{ "cells": [ { "cell_type": "code", "execution_count": 70, "id": "cd3ff75a-808a-4679-b437-eff9f4a0c5a3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "rng = np.random.default_rng()\n", "\n", "num_repetitions = 20000\n", "L = []\n", "for i in np.arange(num_repetitions):\n", " L.append(rng.choice([0, 1], size=100, replace=True, p=[0.74, 0.26]).sum())\n", "\n", "from matplotlib.pyplot import subplots\n", "\n", "fig, ax = subplots(figsize = (8, 8))\n", "ax.hist(L, bins = np.arange(0, 50, 1));\n", "ax.scatter(8, 0, color='red', s=100);" ] }, { "cell_type": "code", "execution_count": 71, "id": "1112d958-44e3-4bfc-b649-57bddcc52d94", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Ethnicity Eligible Panels\n", "0 Asian/PI 0.15 0.26\n", "1 Black/AA 0.18 0.08\n", "2 Caucasian 0.54 0.54\n", "3 Hispanic 0.12 0.08\n", "4 Other 0.01 0.04\n" ] }, { "data": { "image/png": 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79u3SsWNH6datmxw+fNjjfaZMmSJNmzY1xwwdOlSGDBki+/btS7FNR48eldatW0toaKh89dVXpl0a8K5cuZKLVwIAAPgKn+zuXLp0qalouUtISEj1eA1T//3vf6VWrVrmsVbM3F2+fFmmT58uzZs3N4/nzZsntWvXls2bN0uzZs2kQYMGZnOaMGGCfPLJJ7JkyRIZNmyYa78GOQ1n6vHHH5eXX35Z1q5dKzVr1kzWphkzZkhERIQsXLjQFRhr1KiR6vcQHx9vNqezZ8+mcYUAAICv88mQpl2Xb7zxhse+TZs2SZ8+fVI8ftSoUTJw4ECZP3++tGvXTm6//XapWrWq6/mQkBDThemkYU67QPfu3WtCmlbSxo0bJ8uWLTOD/7Xa9e+//yarpNWvX9/1tXZt6hi0kydPptgm7fbU7k33il5aYmJiZPz48cn217s4W4IdhTJ0joAxepnYInZSF283AQDgo3yyu7Nw4cJSrVo1j618+fKpHq8BS8d/denSxXQt1qlTx1TCMio6Otocr2POvv32WxOwrr32Wrl06ZLHcUkDlwa1xMTEFM+Z2YkNY8aMkbi4ONfmPmYOAAD4H58MaVmhXYkjR46UVatWSc+ePWXOnDmu57Qy5pxIoHQcmY5L0y5PpePUdOmPW2+91YQzrZDpRIDs0KqbBj7tas0IHbumEwzcNwAA4L/8PqRpt6SOG9N11A4dOmQCl04gcAYwZwVs+PDhpstUB/BrIGvRooXp6nSOYfv4449NBU1nit59992pVsgyStuk48p0goIGxF9//dV0x6Y20QAAAAQWvw9puj7ZqVOn5N577zXVtN69e0unTp08xncVKlTIDPTX8HX99debSQkffPCB6/mpU6dKsWLFpFWrVmZWpy7R0bhx42y1q3jx4qbrVce7tWnTxiwLMmvWrAyPUQMAAP4tyOFwOCSA6TppI0aMMN2bvkSrcDo7NHLEIgkOZeKArZg4AABI6fNbx5enN3TJ7ytpAAAAvoiQBgAAYKGAD2k6ScDXujoBAID/C/iQBgAAYCNCGgAAgIUIaQAAABYipAEAAFiIkAYAAGAhQhoAAICFCGkAAAAWIqQBAABYiJAGAABgoRBvNwDZs2t8VLo3aAUAAL6HShoAAICFCGkAAAAWIqQBAABYiJAGAABgIUIaAACAhQhpAAAAFiKkAQAAWIiQBgAAYCFCGgAAgIUIaQAAABYipAEAAFiIkAYAAGAhQhoAAICFCGkAAAAWIqQBAABYiJAGAABgIUIaAACAhQhpAAAAFiKkAQAAWIiQBgAAYCFCGgAAgIUIaQAAABYipAEAAFiIkAYAAGAhQhoAAICFCGkAAAAWIqQBAAD4S0hbu3ZtzrcEAAAA2QtpHTt2lKpVq8pzzz0nR44cycopAAAAkNMh7ejRozJs2DBZvHixVKlSRaKiomTRokVy6dKlrJwOAAAAORHSSpQoISNHjpQdO3bIpk2bpEaNGjJ06FApV66cPPzww7Jz586snBYAAAA5NXGgcePGMmbMGFNZO3/+vLzzzjvSpEkTufHGG2X37t3ZPT0AAEBAynJIu3z5sunu7Ny5s1SsWFFWrlwp06dPlz/++EP2799v9t1+++0521oAAIAAEeRwOByZfdHw4cNlwYIFoi/t27evDBw4UOrVq+dxzIkTJ0z3Z2JiYk62F//f2bNnJSIiQuLi4iQ8PNzbzQEAADn8+R0iWbBnzx6ZNm2a9OzZU0JDQ1Mdt8ZSHQAAAHnY3Tl27FjTlZk0oF25ckXWrVtnvg4JCZE2bdpksVkAAACBLUvdnfny5ZPjx49LqVKlPPafOnXK7EtISMjJNiKtcunoMAkPDfJ2cwAA8B/j4qzo7sxSJU1zXVBQ8mCgIa1w4cJZOSUAAACyOiZNx6ApDWj9+/f36O7U6tmPP/4orVq1yswpAQAAkN2QpuU5ZyUtLCxMChYs6HquQIEC0qJFC3nggQcyc0oAAABkN6TNmTPH/FmpUiWJjo6maxMAACCXhGR1dicAAAAsCGl6+6c1a9ZIsWLFpFGjRilOHHDatm1bTrUPAAAgIGU4pHXv3t01UaBHjx652SYAAICAl6V10uB9rJMGAEAu8eV10rZs2SKbNm1Ktl/3/fDDD1k5JQAAALIb0h566CE5cuRIsv1Hjx41zwEAAMALIU1vsK4TCZLSCQX6HAAAALwQ0nQCwR9//JFsv97PU2+sDgAAAC+EtA4dOsiYMWPMoDenM2fOyBNPPCHt27eXQPL111+b5Uj0+wcAAPBqSJs8ebIZk1axYkVp27at2SpXriwnTpyQKVOmZOpc+prhw4dLlSpVTIUuMjJSunXrZtZk8wV6r1KtIDpvmQUAAJATstQ3Wb58eXMz9ffff1927txp7uF53333yV133SX58+fP8HliY2Pl+uuvl6JFi8pLL70k1157rVy+fFlWrlxpJiD8/PPPYju9Z2mZMmW83QwAAOBnslRJU3rfzkGDBsmMGTNMZe3ee+/NVEBTQ4cONV2Fmzdvll69ekmNGjWkbt26MmrUKNm4caM5ZurUqSa86ftplU1fc/78edc5xo0bJw0bNvQ47yuvvGLuL+runXfeMefWal3ZsmVl2LBhrufSe49Dhw6Z6p7ebUGP0fMsX748xe7OU6dOmbCqQbZQoULmvAsWLPBoy0033SQPP/ywPPbYY3L11VebkKffBwAAQKYraUuWLJFOnTqZIKZfp+WWW25J93ynT5+WFStWyPPPP5/ijdq1uqaCg4PltddeM92pv/32mwlQGm5ef/31jDZd3njjDRP8Jk2aZL4HHUu3fv161/PpvYdW9S5duiTr1q0zbdUZrEWKFEnxvS5evChNmjSRxx9/3CxSt2zZMunbt69UrVpVmjVr5jpu3rx5pk26ttyGDRukf//+pqqY2pi++Ph4s7kvhgcAAPxXhu84oEFGx4+VKlXKfJ3qCYOCJCEhId3zafWsefPm8vHHH8utt96a4QYvXrxYHnzwQfnrr7/MY61Affrpp7Jjxw6PSppu2p2qtKql3bHPPfdclt6jfv36ptKX0o3ltZKmY/L+/vtvV7BMqmvXrlKrVi1TcXRW0vQaffvtt65