISsVhRpDY2wVNOy2Mg5jERESnDYEZDSt/w1EoiViLex1tGFItjIOYxEZFR3HzzzbBYLPj1r38ddN+dd94Ji8WCm2++WdM1MJjRiFHyHmJxvGW2ZNZYHQMxj4mIlIjnv7VZWVl466238P3333tva2trw4oVK5Cdna3Z64oYzGhEj3kPUn/RxeOtt956C7179/a7T43jLaMEdUrE6hiIeUxEJFe8/6294IILkJ2djZUrV3pvW7lyJbKyspCXl6fJa/piMKMRveU9hPuL3tLSghtvvFHyL3q0x1t6DOrUoPQYSOlvTMxjIiIl9PBv7YwZM7B06VLv50uWLMEtt9yi2ev5EUzO5XIJAASXyxWX16+vrxdyc3MFAN6P3Nxcob6+PqbraGho8K7D9/V915ebmys0NDSo/tqBr1FVVSW5FiOqqqry+9lWVVUFPaa5uVm46KKLJK9V/N5cdNFFQnNzsyAI8f1ZEVF8fP/998KuXbuE77//PuLniNe/tdOnTxeuvvpq4b///a9gtVqF/fv3C3V1dUJycrLw3//+V7j66quF6dOnS35tuOtW8v7NnRmN6SXvIZ47RWZNZpV7DKT0N6ZweUyrV69Gdna2ZB6TEfOPiEg98f639qyzzsIVV1yB1157DUuXLsUVV1yBs846S9PX9Io2ItM77szoZz1ydjGMQulvQEof39zcHLTzIu7wZGdnC1988YXk8/vu8BCRcaixMyOK9b+14s6MIAjCv/71LyEnJ0fIyckRPvzwQ0EQhJjszDCY0ZBej1fiEVToLagLJBU8iBoaGvwChEiPgaL9HvD4ici81Apm4vFvrW8w09HRIWRkZAgZGRlCR0eHIAixCWZ4zKQRvfZviUeFjN6TWZVWAURazh7tkaPeksqJSF/08G9t165dsXv3buzevRtdu3bV/PW8Ig7FDCJeOzNKkz7Vfm2p387r6+uF7OzsmO4UGWE3IZI1KtnJEan1G5Ped7mISLlod2bi+W+t786MFB4zqSCex0yRvOGp8ZpSQVRDQ4M3kLFard6cC63/osczqFNC6yNBtZ/fTPlHRBR9MGOUf2sDMZiRKd4JwLEWKjr/4osvBKvVKgAQsrOz/YKWeO0UievVy2tqteOh9m9M3JkhMh81cmbi8W9ttJgzQ5JC5VVcddVVaG9vR3Z2NiorK/3yKrQeZBlq5pSYgyL1mtGUGUfaCVOrMno1S631cCZORPoUj/l+uqFFpKUnibYzI9L7b+9abonGq9ooHDVKrY2Qf0REkVGzNNtIuDNDYemlWV8oWrbejqTqR+sdD6nfmMTvQX19Pa666qqomumpNRSUiMiQtIi09IQ7M/rcmRGE2CbdhvseGGnUg9IzcSOeoRMlInGH4sSJE/FeSkydOHGCCcByJGIwo9dmfVK0Drq0mJ2kNq2+B/G+LiKSr6OjQ9i1a5fw3XffxXspMfXdd98Ju3bt8jbY86Xk/dsiCIIQo02guGhpaYHNZoPL5ULv3r3jvRzNORwOFBUVBc3jCDxGsdvtummuVl1djYKCAu/nVVVVyM/Pj/p5fa9ZlJOTg7fffhtjx471e6zL5cLevXsxZMiQoCQ5h8OBlJQUTZPntPgeGPHvAlEia2xsRHNzM9LS0nDGGWfAYrHEe0maEQQBJ06cgNPpRGpqKtLT04Meo+T9m8GMyYiVPE6nM2iwmPgmlpaWplnlklJSAUdgTkg4LpcLra2tQW/GDQ0NKCwsRH19PXJzc1FWVoZp06ahrq4OSUlJqKysxIUXXhi0jsDvTajnB9QLcqL9Hsh9bvH7UFpaaopBn0RmIwgCmpqa0NzcHO+lxExqair69+8vGbgpev9Wd8NIfxLxmMkoeRLRHocpbRC4adMmISkpSQAgJCUlCZs2bZJch/i9i8UxTSyOBI2QP0VEp3V0dAjff/+96T+kjpZ8MWfGh5mDGaMELVLUSLqNpEHgxo0bvQFNTk5O2OBB68TgWCYes2MwERkNgxkfZg1mjJ7cqdb6w+1sZGdnS+4+bNq0ScjJyZG1U6HlzkmsfobcmSEiI2Iw48MMwYzUDozvb/U5OTmGbKCm1s5SJG/WSnYqYt1MT6Tke6Cn4aJERGpgMOPD6MFMuN/elRyZmJ3WwYmej2n0NlyUiEgN7ABsIuE65U6dOhUdHR1ISkpCXV0dCgoKErJKpaGhAaWlpX63lZaWSnbtjaTTr5Lnj4dQf0dcLhe+/fZbAEC/fv28VVfsGExEphOD4CqujL4zIwid522sXLlSt7sGWlOS0xJJwq1RGhBGkjsU6yRxIyesE1HsGeaYyW63C1deeaWQnp4uABBWrVoV8rG/+tWvBADCs88+q+g1zBDMCELoo5GNGzcmbHKn0uBEacKt0QY7Kjk+i3VgYfSEdSKKPcMcMx0/fhwjR47E888/H/ZxH3zwATZt2oSMjIwYrUx/pAZHPv3005g6dapmwxGVcrlccDgckvc5HA64XC5VX0/p4EWbzYby8nLY7fagI7isrCzY7Xa/hnlGG+wod7io2FixqKgo6O9IQ0MDioqKUFJSourPS8vBokREujlmQoidGYfDIQwYMED44osvhIEDB3Jnxue3bjH5Vw+7BvH6zVvrHQYjHY3ofbCmUY7siEgfDHPM5EsqmDl16pQwfvx4YdGiRYIgCLKCmba2NsHlcnk/GhoaDB/MSL0JiH1SkpKShI0bN0o+Ppbb9kY7ktFKvIIfpYFCvAIL9rwhIrlME8w88cQTws9+9jPB7XYLgiAvmHn00Uf9/qEUP4wazIQLEsSARipIiMeuQaL/5h2v3alIA8l4BRZ6LnMnIv0wRTCzZcsWoV+/fsLBgwe9t5llZ0bJb+9GS5xM5N+847U7Fc3fkVgHFon894OIlDFFMPPss88KFotF6Nq1q/cDgNClSxdh4MCBsp9XbzkzkbzxGClvQxCif4M02vX6itfuVCTfM6nAQo0ybnYjJiI1mCKY+e6774SdO3f6fWRkZAhz5swRvvrqK9nPq7dgxuy5JdH+5m20nSgpRth9CPz79u9//9tvOKfvWr/44gshOztb8vsudyeR3YiJSCnDBDOtra3C9u3bhe3btwsAhGeeeUbYvn27cODAAcnHm6Wayay5JWpcl1mCPT3nhUh9j32DDTGgaWhoCDuBXElvHqXPQ0RkmGBm/fr1ksm606dPl3y8WYIZQTDGb+9KqBmEGD3Y0/vPNtTuie8xkNVqFf79738HBTjRdk3WSzdiItI/wwQzsaDXYEYQ9P3bu1JqHw/pPSAIxSiBmJy8Ft8gRmmui1F/fkSkHwxmfOg1mDHjP/ZqJ+4aLdgz8xGZ2SaNE5H+GWacQaKKZHKzEdhsNmRmZkrel5mZ6R0TIIfeJ1VLMdr4Aymhvu8AZI1K6Ox59PzzIyIDi0FwFVd625kxy2/vWjJyzoVZy8p9j5rQyc6M0qM2I3/PiEg7PGbyobdgxgylx1piNUx8hAuyA5OAwwUnWk8yJ6LEwWMmHVM6uTnRhDqqsdls6NevHwDg22+/9U505tRldYT6vlssFu9jrFYrPvroI+Tn53uPzcSjUXFautKjtnDTtMeNGxfy56rFFHYiMrAYBFdxpbedGeocO8jGh9T3Xdw5yc7O9ja6E4Vqptfc3Cxs3rxZcjdF6tjICINUiSj2lLx/WwRBEOIZTGmtpaUFNpsNLpcLvXv3jvdyKEq+OzGiwF0AUpfL5UJra6tfcrfL5UJJSQkOHTqEjz76CMOGDfPeJ/6M0tLSZO8ySv1ck5KS0NHR4ffzDUyet9vtIZPOicjYlLx/85iJDCUrK0tRVQ1FT6pKTTweqq+vx1VXXeV3PBTJsZ/Uz/Wdd97xO8qqrq72C2QqKioYyBARAAYzZDAs+dWHzMzMoLyZaIINqZ/r7NmzsXz5cu9rFBQU+D03A1giEjGYIcMwa38eo/JN6o0m2Aj3c506dSqefvppv8dzJ46IAjFnhgzB4XCgqKgo6M2SORTxV11djYKCAu/nVVVVyM/Pl/W1cn6uYu6MiDszRImBOTNkOmbormtG0R77hfu5Ll++3BvI5OTkcCeOiELizgwZhlRVjcjhcCAlJSVh+/PEQ+CuWFlZGUpLSxUfNUn9XH13bHJycrBhwwbuxBElGO7MkCmpOfuJouNwOIKSfcM10wtH6ufqu2MjBjKAZ8dm9erVyM7OltyJYzM9osSUFO8FEJHxiMEGAMljP7HPTKTHfmKnbKn+NrfddhsAYPHixX4BbCT9bYjIHBjMEJFioYIN4PRYjmiP/Ww2W9DXB/a3kUoYFh/HYIYocfCYiYgiEo9jP7X72xCROTABmIgMh2MtiMyPCcBEZGoca0FEvhjMEJHhcKwFEfliMENEhsKxFkQUiMEMERmGmv1tiMg8WJpNRIahdX8bIjImVjOR+twCsO8I4GoDbMnA4L5AF0u8V0UmwbEWRIlByfs3d2ZIXdsbgXd3Ac1tp29LTQauHwrkpcdvXWQaUs30ROwvQ5SYmDND6tneCLyyzT+QATyfv7LNcz8REZHKGMyQOtyCZ0cmnPd2eR5HRESkIgYzCrlcrpCVEgk9sXffkeAdmUBH2zyPIyIiUhGDGQVcLhdKSkpQVFQU1MuioaEBRUVFKCkpScyAxtVJIKP0cURERDIxmFFAnNgb2JzLt4mX0+lEa2trnFcaB7ZkdR9HREQkE4MZBTixN4zBfT1VS+H0+b8ybSIiIhUxmFFIbM4lBjQFBQV+gUzCDrrrYvGUX4dz3VD2myEiItUxmIkAJ/aGkJcO3H5B8A5Nn2TP7ewzQ0REGlDcNK+8vBy9evVCYWEhAOCFF17AK6+8gqFDh+KFF15Anz59VF+k3oSa2JvQOzOivHRgZH92ACYiophRvDNz//33o6WlBQCwc+dO3Hfffbj88stRW1uLe++9V/UF6g0n9srQxQIMORMYM8DzJwMZIiLSkOJgZv/+/Rg61JMb8f777+PKK6/EE088gb/97W/4+OOPVV+gnnBiL5F+sQcUUeJSHMx0794dJ06cAAB8+umnmDhxIgCgb9++3h0bsxIn9gYm+/omBXNiL1HssQcUUWJTnDNTWFiIe++9FwUFBdi8eTPefvttAMDevXtNX5Jss9lQXl4uObE3KysLdrudE3uJ4iCwB5T4y4bvsbD4OP7/SWQ+indmnn/+eSQlJeG9997Diy++iAEDBgAAPv74Y5SUlKi+QL2x2Wwhg7bMzEz+Q0kUB+wBRZTYLIIgmHryX0tLC2w2G1wuF3r37h3v5RCRhgJ3YgCwBxSRQSl5/1a8M7Nt2zbs3LnT+/k//vEPXHPNNXj44Ydx8uRJ5aslUoNbAPYeBj476PmT07kTEntAESUmxcHMHXfcgb179wIAamtrceONN+KMM87Au+++iwceeED1BRJ1ansjMG8dsGgjsLTG8+e8dZ7bKaGE6gHFlglE5qY4mNm7dy9GjRoFAHj33Xcxbtw4LF++HMuWLcP777+v9vqIwtveCLyyDWgOmMbd3Oa5nQFNwmAPKKLEpTiYEQQBbrcbgKc0+/LLLwfg2d797rvv1F2dnvFYI/7cAvDurvCPeW8XfzYJgD2giBKb4tLs0aNHY8GCBbjkkktgt9vx4osvAvA00+vXr5/qC9Sl7Y2eN1Hf3YDUZM+gRSPMH3IL5hg3sO9I8I5MoKNtnscNOTM2a6K4EHtAAZDsAVVcXMweUEQmpjiYWbRoEaZNm4YPPvgAc+fOxeDBgwEA7733HvLz81VfoO6IxxqBxGMNvQ9UVDMQi3dQ5OokkFH6ODIs9oAiSmyqlWa3tbWha9eu6NatmxpPpxpVS7PdgiexNNxuQJ9k4LEJ+tzpCBWIiZQEYnrYndp72JPs25lZF3FnhojIYDQtzQaA5uZmLF68GA899BCOHDkCANi1axecTmckT2ccSo41tBBNno6a+SV6Sbod3NcTQIXT5/92jIiIyLQUHzPt2LEDP/3pT5Gamoq6ujrcfvvt6Nu3L1atWoUDBw7g9ddf12Kd+hDPY41wOyEj+3d+3KNWfoncoGhkf+13p7pYPNcfbrfpuqH63CUjIiLVKA5m7r33XsyYMQN//vOf/ZLpLrvsMkydOlXVxemOrZNdAKWPk6uzPJ2e3YDjP5y+Xeq4R61ATG9Jt3npnuOxwECvT7InkNFz/hIREalCcTDz2Wef4e9//3vQ7QMGDEBTU5Mqi9It8Vijs5wZNY815OyE+AYygHQyslqBmB6TbvPS5e1OERGRKSnOmUlOTkZLS0vQ7Xv27MHZZ5+t6Lk2bNiAn//858jIyIDFYsEHH3zgve+HH37AnDlzMHz4cPTs2RMZGRm46aabcOjQIaVLVo94rBGO2scacnZCQvHNgYkmv8Q3V6elXd5rq7071ZkuFs9O0JgBnj8ZyBARJQzFwczVV1+NP/7xj/jhB89ugMViQX19PR588EFce+21ip7r+PHjGDlyJJ5//vmg+06cOIFt27bhkUcewbZt27By5Urs3bsXV111ldIlq0s81ggMDPoka1OWHc0Oh28ycqSBWOCogPd3A53FCUy6JVLE5XKFbOjncDjgcrlivCIiY1Fcmt3S0oLLL78cX375JVpbW5GRkYGmpiZcfPHF+Oijj9CzZ8/IFmKxYNWqVbjmmmtCPuazzz7D2LFjceDAAWRnZ8teryZTs2PVY0Vu+XEoM0Z5ditEUonEYn5J4FHNsZPA4jDJtaHovdcOkY64XC6UlJTA6XQGTfcWRzSkpaWhvLycfXIooSh5/1acM9O7d29UVlZi3bp12LZtG9xuNy644AJccsklES9YLpfLBYvFgtTU1JCPaW9vR3v76aMQqSMxVYjHGlqTk6cTTuBxT6j8ks+bgnvodBabWQD4hsJMuiVSrLW1FU6n0ztyQQxofGdNiY9jMEMkTXEwI5owYQImTJig5lrCamtrw4MPPoipU6eGjdAWLlyI+fPnx2xdmpNTfhxKqOOewEAsVLVUZ3t2AoBrzwd6W5l0S7rlcrkkOwMDniOceHcGzszM9I5cEAOasrIylJaW+s2aklo/EXlE1AF47dq1WLt2LZxOp3fopGjJkiWRLSTMMdMPP/yA66+/HvX19aioqAgbzEjtzGRlZal/zBRrUsdDgSXZgeQc98jpahxO4DEWkY4Y6QgncCcGgDeQ8V03UaLQ9Jhp/vz5+OMf/4jRo0cjPT0dFou2v4n/8MMPuOGGG7B//36sW7eu0wuyWq2wWq2arikuwh0PRdNjJZpqKSD2VUtEChjpCCcrKwtlZWUoKCjw3lZWVsZAhkgGxcHMSy+9hGXLlqG0tFSL9fgRA5mvv/4a69evx5lnJvh8Hak8nWh7rERTLcWqJdI5Ix3hNDQ0BP27Wlpayp0ZIhkUl2afPHlStenYx44