jNMD95z//MUEyJfp9jh8/Ptn+yBGLJDi0UIa+LwAAkL7YSV3EhjsOZLiSlpiYmOLXWZXRu1GtXr1aYmJizPg0/cauXLliqlUXLlww3YnpOXnypBw7dkxuvvnmLL+Hdk0OGTJEVq1aJe3atTOBTYNbSjR8TZw4URYtWmQW99UKnFbAkrY16eu1C1bbmhqdTauVNydtp3bNAgAA/5TlMWnZVb16dVN1S2tygFbCtAqlgeajjz6SrVu3mjFwSsOP0qpe0sCnkw+cdFJDWjLyHgMHDjTdoNpt+dNPP0nTpk1l2rRpKZ5PJ0C8+uqrprtz7dq1psIXFRXlOpdT0vF7ei3SCr86lk4Tt/sGAAD8V5ZXntUlMnTT6k/ScKGD9NOjA+Y1vGgg0kpV0nFpOhBfA5OeW5f1cHaxaoXKXcmSJU03rAY1DTrKveszLCzMTCLQtmq3ZFIZeQ+lVSvtAtVNq1qzZs0yS4ckpWPdunfvLn369DGP9dy//PKL1KlTJ91rAgAAkK1Kmo6N0gVtNfjouC0dj+W+ZZQGNO0e1PFYWsX69ddfZe/evWYQf8uWLaVatWqmKqZVK61kzZ8/X958802Pc+j4rj///FNefPFFOXDggDnnF198kWw8l4YwPa++x7Zt21yVsIy8x4gRI8yyIAcPHjSv1QpZ7dq1U60Qfvnll/L999+b72Xw4MEp3p0BAAAgx0Oahpi5c+eamYk6aP+TTz7x2DJKF7DV0KMVrkcffVTq1atnZjdq+NMZmQ0aNDDLY7zwwgvmOV2XTceOudOwpLMwNZzp8TohITo62uOYfv36mYkEepwun6HdmxrWVEbeQ4OkzvDU9+rYsaNZKiS12aVPPfWUua+pVgk1QOryGj169MjE1QUAAMjE7E53xYsXN2FIl5WAdzhnhzC7EwAA/5zdmaVKmg6k/9///pfV9gEAACA3Jg7o8hQzZ840S1forMikMxW1+xAAAAB5HNL0vp3OWzHt2rXL4znnDEsAAADkcUjT2Y0AAADww8VsAQAAkMOVtH/++cfcYzK1xWx1vTEAAADkcUjT2Z3ffPONuU2S3nOScWgAAAAWhDRd0X/ZsmVy/fXX53BzAAAAkOUxacWKFTP33gQAAIBFIW3ChAnyzDPPyIULF3K+RQAAAMh4d2ejRo08xp7t379fSpcuLZUqVUq2mK3ejxMAAAB5ENK4STgAAIDlN1iH92XmBq0AAMAOuX6D9SpVqsipU6eS7T9z5ox5DgAAANmTpZAWGxsrCQkJyfbHx8fL77//ns0mAQAAIFPrpC1ZssT19cqVK025zklDm96BoHLlyjnbQgAAgACUqZDmnDygszz79evn8ZzO8NSZnlOmTMnZFgIAAASgTIU05z06tVq2ZcsWKVGiRG61CwAAIKBlakxa586dzWyEgwcPmoCmN1nXyQJOOpmgTp06udFOAACAgJKpkLZixQozOcBp4sSJcvr0adfjK1euyL59+3K2hQAAAAEoS7M7nVhiDQAAwMKQBgAAAAtCms7qdL9/p3MfAAAAvDi7U7s3+/fvL6GhoebxxYsX5cEHH5TChQubx+7j1QAAAJBHIS3p2mh9+vRJdsy9996bjeYAAAAg0yFtzpw5XDUAAIA8wMQBAAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAsR0gAAACxESAMAALAQIQ0AAMBChDQAAAALEdIAAAAsREgDAACwECENAADAQoQ0AAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAsR0gAAACxESAMAALAQIQ0AAMBChDQAAAALEdIAAAAsREgDAACwUIi3G4BsiqkgEhrk7VbYZVyct1sAAEC2UUkDAACwECENAADAQoQ0AAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAsR0gAAACxESAMAALAQIQ0AAMBChDQAAAALEdIAAAAsREgDAACwkF+EtNjYWAkKCpIdO3bk2Dn1fJ9++mmOnQ8AAMDvQlr//v1NaHJuxYsXl44dO8qPP/7o7aZJ27Zt5e233/bYFxUVJfny5ZMtW7ak+dpatWpJaGionDhxIpdbCQAAfI1PhDSloez48eNmW7NmjYSEhEjXrl292qbTp0/L+vXrpVu3bq59hw8flu+//16GDRsm77zzTqqv/e677+Tff/+V2267TebNm5dHLQYAAL7CZ0KaVpzKlCljtoYNG8ro0aPlyJEj8ueffyY7NiEhQQYMGCCVK1eWggULSs2aNeXVV19NdpyGqLp165pzly1b1gSr1IwdO9Yc4169W7ZsmTRu3FhKly7t2jdnzhwTHocMGSILFiwwQSwls2fPlrvvvlv69u2bZphzio+Pl7Nnz3psAADAf4WIDzp//ry89957Uq1aNdP1+c8//3g8n5iYKBUqVJAPP/zQPK+VrUGDBpmQ1bt3b3PMG2+8IaNGjZJJkyZJp06dJC4uzlTFknI4HPLwww/L0qVL5dtvvzXv6bRkyRLp3r27x7Ea0mbMmGG6MvXYxYsXmyDm7ty5c6ZtmzZtMsfpe+u5b7zxxlS/55iYGBk/fnyy/fUuzpZgR6FMXkE/N3qZt1vgt2IndfF2EwAgYPhMSNOQVKRIEfO1hjINXLovODh5MTB//vwegUYrahs2bJBFixa5Qtpzzz0njz76qDzyyCOu46677jqP81y5ckX69Okj27dvN92T5cuX96hsrVixQsaNG+fat3r1arlw4YIZk6b0tVoxSxrSFi5cKNWrVzdVPHXnnXea49IKaWPGjDGh0kkraZGRkRm6dgAAwPf4THenDtDX2Zu6bd682QQhrYAdOnQoxeO1mtWkSRMpWbKkCXczZ84048XUyZMn5dixY3LzzTen+Z4jR4401a5169Z5BDT11VdfSalSpVxBS2m35R133GHGy6m77rrLVOcOHDjg8Vo9TgOck36tlTWtsKVGu2TDw8M9NgAA4L98JqQVLlzYdB/qphUvnVGpFbVZs2YlO1YrVdHR0WZc2qpVq0ywu+++++TSpUvmeR2nlhHt27eXo0ePysqVK5M9p12dt9xyi8ckgk8++URef/11E9J002Cn1Tj3MWd79uyRjRs3ymOPPeY6rkWLFqYCp+0GAADwqe7OpHQpDu3qTGlgvlavWrVqJUOHDnXtc69mhYWFSaVKlcwsUa3QpUZDmM7c1AH+uqSGdks6x559/vnnZlyc0/vvv2/GwSVdW01D4pQpU+TZZ58159BuzdatW5tKnzsdy6bPPfDAA1m8IgAAwJ/4TEjTMWDO9cT+/vtvmT59uplA4L78hZOO93r33XdNBUzHo82fP9+sWaZfO+lYsgcffNB0WWq3qXY1argbPny4x7luvfVW83odV6ZVL10yY+vWrabydcMNN7iO04Clz9WrV8/j9TpuTMeT6fi1Dh06mHNpYEt63MCBA2Xq1Kmye/dujy5UAAAQmHwmpGnI0ckCzkqYzorUcVw33XSTueOAu8GDB5vB/jo+TCtuOjZMq2pffPGF65h+/frJxYsX5eWXXzZdoyVKlDAhKyW6X2eMalDT6p2eu3Pnzq6xZxradu7cmWLXa0REhBn7piFO3+/UqVMm+CVVu3Zts+lxGtYAAEBgC3Jo3x0ypX79+vLUU0+5Zop6g87u1AAYOWKRBIeyBAfyBktwAEDOfH7r8lvpTQL0mYkDttDJB7169TJdpAAAABLo3Z22KFCggLn7AAAAQG6ikgYAAGAhQhoAAICFCGkAAAAWIqQBAABYiJAGAABgIUIaAACAhQhpAAAAFiKkAQAAWIiQBgAAYCHuOODjdo2PSvfeXwAAwPdQSQMAALAQIQ0AAMBChDQAAAALEdIAAAAsREgDAACwECENAADAQoQ0AAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAsR0gAAACxESAMAALAQIQ0AAMBChDQAAAALEdIAAAAsREgDAACwECENAADAQoQ0AAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAsR0gAAACxESAMAALAQIQ0AAMBChDQAAAALEdIAAAAsREgDAACwECENAADAQoQ0AAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAsR0gAAACwU4u0GIJtiKoiEBnm7FUBgGxfn7RYA8ENU0gAAACxESAMAALAQIQ0AAMBChDQAAAALEdIAAAAsREgDAACwECENAADAQoQ0AAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAsR0gAAACwU0CEtNjZWgoKCZMeOHd5uCgAAgH+GtA0bNki+fPmkS5cuGX5NZGSkHD9+XOrVq5crbWrbtq28/fbbrjDo3IoXLy4dOnSQ7du3u4696aabZMSIEbnSDgAA4Hv8JqTNnj1bhg8fLuvWrZNjx45l6DUa6sqUKSMhISE53p7Tp0/L+vXrpVu3bq59q1evNqFw5cqVcv78eenUqZOcOXMmx98bAAD4Pr8IaRp4PvjgAxkyZIippM2dO9f13N9//y333HOPlCxZUgoWLCjVq1eXOXPmpNjdmZCQIAMGDJDKlSubY2vWrCmvvvqqx3v1799fevToIZMnT5ayZcuaqthDDz0kly9f9jhu2bJl0rhxYyldurRrnx6robBp06bm9X/88Yds2rQpQ99jfHy8nD171mMDAAD+K+dLSF6waNEiqVWrlglVffr0Md2GY8aMMQHs6aeflj179sgXX3whJUqUkP3798u///6b4nkSExOlQoUK8uGHH5pA9f3338ugQYNMGOvdu7fruLVr15p9+qee74477pCGDRvKAw884DpmyZIl0r1791TbrCFQXbp0KUPfY0xMjIwfPz7Z/noXZ0uwo1CGzgH/FTsp4938AADfEOIvXZ0azlTHjh0lLi5OvvnmGzPO6/Dhw9KoUSNTvVKVKlVK9Tz58+f3CEJaUdOxbhoC3UNasWLFZPr06aa7VMOhVu/WrFnjCmla9VqxYoWMGzcuxffRLs4JEyZIkSJFpFmzZhn6HjV0jho1yvVYK2k6pg4AAPgnn+/u3Ldvn2zevFnuuusu81jHl2llS4Ob0i7QhQsXmkrXY489ZqpjaZkxY4Y0adLEdI9qiJo5c6YJeu7q1q1rApqTVtVOnjzpevzVV19JqVKlzHHuWrVqZc6pIW/nzp2mi9a9OzQtoaGhEh4e7rEBAAD/5fOVNA1jV65ckXLlyrn2ORwOE2q02qWD8w8dOiTLly+XL7/8Um6++WYzhkzHhCWlYS46OlqmTJkiLVu2lLCwMHnppZeSjRvTips77VbVrlL3rs5bbrkl2fk1lNWpU8d0pRYtWjSHrgAAAPBHPl1J03D27rvvmlClg/+dm1apNLQtWLDAHKdVsX79+sl7770nr7zyiqmOpURnY2q1a+jQoaaLtFq1anLgwIFMtUkD4ueff57ieDTtnqxatSoBDQAA+HclbenSpWb2ps7IjIiI8HiuV69epsqmy3Fo96V2PepYMX1N7dq1UzyfzvzU0KdLZOh4tPnz58uWLVvM1xm1detWuXDhgtxwww3Z/v4AAEDg8ulKmoawdu3aJQtozpD2ww8/mDFqOui+fv360rp1azOWTLs1UzJ48GDp2bOnGdPWvHlzOXXqlKmqZcZnn30mnTt3zpW11wAAQOAIcmj/HHKMhsGnnnrKYzZobtDZnRpOI0cskuBQluAIdCzBAQC+wfn5rStRpDcJ0KcrabbRNc+0gqeTFQAAALKDPrkcVKBAARk7dqy3mwEAAPwAlTQAAAALEdIAAAAsREgDAACwECENAADAQoQ0AAAACxHSAAAALERIAwAAsBAhDQAAwEKENAAAAAtxxwEft2t8VLr3/gIAAL6HShoAAICFCGkAAAAWIqQBAABYiJAGAABgIUIaAACAhQhpAAAAFiKkAQAAWIiQBgAAYCFCGgAAgIUIaQAAABYipAEAAFiIkAYAAGAhQhoAAICFCGkAAAAWIqQBAABYiJAGAABgoRBvNwBZ43A4zJ9nz571dlMAAEAGOT+3nZ/jaSGk+ahTp06ZPyMjI73dFAAAkEnnzp2TiIiINI8hpPmoq6++2vx5+PDhdP+SkbXfdDQAHzlyRMLDw73dHL/Ctc09XNvcxfXNPYF0bR0Ohwlo5cqVS/dYQpqPCg7+f8MJNaD5+w+0N+m15frmDq5t7uHa5i6ub+4JlGsbkcHiChMHAAAALERIAwAAsBAhzUeFhobK2LFjzZ/IeVzf3MO1zT1c29zF9c09XNuUBTkyMgcUAAAAeYpKGgAAgIUIaQAAABYipAEAAFiIkAYAAGAhQprFZsyYIZUqVZKrrrpKmjdvLps3b07z+A8//FBq1apljr/22mtl+fLledZWf762u3fvll69epnjg4KC5JVXXsnTtvr79Z01a5bceOONUqxYMbO1a9cu3Z/1QJaZa/vxxx9L06ZNpWjRolK4cGFp2LChzJ8/P0/b6+//7jotXLjQ/PvQo0ePXG9jIFzbuXPnmuvpvunrAg0hzVIffPCBjBo1ykxJ3rZtmzRo0ECioqLk5MmTKR7//fffy1133SUDBgyQ7du3m38odNu1a1eet93fru2FCxekSpUqMmnSJClTpkyet9ffr+/XX39tfnbXrl0rGzZsMLeG6dChgxw9ejTP2+5v11ZvH/fkk0+a6/rjjz/KfffdZ7aVK1fmedv98fo6xcbGSnR0tPllAzl3bcPDw+X48eOu7dChQxJwdAkO2KdZs2aOhx56yPU4ISHBUa5cOUdMTEyKx/fu3dvRpUsXj33Nmzd3DB48ONfb6u/X1l3FihUdL7/8ci63MHCvr7py5YojLCzMMW/evFxsZWBeW9WoUSPHU089lUstDLzrqz+vrVq1crz99tuOfv36Obp3755HrfXvaztnzhxHRESEI9BRSbPQpUuXZOvWrabbx/1enfpYfyNOie53P17pbympHR+osnJtkbfXVyuXly9fNlUg5Ny11SUx16xZI/v27ZPWrVvncmsD5/o+++yzUqpUKdOLgZy9tufPn5eKFSua6nr37t3N0JNAQ0iz0F9//SUJCQlSunRpj/36+MSJEym+Rvdn5vhAlZVri7y9vo8//riUK1cu2S8dgS6r1zYuLk6KFCkiBQoUkC5dusi0adOkffv2edBi/7++3333ncyePduMq0TOXtuaNWvKO++8I5999pm89957kpiYKK1atZLff/9dAkmItxsAAE467k8HYOs4tUAcJJwbwsLCZMeOHaYqoZU0HRekYyxvuukmbzfNp507d0769u1rAlqJEiW83Ry/07JlS7M5aUCrXbu2vPXWWzJhwgQJFIQ0C+n/8Pny5ZM//vjDY78+Tm3guu7PzPGBKivXFnlzfSdPnmxC2urVq6V+/fq53NLAubbarVStWjXztc7u3Lt3r8TExBDSsnl9Dxw4YCYMdOvWzbVPqz0qJCTEdCtXrVo1D1oeGP/u5s+fXxo1aiT79++XQEJ3p4W0W6JJkybmt173//n1sftvFu50v/vx6ssvv0z1+ECVlWuL3L++L774ovnteMWKFWbJCOTez66+Jj4+PpdaGTjXV5c7+umnn0yV0rndcsst0rZtW/O1jqNCzv3sJiQkmOtdtmxZCSjenrmAlC1cuNARGhrqmDt3rmPPnj2OQYMGOYoWLeo4ceKEeb5v376O0aNHu45fv369IyQkxDF58mTH3r17HWPHjnXkz5/f8dNPP3nxu/CPaxsfH+/Yvn272cqWLeuIjo42X//6669e/C785/pOmjTJUaBAAcfixYsdx48fd23nzp3z4nfhH9d24sSJjlWrVjkOHDhgjtd/H/TfiVmzZnnxu/Cf65sUsztz7tqOHz/esXLlSvOzu3XrVsedd97puOqqqxy7d+92BBJCmsWmTZvmuOaaa8wHmE5f3rhxo+u5Nm3amH8Q3C1atMhRo0YNc3zdunUdy5Yt80Kr