dQ01NDWpqagAA+/fvR01NDerr69HR0YHrrrsOW7ZswZtvvolTp06hqakJTU1NOHnypCqvbxrR9FiJZmclkUYFRDMXi+IqKysLFRUVyM3NRW1tLQoKCvwCGT0ECr47Rbm5uaiqqvKut7i4GA0NDfFeIpGuKc6ZmTNnDnr16oVHHnkk6hevqKjA+PHjg26fPn06/vCHP2DQoEGSX7d+/XoUFxfLeg3NSrPNQk7OTLyqlmJV/t4ZPUwIp6hVV1f7HeFUVVWp9otZNBwOB4qKioICrMAAx26362IHiShWNM2ZaWtrw8svv4xPP/0UI0aMQLdu3fzuf+aZZ2Q/V3FxMcLFUhHkJpNScqqlbskDUqyxDSr0EkB0NhfLjD119BJEqkjPRzgpKSlIS0sDAL/1iDtKYpKy7yw8IvKneGdGaifF+2QWC9atWxf1otTEnRmZwjXTi/WbdagAQqRlAOH7Rp5iBV7/vPNZXI9NMPybvZdegkgVBe5wSOXMxDug0Xv5OFE8KHn/jqg020gMGczE6zdjPfxGLufYS6sAQuqNXI5ZF8WmgaLW4hlEaoRHOETGpekxE0VASZCg5m/GSoMTrbsay1mPnFJxceaUmmvt7I08nFhOCNeKnOns7+3yVM4ZaBdKzSOcULsnLpcLe/fuxZAhQ4J2T7irQhQbsoKZSZMmyX7ClStXRrwYU1ISnKiZn6G34wK565EbGKgZQMh5Iw/HDL124hVEasxms6G8vFwyCMnKyoLdbpcVbIRqvudyuTBhwgTs2LEDw4cPx/r1673P9eWXX+Lyyy9HRkZGUFM+BjlE6pJVmm2z2bwfvXv3xtq1a7Flyxbv/Vu3bsXatWv5P2YgMTgJfJMQg5Ptjadvk/ubsZySYCWvGwtK1iM3MFAzgIimcaBZeu3EI4iMEZvNFvIIKTMzU9a/W4HN98RS6T179mDHjh3o6OjAzp07sWfPHgCeQOYnP/kJ6uvrcejQIbS2tnqfq6GhAUVFRSgpKeE0bCKVyNqZWbp0qfe/58yZgxtuuAEvvfQSunbtCgA4deoU7rzzTuPkpMSC0m17tX4z1ttxgdL1yBmsqXYAEc0btFq9duKdrxSPINJAwjXf6+joQFJSEjo6OjBlyhSUlZVhypQpfmNVxNREPXYeJjIDxU3zlixZgtmzZ3sDGQDo2rUr7r333ojnMpmSkuAEUO83Y6WvqzWl6xFLxcNRu1lfJG/QfZLVS4jd3uhJel60EVha4/lz3rrY7qCJQWQ4SoNInTcadLlccDgckvc5HI6gXZNwzfcqKyv9bq+vr0d2djays7NRX1+P4uJiVFdX+yUd66XzMJEZKE4A7ujowO7du3Huuef63b579+6goZMJTWlwotZvxno7LohkPXnpnkBBy1LxwBLsznaDUq3ATaOA1nZ1d04izZNSeydHTr8hJUGk3nK2AkQ6gDLU/KQLL7ww6PYVK1YgKyvLG8CI9+mlHJzITBQHMzNmzMAtt9yCffv24aKLLgIAbNy4EU8++SRmzJih+gINS2lwotbxit6OC+S+Tku75zd48Y052plT4Ui90Z7RLfTjAeD6YcB5Z0X/2r4iPRLUKlBQK4g0QKPBcAMox40bh7q6Ou/jfIOZzZs3Y9q0aX7PVVpaiuXLl4dsysfhkUTaUxzMPP300+jfvz+effZZNDZ6tsHT09PxwAMP4L777lN9gYalNDhR6zfjeOScRLseC4D3d5/+3PeNWe3KmVBvtCd+8PzZsxtw/IfTt2vZODCSPCmtA4Vog0i95WyFECoHZtq0aairq0NSUhKWL1/udwy0adMmFBYWoqOjAzk5OXjzzTe9zffE2wOb8hUWFga9tl46DxOZSVRN81paWgBA14m/cW2a11nvklslxgR83qTdb8aiWP9mHGkPF7XXKachn1bHSVI+O+jJkenMjFGeIaLxbCgo197Dnpyfzuik0WBgQi4AbzKv73HQ5s2bUVBQ4E32raqqwtixY0Pe3tDQgMLCQtTX1wMAsrOzsWLFCt11HibSMyXv34oTgAFP3synn36KFStWwGLx/KN56NAhHDt2LJKnMy9x2z4wsbJPMnDJIM9uRGDSJwAsmOD5x37GKM+fj01Q9qYe7nXjscUfaj2dvd/KLUWXS85OSHO7JxCIZAK5UkqP4Nbv11dytxS95Wx1QsyB8fXOO+/4Tayurq7G5MmTvQFLZWUlxo4dCwA499xzMWLECCQlJWH48OHeXELx30UAsFqt+Oijj5Cfn++XQFxcXBwyAZmIlFF8zHTgwAGUlJSgvr4e7e3t+NnPfoaUlBT8+c9/RltbG1566SUt1mlcUtv2x04CizXOKdAy50SN9bS0+x8tSVG7SZve3mgjOYKTI56Bgt5ytjohNYBy9uzZWL58OaZOneqXuJuTk4O3337bG8gAnh4269atC+oAnJKSgoyMDADARx99hGHDhgHg8EgirSjemZk5cyZGjx6No0ePokePHt7bf/GLX2Dt2rWqLs40xDEBYwZ43sDeU6k5npLX1XqXQel6elvlfY2ab8x6e6OVU4YeyV+DeAYKWpR4ayRwPlNVVZV312Tq1Kl4+umn/R7/5ptv+gUyIpvNhjFjxvglCoudh6uqqryBjEjsPBxYKUVEkVMczFRWVmLevHno3r273+0DBw7EwYMHVVuYYXXWW0NvfWDiJR6BRSzfaOX2WIn0CC6UeAcK8egTFAGHwxHU8yXwGOiGG27w+5rS0lJv51851Og8TETyKD5mcrvdOHXqVNDt4qyRhCanZFZvRx3xEo+qK7V7qQDS/V6kkrjDlU5HcgSn1vq1EIs+QVEKN4By+fLlIauWfMu4iUg/FFczTZ48GTabDS+//DJSUlKwY8cOnH322bj66quRnZ3tN/pAD2JWzSS3gshg1R6ailfVlVTQGckbbah+NSd+CP01cq5JbpWTLx0FCl7xHtHQCakp2A6HA0VFRaitrUVOTg42bNjg7T/ju5Njt9vZvZdIY0revxXvzDz77LMYP348hg4dira2NkydOhVff/01zjrrLKxYsSLiRRuakt4aeusDE0+x7vSrZkO+zvrVhCKnx4rco7Vrz/fkHukwUABwOkdKp8Thub7C7dgwcZdIvxQHMxkZGaipqcGKFSuwbds2uN1u3HrrrZg2bZpfQnBCUdr8TO2jDiOLdadfNRryyQleQ5FToSU34B0/KHH+nsSImLgbuGMDnE7cTUlJiTjfRWo3SCQe1TOXhki5qJrmGUFMjpmUNj8D1DvqIGlaHmHJPSoMxffvQSh6a3wYjs6Pk/Qi0nlQRIlK9WOm1atXy37xq666SvZjTSOSyhy99YExE61b6kebnC3n74sBkmgB6H6gpJ6Emwfl24U4cB4UEXVOVjBzzTXX+H1usVgQuKEjdryUqnQyvUjzYHSeU2BYkcw8UiKacvFUqyfY8h2qGSqg0nvAa4CBknoSah5U4IgDJhYTKSerz4zb7fZ+fPLJJxg1ahQ+/vhjNDc3w+Vy4eOPP8YFF1yA8vJyrderTwbprZEwtC5/l9OvJpQf3MBzm/xHWGxvDP14vTU+FMnd/VJzHIUJiInEYi+bgoICzmoiUoHipnmzZs3CX/7yF1x66aXo3bs3UlJScOmll+KZZ57B3XffrcUajUFv85DMrLOGdFo35JMTvPbsJv358YBqJ3EXI1xAo0So743cJn5ysfljxKTmQZWVlTGQIYqC4mqmb775RvI812azoa6uTo01GZfejwXMQE6ORizK3zvLafH9e5BiBV7/HECYsu1ocnhEob43o9OBLY3q5rWw+WPEpOZBlZaWcmeGKAqKd2bGjBmDWbNmobHx9G+STU1NuO+++yTnliQcvR4LmIGYoxEYpATubsTq2C8vPfSEc9+/B10s2u9ihPvefCoxbTvaHSG9zbkyiHDzoIqLixWNSyCi0xQHM0uWLIHT6cTAgQMxePBgDB48GNnZ2WhsbMSrr76qxRqJlOdoxOrYT07wqvUuRjR9byLNazHQQEm9kDMPqri4GA6HI95LJTIcxcdMgwcPxo4dO7BmzRp89dVXEAQBQ4cOxSWXXOKtaCJSXSQVSno59tN6F0PO9yaUSKu6tJhzZXLsLkykHcXBDOApw544cSImTpyo9nqIpEW6u6GH8netc3iizUuJ9OuN0gtHJ7TuLkyUyCIKZtauXYu1a9fC6XTC7Xb73bdkyRJVFkbkx8g5GlrvYkR7zdF8vV52vwxCah6UiP1liCKnOGdm/vz5mDhxItauXYvvvvsOR48e9fsg0oTRczS0zOGJpu+NGt8zJr1rxuVyhcyhcTgccLlcMV4RkT4p3pl56aWXsGzZsqDSQiJNmSFHQ6tdDDnfm1D0/j1LYJzlRCSf4p2ZkydPIj8/X4u1EIVnhsaEWu1ihPveXDIo9PdsZH91m+mRagJnOYll277l3U6nE62trXFeKVH8KZ6aPWfOHPTq1QuPPPKIVmtSVUymZlNscUpzaKG+N1K3f97EIZE6F9iXRmqWExvtkVkpef9WHMzMnDkTr7/+OkaMGIERI0agWzf/tu3PPPOM8hVriMEMkYRQQyJFUjtdDCLjInCqNgAGMpQQlLx/K86Z2bFjB0aNGgUA+OKLL/zuY58ZIgOQ24DQd7yCnDESpAlxllNBQYH3Ns5yIvKnOJhZv369FusgolhR2oAw1C6OOBLBKPlKBsVZTkSdU5wATEQGp6QBodIxEqQqznIikieipnmfffYZ3n33XdTX1+PkyZN+961cuVKVhRGRRpQ0IIxkjESkmJPjR2qWk+/oAzGgsdvtbLhHCU9xMPPWW2/hpptuwsSJE7FmzRpMnDgRX3/9NZqamvCLX/xCizUSkZqUjFfYekjec0Y7UoE5OUE4y4lIPsXHTE888QSeffZZ/Otf/0L37t3xl7/8Bbt378YNN9yA7OxsLdZIRGoSm+yFIzbTi8UYCTEnJzC4EnNytjdG/twGJs5ystvtQbkx4iwnNswj8lAczHzzzTe44oorAABWqxXHjx+HxWLBPffcg5dffln1BRKRBuQ2INR6jIScnJx3vwS++i4hG/vZbLaQR0iZmZkMZIj+j+Jjpr59+3o7Tg4YMABffPEFhg8fjubmZpw4cUL1BRKRRuSMV9B6jIScnJzmduC5Tac/T/DjJyIKpnhn5n/+53+wZs0aAMANN9yAmTNn4vbbb8eUKVPw05/+VPUFEpGG5IxX0HKMRCS5Ngl+/EREwRTvzDz//PNoa/P8A/TQQw+hW7duqKysxKRJkwwz4oCIFNJqSGY0uTaBjf0SiMvlQmtrq+QRlMPhQEpKCo+gKKEoHmdgNBxnQKRjbgGYt67zo6ZQZl0UfUm4wXCaNiUKTccZAIDb7ca+ffvgdDrhdrv97hs3blwkT0lEiUhOTk440ZaEG1DgNG0xoAmc4dTa2spghhKG4mBm48aNmDp1Kg4cOIDATR2LxYJTp06ptjgiMqHA5ngj+3tybwL7zMgRzTGVQWVmZgY1zpOaps1GepRIFB8zjRo1CkOGDMH8+fORnp4eNFxSb78J8JiJSEfCNcfzzclJsQKvf955Y7/HJiRkzgzAadpkfkrevxUHMz179sTnn3+OwYMHR7XIWGEwQ6QToQZWigIro5Q+PgFVV1f7TdOuqqpCfn5+HFdEpB4l79+KS7MvvPBC7Nu3L+LFEVECimRgpZYl4SYQapo2h09SIpKVM7Njxw7vf//ud7/Dfffdh6amJgwfPhzdunXze+yIESNkv/iGDRvw1FNPYevWrWhsbMSqVatwzTXXeO8XBAHz58/Hyy+/jKNHj+LCCy/ECy+8gGHDhsl+DSLSgUgHVmpVEm5wgdO0fXNmfJOCiRKFrGBm1KhRsFgsfgm/t9xyi/e/xfuUJgAfP34cI0eOxIwZM3DttdcG3f/nP/8ZzzzzDJYtW4YhQ4ZgwYIF+NnPfoY9e/ZwuBqRkcitOpJ6nNjYjwBwmjaRFFnBzP79+zV58csuuwyXXXaZ5H2CIGDRokWYO3cuJk2aBAB47bXX0K9fPyxfvhx33HGH5Ne1t7ejvb3d+3lLS4v6CyciZbQYWBlYFZUgOzbhpmmvXr0al19+edA0bZfLhb1792LIkCFBRRpsskdmICuYGThwoPe/N2zYgPz8fCQl+X9pR0cHqqur/R4bjf3796OpqQkTJ0703ma1WlFUVITq6uqQwczChQsxf/58VdZARCoRB1Z2Vp0kd2BluKook+fSiNO0AzsAu1wu3HbbbQCAxYsXe4MTl8uFCRMmYMeOHRg+fDjWr1/vvY9N9sgsFCcAjx8/HkeOHAm63eVyYfz48aosCgCampoAAP369fO7vV+/ft77pDz00ENwuVzeDybDEemA2BwvHLkDK8Uqp8DAKIFmNklN0xab6dXX1+Oqq67y/tu3Z88e7NixAx0dHdi5cyf27NkDwD/vxul0egcIExmR4mBGzI0JdPjwYfTs2VOVRfkKfK1Qry+yWq3o3bu33wcR6YAa1UmRVEUlCLGZXm5urjdvprq6GlOmTEFHRweSkpLQ0dGBKVOmoLq6Oijvhvk1ZGSyOwCLeSsWiwU333wzrFar975Tp05hx44dqvY36N+/PwDPDk16+ul/5JxOZ9BuDREZRLTVSZFWRSWIwERgsQdNbm4uli9fjqlTpwbdzsonMgPZOzM2mw02mw2CIHiTxcSP/v3741e/+hXeeOMN1RY2aNAg9O/fH2vWrPHedvLkSdjtdjaFIjIysTppzADPn0qSdqOpikoQWVlZKCsr87utrKwMF154oeTtDGTIDGTtzNx77714/vnn0bNnT9TV1WHx4sXo1atX1C9+7NgxvwZ8+/fvR01NDfr27Yvs7GzMmjULTzzxBM455xycc845eOKJJ3DGGWdg6tSpUb82ERmQFlVRJhOqmd7y5cslb+fODJmBrJ2Zv/71rzh27BgATzXTiRMnVHnxLVu2IC8vD3l5eQA8QVNeXh5+//vfAwAeeOABzJo1C3feeSdGjx6NgwcP4pNPPmGPGaJEJVZFhaOkKspkApvpVVVVeXNoCgsLJW8vLi5moQQZnqzZTOeccw5uuOEGTJw4EePHj8eqVavQp08fyceOGzdO9UVGg7OZiEyGM5skORwOFBUVBTXT27x5MwoKCrxJwFVVVRg7dmxQ4MMme6Q3qg+a/OCDD/DrX/8aTqczqBOw35Mp7AAcCwxmiExIqs9Mn2RPebfSQMYkzfdcLhdKSkrgdDr9jo7YZ4aMSrOp2ceOHUPv3r2xZ88ebwfKQHr7n4HBDJFJqRGEmKz5nsvlCmqmJ97ODsBkNJoFMwBgt9tRUFAQ1AFYrxjMEJEkHlcR6ZqS92/FTfOKiopw4MABzJs3D1OmTIHT6QQAlJeX48svv4xsxUREscTme0SmojiYsdvtGD58ODZt2oSVK1d6q5x27NiBRx99VPUFEhGpTknzPSLSPcXBzIMPPogFCxZgzZo16N69u/f28ePH4z//+Y+qiyMi0gSb7xGZiuLEl507d2L58uVBt5999tk4fPiwKosiIlKdb8JwS7u8r0ng5ntERqI4mElNTUVjYyMGDRrkd/v27dsxYMAA1RZGRKQaqaolC4BwKTEJ3HyPyGgUHzNNnToVc+bMQVNTEywWC9xuN6qqqjB79mzcdNNNWqyRiChyYtVSYI5MZ7m91w01ZL8ZokSkOJh5/PHHkZ2djQEDBuDYsWMYOnQoxo0bh/z8fMydO1eLNRIRRUZO1VJgvNInmWXZRAajuM+MqLa2Ftu2bYPb7UZeXh7OOecctdemCvaZIUpgew8DizZ2/rhrzwd6Ww3dAZjIbJS8f0fc+S43Nxe5ubnezz///HNccMEFuhtnQEQJxjfRt/GYvK/pbQXGJF7OX6iOwQA7A5OxqNrGN8JNHiIidUgl+sqRgFVLoWY5AZzZRMajOGcmHIuFW7NEFCehEn07k6BVS62trXA6naitrUVxcTEaGhoAwG+attPpRGtra5xXStQ5VYMZIqK4kJPoG0qCVi1lZmaioqICubm53oCmurraG8jk5uaioqJC8giKSG9kHzO1tLSEvZ/ROxHFjZzxBIH6JHsCmQSuWsrKykJFRYU3gCkoKAAAbyDje/REpGeyg5nU1NSwx0iCIPCYiYjiQ+7YgZIfAekprFrykZWVhbKyMm8gAwBlZWUMZMhQZAcz69ev13IdRESRk5vAe97ZwJAztV2LwTQ0NKC0tNTvttLSUu7MkKHIDmaKioq0XAcRUeQG9wVSk8MfNSVoom84vsm+ubm5KCsrQ2lpqTeHhgENGQUTgInI+LpYgOuHhn9Mgib6huJwOIKSffPz84OSgh0OR7yXStQpBjNEZA556Z4xBKkBR04cTyApJSUFaWlpQcm+YlJwbm4u0tLSkJKSEueVEnUu4nEGRsFxBkQJxrcDMBN9w2IHYNIz1ccZ7NixAz/+8Y/RpQs3cohI57pYmOQrk81mCxmssL8MGYms6CQvLw/fffcdAE//gcOHD2u6KCIiIiK5ZAUzqamp2L9/PwCgrq4Obrdb00URERERySXrmOnaa69FUVER0tPTYbFYMHr0aHTt2lXysbW1taoukIiIjCmRcnIS6Vr1SFYw8/LLL2PSpEnYt28f7r77btx+++3McCeixMQEY1kSaSp3Il2rXslumldSUgIA2Lp1K2bOnMlghogSz/ZGz0BL3+Z8qcmeHjcs/fYTOJVbfJP3bdQnPs7ob/CJdK16FVVptsPhgMViwYABA9Rck6pYmk1EEQncgTl2Eli8LfTjQ/WySeCdnHAdhs02zDKRrjVWlLx/Kw5m3G43FixYgP/93//FsWPHAHiaL913332YO3eu7sq3GcwQkWJSOzAWAOH+teyTDDw2wT9Q4U5O0O4EYN6p3Il0rbGg5P1bceQxd+5cPP/883jyySexfft2bNu2DU888QT++te/4pFHHol40UREurC9EXhlW/Ccp85+7Tva5tmB6ex5mts8t29vVGW5eidO5fZl1qnciXSteqM4mHnttdewePFi/OY3v8GIESMwcuRI3HnnnXjllVewbNkyDZZIRBQjbsGzkxIpV5v853lvl+dxOuRyuULOZHI4HHC5XLKfS2oq97Rp07B582ZVnl9PQk0gb2hoiNOKEofiYObIkSM477zzgm4/77zzcOTIEYmvICIyiH1Hwk/e7owtWf7zBO7k6IRYmVNUVBT0JtzQ0ICioiKUlJT4BRyhgp+GhgYUFhZ680aqqqqQk5ODuro6FBQUYNOmTbKe3wgCc2aqqqr8BnYyoNGW4mBm5MiReP7554Nuf/755zFy5EhVFkVEFBeuKAKZPv+X3KvkeaJ5PY0EVuaIb8K+b9ZOpxOtra0AQgc/DocDhYWFqK+vh9VqxerVq5Gfn4+3334bSUlJ6OjoQGFhoXeHJtTzi6+h1k6RFjiBPP4UBzN//vOfsWTJEgwdOhS33norbrvtNgwdOhTLli3DU089pcUaiYhiw5bc+WNCmXS+Z6fls4NAS7v2r6eRzMzMoDfh6urqoDdrsTlcqODH5XLh22+/BQD069fPW5I8duxYVFZWegOayZMnh33+SHaKYo0TyOMvotLsQ4cO4YUXXsBXX30FQRAwdOhQ3HnnncjIyNBijVFhNRMRyeYWgHnrwh8RBVY19UkGfpIObGmMvvpJR5RU5oQrS87OzkZlZWXQ12zevBmTJ09GXV1d2Od3OBwoKioKKnEOfE273R7X4ZjsAKw+TUuzjYbBDBEpIlYhhXJrHpBild9/JpRQfWl0pLq6GgUFBd7Pq6qqkJ+fL/nYSMqS5T4/e7gkJgYzPhjMEJFiUv1h+iQD1wX0h4l0JyfweXRIy+AkkudnD5fEo2mfGSIi08tLBxZMAGZdBMwY5fnzsQnBAYicqiUBwLXnh38enYmkMkdJWXIkzy+3h4vek4VJGwxmiIikdLEAQ84Exgzw/CmV2yK3Gqm3Nfzz6EgklTlKgpNIK3/kBEtGSBYmbTCYISKKlNxqJB1WLYUSrjJn9erVyM7O9qvM8Q1OcnJyOg1OIqn8kRssKS0rJ/OIKJjp6OjAp59+ir///e/evxSHDh3yzmoiIkoIg/t6Zi2F49t/xgBsNhvKy8tht9v9jnBcLhduu+02AMDixYu9lTkpKSlITU1FUlIS+vTp481tCBWchHp+8WvsdjvKy8u9z69kJ0dpWXkkeIylT4qDmQMHDmD48OG4+uqrcdddd+G///0vAE//mdmzZ6u+QCIi3epi8QyNDOe6obo/Wgpks9mC3vDFXY/6+npcddVV3l2PlpYWHDlyBB0dHd7yZJFUcBLq+UWZmZl+j1W6k+N7e21tLQoKClSreuIxln4pDmZmzpyJ0aNH4+jRo+jRo4f39l/84hdYu3atqosjItK9vHRPmXXgDk2fZEOUX8sVbtejrq4u5K5HYHCilNKdHPF2LQY+8hhLvxSXZp911lmoqqrCueeei5SUFHz++efIzc1FXV0dhg4dihMnTmi11oiwNJuIYsIteKqbxP4zg/sabkdGDiOUSGu5Rva8iR1NS7PdbjdOnToVdLvY4ZCIKCHJqX4SuQVg72HP6IO9h3U7PVuKVrseatF64KOWx1gUOcXBzM9+9jMsWrTI+7nFYsGxY8fw6KOP4vLLL1dzbURE5rO90dNob9FGYGmN58956zy3G4CSfjKxFquBj3oP6BKR4mDm2Wefhd1ux9ChQ9HW1oapU6ciJycHBw8exJ/+9Cct1khEZA7iqITARnvNbZ7btx7S9Y6N1rse0VYKxWrgo54DukQV0TiD77//Hm+99Ra2bt0Kt9uNCy64ANOmTfNLCNYL5swQkS5EMvogNdlTLRUqiTiGeTpaD3wUK4WcTmfQcY34GmlpaUHJvlLPo+XAR+bMxI6ms5k2bNiA/Px8JCUl+d3e0dGB6upqjBs3TvmKNcRghoh0Ye9hz5FSJKSqoqTmR3UW/ERBrWAjFCNMxzbCGs1E0wTg8ePH48iRI0G3u1wujB8/XunThdXR0YF58+Zh0KBB6NGjB3Jzc/HHP/4Rbrdb1dchItKc3NEHUt7b5X/k1NlxlQb5N5GUSCsRi4Z30YrVMRYpl9T5Q/wJggCLJXgb8/Dhw+jZs6cqixL96U9/wksvvYTXXnsNw4YNw5YtWzBjxgzYbDbMnDlT1dciItJUNCMNjrZ5jpOGnOkJat7dFf7x7+0CRvZX/cjJZrOFDFbUCDLEoEAMYMQJ3Ho5vhEDOqljLDGgi/YYiyIjO5iZNGkSAE/10s033wyr1eq979SpU9ixY0fIUe+R+s9//oOrr74aV1xxBQAgJycHK1aswJYtW1R9HSIizYmjDzqbsh2KuLMjZ1K3b/BjMGKlkBjIAKErhbTOj5GidUBHkZF9zCT+AAVB8P4FET/69++PX/3qV3jjjTdUXVxhYSHWrl2LvXv3AgA+//xzVFZWhi0Bb29vR0tLi98HEVHcyRl9EI64syP3uCqaY604klspxNEC5Ev2zszSpUsBeHZHZs+erfqRkpQ5c+bA5XLhvPPOQ9euXXHq1Ck8/vjjmDJlSsivWbhwIebPn6/52oiIFBNHHwQm7gZWMQXyHVZpwkndonCVQsXFxX5HTYGjBaSSccXH8djH/CIqzY6Vt956C/fffz+eeuopDBs2DDU1NZg1axaeeeYZTJ8+XfJr2tvb0d7e7v28paUFWVlZrGYiIv0ILKk+dhJYvC3042/NA1KsnsenWIHXPw9/1NQnGXhsgqHGKURSKcQyaXPTtDQbAN577z288847qK+vx8mTJ/3u27YtzP+QCmVlZeHBBx/EXXfd5b1twYIFeOONN/DVV1/Jeg6WZhORIUiVWvdJBn6SDmxp9L/9jG7AiR9CP5cBB1xGWvpthFlRFBkl79+Kq5mee+45zJ07F9OnT8c//vEPzJgxA9988w0+++wzv6BDDSdOnECXLv5pPV27dmVpNhGZT166pwJJzo6NGMj07AYc9wlq+iQD12nTZ0ZrkVYKKUkYJvNSHMz87W9/w8svv4wpU6bgtddewwMPPIDc3Fz8/ve/l+w/E42f//znePzxx5GdnY1hw4Zh+/bteOaZZ3DLLbeo+jpERLogDqsETncMDqdbF+DuC4HWdlNM6o6kUihUwjB3ZhKL4qZ59fX13hLsHj16oLW1FYDnL8+KFStUXdxf//pXXHfddbjzzjtx/vnnY/bs2bjjjjvw2GOPqfo6RES6I6cEu7ndE7zImdRtQlrPiiLjUBzM9O/fH4cPHwYADBw4EBs3etpz79+/H2rnEqekpGDRokU4cOAAvv/+e3zzzTdYsGABunfvrurrEBHpTjQl2G5B1wMr1RCrCdlkDIqPmSZMmIB//vOfuOCCC3DrrbfinnvuwXvvvYctW7Z4G+sREVGUIi3BjvHMpngRRwsAkBwtICYMc7RAYlBczeR2u+F2u72DJt955x1UVlZi8ODB+PWvf627XRNWMxGRIcmZsh1Ygi3ObArFgFVO4cSjAzDFjual2aEcPHgQAwYMUOvpVMFghogMS0lwEknwk0AY+BiPplOzpTQ1NeF3v/sdBg8erMbTERERcLpjcGrAUVKf5OBdFiUzmxIMRx+Yn+xgprm5GdOmTcPZZ5+NjIwMPPfcc3C73fj973+P3NxcbNy4EUuWLNFyrUREiScvHVgwAZh1ETBjlOfPxyYEHxeZfGZTNAJHH4gBjW81lNPp9FbnkvHITgB++OGHsWHDBkyfPh3l5eW45557UF5ejra2Nnz88ccoKirScp1ERInLt/9MKCae2RStzMxMb1KwGND4jj7Izs5GRUVF0BEUj5+MQ/bOzIcffoilS5fi6aefxurVqyEIAoYMGYJ169YxkCEiirfBfYOPowL5DqxMMGKVk1i2XVBQgNraWlitVsnH8/jJWGQHM4cOHcLQoZ7x9bm5uUhOTsZtt92m2cKIiEiBLhZP+XU41w2VTv5NgL40wOnRB77OPPNM1NfX8/jJ4GQfM7ndbnTr1s37edeuXdGzZ09NFkVERBEQE4alBlaGmtmUIH1pAOnRB0lJScjOzpY8fhKb8YUapaBniVa9Jbs0u0uXLrjsssu8W3L//Oc/MWHChKCAZuXKleqvMgoszSaihOMW/AdWhprZlEB9aQJHHwTmzACecT0iI0/ejnQCeajnildQpMnU7OnTp/t9/stf/jKy1RERkbbkJAy7Bc+OTDjv7fJM8jZ4Xxqp0Qe+nYJra2uRkZHh9zVGnrwdWL0lXq9vQCc+LlwgomZQpDXZwczSpUu1XAcREcWSkr40nQVGOtfZ6IPCwkJ8++23fl9j5MnbnVVvyT0+UysoigVVmuYREZHBJFBfGpvNhvLyctjt9pDBSXt7u6kmb4eq3lJyfCYGRb7fj+rq6qBdLj3kFDGYISJKFL5VSy3t8r7GJH1pbDabZB+Z4uJi1NfXG3bytsvlCrk+i8WCF1980e82pcdnagRFsaB4ajYRERmQVNWSBUC4EhCT96Ux+uTtznJa1Do+E0vaCwoKvLfpLaeIOzNERGYnVi0F5sh0Vssaqi+NSYQ7fsrKyoLdbtdFcmso4cY0FBYWor6+Hu3t7cjOzo7q+EyqpL20tFRXR3AMZoiIzExO1VJgvCI1yNKkpI6fRJmZmboNZIDQOS1iIAMA2dnZqKysjPj4LLCkXa85RbL7zBgV+8wQUULbexhYtLHzx117PtDbGr4vTQIxUtO5wOoiALBarejXrx8qKysjLql2OBwoKioKypEJDHDsdrsmScBK3r+5M0NEZGZyq5F6W4ExAzxl2AxkUFJSgqKioqCdBz3ObJIa07B69WpUVVVFdXwm5hTl5uaiYt16ZH1/BvDZQWR9fwYq1q1Hbm6ubnKKmABMRGRmnKatmJH6qwDSOS2/+c1vUFFRIfl4ubsoYk5R63/2IfPvX/vlXGWlJsP+wjtIuXiwLr4H3JkhIjIzTtNWzEj9VbTOabHVnkDm6qbg5PHmNmSuboKt9kRUz68WBjNERGYWzTRtpUw0fVvr/irh+sM4HI6gIyypx/uOacjJyVG/T47ckRc6+DkzmCEiMjtxmnbgDo2aVUvbG4F56zzJxktrPH/OW+e53aCkclHU6K+iNCcn1ONTUlKQmpqKpKQk9OnTx5sk6xuIRZXTomTkRZwxZ4aIKBHkpXuGRsqZpq1UqOnbzW2e2w1a5h2qv0q0OzNKc3JCPb6lpQVHjhxBR0eHt/pKzF8RE32jqroy0MgL7swQESUKcZq2mlVLBjqKUELLXBSlOTnhHl9XVxcyhyfqPjkGSh5nMENERJEz0FGEXL65KFrNbFKakxOXGUkGSh5nMENERJEz0FGEXH79VSRmNqnVX0VpTo5WOTwhxTJ5PErsAExERMq4hdO5Ny3twPu7O/+aWRd5jrYMIhYdgKU694bbaVH6eNVIDSntk+wJZDTMhVLy/s1ghoiI5Itk+naqFbhpFNDaznEJ/ycwJ6esrAylpaUhj46UPl51vgFsjH6GDGZ8MJghIlJJqKqlzvTsBhz/4fTnqcme4wsDVjipQenMo3jPSIoXzmYiIiJ1RTJ9u2c3z5++gQxwumTbwD1ooqE0JydWOTxGxp0ZIiLqnNLp2ylW4PXPw1c69UkGHpsQ2yOnOByXSFGak2OkKd5qUfL+zaZ5RETUOaXTt/cell+yHZgYrFXAIZXvE6cjL5vNFjL4kApYlD4+0TCYISIiaYFVS3KIDdQiLdnWKuAwaZdi8mAwQ0REwSKpWvJtoBZJ91itAg65XYpH9k/YKiujH2MxAZiIiPyJQUXgMVFnGZa+DdSUdo/VciyCCbsUq0np4Es9YjBDRESnRVK1JDV9W2n3WC0DDhN2KY6Uy+UKGsPgO8hy3Lhx3oDGt/Tb6XSitbU1HkuWhcEMERGdJieoEOCpWpoxytPZ97EJ0sc/eemeICdwh0Yq+NEy4DDQwEQthdqByczMxPLly5GUlIS6ujqMGzcu7OBLPWLODBERnaa0aqkzeemeXJTOqpO0DDjEI6/OysR1MDBRS747MMXFxX7N96ZOnYqOjg5vQFNQUAAgRuMSVMCdGSIiOk2LoKKLxVN+PWaA50+pJFstJzQbaGCiljIzM4MmfwfuwLzzzjt+X6PpIEsVMZghIqLTtAwqwtE64FBy5GVivl2Da2trUVBQ4A1kli9fjtmzZ/s9vrS0NCgpWI/YAZiIiPx1NoNJrTd/qeZ4nzeFntAs57gqktc0+Y6MlOrqau9REgCsXLkSs2fPjt8gSwkcNOmDwQwRUQSk+syIQYUagUy45nhSQYtUkJPgAysj5VulJEpKSkJHR4euBlkymPHBYIaIKEJajhVQsvMTq52iBBAYoJSVlWHatGmoq6tDUlISKisrceGFFwY9Pi0tDeXl5TFtnMep2UREFD05ibtKKW2Op2UzvQTjcDiCyq3z8/OxYcMG5OTkoKOjA1OnTvXrQ5OVlQW73R7zQEYpBjNERBQ7SpvjsXuvalJSUpCWlhaUA5OVlYUNGzYgNzcXaWlpSElJ8fu6zMxMXQcyAPvMEBFRLCltjsfuvaqx2WwoLy+XnMEk7sDofQZTKAxmiIgodpT2sTF7994YV1fZbLaQwYqeO/x2hsEMERHFjtJuvGbu3huuoitUQjNLyyUxmCEiotgRm+OFq07ybY6n9PFGEapCq7nNc7tUhVYkwU+CYAIwERHFltJuvGbr3htJhZYY/ATuUInBz/ZG9ddpILrfmTl48CDmzJmDjz/+GN9//z2GDBmCV199FT/5yU/ivTQiIoqU3AGUkT4+XuQcAymp0BpypvzgZ2R//X0/YkTXwczRo0dRUFCA8ePH4+OPP0ZaWhq++eYbpKamxntpREQULbGPjVaPjzW5x0BKK7SUBj8JSNfBzJ/+9CdkZWVh6dKl3ttycnLityAiIiIpSnJglFZosTy9U7rOmVm9ejVGjx6N66+/HmlpacjLy8Mrr7wS9mva29vR0tLi90FERBSWWwD2HgY+O+j5U0lHYaU5MEonk5u9PF0Fug5mamtr8eKLL+Kcc87Bv//9b/z617/G3Xffjddffz3k1yxcuNBbR2+z2WI+5ZOIiAxmeyMwbx2waCOwtMbz57x18pNqlXYpFiu0wvGt0FIa/CQgXQ+a7N69O0aPHo3q6mrvbXfffTc+++wz/Oc//5H8mvb2drS3t3s/b2lpQVZWFgdNEhFRMDWGWH520BMEdWbGKM+cK9/XljuZPAGHbSoZNKnrnJn09HQMHeofvZ5//vl4//33Q36N1WqF1WrVemlERKRncqqK1KoSivQYSEmFllieLjf4iZRBm/LpOpgpKCjAnj17/G7bu3cvBg4cGKcVERGR7smtKlKrSiiaLsVKKrS0Lk83cFM+XefM3HPPPdi4cSOeeOIJ7Nu3D8uXL8fLL7+Mu+66K95LIyIiPVLSXE6tKiGlOTDREIOfMQM8f6oZyBi4KZ+ug5kxY8Zg1apVWLFiBX784x/jsccew6JFizBt2rR4L42IiPRGaVWRmlVCRutS7Fu99dV3yjsS64yuj5kA4Morr8SVV14Z72UQEZHeKT02UnuIpVG6FEsdJ3VG5035dB/MEBERyaL02EiLIZZG6FIc7nrD0XFTPl0fMxEREckWybFRrI6HomnKpxY5x3Dh6LgpH3dmiIjIHCI9NkqUKiE5x3Ch6LwpH3dmiIjIHKKpKkqEKqFojonUqsbSCIMZIiIyNt8jnJ7dgdt0UlWktLoq8GvVPpaK5JhIr9VYAXjMRERExhXqCOfa84EUq3ZVRXI65UbalE+rYyk5x3CpVuCmUUBru36rsSQwmCEiImMKVZnT3Aa8ut2zo+A7C0nN15UTbETSlC/cNb2yLbpdEjnVW9cPA847K7LnjyMeMxERkfFEc4QTDSU5MLZkuNqPw3HsO8mnchz7Dq7246ePf2JxTUZr7icTd2aIiMh41JqrpITCwZSus7uipPwPcB47ioqrFyKr19nehzUc+y+K//EQ0lL6oPzpibABsbsmozT3U4A7M0REZDxqzVVSQkmwAaD1+DE4u5xAbUsTiv/xEBqO/RfA6UCmtqUJTssJtB4/pmytalyTVtVbccJghoiIjEfNuUpyKQw2MjMzUVH9/5CbOdAb0FQ37fYGMrmZA1FR/f+QmZmpbK06bl4XLwxmiIjIeMTKnHDUaPTmWyLd0i7va3yCjaysLE9Ak5uL2pYmFKy63xPI5Oaiovr/ISsr6/TXxeqaTIjBDBERGU80DfLk2t4IzFsHLNoILK0B3t8NdPZ0EsFGVlYWysrK/G4rKyvzD2SA2FyTSTGYISIiY9KyMidU1VJnhUQSwUZDQwNKS0v9bistLUVDQ0Pw15u02khrrGYiIiLj0qIyR07VkgX+gU2fZE8gExBsNDQ0oLi4GLW1tcjNzUVZWRlKS0tRW1uL4uJiVFRUBO/QmLDaSGsMZoiIyNjEyhy1yKlaEuDpMtzbGjLYcDgcfoGMGLhUVFR4by8uLobdbj+dBKzVNZkcgxkiIiJfcquWelvDdhhOSUlBWloaAPjtwPgGNGlpaUhJSYl6yYmOwQwREZEvlUqkbTYbysvL0draGrTzkpWVBbvdjpSUFNhstkhXGhcul0vymgDPblQ8rokJwERERL5ULJG22WySb/qApw+NEQOZkpISFBUVBSUwNzQ0oKioCCUlJXC5XDFdF4MZIiIiXyyRDqm1tRVOp9Ob7yMGNL6Jzk6nE62trTFdF4MZIiKiQCyRlpSZmYmKigpPE8D/C2iqq6uDEp1D7UZpxSIIgsojRfWlpaUFNpsNLpcLvXv3jvdyiIjISNwCS6Ql+O7EiHwrttSg5P2bOzNEREShmGwgo1pkdzWOEQYzREREOuFyueBwOCTvczgcyhNrfWdL7T3s+VwFiroaxwCDGSIiIh1QvVIocLbUoo2ez7c3RrXOwK7GVVVVfjk08QhoGMwQERHpgKqVQqFmSzW3eW6PMKCR6mqcn58flBQcandJKwxmiIiIdCCqSiHf46Svvut8ttR7uyI6chK7Ggcm+4pdjXNzc+PS1ZjVTERERDqiuFJoe6MneOlsnlSgWRdFNP8pVh2AWc1ERERkUIoqhUIdJ8khdwZVAD12NWYwQ0REpCOyK4XcQufHSeHInUEVikaVUpHgoEkiIiKdCKwUKisrQ2lpqTeHxu+oad+RyHZkANmzpUKSOtpKTfaMgYhDd2TuzBAREemA4kqhCI+JAEQ3W0qjSqloMJghIiLSAcWVQpEcE0U7W0rO0VaElVLR4DETERGRDthsNpSXl0tWCmVlZcFut/tXCg3u6znaCXfUlGoFbhoFtLarM1tKztHW0TbP4yKolIoUgxkiIiKdsNlsIauBgiqIulg8OSqvbAv9hNcPA847S70Fyj3aiuYILAI8ZiIiIjKqvHTPsVFqwJFTtMdJocg92oq2Ukoh7swQEREZWV46MLK/52jH1abOcVIoco62oq2UigB3ZoiIiIyui8WTozJmgOdPLQIZ8XWuHxr+MdFUSkWIwQwRERHJF+ujLRl4zERERETKxPJoSwYGM0RERKSceLSlAzxmIiIiIkNjMENERESGxmCGiIiIDI3BDBERERkagxkiIiIyNAYzREREZGgMZoiIiMjQGMwQERGRoTGYISIiIkMzfQdgQRAAAC0tLXFeCREREcklvm+L7+PhmD6YaW1tBQBkZWXFeSVERESkVGtrK2w2W9jHWAQ5IY+Bud1uHDp0CCkpKbBY1B2A1dLSgqysLDQ0NKB3796qPrfe8FrNK5Gul9dqXol0vYlyrYIgoLW1FRkZGejSJXxWjOl3Zrp06YLMzExNX6N3796m/gvli9dqXol0vbxW80qk602Ea+1sR0bEBGAiIiIyNAYzREREZGgMZqJgtVrx6KOPwmq1xnspmuO1mlciXS+v1bwS6XoT6VrlMn0CMBEREZkbd2aIiIjI0BjMEBERkaExmCEiIiJDYzBDREREhsZgphMbNmzAz3/+c2RkZMBiseCDDz7wu18QBPzhD39ARkYGevTogeLiYnz55ZfxWWyUFi5ciDFjxiAlJQVpaWm45pprsGfPHr/HmOV6X3zxRYwYMcLbdOriiy/Gxx9/7L3fLNcpZeHChbBYLJg1a5b3NjNd7x/+8AdYLBa/j/79+3vvN9O1AsDBgwfxy1/+EmeeeSbOOOMMjBo1Clu3bvXeb6brzcnJCfrZWiwW3HXXXQDMda0dHR2YN28eBg0ahB49eiA3Nxd//OMf4Xa7vY8x0/VGTaCwPvroI2Hu3LnC+++/LwAQVq1a5Xf/k08+KaSkpAjvv/++sHPnTmHy5MlCenq60NLSEp8FR+HSSy8Vli5dKnzxxRdCTU2NcMUVVwjZ2dnCsWPHvI8xy/WuXr1a+PDDD4U9e/YIe/bsER5++GGhW7duwhdffCEIgnmuM9DmzZuFnJwcYcSIEcLMmTO9t5vpeh999FFh2LBhQmNjo/fD6XR67zfTtR45ckQYOHCgcPPNNwubNm0S9u/fL3z66afCvn37vI8x0/U6nU6/n+uaNWsEAML69esFQTDXtS5YsEA488wzhX/961/C/v37hXfffVfo1auXsGjRIu9jzHS90WIwo0BgMON2u4X+/fsLTz75pPe2trY2wWazCS+99FIcVqgup9MpABDsdrsgCOa/3j59+giLFy827XW2trYK55xzjrBmzRqhqKjIG8yY7XofffRRYeTIkZL3me1a58yZIxQWFoa832zXG2jmzJnCj370I8HtdpvuWq+44grhlltu8btt0qRJwi9/+UtBEMz/s1WKx0xR2L9/P5qamjBx4kTvbVarFUVFRaiuro7jytThcrkAAH379gVg3us9deoU3nrrLRw/fhwXX3yxaa/zrrvuwhVXXIFLLrnE73YzXu/XX3+NjIwMDBo0CDfeeCNqa2sBmO9aV69ejdGjR+P6669HWloa8vLy8Morr3jvN9v1+jp58iTeeOMN3HLLLbBYLKa71sLCQqxduxZ79+4FAHz++eeorKzE5ZdfDsDcP9tImH7QpJaampoAAP369fO7vV+/fjhw4EA8lqQaQRBw7733orCwED/+8Y8BmO96d+7ciYsvvhhtbW3o1asXVq1ahaFDh3r/ITDLdQLAW2+9hW3btuGzzz4Lus9sP9cLL7wQr7/+OoYMGYJvv/0WCxYsQH5+Pr788kvTXWttbS1efPFF3HvvvXj44YexefNm3H333bBarbjppptMd72+PvjgAzQ3N+Pmm28GYL6/x3PmzIHL5cJ5552Hrl274tSpU3j88ccxZcoUAOa73mgxmFGBxWLx+1wQhKDbjOa3v/0tduzYgcrKyqD7zHK95557LmpqatDc3Iz3338f06dPh91u995vlutsaGjAzJkz8cknnyA5OTnk48xyvZdddpn3v4cPH46LL74YP/rRj/Daa6/hoosuAmCea3W73Rg9ejSeeOIJAEBeXh6+/PJLvPjii7jpppu8jzPL9fp69dVXcdlllyEjI8PvdrNc69tvv4033ngDy5cvx7Bhw1BTU4NZs2YhIyMD06dP9z7OLNcbLR4zRUGskBAjZJHT6QyKlo3kd7/7HVavXo3169cjMzPTe7vZrrd79+4YPHgwRo8ejYULF2LkyJH4y1/+Yrrr3Lp1K5xOJ37yk58gKSkJSUlJsNvteO6555CUlOS9JrNcb6CePXti+PDh+Prrr033s01PT8fQoUP9bjv//PNRX18PwHz/z4oOHDiATz/9FLfddpv3NrNd6/33348HH3wQN954I4YPH47S0lLcc889WLhwIQDzXW+0GMxEYdCgQejfvz/WrFnjve3kyZOw2+3Iz8+P48oiIwgCfvvb32LlypVYt24dBg0a5He/2a43kCAIaG9vN911/vSnP8XOnTtRU1Pj/Rg9ejSmTZuGmpoa5Obmmup6A7W3t2P37t1IT0833c+2oKAgqH3C3r17MXDgQADm/X926dKlSEtLwxVXXOG9zWzXeuLECXTp4v8W3bVrV29pttmuN2pxSjw2jNbWVmH79u3C9u3bBQDCM888I2zfvl04cOCAIAie0jibzSasXLlS2LlzpzBlyhTDlsb95je/EWw2m1BRUeFX/njixAnvY8xyvQ899JCwYcMGYf/+/cKOHTuEhx9+WOjSpYvwySefCIJgnusMxbeaSRDMdb333XefUFFRIdTW1gobN24UrrzySiElJUWoq6sTBMFc17p582YhKSlJePzxx4Wvv/5aePPNN4UzzjhDeOONN7yPMdP1CoIgnDp1SsjOzhbmzJkTdJ+ZrnX69OnCgAEDvKXZK1euFM466yzhgQce8D7GTNcbLQYznVi/fr0AIOhj+vTpgiB4yuMeffRRoX///oLVahXGjRsn7Ny5M76LjpDUdQIQli5d6n2MWa73lltuEQYOHCh0795dOPvss4Wf/vSn3kBGEMxznaEEBjNmul6x10a3bt2EjIwMYdKkScKXX37pvd9M1yoIgvDPf/5T+PGPfyxYrVbhvPPOE15++WW/+812vf/+978FAMKePXuC7jPTtba0tAgzZ84UsrOzheTkZCE3N1eYO3eu0N7e7n2Mma43WhZBEIS4bAkRERERqYA5M0RERGRoDGaIiIjI0BjMEBERkaExmCEiIiJDYzBDREREhsZghoiIiAyNwQwREREZGoMZIiIiMjQGM0RERGRoDGaISJeqq6vRtWtXlJSUxHspRKRzHGdARLp02223oVevXli8eDF27dqF7OzseC+JiHSKOzNEpDvHjx/HO++8g9/85je48sorsWzZMr/7V69ejXPOOQc9evTA+PHj8dprr8FisaC5udn7mOrqaowbNw49evRAVlYW7r77bhw/fjy2F0JEMcFghoh05+2338a5556Lc889F7/85S+xdOlSiJvIdXV1uO6663DNNdegpqYGd9xxB+bOnev39Tt37sSll16KSZMmYceOHXj77bdRWVmJ3/72t/G4HCLSGI+ZiEh3CgoKcMMNN2DmzJno6OhAeno6VqxYgUsuuQQPPvggPvzwQ+zcudP7+Hnz5uHxxx/H0aNHkZqaiptuugk9evTA3//+d+9jKisrUVRUhOPHjyM5OTkel0VEGuHODBHpyp49e7B582bceOONAICkpCRMnjwZS5Ys8d4/ZswYv68ZO3as3+dbt27FsmXL0KtXL+/HpZdeCrfbjf3798fmQogoZpLivQAiIl+vvvoqOjo6MGDAAO9tgiCgW7duOHr0KARBgMVi8fuawA1mt9uNO+64A3fffXfQ8zORmMh8GMwQkW50dHTg9ddfx//+7/9i4sSJfvdde+21ePPNN3Heeefho48+8rtvy5Ytfp9fcMEF+PLLLzF48GDN10xE8cecGSLSjQ8++ACTJ0+G0+mEzWbzu2/u3Ln46KOPsHLlSpx77rm45557cOutt6Kmpgb33XcfHA4HmpubYbPZsGPHDlx00UWYMWMGbr/9dvTs2RO7d+/GmjVr8Ne//jVOV0dEWmHODBHpxquvvopLLrkkKJABPDszNTU1OHr0KN577z2sXLkSI0aMwIsvvuitZrJarQCAESNGwG634+uvv8b//M//IC8vD4888gjS09Njej1EFBvcmSEiw3v88cfx0ksvoaGhId5LIaI4YM4MERnO3/72N4wZMwZnnnkmqqqq8NRTT7GHDFECYzBDRIbz9ddfY8GCBThy5Aiys7Nx33334aGHHor3sogoTnjMRERERIbGBGAiIiIyNAYzREREZGgMZoiIiMjQGMwQERGRoTGYISIiIkNjMENERESGxmCGiIiIDI3BDBERERna/wfuEo+K9bBciQAAAABJRU5ErkJggg==","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# import libraries\n","\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# load the data\n","data_url_1 = \"https://gist.githubusercontent.com/mbonsma/8da0990b71ba9a09f7de395574e54df1/raw/aec88b30af87fad8d45da7e774223f91dad09e88/lh_data.csv\"\n","lefthanded_data = pd.read_csv(data_url_1)\n","\n","\n","# plot male and female left-handedness rates vs. age\n","\n","fig,ax = plt.subplots() # create figure and axis objects\n","ax.scatter(y=lefthanded_data[\"Female\"] ,x= lefthanded_data[\"Age\"], marker = 'o',color=\"hotpink\") # plot \"Female\" vs. \"Age\"\n","ax.scatter(y=lefthanded_data[\"Male\"] ,x=lefthanded_data[\"Age\"], marker = 'x', color=\"k\") # plot \"Male\" vs. \"Age\"\n","ax.legend(\"FM\") # add a legend\n","ax.set_ylabel(\"Rate of Lefthandedness\")\n","ax.set_xlabel(\"Age\")\n"]},{"cell_type":"markdown","id":"7de83377","metadata":{"papermill":{"duration":0.008463,"end_time":"2023-07-01T16:09:29.626621","exception":false,"start_time":"2023-07-01T16:09:29.618158","status":"completed"},"tags":[]},"source":["*From the above plot we can infer that Men have a higher rate of lefthandedness than women and prior a the rate decreases with age, that too Sharply after 50*"]},{"cell_type":"markdown","id":"c0443f92","metadata":{"dc":{"key":"11"},"deletable":false,"papermill":{"duration":0.008047,"end_time":"2023-07-01T16:09:29.643076","exception":false,"start_time":"2023-07-01T16:09:29.635029","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 2. Rates of left-handedness over time\n","<p>Let's convert this data into a plot of the rates of left-handedness as a function of the year of birth, and average over male and female to get a single rate for both sexes. </p>\n","<p>Since the study was done in 1986, the data after this conversion will be the percentage of people alive in 1986 who are left-handed as a function of the year they were born. </p>"]},{"cell_type":"code","execution_count":2,"id":"2a925db0","metadata":{"execution":{"iopub.execute_input":"2023-07-01T16:09:29.661405Z","iopub.status.busy":"2023-07-01T16:09:29.661087Z","iopub.status.idle":"2023-07-01T16:09:29.677413Z","shell.execute_reply":"2023-07-01T16:09:29.676319Z"},"papermill":{"duration":0.027759,"end_time":"2023-07-01T16:09:29.679602","exception":false,"start_time":"2023-07-01T16:09:29.651843","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Age</th>\n","      <th>Male</th>\n","      <th>Female</th>\n","      <th>Mean_Lh</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>10</td>\n","      <td>12.717558</td>\n","      <td>12.198041</td>\n","      <td>12.457800</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>11</td>\n","      <td>15.318830</td>\n","      <td>11.144804</td>\n","      <td>13.231817</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>12</td>\n","      <td>14.808281</td>\n","      <td>11.549240</td>\n","      <td>13.178760</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>13</td>\n","      <td>13.793744</td>\n","      <td>11.276442</td>\n","      <td>12.535093</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>14</td>\n","      <td>15.156304</td>\n","      <td>11.572906</td>\n","      <td>13.364605</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Age       Male     Female    Mean_Lh\n","0   10  12.717558  12.198041  12.457800\n","1   11  15.318830  11.144804  13.231817\n","2   12  14.808281  11.549240  13.178760\n","3   13  13.793744  11.276442  12.535093\n","4   14  15.156304  11.572906  13.364605"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["import statistics as st\n","a=lefthanded_data.copy()\n","\n","a[\"Mean_Lh\"]=(lefthanded_data[\"Female\"]+lefthanded_data[\"Male\"])/2\n","a.head()"]},{"cell_type":"code","execution_count":3,"id":"256653be","metadata":{"dc":{"key":"11"},"execution":{"iopub.execute_input":"2023-07-01T16:09:29.69989Z","iopub.status.busy":"2023-07-01T16:09:29.699096Z","iopub.status.idle":"2023-07-01T16:09:29.977231Z","shell.execute_reply":"2023-07-01T16:09:29.976376Z"},"papermill":{"duration":0.290114,"end_time":"2023-07-01T16:09:29.979176","exception":false,"start_time":"2023-07-01T16:09:29.689062","status":"completed"},"tags":["sample_code"]},"outputs":[{"data":{"text/plain":["Text(0, 0.5, 'Mean Rate of Lefthandedness')"]},"execution_count":3,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# create a new column for birth year of each age\n","lefthanded_data[\"Birth_year\"]=1986-lefthanded_data[\"Age\"]\n","\n","\n","# create a new column for the average of male and female\n","lefthanded_data[\"Mean_lh\"]=(lefthanded_data[\"Female\"]+lefthanded_data[\"Male\"])/2\n","\n","# create a plot of the 'Mean_lh' column vs. 'Birth_year'\n","fig, ax = plt.subplots()\n","ax.plot(lefthanded_data[\"Birth_year\"],lefthanded_data[\"Mean_lh\"],marker=\"*\",color=\"r\") # plot 'Mean_lh' vs. 'Birth_year'\n","ax.set_xlabel(\"Birth Year\") # set the x label for the plot\n","ax.set_ylabel(\"Mean Rate of Lefthandedness\") # set the y label for the plot"]},{"cell_type":"markdown","id":"8df8e359","metadata":{"papermill":{"duration":0.008683,"end_time":"2023-07-01T16:09:29.996776","exception":false,"start_time":"2023-07-01T16:09:29.988093","status":"completed"},"tags":[]},"source":["*From the above plot we can infer that Mean Rate of Lefthandedness has increased from 1900 to 1976 and a Sharp increase was seen between 1920 to 1940*"]},{"cell_type":"markdown","id":"81b992a6","metadata":{"dc":{"key":"18"},"deletable":false,"papermill":{"duration":0.008554,"end_time":"2023-07-01T16:09:30.01425","exception":false,"start_time":"2023-07-01T16:09:30.005696","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 3. Applying Bayes' rule\n","<p>The probability of dying at a certain age given that you're left-handed is <strong>not</strong> equal to the probability of being left-handed given that you died at a certain age. This inequality is why we need <strong>Bayes' theorem</strong>, a statement about conditional probability which allows us to update our beliefs after seeing evidence. </p>\n","<p>We want to calculate the probability of dying at age A given that you're left-handed. Let's write this in shorthand as P(A | LH). We also want the same quantity for right-handers: P(A | RH). </p>\n","<p>Here's Bayes' theorem for the two events we care about: left-handedness (LH) and dying at age A.</p>\n","<p>$$P(A | LH) = \\frac{P(LH|A) P(A)}{P(LH)}$$</p>\n","<p>P(LH | A) is the probability that you are left-handed <em>given that</em> you died at age A. P(A) is the overall probability of dying at age A, and P(LH) is the overall probability of being left-handed. We will now calculate each of these three quantities, beginning with P(LH | A).</p>\n","<p>To calculate P(LH | A) for ages that might fall outside the original data, we will need to extrapolate the data to earlier and later years. Since the rates flatten out in the early 1900s and late 1900s, we'll use a few points at each end and take the mean to extrapolate the rates on each end. The number of points used for this is arbitrary, but we'll pick 10 since the data looks flat-ish until about 1910. </p>"]},{"cell_type":"code","execution_count":4,"id":"4c132726","metadata":{"dc":{"key":"18"},"execution":{"iopub.execute_input":"2023-07-01T16:09:30.033959Z","iopub.status.busy":"2023-07-01T16:09:30.033158Z","iopub.status.idle":"2023-07-01T16:09:30.041168Z","shell.execute_reply":"2023-07-01T16:09:30.040304Z"},"papermill":{"duration":0.021097,"end_time":"2023-07-01T16:09:30.044136","exception":false,"start_time":"2023-07-01T16:09:30.023039","status":"completed"},"tags":["sample_code"]},"outputs":[],"source":["# import library\n","import numpy as np\n","\n","# create a function for P(LH | A)\n","def P_lh_given_A(ages_of_death, study_year = 1990):\n","    \"\"\" P(Left-handed | ages of death), calculated based on the reported rates of left-handedness.\n","    Inputs: numpy array of ages of death, study_year\n","    Returns: probability of left-handedness given that subjects died in `study_year` at ages `ages_of_death` \"\"\"\n","    \n","    # Use the mean of the 10 last and 10 first points for left-handedness rates before and after the start \n","    early_1900s_rate =lefthanded_data[-10:][\"Mean_lh\"].mean()\n","    late_1900s_rate =lefthanded_data[:10][\"Mean_lh\"].mean()\n","    middle_rates = lefthanded_data.loc[lefthanded_data['Birth_year'].isin(study_year - ages_of_death)]['Mean_lh']\n","    youngest_age = study_year - 1986 + 10 # the youngest age is 10\n","    oldest_age = study_year - 1986 + 86 # the oldest age is 86\n","    \n","    P_return = np.zeros(ages_of_death.shape) # create an empty array to store the results\n","    # extract rate of left-handedness for people of ages 'ages_of_death'\n","    P_return[ages_of_death > oldest_age] = early_1900s_rate/100\n","    P_return[ages_of_death < youngest_age] =  late_1900s_rate/100\n","    P_return[np.logical_and((ages_of_death <= oldest_age), (ages_of_death >= youngest_age))] = middle_rates / 100\n","    \n","    return P_return"]},{"cell_type":"markdown","id":"3a6d8188","metadata":{"dc":{"key":"25"},"deletable":false,"papermill":{"duration":0.008762,"end_time":"2023-07-01T16:09:30.062445","exception":false,"start_time":"2023-07-01T16:09:30.053683","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 4. When do people normally die?\n","<p>To estimate the probability of living to an age A, we can use data that gives the number of people who died in a given year and how old they were to create a distribution of ages of death. If we normalize the numbers to the total number of people who died, we can think of this data as a probability distribution that gives the probability of dying at age A. The data we'll use for this is from the entire US for the year 1999 - the closest I could find for the time range we're interested in. </p>\n","<p>In this block, we'll load in the death distribution data and plot it. The first column is the age, and the other columns are the number of people who died at that age. </p>"]},{"cell_type":"code","execution_count":5,"id":"342078d5","metadata":{"dc":{"key":"25"},"execution":{"iopub.execute_input":"2023-07-01T16:09:30.081405Z","iopub.status.busy":"2023-07-01T16:09:30.081141Z","iopub.status.idle":"2023-07-01T16:09:30.436293Z","shell.execute_reply":"2023-07-01T16:09:30.435342Z"},"papermill":{"duration":0.366903,"end_time":"2023-07-01T16:09:30.438447","exception":false,"start_time":"2023-07-01T16:09:30.071544","status":"completed"},"tags":["sample_code"]},"outputs":[{"name":"stdout","output_type":"stream","text":["5\n","0\n"]},{"data":{"text/plain":["Text(0, 0.5, 'Number of Deaths in Both Sexes')"]},"execution_count":5,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Death distribution data for the United States in 1999\n","data_url_2 = \"https://gist.githubusercontent.com/mbonsma/2f4076aab6820ca1807f4e29f75f18ec/raw/62f3ec07514c7e31f5979beeca86f19991540796/cdc_vs00199_table310.tsv\"\n","\n","# load death distribution data\n","death_distribution_data=pd.read_csv(data_url_2,sep='\\t',skiprows=[1])\n","\n","# drop NaN values from the `Both Sexes` column\n","print(death_distribution_data[\"Both Sexes\"].isna().sum())\n","death_distribution_data.dropna(subset=\"Both Sexes\",inplace=True)\n","print(death_distribution_data[\"Both Sexes\"].isna().sum())\n","#plot number of people who died as a function of age\n","fig, ax = plt.subplots()\n","ax.plot(\"Age\", \"Both Sexes\", data =death_distribution_data ,color=\"g\", marker='o') # plot 'Both Sexes' vs. 'Age'\n","ax.set_xlabel(\"Age\") \n","ax.set_ylabel(\"Number of Deaths in Both Sexes\")"]},{"cell_type":"code","execution_count":6,"id":"18b333ee","metadata":{"execution":{"iopub.execute_input":"2023-07-01T16:09:30.45939Z","iopub.status.busy":"2023-07-01T16:09:30.458641Z","iopub.status.idle":"2023-07-01T16:09:30.466285Z","shell.execute_reply":"2023-07-01T16:09:30.465382Z"},"papermill":{"duration":0.020926,"end_time":"2023-07-01T16:09:30.46892","exception":false,"start_time":"2023-07-01T16:09:30.447994","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0        0\n","1        1\n","2        2\n","3        3\n","4        4\n","      ... \n","115    115\n","117    117\n","118    118\n","119    119\n","120    120\n","Name: Age, Length: 120, dtype: int64"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["death_distribution_data[\"Age\"]"]},{"cell_type":"markdown","id":"7c076f19","metadata":{"dc":{"key":"32"},"deletable":false,"papermill":{"duration":0.009796,"end_time":"2023-07-01T16:09:30.488216","exception":false,"start_time":"2023-07-01T16:09:30.47842","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 5. The overall probability of left-handedness\n","<p>In the previous code block we loaded data to give us P(A), and now we need P(LH). P(LH) is the probability that a person who died in our particular study year is left-handed, assuming we know nothing else about them. This is the average left-handedness in the population of deceased people, and we can calculate it by summing up all of the left-handedness probabilities for each age, weighted with the number of deceased people at each age, then divided by the total number of deceased people to get a probability. In equation form, this is what we're calculating, where N(A) is the number of people who died at age A (given by the dataframe <code>death_distribution_data</code>):</p>\n","<p><img src=\"https://i.imgur.com/gBIWykY.png\" alt=\"equation\" width=\"220\"></p>\n","<!--- $$P(LH) = \\frac{\\sum_{\\text{A}} P(LH | A) N(A)}{\\sum_{\\text{A}} N(A)}$$ -->"]},{"cell_type":"code","execution_count":7,"id":"494caf91","metadata":{"dc":{"key":"32"},"execution":{"iopub.execute_input":"2023-07-01T16:09:30.510108Z","iopub.status.busy":"2023-07-01T16:09:30.50957Z","iopub.status.idle":"2023-07-01T16:09:30.521703Z","shell.execute_reply":"2023-07-01T16:09:30.520522Z"},"papermill":{"duration":0.025247,"end_time":"2023-07-01T16:09:30.524079","exception":false,"start_time":"2023-07-01T16:09:30.498832","status":"completed"},"tags":["sample_code"]},"outputs":[{"name":"stdout","output_type":"stream","text":["0.07766387615350638\n"]}],"source":["def P_lh(death_distribution_data, study_year = 1990): # sum over P_lh for each age group\n","    \"\"\" Overall probability of being left-handed if you died in the study year\n","    Input: dataframe of death distribution data, study year\n","    Output: P(LH), a single floating point number \"\"\"\n","    p_list = P_lh_given_A(death_distribution_data['Age'], study_year = study_year)*death_distribution_data[\"Both Sexes\"] # multiply number of dead people by P_lh_given_A\n","    p = p_list.sum() # calculate the sum of p_list\n","    return p/death_distribution_data[\"Both Sexes\"].sum() # normalize to total number of people (sum of death_distribution_data['Both Sexes'])\n","\n","print(P_lh(death_distribution_data))"]},{"cell_type":"markdown","id":"80d8c7d8","metadata":{"dc":{"key":"39"},"deletable":false,"papermill":{"duration":0.009727,"end_time":"2023-07-01T16:09:30.545029","exception":false,"start_time":"2023-07-01T16:09:30.535302","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 6. Putting it all together: dying while left-handed (i)\n","<p>Now we have the means of calculating all three quantities we need: P(A), P(LH), and P(LH | A). We can combine all three using Bayes' rule to get P(A | LH), the probability of being age A at death (in the study year) given that you're left-handed. To make this answer meaningful, though, we also want to compare it to P(A | RH), the probability of being age A at death given that you're right-handed. </p>\n","<p>We're calculating the following quantity twice, once for left-handers and once for right-handers.</p>\n","<p>$$P(A | LH) = \\frac{P(LH|A) P(A)}{P(LH)}$$</p>\n","<p>First, for left-handers.</p>\n","<!--Notice that I was careful not to call these \"probability of dying at age A\", since that's not actually what we're calculating: we use the exact same death distribution data for each. -->"]},{"cell_type":"code","execution_count":8,"id":"4c79912d","metadata":{"execution":{"iopub.execute_input":"2023-07-01T16:09:30.566821Z","iopub.status.busy":"2023-07-01T16:09:30.565995Z","iopub.status.idle":"2023-07-01T16:09:30.575542Z","shell.execute_reply":"2023-07-01T16:09:30.574451Z"},"papermill":{"duration":0.022706,"end_time":"2023-07-01T16:09:30.577782","exception":false,"start_time":"2023-07-01T16:09:30.555076","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["2     0.000575\n","34    0.002346\n","55    0.008009\n","67    0.015317\n","Name: Both Sexes, dtype: float64"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["(death_distribution_data.loc[death_distribution_data[\"Age\"].isin([34,2,67,55])][\"Both Sexes\"])/death_distribution_data[\"Both Sexes\"].sum()"]},{"cell_type":"code","execution_count":9,"id":"078ae7b8","metadata":{"execution":{"iopub.execute_input":"2023-07-01T16:09:30.600899Z","iopub.status.busy":"2023-07-01T16:09:30.600121Z","iopub.status.idle":"2023-07-01T16:09:30.608919Z","shell.execute_reply":"2023-07-01T16:09:30.607783Z"},"papermill":{"duration":0.022627,"end_time":"2023-07-01T16:09:30.611192","exception":false,"start_time":"2023-07-01T16:09:30.588565","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["67    0.015317\n","Name: Both Sexes, dtype: float64"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["death_distribution_data.loc[death_distribution_data[\"Age\"]==67][\"Both Sexes\"]/death_distribution_data[\"Both Sexes\"].sum()"]},{"cell_type":"code","execution_count":10,"id":"0b82a596","metadata":{"dc":{"key":"39"},"execution":{"iopub.execute_input":"2023-07-01T16:09:30.633556Z","iopub.status.busy":"2023-07-01T16:09:30.632837Z","iopub.status.idle":"2023-07-01T16:09:30.63861Z","shell.execute_reply":"2023-07-01T16:09:30.637781Z"},"papermill":{"duration":0.018671,"end_time":"2023-07-01T16:09:30.640649","exception":false,"start_time":"2023-07-01T16:09:30.621978","status":"completed"},"tags":["sample_code"]},"outputs":[],"source":["def P_A_given_lh(ages_of_death, death_distribution_data, study_year = 1990):\n","    \"\"\" The overall probability of being a particular `age_of_death` given that you're left-handed \"\"\"\n","    P_A = (death_distribution_data.loc[death_distribution_data[\"Age\"].isin(ages)][\"Both Sexes\"])/death_distribution_data[\"Both Sexes\"].sum()\n","    P_left = P_lh(death_distribution_data,study_year=study_year) # use P_lh function to get probability of left-handedness overall\n","    P_lh_A = P_lh_given_A(ages_of_death, study_year) # use P_lh_given_A to get probability of left-handedness for a certain age\n","    return P_lh_A*P_A/P_left"]},{"cell_type":"markdown","id":"9a1061db","metadata":{"dc":{"key":"46"},"deletable":false,"papermill":{"duration":0.009652,"end_time":"2023-07-01T16:09:30.660478","exception":false,"start_time":"2023-07-01T16:09:30.650826","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 7. Putting it all together: dying while left-handed (ii)\n","<p>And now for right-handers.</p>"]},{"cell_type":"code","execution_count":11,"id":"b239b3c9","metadata":{"dc":{"key":"46"},"execution":{"iopub.execute_input":"2023-07-01T16:09:30.681608Z","iopub.status.busy":"2023-07-01T16:09:30.681339Z","iopub.status.idle":"2023-07-01T16:09:30.686786Z","shell.execute_reply":"2023-07-01T16:09:30.685871Z"},"papermill":{"duration":0.018455,"end_time":"2023-07-01T16:09:30.688786","exception":false,"start_time":"2023-07-01T16:09:30.670331","status":"completed"},"tags":["sample_code"]},"outputs":[],"source":["def P_A_given_rh(ages_of_death, death_distribution_data, study_year = 1990):\n","    \"\"\" The overall probability of being a particular `age_of_death` given that you're right-handed \"\"\"\n","    P_A =  (death_distribution_data.loc[death_distribution_data[\"Age\"].isin(ages)][\"Both Sexes\"])/death_distribution_data[\"Both Sexes\"].sum()\n","    P_right = 1-P_lh(death_distribution_data,study_year=study_year) # either you're left-handed or right-handed, so P_right = 1 - P_left\n","    P_rh_A = 1-P_lh_given_A(ages_of_death, study_year) # P_rh_A = 1 - P_lh_A \n","    return P_rh_A*P_A/P_right"]},{"cell_type":"markdown","id":"377e51a0","metadata":{"dc":{"key":"53"},"deletable":false,"papermill":{"duration":0.010107,"end_time":"2023-07-01T16:09:30.708941","exception":false,"start_time":"2023-07-01T16:09:30.698834","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 8. Plotting the distributions of conditional probabilities\n","<p>Now that we have functions to calculate the probability of being age A at death given that you're left-handed or right-handed, let's plot these probabilities for a range of ages of death from 6 to 120. </p>\n","<p>Notice that the left-handed distribution has a bump below age 70: of the pool of deceased people, left-handed people are more likely to be younger. </p>"]},{"cell_type":"code","execution_count":12,"id":"45fab7fc","metadata":{"dc":{"key":"53"},"execution":{"iopub.execute_input":"2023-07-01T16:09:30.730994Z","iopub.status.busy":"2023-07-01T16:09:30.730224Z","iopub.status.idle":"2023-07-01T16:09:31.047274Z","shell.execute_reply":"2023-07-01T16:09:31.04636Z"},"papermill":{"duration":0.3303,"end_time":"2023-07-01T16:09:31.049503","exception":false,"start_time":"2023-07-01T16:09:30.719203","status":"completed"},"tags":["sample_code"]},"outputs":[{"data":{"text/plain":["Text(0, 0.5, 'Probability of being age A at death')"]},"execution_count":12,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["ages = np.arange(6, 115, 1) # make a list of ages of death to plot\n","\n","\n","# calculate the probability of being left- or right-handed for each \n","left_handed_probability = P_A_given_lh(ages, death_distribution_data, study_year = 1990)\n","right_handed_probability = P_A_given_rh(ages, death_distribution_data, study_year = 1990)\n","\n","# create a plot of the two probabilities vs. age\n","fig, ax = plt.subplots() # create figure and axis objects\n","ax.plot(ages, left_handed_probability, label = \"Left-handed\")\n","ax.plot(ages, right_handed_probability, label = \"Right-handed\")\n","ax.legend(\"LR\") # add a legend\n","ax.set_xlabel(\"Age at death\")\n","ax.set_ylabel(r\"Probability of being age A at death\")"]},{"cell_type":"markdown","id":"14505089","metadata":{"dc":{"key":"60"},"deletable":false,"papermill":{"duration":0.011069,"end_time":"2023-07-01T16:09:31.07179","exception":false,"start_time":"2023-07-01T16:09:31.060721","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 9. Moment of truth: age of left and right-handers at death\n","<p>Finally, let's compare our results with the original study that found that left-handed people were nine years younger at death on average. We can do this by calculating the mean of these probability distributions in the same way we calculated P(LH) earlier, weighting the probability distribution by age and summing over the result.</p>\n","<p>$$\\text{Average age of left-handed people at death} = \\sum_A A P(A | LH)$$</p>\n","<p>$$\\text{Average age of right-handed people at death} = \\sum_A A P(A | RH)$$</p>"]},{"cell_type":"code","execution_count":13,"id":"f8ca44b7","metadata":{"dc":{"key":"60"},"execution":{"iopub.execute_input":"2023-07-01T16:09:31.096229Z","iopub.status.busy":"2023-07-01T16:09:31.09541Z","iopub.status.idle":"2023-07-01T16:09:31.103211Z","shell.execute_reply":"2023-07-01T16:09:31.101743Z"},"papermill":{"duration":0.022458,"end_time":"2023-07-01T16:09:31.105558","exception":false,"start_time":"2023-07-01T16:09:31.0831","status":"completed"},"tags":["sample_code"]},"outputs":[{"name":"stdout","output_type":"stream","text":["The average age of left handed individual is 67.245037 and right handed individual is 72.791719\n","The difference in average ages is 5.5 years.\n"]}],"source":["# calculate average ages for left-handed and right-handed groups\n","# use np.array so that two arrays can be multiplied\n","average_lh_age =  np.nansum(ages*np.array(left_handed_probability))\n","average_rh_age =  np.nansum(ages*np.array(right_handed_probability))\n","\n","# print the average ages for each group\n","print(\"The average age of left handed individual is %f and right handed individual is %f\"%(average_lh_age,average_rh_age))\n","\n","# print the difference between the average ages\n","print(\"The difference in average ages is \" + str(round(average_rh_age -average_lh_age, 1)) + \" years.\")"]},{"cell_type":"markdown","id":"b7c7f467","metadata":{"dc":{"key":"67"},"deletable":false,"papermill":{"duration":0.01063,"end_time":"2023-07-01T16:09:31.128301","exception":false,"start_time":"2023-07-01T16:09:31.117671","status":"completed"},"run_control":{"frozen":true},"tags":["context"]},"source":["## 10. Final comments\n","<p>We got a pretty big age gap between left-handed and right-handed people purely as a result of the changing rates of left-handedness in the population, which is good news for left-handers: you probably won't die young because of your sinisterness. The reported rates of left-handedness have increased from just 3% in the early 1900s to about 11% today, which means that older people are much more likely to be reported as right-handed than left-handed, and so looking at a sample of recently deceased people will have more old right-handers.</p>\n","<p>Our number is still less than the 9-year gap measured in the study. It's possible that some of the approximations we made are the cause: </p>\n","<ol>\n","<li>We used death distribution data from almost ten years after the study (1999 instead of 1991), and we used death data from the entire United States instead of California alone (which was the original study). </li>\n","<li>We extrapolated the left-handedness survey results to older and younger age groups, but it's possible our extrapolation wasn't close enough to the true rates for those ages. </li>\n","</ol>\n","<p>One thing we could do next is figure out how much variability we would expect to encounter in the age difference purely because of random sampling: if you take a smaller sample of recently deceased people and assign handedness with the probabilities of the survey, what does that distribution look like? How often would we encounter an age gap of nine years using the same data and assumptions? We won't do that here, but it's possible with this data and the tools of random sampling. </p>\n","<!-- I did do this if we want to add more tasks - it would probably take three more blocks.-->\n","<p>To finish off, let's calculate the age gap we'd expect if we did the study in 2018 instead of in 1990. The gap turns out to be much smaller since rates of left-handedness haven't increased for people born after about 1960. Both the National Geographic study and the 1990 study happened at a unique time - the rates of left-handedness had been changing across the lifetimes of most people alive, and the difference in handedness between old and young was at its most striking. </p>"]},{"cell_type":"code","execution_count":14,"id":"8776a7f5","metadata":{"dc":{"key":"67"},"execution":{"iopub.execute_input":"2023-07-01T16:09:31.151816Z","iopub.status.busy":"2023-07-01T16:09:31.151169Z","iopub.status.idle":"2023-07-01T16:09:31.167684Z","shell.execute_reply":"2023-07-01T16:09:31.166339Z"},"papermill":{"duration":0.030499,"end_time":"2023-07-01T16:09:31.169756","exception":false,"start_time":"2023-07-01T16:09:31.139257","status":"completed"},"tags":["sample_code"]},"outputs":[{"name":"stdout","output_type":"stream","text":["The difference in average ages is 2.3 years.\n"]}],"source":["# Calculate the probability of being left- or right-handed for all ages\n","left_handed_probability_2018 = P_A_given_lh(ages, death_distribution_data, study_year = 2018)\n","right_handed_probability_2018 = P_A_given_rh(ages, death_distribution_data, study_year = 2018)\n","\n","# calculate average ages for left-handed and right-handed groups\n","average_lh_age_2018 = np.nansum(ages*np.array(left_handed_probability_2018))\n","average_rh_age_2018 = np.nansum(ages*np.array(right_handed_probability_2018))\n","\n","print(\"The difference in average ages is \" + \n","      str(round(average_rh_age_2018 - average_lh_age_2018, 1)) + \" years.\")"]},{"cell_type":"code","execution_count":null,"id":"139bd450","metadata":{"papermill":{"duration":0.010436,"end_time":"2023-07-01T16:09:31.191494","exception":false,"start_time":"2023-07-01T16:09:31.181058","status":"completed"},"tags":[]},"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":"3.10.10"},"papermill":{"default_parameters":{},"duration":15.221036,"end_time":"2023-07-01T16:09:31.920788","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-07-01T16:09:16.699752","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}