/e/aHjx40KG/zyTd9Dhk//rqsiYpXV/9RQPZu7ZPPvmko1q1aubDrVixYo6WLVuaD0vk3L+77ghpOXdtR4wY4Tq2dOnSjs6dOzu2bdvmCDRB+h9vV/MAAADgiTFpAAAAFiKkAQAAWIiQBgAAYCFCGgAAgIUIaQAAABYipAEAAFiIkAYAAGAhQhoAAICFCGkAAAAWIqQBAABYiJAGAABgIUIaAACA2Of/AM1dVxBuQnc3AAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "\n", "data = [['Asian/PI', 0.15, 0.26], \n", " ['Black/AA', 0.18, 0.08], \n", " ['Caucasian', 0.54, 0.54],\n", " ['Hispanic', 0.12, 0.08],\n", " ['Other',0.01, 0.04]]\n", "\n", "df = pd.DataFrame(data, columns=['Ethnicity', 'Eligible', 'Panels'])\n", "print(df)\n", "df.plot.barh('Ethnicity');" ] }, { "cell_type": "code", "execution_count": 72, "id": "0a052fe2-61ea-4da8-aa7a-c8d51dcd5718", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.14659326 0.18995182 0.53613214 0.12044047 0.00688231]\n", " Ethnicity Eligible Panels Random\n", "0 Asian/PI 0.15 0.26 0.146593\n", "1 Black/AA 0.18 0.08 0.189952\n", "2 Caucasian 0.54 0.54 0.536132\n", "3 Hispanic 0.12 0.08 0.120440\n", "4 Other 0.01 0.04 0.006882\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x, y = np.unique(rng.choice(df['Ethnicity'], size=1453, replace=True, p=df['Eligible']), return_counts=True)\n", "y = y/1453\n", "print(y)\n", "df.insert(loc=3, column='Random', value=y)\n", "print(df)\n", "df.plot.barh('Ethnicity')" ] }, { "cell_type": "code", "execution_count": 89, "id": "9b0185e9-9abb-45c3-ae65-d6bbe4fd2895", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def calculate_tvd(X, Y):\n", " A = np.abs(X - Y).sum()/2\n", " return A\n", "\n", "L=[]\n", "for i in range(10000):\n", " x, y = np.unique(rng.choice(df['Ethnicity'], size=1453, replace=True, p=df['Eligible']), return_counts=True)\n", " y = y/1453\n", " L.append(calculate_tvd(df['Eligible'], y))\n", "\n", "fig, ax = subplots(figsize = (8, 8))\n", "ax.hist(L, bins=np.arange(0.0, 0.2, 0.001));\n", "ax.scatter(0.14, 0, color='red')\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "ac66ce72-ccb4-4df0-80e0-65324ec8cecc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }