"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import scipy.stats as stats\n",
"import math\n",
"\n",
"mu = 100\n",
"sigma = 15\n",
"x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
"y = stats.norm.pdf(x, mu, sigma)\n",
"\n",
"sns.lineplot(x=x,y=y)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '100 samples')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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gnqD9HuCdw59ORETyTSzsACKFrqenh8bGxle1L1q0iNLS0hASiUguM7MosAqYCfy3uzeaWa277wFw9z1mNiHUkCIikhdU7IlkWWNjI3feu4S6htkn2lqbN3ArsHjx4tByiUhucvckMN/MxgIPmtncgR5rZrcAtwBMnTo1OwFFRojJ9VNp3b0r7Bj9qptST8uunWHHkDygYk9kGNQ1zGbmvIVhxxCRPOLuB83sCeAGoM3MJgWjepOA9lMcczdwN8CCBQt82MKKFKDW3bv4wF3Phh2jX/d+7OqwI0ie0DV7IiIiOcLMaoIRPcysDHgDsBF4CLg52O1m4BehBBQRkbyikT0REZHcMQm4J7huLwLc5+4Pm9lzwH1m9lFgJ/C+MEOKiEh+ULEnIiKSI9x9DXBpP+37geuHP5GIiOQzTeMUEREREREpQCr2RERERERECpCKPRERERERkQKkYk9ERERERKQAZbXYM7PtZvaSmTWZ2cqgbbyZLTWzzcHPcRn7f9bMtpjZy2b25mxmExERERERKWTDsRrn6929I+P1bcBj7n6Hmd0WvP47M7sIuAmYA9QBvzWz8909OQwZRYZVIt5HU1PTq9oXLVpEaWnp8AcSERERkYITxq0XbgSuDZ7fAzwB/F3Q/hN37wW2mdkWYCHwXAgZRbKqbWczGzs72ZSoOtHW2ryBW4HFixeHlktERERECke2iz0HlpiZA3e5+91ArbvvAXD3PWY2Idh3MvB8xrG7gzaRglRT38DMeQvDjiEiIiIiBSrbxd417t4aFHRLzWzjafa1ftr8VTuZ3QLcAjB16tShSSkiIiIiIlJgsrpAi7u3Bj/bgQdJT8tsM7NJAMHP9mD33UB9xuFTgNZ+3vNud1/g7gtqamqyGV9ERERERCRvZa3YM7MKMxt1/DnwJmAt8BBwc7DbzcAvgucPATeZWYmZTQdmAcuzlU8kDIlkim0d3bSWTKXNxtHZ3Rd2JBEREREpUNmcxlkLPGhmxz/nR+7+qJmtAO4zs48CO4H3Abj7OjO7D1gPJIBPaCVOKSQHj/bx0IutHDgah+LJYMb3n9/B7EmjWDxLo9QiIiIiMrSyVuy5+1bgkn7a9wPXn+KY24Hbs5VJJCyH4lEeW7kLgLfNm8TOpx/EK6opmXEFq3YeYPeBY1xWltVZ1SIiIiIywujbpUiW9SSc1YcriEUifGBBPQ01lURIUUKca2ZW8/7L60mmnOcOjmLbIQ1mi4iIiMjQULEnkmU/2tDL0WSEN8+pZWx58au2TxxTyvsun0LUnC+tOMamtsMhpBQRERGRQqNiTySLnm3uYNnuBA1lPUwZV37K/caWF3Pl2CMURYwPf2s5uw8cHcaUIiIiIlKIsn2fPZERy92549cbGV9qzKroOeP+5dEUf3NFKV9cGefD31rOTz9+FRUxp7Gx8VX7Llq0iNLS0mzEFhEREZECoWJPJEt+9dIe1uw+xJ9eXELr3oEdUz8qyrc/Mp8PfrORj3xnBZ+8KMH/3L+UuobZJ/Zpbd7ArcDixYuzkltERERECoOKPZEsSKacLy/ZxAW1o7h6coqfDbDYA1gwbTxf++Bl/On3VnJXPMqkGbOZOW9h9sKKiIiISEHSNXsiWfDrtXvY1tHNrW+YRSR9r8lBuX52Lf/4totY3Z7k5W5N1xQRERGRwVOxJzLE3J2vP9HMjJoK3jxn4lm/z0eunsa19TGaj5Wxdd+RIUwoIiIiIiOBpnGKDLGnNnewrrWLL7znYiKRgY/qJeJ9NDU1vaLt4vjLrI6ex5L1bfzRohJGlxYNcVoRERERKVQq9kSG2Dee2krt6BLeeenkQR3XtrOZjZ2dbEpUnWhb89Ryps5yNhWfz9J1bbz7ssG9p4iIiIiMXCr2RIbQSzs7eGpzB+89v5jnn3kagKamJpLJsQM6vqa+4RWLsbQ0byBGnNfOquaxje2s29OFruATERERkYFQsScyhL78y1WYJzm0v537DjgAa55aQe2s+VxwDu87p240G/ce5unNHbxmzOAXfBERERGRkUfFnsgQOXi0j2dbEkwpS3DR/CtOtLc0bzjn9zYzrr9wAj9s3Mmm7rJzfj8RERERKXwq9kQGoKenh8bGxle1L1q0iNLS9MTKHy/fRV8KppX1ZCXDuIpiLp4yhqZdKVqPpLLyGSIiIiJSOFTsiQxAY2Mjd967hLqG2SfaWps3cCuwePFi4skU33tuOxdVRRkdyV4hdsW0cby0q5NvLW+nrnLZifbMolNEREREBFTsiQxYXcPsVyyekuk36/ay51AP77+slC27spehvDhGTc9utjCVbzzXypii5CuKThERERGR41TsiQyB7zyznanjy5k/wbJa7AHU9O2hvWQy7SV1XD53UnY/TERERETyViTsACL5bs3ug6zacYCPXD2NiGV/pcwoSWr8IJvbj9B1LJ71zxMRERGR/KRiT+QcfeeZ7VSWxHjfginD9pm13okBL+w6OGyfKSIiIiL5RcWeyDk42JPi4TWtvPfyKYwqLRq2zy0mwfm1o1jXeoiEFuYUERERkX6o2BM5B7/bFSeRcj5y9bRh/+x5U8YQTzqtvcXD/tkiIiIikvtU7ImcpZTD4zsTXHfBBKZVVwz7508cXUpVZTE7e0qG/bNFJDvMrN7MHjezDWa2zsw+FbR/zsxazKwpeLw17KwiIpL7VOyJnKW9vUV09Tl/fNV5oXy+mXFx3RgOJWJsP5QMJYOIDLkE8Gl3nw1cCXzCzC4Ktv2Hu88PHo+EF1FERPKFij2Rs7Szp4TqMuN1s2pCy3DhxFFEcJ7YpVU5RQqBu+9x99XB88PABmByuKlERCRfqdgTOQsHjvaxP17EtfVFRCPZv93CqZQURZlY0sfyvQl6ExrdEykkZjYNuBRoDJo+aWZrzOzbZjYuvGQiIpIvVOyJnIW1LYcwnNdOiYUdhcmlfXTH4YmX94UdRUSGiJlVAvcDt7p7F/B1oAGYD+wBvnyK424xs5VmtnLfPv2bICIy0qnYExmkRDLF+tYuJpbEGVsS/v9C1UUJRhcbP3+hJewoIjIEzKyIdKH3Q3d/AMDd29w96e4p4BvAwv6Odfe73X2Buy+oqQlvirmIiOSG8L+piuSZLfuO0JNIMbW0N+woAEQMrpwU47GN7Rw6pmv3RPKZmRnwLWCDu38lo31Sxm7vAtYOdzYREck/KvZEBuml3YcYW1ZEVVEi7CgnXD05Rl8ixW/W7g07ioicm2uADwHXnXSbhS+a2UtmtgZ4PfBXoaYUEZG8EP4FRyJ5ZP+RXloP9fDamdXYofaw45wwbXSE+vFlPLJ2D++/oj7sOCJyltz9aaC/VZ90qwURERk0jeyJDML6PV1EDGZPGh12lFcwM94ydxLPbOnQVE4RERERAVTsiQyYO7zcdphpVRWUFUfDjvMqN8ydSDzp/G5jW9hRRERERCQHaBqnyAB1xGN09ya5cNYoABLxPpqaml6xT1NTE8nk2GHNdTzHPHfGlRjff2It47uaWbRoEaWlpcOaRURERERyh4o9kQFq6SmmOBZhenUFAG07m9nY2cmmRNWJfdY8tYLaWfO5YBhzZeYYEymjqa2EL9/7Wz4NLF68eBiTiIiIiEguUbEnMgC9SWdvbzEX1lUSi/5+9nNNfQMz5/3+dlctzRvCiHciR+mBo2xf3UJsytxQcoiIiIhI7tA1eyIDsGZfkiTG+bWjwo5yWnVjyygrirKntzjsKCIiIiISMhV7IgOwcm+CIksxZWxZ2FFOK2JGQ00F7b1F9CU97DgiIiIiEiIVeyJn0JtI0tSeoLYkTiTS3+2vcsvMCZUkMdZ2JMOOIiIiIiIh0jV7Iifp6emhsbHxxOum9gQ9Sagt6wkx1cBNGVdOkaVYsTcRdhQRERERCZGKPZGTNDY2cue9S6hrmA3Ai4fLsaThh/aEnGxgohGjtjhOU3uEeDJFUVQD+CIiIiIjkb4FivSjrmE2M+ctZPrcK+hIlDE2eYgI+XMNXG1JnKMJWL6tM+woIiIiIhKSrBd7ZhY1sxfM7OHg9XgzW2pmm4Of4zL2/ayZbTGzl83szdnOJnImLQeP0ZNIMSaxP+wog1JTHKc4AkvXt4UdRURERERCMhwje58CMm8+dhvwmLvPAh4LXmNmFwE3AXOAG4CvmVl0GPKJnNKW9iPEIsboxMGwowxK1GBOdZQl6/binj8jkiIiIiIydLJa7JnZFOAPgG9mNN8I3BM8vwd4Z0b7T9y91923AVuAhYiExN1p3neEadUVREiFHWfQLquN0Xqoh3WtXWFHEREREZEQZHtk707gM/CKb8q17r4HIPg5IWifDOzK2G930PYKZnaLma00s5X79u3LSmgRgNZDPRztSzKzpjLsKGdlfk2MiMESTeUUERERGZGyVuyZ2duAdndfNdBD+ml71fwzd7/b3Re4+4KamppzyihyOs3tR4iaMa26POwoZ2V0ibHgvPEsWbc37CgiIiIiEoJsjuxdA7zDzLYDPwGuM7MfAG1mNgkg+Nke7L8bqM84fgrQmsV8IqfkDs37jlA/voySWP5eOvqmObVs3HuYXZ1Hw44iIiIiIsMsa8Weu3/W3ae4+zTSC6/8zt3/GHgIuDnY7WbgF8Hzh4CbzKzEzKYDs4Dl2concjqHkxG6ehLMyNMpnIl4H01NTYw+vAOArz/0DMuWLaOnJz9uDC8iIiIi5y6Mm6rfAdxnZh8FdgLvA3D3dWZ2H7AeSACfcPdkCPlEaOstBmBGdUXISc5O285mNnZ2MjdRxajoaB5t7mbD8ie4FVi8eHHY8URERERkGAxLsefuTwBPBM/3A9efYr/bgduHI5PI6bT1FTFxdCkVJWH8PmRo1NQ3MHPeQtqbO1i5/QCXT78o7EgiIiIiMoyG4z57Inml81iKQ4kYM2ryc1TvZA01lTjQ3lcUdhQRERERGUYq9kRO8kJ7evZwQ55er3eyCaNKqCyJsbdXxZ6IiIjISKJiT+Qkq9sTVESTjK8oDjvKkDAzZlRX0NFXRF/yVXczEREREZECpWJPJENXT5wN+5PUFsfDjjKkZtRUkMRY16E1j0RERERGChV7IhmeeHkfSYfakr6wowypKePKiZmzuj0RdhQRERERGSYq9kQyLF3fxuhiY1yssEbAohFjQnGcF9qTJFOayikiIiIyEqjYEwn0JVI8sbGd+ROimIWdZujVFvdxuM9ZvfNA2FFEREREZBio2BMJNG7bz+HeBJdNyN97651OTXGcqKVHL0VERESk8KnYEwksWddGWVGUOdXRsKNkRVEELqqKsmTdXtw1lVNERESk0KnYEwHcnd9uaON151dTHC3AOZyBSyfE2L7/KFvaj4QdRURERESyTMWeCLC2pYs9h3p440UTw46SVZfWpkctl2gqp4iIiEjBU7EnAixZv5eIwfUXTgg7SlaNL41wyZQxLFm3N+woIiIiIpJlKvZESF+vd8W08YyrKA47Sta9ee5EXtx9iJaDx8KOIiIiIiJZVJjLDor0o6enh8bGxle118yYy8tth/nc2y8KIdXwScT7aGpqYuKsFAD//fOnefP0YhYtWkRpaWnI6URERERkqKnYkxGjsbGRO+9dQl3D7BNtrc0bmH55HwBvuXhSWNGGRdvOZjZ2djI3UcWo6Ch+vSXBhuVPcCuwePHisOOJCGBm9cD3gIlACrjb3b9qZuOBe4FpwHbg/e6um2aKiMhpqdiTEaWuYTYz5y18RduKvQkWnDeO2tGFP7pVU9/AzHkL2b9tP89v7eSyaYU9mimShxLAp919tZmNAlaZ2VLgI8Bj7n6Hmd0G3Ab8XYg5RUQkD+iaPRnRjiQi7Dqc4q0FPqp3slkTRgGwt6/wr1EUySfuvsfdVwfPDwMbgMnAjcA9wW73AO8MJaCIiOQVFXsyou3tTRc7b7m4sG+5cLLxFcWMLy9mT29R2FFE5BTMbBpwKdAI1Lr7HkgXhEBhLx0sIiJDQsWejGh7+oqYOTbCpDFlYUcZdjMnVNIZj9HVmwo7ioicxMwqgfuBW929axDH3WJmK81s5b59+7IXUERE8oKKPRmxDh7toysR44qJI/PS1ZkTKgFjVVsy7CgiksHMikgXej909weC5jYzmxRsnwS093esu9/t7gvcfUFNTc3wBBYRkZylYk9GrM3tRwBGbLFXXVlMeTTJyrZE2FFEJGBmBnwL2ODuX8nY9BBwc/D8ZuAXw51NRETyj4o9GbG2tB9hbCxBVdnI/N/AzJhUHGfD/iQHj/aFHUdE0q4BPgRcZ2ZNweOtwB3AG81sM/DG4LWIiMhpjcwhDRnxDh2L0364lwsrRnaRM7Gkj+ZjpSxd38b7FtSHHUdkxHP3pwE7xebrhzOLiIjkv5E5pCEj3ub2wwBMKomHnCRcY2JJqsuMX6/dG3YUERERERliKvZkRNrSfoQJo0ooj47slSjNYEFtjKc3d9DVM7ILXxEREZFCo2JPRpyuY3HaunqZVVsZdpSccMXEGH3JFL/b0O/ifiIiIiKSp3TNnow4W4JVOGfWVLK3o4+mpqZXbG9qaiKZHDv8wUKQiPdxePtLjC2ZxfefeIlxXVsAWLRoEaWlpSGnExEREZFzoWJPRpzN7UeoGVXC2PJiXtzZzMbOTjYlqk5sX/PUCmpnzeeCEDMOl7bg/MfOnsILbSX8aMUu2rdu4FZg8eLFYccTERERkXOgYk9GlGNJY29XD1c3/L64q6lvYOa8hSdetzRvCCNaaGrqGzhvdgPbV7cQnTibulOtAygiIiIieUXX7MmIsre3GICZE3S9Xqa6sWWUFUVP3GheRERERPKfij0ZUfb0FlNdWcy48uKwo+SUiBkNEyrYvr+bpIedRkRERESGgoo9GTE6e1IcSMSYNWFU2FFy0qwJo4gnnX19RWFHEREREZEhoGJPRoxVexOApnCeyuSxZZQWRdjTq2JPREREpBCo2JMRY8XeBJXRJOMrNIWzP9GIMaO6kva+YuKayykiIiKS91TsyYjQfriHTQdSTCrpCztKTps1oZKEG+v2J8OOIiIiIiLnSLdekBHhN2v34qBi7wzqx5cTsxQrgymvIiIikoMiMcxy715JdVPqadm1M+wYkkHFnowIj7y0l0kVRmU0FXaUnBaNGLXFcVa3R4gnUxRFNfgvIiKSc1IJPnDXs2GneJV7P3Z12BHkJPomJwWv40gvjdv2s3BijBz8JVjOmVgSpzsOz2/dH3YUERERETkHKvak4C1Z10bKYcFEDWQPRE1xnJIo/Gbd3rCjiIiIiMg5ULEnBe836/ZyXlU59aP0130gogZzqqM8vnEf7lqVU0RERCRfZe3br5mVmtlyM3vRzNaZ2b8E7ePNbKmZbQ5+jss45rNmtsXMXjazN2crmxS+np4eli1bxqOPPcEzm/cxe1ScF198kWRSq0wOxCU1MVoOHmNT25Gwo4iIiIjIWcrmUEcvcJ27XwLMB24wsyuB24DH3H0W8FjwGjO7CLgJmAPcAHzNzKJZzCcFrLGxkTvvXcLdjW0kHA4e6ORnj6+gY9++sKPlvES8j1j7RgC++cjzLFu2jGXLltHT0xNyMhEREREZjAEVe2Z2zUDaMnna8WGBouDhwI3APUH7PcA7g+c3Aj9x91533wZsARYOJJ9If+oaZtNdNomyoihXXHYpVXXnhR0pL7TtbGbJ8nWMjiX47dZu7lu5izvvXUJjY2PY0UTyytn0nSIiIkNpoCN7/znAtlcws6iZNQHtwFJ3bwRq3X0PQPBzQrD7ZGBXxuG7g7aT3/MWM1tpZiv3aZRGTiPlsH1/N9Oqy4lEtAznYNTUN3DhlAkcTBQxZfbl1DXMDjuSSD46q75TRERkqJx2eUIzuwq4Gqgxs7/O2DQaOOMUS3dPAvPNbCzwoJnNPd3H9fcW/bzn3cDdAAsWLNDqEXJKnfEYvYkUDTWVYUfJS9OrK1i+vZPt+7t1Q06RQTjXvlNERGSonGlkrxioJF0Ujsp4dAHvHeiHuPtB4AnS1+K1mdkkgOBne7DbbqA+47ApQOtAP0PkZG19RUQjxtTx5WFHyUu1o0soK4qyveNo2FFE8s2Q9J0iIiLn6rS/sHf3ZcAyM/uuu+8YzBubWQ0Qd/eDZlYGvAH4AvAQcDNwR/DzF8EhDwE/MrOvAHXALGD5YD5T5Dh3Z29vMeeNL6coqlsunA0zY1pVOds6upkxNuw0IvnjXPpOERGRoTTQ2VklZnY3MC3zGHe/7jTHTALuCVbUjAD3ufvDZvYccJ+ZfRTYCbwveK91ZnYfsB5IAJ8IpoGKDNrOrhQ9qQgzairCjpLXpldXsGHvYQ4mNPNM5CycTd8pIiIyZAZa7P0U+B/gm8CACjB3XwNc2k/7fuD6UxxzO3D7ADOJnNLq9gTgTK9WsXcuplaVEzFo7y0KO4pIPhp03ykiIjKUBlrsJdz961lNIjKEVrclGRdLUF6spUXORUksSt2YMtoP63uqyFlQ3ykiIqEa6MVMvzSzPzezSWY2/vgjq8lEztKuzqPsPJyitiQedpSCMK26gsPJKJ3HUmFHEck36jtFRCRUAx32uDn4+bcZbQ7MGNo4IufutxvaAJhYrGJvKBxfzXTt/iTvCjmLSJ5R3ykiIqEaULHn7tOzHUTkbPX09NDY2Hji9U+XH2N8rI9SU7E3FKoriymxFGs7NJVTZDDUd4qISNgGVOyZ2Yf7a3f37w1tHJHBa2xs5M57l1DXMJu+lLFh/xgqOzbT0RvhgrDDFQAzo7o4zrqOCKmUE4lY2JFE8oL6ThERCdtAp3FekfG8lPRqmqsBdViSE+oaZjNz3kI27unC97cxsSzJwC9JlTOpLk7QcriEda1dXDxlTNhxRPKF+k4REQnVQKdx/kXmazMbA3w/K4lEzkFzRzcVxVHKu44A48KOUzCqg+sfn9y8T8WeyACp7xQRkbCd7dDHUWDWUAYROVeJZIod+7uZXl2BJhoOrdKIM3VUhKc27ws7ikg+U98pIiLDaqDX7P2S9ApiAFFgNnBftkKJnI1dB44RTzoNNZXs2Bh2msIztzrK0h0H6O5NUFGi+xeKnIn6ThERCdtAv7H9e8bzBLDD3XdnIY/IWdu67whFUWPK+DJ2hB2mAM2tjvLItjiN2/Zz3YW1YccRyQeD7jvN7NvA24B2d58btH0O+FPg+ND637v7I0MfV0RECs2ApnG6+zJgIzCK9IVQfdkMJTJY7rC1o5vzqiqIRbQwSzbMGhelJBbhyU0dYUcRyQtn2Xd+F7ihn/b/cPf5wUOFnoiIDMiAvhWb2fuB5cD7gPcDjWb23mwGExmMg4koR/uSNFRXhB2lYBVHjUUzqnTdnsgAnU3f6e5PAp3DEE9EREaAgQ6B/ANwhbvf7O4fBhYC/5i9WCKD09ZbhBlMU7GXVa+bVU3zvm5aDh4LO4pIPhjKvvOTZrbGzL5tZqdcatjMbjGzlWa2ct8+/WJGRGSkG2ixF3H39ozX+wdxrEjWtfUVM3lsGaVF0bCjFLTXzqoB4KlN+hIpMgBD1Xd+HWgA5gN7gC+fakd3v9vdF7j7gpqamrP4KBERKSQD7XQeNbPfmNlHzOwjwK8AXTMgOWFvd4ojySgNNZVhRyl459dWUju6hKe26Lo9kQEYkr7T3dvcPenuKeAbpEcIRUREzui0q3Ga2Uyg1t3/1szeDbwGMOA54IfDkE/kjFa3JQCYoSmcWZOI99HU1ATAzFFJlm3Yw+NPPMFVV15JaWlpuOFEcsxQ951mNsnd9wQv3wWsHbKwIiJS0M5064U7gb8HcPcHgAcAzGxBsO3tWcwmMiCr2xOMjiUYXVYUdpSC1bazmY2dnWxKVHGkp5gj8Qru+Nmz/L0ZixcvDjueSK65k7PsO83sx8C1QLWZ7Qb+GbjWzOaTvmffduBj2QouIiKF5UzF3jR3X3Nyo7uvNLNp2YkkMnAdR3rZciDFzPJ42FEKXk19AzPnLWRSb4IXn94GtbPCjiSSq86673T3P+yn+VtDFUxEREaWM12zd7r5WWVDGUTkbDy2oQ0HaotV7A2XipIYVZXFdPRpJFXkFNR3iohITjhTsbfCzP705EYz+yiwKjuRRAZu6fo2qkqN0bFk2FFGlKnjyzkQj9Gb9LCjiOQi9Z0iIpITzjSN81bgQTP7IL/voBYAxaQvEhcJzdG+BE9t7uB1k2PY0bDTjCxTx5fzws6DbOpM8qaww4jknltR3ykiIjngtMWeu7cBV5vZ64G5QfOv3P13WU8mcgZPbe6gN5Hi0gnFrNsedpqRZfLYMiI4a/drRFXkZOo7JRdNrp9K6+5dYcfoV92Uelp27Qw7hkhBOtPIHgDu/jjweJaziAzKknVtjC6NccH4qIq9YVYUjTCuKMG6Dt3EXuRU1HdKLmndvYsP3PVs2DH6de/Hrg47gkjBGuhN1UVySiKZ4ncb27juwgnEIhZ2nBGpujjBrsMp9h3uDTuKiIiIiPRDxZ7kpVU7DnDgaJw3XjQx7CgjVnVRegXUZ7Z0hJxERERERPqjYk/y0tL1bRRHIyy+oCbsKCPWmFiSiqL0tZMiIiIikntU7EnecXeWrG/jqoYqKksGdNmpZIEZXFQV4+kt+3DXLRhEREREco2KPck7m9qOsLPzKG+aUxt2lBFvbnWUtq5etrQfCTuKiIiIiJxExZ7knaXr9wLwhtkq9sI2pyq9GqemcoqIiIjkHhV7kld6enq4v3ELM8ZE2PhCI8uWLaOpqYlkUvd7C0NNeYRpVeU8rUVaRERERHKOLniSvPLrZc+x7VCKCyqOcd/K/QCseWoFtbPmc0HI2Uaq186q4f7Vu+lLpCiO6fdHIiIiIrlC38wkr7zQlh7Bu2LuBcyct5CZ8xZSVXdeyKlGttfMquZoX5IXdh4IO4qIiIiIZFCxJ3nlhfYE5dEk4yuKw44igasaqohGTFM5RURERHKMpnFKzurp6aGxsfHE66NxZ11HgqmlvZhZiMkk0+jSIi6ZMoanNnfw6TdpMq2IiIhIrlCxJzmrsbGRO+9dQl3DbABae4pIUUlJd3vIyQQgEe+jqakJgPriXh7aEueR3z7Bda+5ktLS0nDDiYiIiIiKPcltdQ2zmTlvIQCb1+4hdugAlRwLOZUAtO1sZmNnJ5sSVXTGozij+dJDq6goMhYvXhx2PBEREZERT8We5IVkytm+/yijE51YNOw0clxNfQMz5y0kmXJWPbmV5PjpYUcSERERkYAWaJG8sPvAUfoSKcYkOsOOIv2IRozJ48ro6NPvj0RERERyhYo9yQtbO7qJRYxRiUNhR5FTmDq+nKOpKPuOpsKOIiIiIiKo2JM84O5s3dfN1PHlRFAhkaumji8HYG1HMuQkIiIiIgJZLPbMrN7MHjezDWa2zsw+FbSPN7OlZrY5+Dku45jPmtkWM3vZzN6crWySX9oP93KkN8GMmoqwo8hpjCsvoiySpGlfIuwoIiIiIkJ2R/YSwKfdfTZwJfAJM7sIuA14zN1nAY8Frwm23QTMAW4AvmampTgEmvcdwQxmVFeGHUVOw8yYUBxn/f4kPXGN7omIiIiELWvFnrvvcffVwfPDwAZgMnAjcE+w2z3AO4PnNwI/cfded98GbAEWZiuf5I8t7UeYPLaMsmLV/rluQkmcviQ8v3V/2FFERERERrxhuWbPzKYBlwKNQK2774F0QQhMCHabDOzKOGx30CYj2OFEhANH48ys0ahePqgqSlAchcc36sb3IiIiImHLerFnZpXA/cCt7t51ul37afN+3u8WM1tpZiv37ds3VDElR7X1FgPoer08ETW4qCrKYxvbcX/V/74iIiIiMoyyWuyZWRHpQu+H7v5A0NxmZpOC7ZOA40MAu4H6jMOnAK0nv6e73+3uC9x9QU1NTfbCS07Y21dE7egSRpUWhR1FBmh+TYzdB46xpf1I2FFERERERrRsrsZpwLeADe7+lYxNDwE3B89vBn6R0X6TmZWY2XRgFrA8W/kk9+0/luJQIqYpnHlmXk362srfaSqniIiISKiyObJ3DfAh4DozawoebwXuAN5oZpuBNwavcfd1wH3AeuBR4BPuriX9RrBVbekl/BsmqNjLJ1VlES6cOErFnoiIDEwkhpnl3EOkEMSy9cbu/jT9X4cHcP0pjrkduD1bmSS/rGpLUBlNMq68OOwoMkCJeB9NTU3MLJ/NI9sO88hvn6CiyFi0aBGlpaVhxxMRkVyUSvCBu54NO8Wr3Puxq8OOIHLOslbsiZyLjiO9vNyZoqG8L+woMghtO5vZ2NlJ3fwJpHw032hsg5Y13AosXrw47HgiIiIiI8qw3HpBZLB+u74NByaWxMOOIoNUU9/AgssuozQW4VjFJOoaZocdSURERGREUrEnOelXL+2hpswYHdVlm/koYsZ51RVs399NSndgEBEREQmFij3JOR1Henm2eT+LJsXQ9dH5q6Gmgp54is64ZouLDJSZfdvM2s1sbUbbeDNbamabg5/jwswoIiL5Q8We5Jxfr91LMuUsmqQiIZ9Nq6ogFjH29uoeiSKD8F3ghpPabgMec/dZwGPBaxERkTNSsSc555dNrcyaUEn9KP31zGdF0QjnVZWzt6+YlGsup8hAuPuTQOdJzTcC9wTP7wHeOZyZREQkf+nbtOSUPYeOsXx7J2+/pE73uCkAMydU0puKsOVgKuwoIvms1t33AAQ/J4ScR0RE8oSKPckpD7+4B4C3X1IXchIZCtOrK4jgrNybCDuKyIhgZreY2UozW7lv376w44iISMhU7ElO+eWaVi6ePIbp1RVhR5EhUBKLUl0cZ1VbAtdUTpGz1WZmkwCCn+2n2tHd73b3Be6+oKamZtgCiohIblKxJzljW0c3a3Yf4h0a1SsoE0vidBxzXmo5FHYUkXz1EHBz8Pxm4BchZhERkTyiYk9yxsMvtgLwB/MmhZxEhlJtcZyIpVdZFZHTM7MfA88BF5jZbjP7KHAH8EYz2wy8MXgtIiJyRlrbXnKCu/PQi60snDaeurFlYceRIVQccWaPj/Lo2r185s0XaOEdkdNw9z88xabrhzWIiIgUBI3sSU54YXsHm9uPcGF5N8uWLWPZsmU0NTWRTCbDjiZDYMHEGNs6unm57XDYUURERERGDBV7khO+9ugLmKfY29bGfSt3cd/KXfzs8RV0aDW5gnB5bZSI/X61VRERERHJPk3jlND1xJM81xpnUmmC2fOvONHe0rwhxFQylMaURLhmZjU/b2rhr994PpGIpnKKiIiIZJtG9iR0S9e30R2H+tLesKNIFr3r0snsPnCMVTsPhB1FRCRrJtdPxcxy7iEyLCKx0P+un+oxuX5q2H86odDInoTuvpW7qCo1qop04+1C9uY5EykrWsuDL7RwxbTxYccREcmK1t27+MBdz4Yd41Xu/djVYUeQkSCVyMm//zBy/x/QyJ6EquXgMZ7e0sFrp8TQLx4LW0VJjDfNqeVXa/bQl0iFHUdERESk4KnYk1Ddv2o37vCayUVhR5EsScT7aGpqYtmyZTTEOjl0LM5/P/A4PT09YUcTERERKWiaximhSaWcn67axTUzq6gp1/V6haptZzMbOzvZlKgi5VBsY7incTeX1TayePHisOOJiIiIFCyN7Elont+2n12dx3j/gvqwo0iW1dQ3MHPeQs6/ZCGzJ4+jq6iK7riHHUtERESkoKnYk9D8ePkuRpXGePOciWFHkWF04cTRpDBW7tWCPCIiIiLZpGJPQtF+uIdH1+7hvZdPobQoGnYcGUa1o0uoiCZ5tlXFnoiIiEg2qdiTYdfT08Md9z1FPOlcGG1n2bJlNDU1kUwmw44mw8DMmFzSx8bOJDv2d4cdR0RERKRgaYEWybqenh4aGxtPvF79QhO/bK2nuiTCkxtaAFjz1ApqZ83ngrBCyrCqL+1l89Ey7l2xi8/ccGHYcUREREQKkoo9ybrGxkbuvHcJdQ2zAXj+hT3Ep8zkytmTaKipBKCleUOYEWWYxVK9TCvp5ofPbWVByV6ikfRNFhctWkRpaWnI6UREREQKg4o9GRZ1DbOZOW8hAMv2GMUeZ3pVRcipJCxtO5s5cqyIQ7VX8LXn2qgtidPavIFbQbdjEBERERkiKvZkWB3o7uNIbCx1qXYiwWiOjEz11ZV0F0fZX1zLNfPqwo4jIiIiUnC0QIsMqzUthzBPUeMHw44iIYsAF00azfaObo70aGVOERERkaGmYk+GTV8ixfo9XYxJ7KcIrbwpMKduNA6s39MVdhQRERGRgqNiT4bNhj1d9CVS1PTtCTuK5Iix5cVMGVfGutZDuIedRkRERKSwqNiTYeEOL+w6yMTRpVQkj4QdR3LInLrRdPUk6IjrEmIRERGRoaRiT4ZFe18Rh47FuXTq2LCjSI6ZWVNJWVGU7cdKwo4iIiIiUlBU7Mmw2HqshMqSGDOD++qJHBeLRrh48hja+4poP5oKO46IiIhIwVCxJ1m3oytJZ7yI+fVjdbsF6dfFk8dgwGM74mFHERERESkYKvYk636zPU4UZ27d6LCjSI6qLI0xqSTOk7vjdPfqNgwiIiIiQ0HFnmRVe1cPz7cmmFLaS0lRNOw4ksPOK+vhaAIeeKEl7CgiIiIiBUHFnmTVPc9tJ+Uwvaw37CiS48bFkkwbHeGeZ7fjug+DiIiIyDlTsSdZc7gnzvef28HltVEqYlp4Q07PDN44rYgt7Ud4Zsv+sOOIiIiI5D0Ve5I1P2rcSVdPgj+YURx2FMkTiybGqK4s5tvPbAs7ioiIiEjey1qxZ2bfNrN2M1ub0TbezJaa2ebg57iMbZ81sy1m9rKZvTlbuWR49MSTfPPpbVwzs4oZY3WtngxMUdT48FXT+N3Gdta3doUdR0RERCSvZXNk77vADSe13QY85u6zgMeC15jZRcBNwJzgmK+ZmSqEPPbgCy3sO9zLny2eGXYUyTM3Xz2NUSUx/vvxLWFHERERkUIRiWFmOfeYXD81q6cdy9Ybu/uTZjbtpOYbgWuD5/cATwB/F7T/xN17gW1mtgVYCDyXrXySPcmUc9eyZuZNGcM1M6t4sjXsRJIPEvE+mpqaALh2ivHwS3v48a8e513XX0VpaWm44URERCS/pRJ84K5nw07xKvd+7Oqsvn/Wir1TqHX3PQDuvsfMJgTtk4HnM/bbHbRJHvr12j1s33+Ur3/wMsx0E3UZmLadzWzs7GRToorelBFhDF98bDt1lREWL14cdjwRERGRvDPcxd6p9FcR9Lv2upndAtwCMHVqdoc9ZfDcna8/0cyM6greNGdi2HEkz9TUNzBz3kIAOjfv44Wd0NatlVxFREREzsZwr8bZZmaTAIKf7UH7bqA+Y78pQL+T/9z9bndf4O4LampqshpWBm/p+jbWtXbx8WsbiEY0qidn77Kp4zDgV1v7wo4iIiIikpeGu9h7CLg5eH4z8IuM9pvMrMTMpgOzgOXDnE3OUSrl/MdvNzOtqpx3X6pZuHJuKkpi1Jf28nRLgt0HjoYdRyR0ZrbdzF4ysyYzWxl2HhERyX1Zm8ZpZj8mvRhLtZntBv4ZuAO4z8w+CuwE3gfg7uvM7D5gPZAAPuHuyWxlk+z4zbq9bNjTxS3zSnjm6adOtDc1NZFMjg0vmOSthvIeWvtK+cqSTXzlA/PDjiOSC17v7h1hhxARkfyQzdU4//AUm64/xf63A7dnK48MvZ6eHhobGwFIufP5p48xLtrH88uepuX8OSf2W/PUCmpnzeeCsIJK3iqLOm88r4gHm1r4k9dMZ+7kMWFHEhEREckbubJAi+ShxsZG7rx3CXUNs2ntKaLlSCVVLSsonlh1YpENgJbmDSGmlHz3toZinmuDO369ke9/dKFWeJWRzIElZubAXe5+d9iBREQktw33NXtSYOoaZjPj4ivYnhxHVUUxU0bpi7gMrYoi4y+vn8XTWzpYtmlf2HFEwnSNu18GvAX4hJm97uQdzOwWM1tpZiv37dP/LyIiI52KPTlnG/ce5sDROItmjO/3Hhoi5+qDi85jWlU5//eRjSRT/d6VRaTguXtr8LMdeBBY2M8+WrFaREROULEn5yTp8FzzfiaMKmFmTWXYcaRAFccifOaGC3m57TD3r9oddhyRYWdmFWY26vhz4E3A2nBTiYhIrlOxJ+dk69FSjvQmeN2sGl1LJVn1lrkTuWzqWL605GUO98TDjiMy3GqBp83sRdK3JvqVuz8aciYREclxWqBFztqBnhTNR0uZWVPJ5HFlYceRApSI99HU1HTi9dsnJ/nXnb38zbd/yx/NKX/FvosWLaK0tHSYE4oMD3ffClwSdg4REckvKvbkrN2/qQ8HrplZFXYUKVBtO5vZ2NnJpsTv/46VH+zkNz6dnsMtVMZSALQ2b+BWYPHixeEEFREREclBKvbkrKxtOcTTLQmml/Uytrw47DhSwGrqG15xK4/tW3/ERqax3SZx48V1mj4sIiIicgq6Zk8Gzd35/K/WU1EEM8t7wo4jI0yRx6nzfezoPMq2ju6w44iIiIjkLBV7MmgPrG7h+a2dvOf8EooiWgZfhl+NH2B8RTFPbu4gkUyFHUdEREQkJ6nYk0Hp7O7j879az2VTx3JtvWYBSzgiwOLzazh0LM7qnQfDjiMiIiKSk1TsyaD828PrOdyT4P++ex4RXSslIZo6vpyZNZWs2N7JsaT+LoqIiIicTMWeDNiv1uzhwRda+PPXz+SCiaPCjiPCa2dV48CG7vIz7isiIiIy0qjYkwFp6+rhH37+EpdMGcNfXDcz7DgiAIwuK2LBeePY01vMxv3JsOOIiIiI5BQVe3JG8WSKv/jRC/TGU3zlA/MpiuqvjeSOBeeNoyyS5PsberVYi4iIiEgGrbAhZ3THrzeyfHsnH7+khN3rV7E7aG9qaiKZHBtmNBFi0QizK4+xuivKDxt3cvPV08KOJCIiIpITVOzJad23YhffenobbziviManHmdXw+wT29Y8tYLaWfO5IMR8IgATi+PMqYry70te5i1zJzJhdGnYkURERERCp/l4ckpPbtrHZx98idfOquYPLyymrmE2M+ctPPGoqjsv7IgiAJjBh+eU0JtI8a8Prw87joiIiEhO0MjeCNfT00NjY+Or2osmXcDHf9DErAmVfO2Dl7G68dkQ0okM3MSKCH/x+pl8eekm3nN5O6+/YELYkURERERCpWJvhGtsbOTOe5dQlzE9c/O2Xewae4SJYyv43p8sZFRpUYgJRQbulsUz+MWLrfzjz9ey9K8WU1YcDTuSiIiISGg0jVNeMT1zzPRL2DZqLpVFxo/+dJGufZK8UhKLcvs757L7wDG++tjmsOOIiIiIhEoje3LC/iO9PPhCC1Gct1duY1NTBZuCbVp5U/LFohlVvH/BFL7x1FbeNm8ScyePCTuSiIiISChU7AkAB4728cALLUQMph5+kce2tdLG778ka+VNyWWJeB9NTU0nXi8e4ywtgk/9eDW/+tTrKC3SdE4REREZeVTsCUeTEZatbsEd3nP5FF5a8hQV9Q3MnLfwxD4tzRtCTChyem07m9nY2cmmRNWJtvGdrTT3zuGOX2/kc++YE2I6ERERkXCo2BvhOo+leP5gJalIivdcNoXxFcVhRxI5KzUn/YICltNQUcR3n93OFdPG8wfzJoWWTURERCQMKvZGsPbDPXxhxTHiHuE9l06mZlRJ2JFEhkwi3seFPRvYOvZ8/vre1RzaWc7kUREWLVpEaakWHhIREZHCp2JvhOrs7uND31xOZ49zxZjDTNSqm1Jgjk/tnHlZNTtTo/m3Zw/TcGg1fwssXrw47HgiIiIiWadibwQ5fgP17rjzheXHaD2S4h1jW+hGI3pSmGrqG5h76RVUd/Vw/6rd7Bx3KUfjHnYskbwxuX4qrbt3hR2jX3VT6mnZtTPsGCIiOU3F3gjS2NjIl+99jB3jL+dwIsrlY46w/NkntcqmFLyJo0v5g3mTeKiphS+uOMaVV/UxTtenipxR6+5dfOCuZ8OO0a97P3Z12BFERHKebqo+ghxLODvHX87hZIw/mFfH1Qsuo6ruvLBjiQyLaVUVXD66m92HU3zg7ufY1Xk07EgiIiIiWaVib4To6onzlZXHOJSI8pa5k5hRUxl2JJFhV1sS568XlLLnUA/v+K+neWrzvrAjiYiIiGSNir0RoL2rhw/c9TzNB1PMH9XNzAkq9GTkuqgqxkOffA3VlSV86FvL+dxD6zjalwg7loiIiMiQU7FX4FZu7+Qd//UMO/Z381eXl1JXGg87kkhoEvE+mpqa2LluJX97ifPG89L34bv2S0/wk+U7SSRTYUcUERERGTJaoKVA9SaSfP2JZv7zd1uYMq6Mn37kKjo2N7FhR9jJRMJz/HYMmxJVABQDDV07OFp0Abc98BL/b8k63jndmF8ToaTklavU6v58IiIikm9U7BWYZMp55KU9/MfSTWzt6OYdl9Tx+XfNZXRpEcs2h51OJHw19Q3MnLfwxOuW5g3ENz/G5fOuZWN3GV97KUpR3wEuqopSV9JHxKC1eQO3ovvziYiISH5RsVcgWg4e4+EXW7l3xS62dnTTUFPBPX+ykMXn14QdTSTnTahv4JorLuWqlPPQI4+wr3QqLx4upblvDJfWj6VmetgJReRVIjHMLOwUIiI5TcVenunp6eH5559n3zFnY2eSlzuTbNifYH9PevuscRH+fH4J88b10rejiWV7fn8vsaamJpLJseEEF8kD0YgxPt5BTXGSKZdfz6odB3hqSwcxG4Nt6GXa3G7Oq6oIO6aIAKQSOXkPQN3/T0RyiYq9PODubOvopnFbJw89v4GVu7uJR9LXExVZilhXC+P6upg3cwqVsRQ7WuCXP3mUSOko5l5xzYn3WfPUCt1AXWQAjPR9+aZVVdDW1cOTa5r57Y4IS/79Ca49v4abr57G62bVEIloVEFERERyl4q9HNHT00NjYyOQLu5ajqRY197LpoOw+ZBzqNcBKI8kqCqFC6fXMGVsGeMrinny588TGzuO+ZctOPF+Lc0biFWMe9W1SSIyOLWjS7l0dDdvmjuWrZHJ/Gj5Tj7ynRVMr67gfQum8PZ5ddSPLw87poiIiMirqNjLAamU89Olz/I/j60jOXoKnfEYfZ6+K0Y0cYzaighTKxOML0rQ/NwjTJw1n0umzA05tcjIkYj3sX3jGi6bH2He1TFW7IWl24/yxUdf5ouPvszMsREWTYoxtzrGO6+7krKysrAji4iIiOResWdmNwBfBaLAN939jpAjDbnOrm5+9lgjzQeTbDqQfnTHgdIZjI7EaJhYxuSxZbQs/zXl5eW85jXvOHHsoR3rwgsuMkKdfMsGgL7GR6mrnMCoC66m9XARPzyYAvr40orHuX7OZC6dOo6L6kZzQe0oyoqj4YWXgjES+kcRERlaOVXsmVkU+G/gjcBuYIWZPeTu68NNdvbaDhzml8tW0HIkxbZDSbYeTLHrcBInfa1PeTRJVVGCkh2rmVxbzXXXvPXEsR3ei6HpYSK5oL9bNsQqxnHNlfMAOHQszvIX1hD3CI+s2c1PV+0G0tf/1ZRBTXmUqjKjqjRCZTHMvWAm4yrLqCyNMbq0iNKiKCWxSPCIUhyLkEr0sXLF8ldl0T3/Rp5C7B9FRCT7cqrYAxYCW9x9K4CZ/QS4EchKZ5ZIpmjZ38Xq1S8A4BnbLpk/n+LiV95U2R082CvlcKQnQVdPnK5jcbp64nR0HeOlzTvY3+Mc6Emx76hzqO/37xqzFGNjSUZ1vMyEcWN4/bWvo7w4/Z9gWfsqYiSycZoiMgzGlBVR3LaW7s5OFi+4hmOpCF2JKJu3buNIWRV9Yyaw5UCE3lR6ijbrB/bPmnmKSMSI4kQMUvFeRi19hMryMooiUBQxopaiJAplRVFKokZJFKIkKYka5cUxSmKcaL/04osYW1lOWXGUsqIosagRi0SIRoxYxIhGjKJohIhxymXt3Z2UQ8odP+nn0WM9rFi5kpQH+wHz51/KxPGjKIlphPMcDGv/KCIihSHXir3JwK6M17uBRdn6sNaDPSz+yjP9b3ziLJdzTiUoTvUSS/ZQlDxGrGUz40ZXMHf2BZRFUpjBmpdeIn5kFK0bK08ctr91B5HSTrasWV7wbbmSo1DaciVHobSd+3uNwgzKoynKoynaO9amV8adll4ZN+Xw4nOPc+DQYWrPm0UyEiNlMdpbd2PFpYyrnYwTwS3CgY52ysdPpGpSPSmHJEbngTYOHkjQUzkatwhuUXp6eiBWQrS4DI9ESVkUt+OFVZJXWLmagYpaemTSg9zwyl+KDdjvnuGnH7+KK6aNP5ujJW1Y+0cRESkM5n5WXXdWmNn7gDe7+/8OXn8IWOjuf5Gxzy3ALcHLC4CXz/LjqoGOc4ibq3Re+UXnlV90XuE6z91rwg4RhoH0j0H7UPWRwyVf/u4dl0958ykr5FfefMoK+ZU3n7JC7uQ9Zf+YayN7u4H6jNdTgNbMHdz9buDuc/0gM1vp7gvOvGd+0XnlF51XftF5SYjO2D/C0PWRwyXf/u7lU958ygr5lTefskJ+5c2nrJAfeSNhBzjJCmCWmU03s2LgJuChkDOJiIiETf2jiIgMWk6N7Ll7wsw+CfyG9NLS33Z33WtARERGNPWPIiJyNnKq2ANw90eAR4bho/Jmmssg6bzyi84rv+i8JDTD2D8Op3z7u5dPefMpK+RX3nzKCvmVN5+yQh7kzakFWkRERERERGRo5No1eyIiIiIiIjIERkyxZ2ZRM3vBzB4OXo83s6Vmtjn4OS7sjINlZmPN7GdmttHMNpjZVQVyXn9lZuvMbK2Z/djMSvP1vMzs22bWbmZrM9pOeS5m9lkz22JmL5vZm8NJfWanOK8vBX8X15jZg2Y2NmNb3p5Xxra/MTM3s+qMtrw+LzP7iyD7OjP7YkZ7XpyX5J986bfM7AIza8p4dJnZrbmY9bh86jvN7FNBznVmdmvQljNZ86nvPkXW9wV/tikzW3DS/qH++55v3x9OkfffgqxNZrbEzOpyJW9/RkyxB3wK2JDx+jbgMXefBTwWvM43XwUedfcLgUtIn19en5eZTQb+Eljg7nNJL0RwE/l7Xt8Fbjiprd9zMbOLSJ/rnOCYr5mduDN2rvkurz6vpcBcd58HbAI+CwVxXphZPfBGYGdGW16fl5m9HrgRmOfuc4B/D9rz6bwk/+RFv+XuL7v7fHefD1wOHAUeJAezQn71nWY2F/hTYCHpvwNvM7NZ5FbW75I/ffd3eXXWtcC7gSczG3MgK+Tf94fv8uq8X3L3ecG/Dw8D/wQ5k/dVRkSxZ2ZTgD8AvpnRfCNwT/D8HuCdwxzrnJjZaOB1wLcA3L3P3Q+S5+cViAFlZhYDyknfSyovz8vdnwQ6T2o+1bncCPzE3XvdfRuwhXRnmHP6Oy93X+LuieDl86TvAwZ5fl6B/wA+A2Re5Jzv5/VnwB3u3hvs0x605815SX7J437reqDZ3XeQ21nzpe+cDTzv7keDPmMZ8C5yKGs+9d2n6I83uPvL/ewe+r/v+fb94RR5uzJeVvD77wah5+3PiCj2gDtJf1FLZbTVuvsegODnhBBynYsZwD7gO5aenvpNM6sgz8/L3VtIjzDsBPYAh9x9CXl+Xic51blMBnZl7Lc7aMtHfwL8Onie1+dlZu8AWtz9xZM25fV5AecDrzWzRjNbZmZXBO35fl6Su/K137oJ+HHwPCez5lnfuRZ4nZlVmVk58FagntzMmqkQ+u58yJoX3x/M7HYz2wV8kGBkjxzNW/DFnpm9DWh391VhZxliMeAy4OvufinQTQ5MzzhXwRz4G4HpQB1QYWZ/HG6qYWP9tOXdcrlm9g9AAvjh8aZ+dsuL8wq+iPwDv/+H/BWb+2nLi/MKxIBxwJXA3wL3mZmR/+cluSvv+i1L38D+HcBPw85yOvnUd7r7BuALpKfuPQq8SLrPyFf59G9mTmfNp+8P7v4P7l5POusng+aczFvwxR5wDfAOM9sO/AS4zsx+ALSZ2SSA4Gf7qd8iJ+0Gdrt7Y/D6Z6Q70Xw/rzcA29x9n7vHgQeAq8n/88p0qnPZTfq3m8dNIT0NJ2+Y2c3A24AP+u/v65LP59VA+svTi8G/IVOA1WY2kfw+L0jnf8DTlpOe+VBN/p+X5K587LfeAqx297bgda5mzau+092/5e6XufvrSE+R20yOZs1QCH13zmbN4+8PPwLeEzzPybwFX+y5+2fdfYq7TyM9FeN37v7HwEPAzcFuNwO/CCniWXH3vcAuM7sgaLoeWE+enxfpKShXmll5MMpwPekL+PP9vDKd6lweAm4ysxIzmw7MApaHkO+smNkNwN8B73D3oxmb8va83P0ld5/g7tOCf0N2A5cF///l7XkFfg5cB2Bm5wPFQAf5f16So/K03/pDfj+FE3I3a171nWY2Ifg5lfRCIj8mR7NmKIS+Oyez5tv3h2BBoePeAWwMnudkXtx9xDyAa4GHg+dVpFdT2hz8HB92vrM4n/nASmAN6S9u4wrkvP6F9P84a4HvAyX5el6kO7A9QJx0ofDR050L6SmDzcDLwFvCzj/I89pCeq56U/D4n0I4r5O2bweqC+G8SBd3Pwj+P1sNXJdv56VH/j3yqd8ivcjJfmBMRltOZg2y5U3fCTxFutB/Ebg+1/5s86nvPkXWdwXPe4E24De5kPU0eXP2+8Mp8t4f/H+2BvglMDlX8vb3sCCYiIiIiIiIFJCCn8YpIiIiIiIyEqnYExERERERKUAq9kRERERERAqQij0REREREZECpGJPRERERESkAKnYExERERERKUAq9kRERERERAqQij0REREREZEC9P8DaK7F7STUIxQAAAAASUVORK5CYII=\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharey=False)\n",
"fig.suptitle('Simulated IQ Data')\n",
"\n",
"\n",
"IQ = np.random.normal(loc=100,scale=15,size=10000)\n",
"sns.histplot(IQ, kde=True,ax=axes[0])\n",
"axes[0].set_title(\"1000000 samples\")\n",
"\n",
"\n",
"IQ = np.random.normal(loc=100,scale=15,size=100)\n",
"sns.histplot(IQ, ax=axes[1])\n",
"axes[1].set_title(\"100 samples\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now suppose I run an experiment. I select 100 people at random and administer an IQ test, giving me a simple random sample from the population. My sample would consist of a collection of numbers like this:\n",
"```\n",
" 106 101 98 80 74 ... 107 72 100\n",
"```\n",
"Each of these IQ scores is sampled from a normal distribution with mean 100 and standard deviation 15. So if I plot a histogram of the sample, I get something like the one shown in Figure \\@ref(fig:IQdist)b. As you can see, the histogram is *roughly* the right shape, but it's a very crude approximation to the true population distribution shown in Figure \\@ref(fig:IQdist)a. When I calculate the mean of my sample, I get a number that is fairly close to the population mean 100 but not identical. In this case, it turns out that the people in my sample have a mean IQ of 98.5, and the standard deviation of their IQ scores is 15.9. These **_sample statistics_** are properties of my data set, and although they are fairly similar to the true population values, they are not the same. In general, sample statistics are the things you can calculate from your data set, and the population parameters are the things you want to learn about. Later on in this chapter I'll talk about how you can estimate population parameters using your sample statistics (Section \\@ref(pointestimates) and how to work out how confident you are in your estimates (Section \\@ref(ci) but before we get to that there's a few more ideas in sampling theory that you need to know about. \n",
"\n",
"\n",
"## The law of large numbers\n",
"\n",
"In the previous section I showed you the results of one fictitious IQ experiment with a sample size of $N=100$. The results were somewhat encouraging: the true population mean is 100, and the sample mean of 98.5 is a pretty reasonable approximation to it. In many scientific studies that level of precision is perfectly acceptable, but in other situations you need to be a lot more precise. If we want our sample statistics to be much closer to the population parameters, what can we do about it?\n",
"\n",
"The obvious answer is to collect more data. Suppose that we ran a much larger experiment, this time measuring the IQs of 10,000 people. We can simulate the results of this experiment using Python. To create the simulated data above, I used the numpy package to sample from a normal distribution with a mean of 100 and a standard deviation of 15. By altering the size of the sample drawn and plotting histograms of the frequencies of the values in the samples, it is easy to create simulated data that illustrate the effect of increasing the sampling size.\n",
"\n",
"\n",
"\n",
"I can compute the mean IQ using the `mean()`function and the standard deviation using the `stdev()` function, both from the `statistics`package, and I can draw a histgram using `histplot()` from `seaborn`. The histogram of this much larger sample is shown in Figure \\@ref(fig:IQdist)c. Even a moment's inspections makes clear that the larger sample is a much better approximation to the true population distribution than the smaller one. This is reflected in the sample statistics: the mean IQ for the larger sample turns out to be 99.9, and the standard deviation is 15.1. These values are now very close to the true population."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 samples. Mean: 91.83513165879515 Standard deviation: 12.080467135962696\n",
"100 samples. Mean: 99.11176856251919 Standard deviation: 14.192870313207116\n",
"10000 samples. Mean: 100.08335406247072 Standard deviation: 14.849937076834848\n"
]
}
],
"source": [
"import numpy as np\n",
"import statistics\n",
"\n",
"IQ_10 = np.random.normal(loc=100,scale=15,size=10)\n",
"IQ_100 = np.random.normal(loc=100,scale=15,size=100)\n",
"IQ_10000 = np.random.normal(loc=100,scale=15,size=10000)\n",
"\n",
"print(\"10 samples. Mean: \", statistics.mean(IQ_10), \" Standard deviation: \", statistics.stdev(IQ_10))\n",
"print(\"100 samples. Mean: \", statistics.mean(IQ_100), \" Standard deviation: \", statistics.stdev(IQ_100))\n",
"print(\"10000 samples. Mean: \", statistics.mean(IQ_10000), \" Standard deviation: \", statistics.stdev(IQ_10000))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I feel a bit silly saying this, but the thing I want you to take away from this is that large samples generally give you better information. I feel silly saying it because it's so bloody obvious that it shouldn't need to be said. In fact, it's such an obvious point that when Jacob Bernoulli -- one of the founders of probability theory -- formalised this idea back in 1713, he was kind of a jerk about it. Here's how he described the fact that we all share this intuition:\n",
"\n",
"> *For even the most stupid of men, by some instinct of nature, by himself and without any instruction (which is a remarkable thing), is convinced that the more observations have been made, the less danger there is of wandering from one's goal* @Stigler1986\n",
"\n",
"Okay, so the passage comes across as a bit condescending (not to mention sexist), but his main point is correct: it really does feel obvious that more data will give you better answers. The question is, why is this so? Not surprisingly, this intuition that we all share turns out to be correct, and statisticians refer to it as the **_law of large numbers_**. The law of large numbers is a mathematical law that applies to many different sample statistics, but the simplest way to think about it is as a law about averages. The sample mean is the most obvious example of a statistic that relies on averaging (because that's what the mean is... an average), so let's look at that. When applied to the sample mean, what the law of large numbers states is that as the sample gets larger, the sample mean tends to get closer to the true population mean. Or, to say it a little bit more precisely, as the sample size \"approaches\" infinity (written as $N \\rightarrow \\infty$) the sample mean approaches the population mean ($\\bar{X} \\rightarrow \\mu$).^[Technically, the law of large numbers pertains to any sample statistic that can be described as an average of independent quantities. That's certainly true for the sample mean. However, it's also possible to write many other sample statistics as averages of one form or another. The variance of a sample, for instance, can be rewritten as a kind of average and so is subject to the law of large numbers. The minimum value of a sample, however, cannot be written as an average of anything and is therefore not governed by the law of large numbers.] \n",
"\n",
"I don't intend to subject you to a proof that the law of large numbers is true, but it's one of the most important tools for statistical theory. The law of large numbers is the thing we can use to justify our belief that collecting more and more data will eventually lead us to the truth. For any particular data set, the sample statistics that we calculate from it **_will be wrong_**, but the law of large numbers tells us that if we keep collecting more data those sample statistics will tend to get closer and closer to the true population parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(samplingdists)=\n",
"## Sampling distributions and the central limit theorem\n",
"\n",
"The law of large numbers is a very powerful tool, but it's not going to be good enough to answer all our questions. Among other things, all it gives us is a \"long run guarantee\". In the long run, if we were somehow able to collect an infinite amount of data, then the law of large numbers guarantees that our sample statistics will be correct. But as John Maynard Keynes famously argued in economics, a long run guarantee is of little use in real life:\n",
"\n",
"> *[The] long run is a misleading guide to current affairs. In the long run we are all dead. Economists set themselves too easy, too useless a task, if in tempestuous seasons they can only tell us, that when the storm is long past, the ocean is flat again.* @Keynes1923\n",
"\n",
"As in economics, so too in psychology and statistics. It is not enough to know that we will *eventually* arrive at the right answer when calculating the sample mean. Knowing that an infinitely large data set will tell me the exact value of the population mean is cold comfort when my *actual* data set has a sample size of $N=100$. In real life, then, we must know something about the behaviour of the sample mean when it is calculated from a more modest data set!\n",
"\n",
"### Sampling distribution of the mean\n",
"\n",
"\n",
"With this in mind, let's abandon the idea that our studies will have sample sizes of 10000, and consider a very modest experiment indeed. This time around we'll sample $N=5$ people and measure their IQ scores. As before, I can simulate this experiment in Python using numpy's `random.normal()` function. We can convert these to integers with `astype(int`"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulated data: [116 91 110 92 127]\n",
"Mean of simulated data: 107\n"
]
}
],
"source": [
"IQ_1 = np.random.normal(loc=100,scale=15,size=5).astype(int)\n",
"print(\"Simulated data: \", IQ_1)\n",
"print(\"Mean of simulated data: \", statistics.mean(IQ_1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not surprisingly, the mean from this sample turn is much less accurate than the previous experiment. Now imagine that I decided to **_replicate_** the experiment. That is, I repeat the procedure as closely as possible: I randomly sample 5 new people and measure their IQ. Again, R allows me to simulate the results of this procedure:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulated data: [128 94 95 92 87]\n",
"Mean of simulated data: 99\n"
]
}
],
"source": [
"IQ_2 = np.random.normal(loc=100,scale=15,size=5).astype(int)\n",
"print(\"Simulated data: \", IQ_2)\n",
"print(\"Mean of simulated data: \", statistics.mean(IQ_2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If I repeat the experiment 10 times I obtain the results shown in Table \\@ref(tab:replications), and as you can see the sample mean varies from one replication to the next. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now suppose that I decided to keep going in this fashion, replicating this \"five IQ scores\" experiment over and over again. Every time I replicate the experiment I write down the sample mean. Over time, I'd be amassing a new data set, in which every experiment generates a single data point. The first 10 observations from my data set are the sample means listed in Table \\@ref(tab:replications), so my data set starts out like this:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
" IQ1 IQ2 IQ3 IQ4 IQ5\n",
"count 5.000000 5.000000 5.000000 5.000000 5.000000\n",
"mean 98.800000 97.000000 95.200000 113.600000 94.600000\n",
"std 7.918333 8.124038 9.011104 8.961027 28.023205\n",
"min 87.000000 91.000000 85.000000 99.000000 66.000000\n",
"25% 96.000000 92.000000 91.000000 114.000000 69.000000\n",
"50% 100.000000 92.000000 93.000000 114.000000 95.000000\n",
"75% 103.000000 100.000000 98.000000 118.000000 111.000000\n",
"max 108.000000 110.000000 109.000000 123.000000 132.000000"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame(\n",
" {'IQ1': np.random.normal(loc=100,scale=15,size=5).astype(int),\n",
" 'IQ2': np.random.normal(loc=100,scale=15,size=5).astype(int),\n",
" 'IQ3': np.random.normal(loc=100,scale=15,size=5).astype(int),\n",
" 'IQ4': np.random.normal(loc=100,scale=15,size=5).astype(int),\n",
" 'IQ5': np.random.normal(loc=100,scale=15,size=5).astype(int)\n",
" }) \n",
"\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What if I continued like this for 10,000 replications, and then drew a histogram? Using the magical powers of Python that's exactly what I did, and you can see the results in Figure \\@ref(fig:sampdistmean). As this picture illustrates, the average of 5 IQ scores is usually between 90 and 110. But more importantly, what it highlights is that if we replicate an experiment over and over again, what we end up with is a *distribution* of sample means! This distribution has a special name in statistics: it's called the **_sampling distribution of the mean_**. "
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"IQ1 | IQ2 | IQ3 | IQ4 | IQ5 | |
---|---|---|---|---|---|

count | 5.000000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 |

mean | 98.800000 | 97.000000 | 95.200000 | 113.600000 | 94.600000 |

std | 7.918333 | 8.124038 | 9.011104 | 8.961027 | 28.023205 |

min | 87.000000 | 91.000000 | 85.000000 | 99.000000 | 66.000000 |

25% | 96.000000 | 92.000000 | 91.000000 | 114.000000 | 69.000000 |

50% | 100.000000 | 92.000000 | 93.000000 | 114.000000 | 95.000000 |

75% | 103.000000 | 100.000000 | 98.000000 | 118.000000 | 111.000000 |

max | 108.000000 | 110.000000 | 109.000000 | 123.000000 | 132.000000 |

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.stats as stats\n",
"import math\n",
"\n",
"# define a normal distribution with a mean of 100 and a standard deviation of 15\n",
"mu = 100\n",
"sigma = 15\n",
"x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
"y = stats.norm.pdf(x, mu, sigma)\n",
"\n",
"# run 10000 simulated experiments with 5 subjects each, and calculate the sample mean for each experiment\n",
"sample_means = []\n",
"for i in range(1,10000):\n",
" sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=5).astype(int))\n",
" sample_means.append(sample_mean)\n",
"\n",
"\n",
"# plot a histogram of the distribution of sample means, together with the population distribution\n",
"fig, ax = plt.subplots()\n",
"sns.histplot(sample_means, ax=ax)\n",
"ax2 = ax.twinx()\n",
"sns.lineplot(x=x,y=y, ax=ax2, color='black')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fig.cap=\"The sampling distribution of the mean for the \\\"five IQ scores experiment\\\". If you sample 5 people at random and calculate their *average* IQ, you'll almost certainly get a number between 80 and 120, even though there are quite a lot of individuals who have IQs above 120 or below 80. For comparison, the black line plots the population distribution of IQ scores.\"\n",
"\n",
"Sampling distributions are another important theoretical idea in statistics, and they're crucial for understanding the behaviour of small samples. For instance, when I ran the very first \"five IQ scores\" experiment, the sample mean turned out to be 95. What the sampling distribution in Figure \\@ref(fig:sampdistmean) tells us, though, is that the \"five IQ scores\" experiment is not very accurate. If I repeat the experiment, the sampling distribution tells me that I can expect to see a sample mean anywhere between 80 and 120. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fig.cap=\"The sampling distribution of the *maximum* for the \\\"five IQ scores experiment\\\". If you sample 5 people at random and select the one with the highest IQ score, you'll probably see someone with an IQ between 100 and 140.\"\n",
"\n",
"### Sampling distributions exist for any sample statistic!\n",
"\n",
"One thing to keep in mind when thinking about sampling distributions is that *any* sample statistic you might care to calculate has a sampling distribution. For example, suppose that each time I replicated the \"five IQ scores\" experiment I wrote down the largest IQ score in the experiment. This might give me a data set that started out like this:\n",
"```\n",
" 110 117 122 119 113 ... \n",
"```\n",
"Doing this over and over again would give me a very different sampling distribution, namely the *sampling distribution of the maximum*. The sampling distribution of the maximum of 5 IQ scores is shown in Figure \\@ref(fig:sampdistmax). Not surprisingly, if you pick 5 people at random and then find the person with the highest IQ score, they're going to have an above average IQ. Most of the time you'll end up with someone whose IQ is measured in the 100 to 140 range. "
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.stats as stats\n",
"import math\n",
"\n",
"# define a normal distribution with a mean of 100 and a standard deviation of 15\n",
"mu = 100\n",
"sigma = 15\n",
"x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
"y = stats.norm.pdf(x, mu, sigma)\n",
"\n",
"# run 10000 simulated experiments with 5 subjects each, and find the maximum score for each experiment\n",
"sample_maxes = []\n",
"for i in range(1,10000):\n",
" sample_max = max(np.random.normal(loc=100,scale=15,size=5).astype(int))\n",
" sample_maxes.append(sample_max)\n",
"\n",
"\n",
"# plot a histogram of the distribution of sample maximums, together with the population distribution\n",
"fig, ax = plt.subplots()\n",
"sns.histplot(sample_maxes, ax=ax)\n",
"ax2 = ax.twinx()\n",
"sns.lineplot(x=x,y=y, ax=ax2, color='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fig.cap=\"The sampling distribution of the *maximum* for the \\\"five IQ scores experiment\\\". If you sample 5 people at random and select the one with the highest IQ score, you'll probably see someone with an IQ between 100 and 140.\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(clt)=\n",
"### The central limit theorem\n",
"\n",
"An illustration of the how sampling distribution of the mean depends on sample size. In each panel, I generated 10,000 samples of IQ data, and calculated the mean IQ observed within each of these data sets. The histograms in these plots show the distribution of these means (i.e., the sampling distribution of the mean). Each individual IQ score was drawn from a normal distribution with mean 100 and standard deviation 15, which is shown as the solid black line)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.stats as stats\n",
"import seaborn as sns\n",
"import statistics\n",
"import math\n",
"\n",
"# define a normal distribution with a mean of 100 and a standard deviation of 15\n",
"mu = 100\n",
"sigma = 15\n",
"x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
"y = stats.norm.pdf(x, mu, sigma)\n",
"\n",
"# run 10000 simulated experiments with 1 subject each, and calculate the sample mean for each experiment\n",
"n = 1\n",
"sample_means = []\n",
"for i in range(1,10000):\n",
" sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int))\n",
" sample_means.append(sample_mean)\n",
"\n",
"\n",
"# plot a histogram of the distribution of sample means, together with the population distribution\n",
"fig, ax = plt.subplots()\n",
"sns.histplot(sample_means, ax=ax, binwidth=4)\n",
"ax2 = ax.twinx()\n",
"sns.lineplot(x=x,y=y, ax=ax2, color='black')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each data set contained only a single observation, so the mean of each sample is just one person's IQ score. As a consequence, the sampling distribution of the mean is of course identical to the population distribution of IQ scores."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n = 2\n",
"sample_means = []\n",
"for i in range(1,10000):\n",
" sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int))\n",
" sample_means.append(sample_mean)\n",
"\n",
"\n",
"# plot a histogram of the distribution of sample means, together with the population distribution\n",
"fig, ax = plt.subplots()\n",
"sns.histplot(sample_means, ax=ax, binwidth=4)\n",
"ax2 = ax.twinx()\n",
"sns.lineplot(x=x,y=y, ax=ax2, color='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we raise the sample size to 2, the mean of any one sample tends to be closer to the population mean than a one person's IQ score, and so the histogram (i.e., the sampling distribution) is a bit narrower than the population distribution."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"n = 10\n",
"sample_means = []\n",
"for i in range(1,10000):\n",
" sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int))\n",
" sample_means.append(sample_mean)\n",
"\n",
"\n",
"# plot a histogram of the distribution of sample means, together with the population distribution\n",
"fig, ax = plt.subplots()\n",
"sns.histplot(sample_means, ax=ax, binwidth=4)\n",
"ax2 = ax.twinx()\n",
"sns.lineplot(x=x,y=y, ax=ax2, color='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By the time we raise the sample size to 10, we can see that the distribution of sample means tend to be fairly tightly clustered around the true population mean. At this point I hope you have a pretty good sense of what sampling distributions are, and in particular what the sampling distribution of the mean is. In this section I want to talk about how the sampling distribution of the mean changes as a function of sample size. Intuitively, you already know part of the answer: if you only have a few observations, the sample mean is likely to be quite inaccurate: if you replicate a small experiment and recalculate the mean you'll get a very different answer. In other words, the sampling distribution is quite wide. If you replicate a large experiment and recalculate the sample mean you'll probably get the same answer you got last time, so the sampling distribution will be very narrow. You can see this visually in Figures \\@ref(fig:IQsampa), \\@ref(fig:IQsampb) and \\@ref(fig:IQsampc): the bigger the sample size, the narrower the sampling distribution gets. We can quantify this effect by calculating the standard deviation of the sampling distribution, which is referred to as the **_standard error_**. The standard error of a statistic is often denoted SE, and since we're usually interested in the standard error of the sample *mean*, we often use the acronym SEM. As you can see just by looking at the picture, as the sample size $N$ increases, the SEM decreases. \n",
"\n",
"Okay, so that's one part of the story. However, there's something I've been glossing over so far. All my examples up to this point have been based on the \"IQ scores\" experiments, and because IQ scores are roughly normally distributed, I've assumed that the population distribution is normal. What if it isn't normal? What happens to the sampling distribution of the mean? The remarkable thing is this: no matter what shape your population distribution is, as $N$ increases the sampling distribution of the mean starts to look more like a normal distribution. To give you a sense of this, I ran some simulations using R. To do this, I started with the \"ramped\" distribution shown in the histogram in Figure \\@ref(fig:cltdemo). As you can see by comparing the triangular shaped histogram to the bell curve plotted by the black line, the population distribution doesn't look very much like a normal distribution at all. Next, I used R to simulate the results of a large number of experiments. In each experiment I took $N=2$ samples from this distribution, and then calculated the sample mean. Figure \\@ref(fig:cltdemob) plots the histogram of these sample means (i.e., the sampling distribution of the mean for $N=2$). This time, the histogram produces a $\\cap$-shaped distribution: it's still not normal, but it's a lot closer to the black line than the population distribution in Figure \\@ref(fig:cltdemoa). When I increase the sample size to $N=4$, the sampling distribution of the mean is very close to normal (Figure \\@ref(fig:cltdemoc), and by the time we reach a sample size of $N=8$ it's almost perfectly normal. In other words, as long as your sample size isn't tiny, the sampling distribution of the mean will be approximately normal no matter what your population distribution looks like!"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# parameters of the beta\n",
"a=2\n",
"b=1\n",
"\n",
"\n",
"\n",
"def plotSamples(n):\n",
" # create normal distribution with mean and standard deviation of the beta\n",
" mu = a / (a+b)\n",
" sigma = math.sqrt( a*b / (a+b)**2 / (a+b+1) )\n",
" x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)\n",
" y = stats.norm.pdf(x, mu, sigma/math.sqrt(n))\n",
"\n",
" # find sample means from samples of \"ramped\" beta distribution\n",
"\n",
" values = []\n",
" for i in range(n):\n",
" v = []\n",
" for j in range(50000):\n",
" v.append(np.random.beta(a,b))\n",
" values.append(v)\n",
" df = pd.DataFrame(values)\n",
" sample_means = df.mean(axis=0)\n",
"\n",
" # plot a histogram of the distribution of sample means, together with the population distribution\n",
" fig, ax = plt.subplots(sharex=True)\n",
" sns.histplot(sample_means)\n",
" ax2 = ax.twinx()\n",
" sns.lineplot(x=x,y=y, ax=ax2, color='black')\n",
" ax.set(yticklabels=[])\n",
" ax2.set(yticklabels=[])\n",
" ax.set(ylabel=None)\n",
" ax2.set(ylabel=None)\n",
" ax.tick_params(left=False)\n",
" ax2.tick_params(right=False)\n",
" ax.set_title(\"Sample size = \" + str(n))\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" ax2.spines['top'].set_visible(False)\n",
" ax2.spines['right'].set_visible(False)\n",
"\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plotSamples(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plotSamples(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plotSamples(4)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
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"source": [
"plotSamples(8)"
]
},
{
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"source": [
"On the basis of these figures, it seems like we have evidence for all of the following claims about the sampling distribution of the mean:\n",
"\n",
"- The mean of the sampling distribution is the same as the mean of the population\n",
"- The standard deviation of the sampling distribution (i.e., the standard error) gets smaller as the sample size increases\n",
"- The shape of the sampling distribution becomes normal as the sample size increases\n",
"\n",
"As it happens, not only are all of these statements true, there is a very famous theorem in statistics that proves all three of them, known as the **_central limit theorem_**. Among other things, the central limit theorem tells us that if the population distribution has mean $\\mu$ and standard deviation $\\sigma$, then the sampling distribution of the mean also has mean $\\mu$, and the standard error of the mean is \n",
"$$\n",
"\\mbox{SEM} = \\frac{\\sigma}{ \\sqrt{N} }\n",
"$$ \n",
"Because we divide the population standard devation $\\sigma$ by the square root of the sample size $N$, the SEM gets smaller as the sample size increases. It also tells us that the shape of the sampling distribution becomes normal.^[As usual, I'm being a bit sloppy here. The central limit theorem is a bit more general than this section implies. Like most introductory stats texts, I've discussed one situation where the central limit theorem holds: when you're taking an average across lots of independent events drawn from the same distribution. However, the central limit theorem is much broader than this. There's a whole class of things called \"$U$-statistics\" for instance, all of which satisfy the central limit theorem and therefore become normally distributed for large sample sizes. The mean is one such statistic, but it's not the only one.] \n",
"\n",
"This result is useful for all sorts of things. It tells us why large experiments are more reliable than small ones, and because it gives us an explicit formula for the standard error it tells us *how much* more reliable a large experiment is. It tells us why the normal distribution is, well, *normal*. In real experiments, many of the things that we want to measure are actually averages of lots of different quantities (e.g., arguably, \"general\" intelligence as measured by IQ is an average of a large number of \"specific\" skills and abilities), and when that happens, the averaged quantity should follow a normal distribution. Because of this mathematical law, the normal distribution pops up over and over again in real data. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimating population parameters\n",
"\n",
"In all the IQ examples in the previous sections, we actually knew the population parameters ahead of time. As every undergraduate gets taught in their very first lecture on the measurement of intelligence, IQ scores are *defined* to have mean 100 and standard deviation 15. However, this is a bit of a lie. How do we know that IQ scores have a true population mean of 100? Well, we know this because the people who designed the tests have administered them to very large samples, and have then \"rigged\" the scoring rules so that their sample has mean 100. That's not a bad thing of course: it's an important part of designing a psychological measurement. However, it's important to keep in mind that this theoretical mean of 100 only attaches to the population that the test designers used to design the tests. Good test designers will actually go to some lengths to provide \"test norms\" that can apply to lots of different populations (e.g., different age groups, nationalities etc). \n",
"\n",
"This is very handy, but of course almost every research project of interest involves looking at a different population of people to those used in the test norms. For instance, suppose you wanted to measure the effect of low level lead poisoning on cognitive functioning in Port Pirie, a South Australian industrial town with a lead smelter. Perhaps you decide that you want to compare IQ scores among people in Port Pirie to a comparable sample in Whyalla, a South Australian industrial town with a steel refinery.^[Please note that if you were *actually* interested in this question, you would need to be a *lot* more careful than I'm being here. You *can't* just compare IQ scores in Whyalla to Port Pirie and assume that any differences are due to lead poisoning. Even if it were true that the only differences between the two towns corresponded to the different refineries (and it isn't, not by a long shot), you need to account for the fact that people already *believe* that lead pollution causes cognitive deficits: if you recall back to Chapter \\@ref(studydesign), this means that there are different demand effects for the Port Pirie sample than for the Whyalla sample. In other words, you might end up with an illusory group difference in your data, caused by the fact that people *think* that there is a real difference. I find it pretty implausible to think that the locals wouldn't be well aware of what you were trying to do if a bunch of researchers turned up in Port Pirie with lab coats and IQ tests, and even less plausible to think that a lot of people would be pretty resentful of you for doing it. Those people won't be as co-operative in the tests. Other people in Port Pirie might be *more* motivated to do well because they don't want their home town to look bad. The motivational effects that would apply in Whyalla are likely to be weaker, because people don't have any concept of \"iron ore poisoning\" in the same way that they have a concept for \"lead poisoning\". Psychology is *hard*.] Regardless of which town you're thinking about, it doesn't make a lot of sense simply to *assume* that the true population mean IQ is 100. No-one has, to my knowledge, produced sensible norming data that can automatically be applied to South Australian industrial towns. We're going to have to **_estimate_** the population parameters from a sample of data. So how do we do this?\n",
"\n",
"### Estimating the population mean\n",
"\n",
"Suppose we go to Port Pirie and 100 of the locals are kind enough to sit through an IQ test. The average IQ score among these people turns out to be $\\bar{X}=98.5$. So what is the true mean IQ for the entire population of Port Pirie? Obviously, we don't know the answer to that question. It could be $97.2$, but if could also be $103.5$. Our sampling isn't exhaustive so we cannot give a definitive answer. Nevertheless if I was forced at gunpoint to give a \"best guess\" I'd have to say $98.5$. That's the essence of statistical estimation: giving a best guess. \n",
"\n",
"In this example, estimating the unknown poulation parameter is straightforward. I calculate the sample mean, and I use that as my **_estimate of the population mean_**. It's pretty simple, and in the next section I'll explain the statistical justification for this intuitive answer. However, for the moment what I want to do is make sure you recognise that the sample statistic and the estimate of the population parameter are conceptually different things. A sample statistic is a description of your data, whereas the estimate is a guess about the population. With that in mind, statisticians often use different notation to refer to them. For instance, if true population mean is denoted $\\mu$, then we would use $\\hat\\mu$ to refer to our estimate of the population mean. In contrast, the sample mean is denoted $\\bar{X}$ or sometimes $m$. However, in simple random samples, the estimate of the population mean is identical to the sample mean: if I observe a sample mean of $\\bar{X} = 98.5$, then my estimate of the population mean is also $\\hat\\mu = 98.5$. To help keep the notation clear, here's a handy table:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|Symbol |What it's calle |Do we know what it is? |\n",
"|:-----------|:-------------------------------|:---------------------------------|\n",
"|$\\bar{X}$ |Sample mean |Yes calculated from the raw data |\n",
"|$\\mu$ |True population mean |Almost never known for sure |\n",
"|$\\hat{\\mu}$ |Estimate of the population mean |Yes identical to the sample mean |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimating the population standard deviation\n",
"\n",
"So far, estimation seems pretty simple, and you might be wondering why I forced you to read through all that stuff about sampling theory. In the case of the mean, our estimate of the population parameter (i.e. $\\hat\\mu$) turned out to identical to the corresponding sample statistic (i.e. $\\bar{X}$). However, that's not always true. To see this, let's have a think about how to construct an **_estimate of the population standard deviation_**, which we'll denote $\\hat\\sigma$. What shall we use as our estimate in this case? Your first thought might be that we could do the same thing we did when estimating the mean, and just use the sample statistic as our estimate. That's almost the right thing to do, but not quite. \n",
"\n",
"Here's why. Suppose I have a sample that contains a single observation. For this example, it helps to consider a sample where you have no intutions at all about what the true population values might be, so let's use something completely fictitious. Suppose the observation in question measures the *cromulence* of my shoes. It turns out that my shoes have a cromulence of 20. So here's my sample:\n",
"\n",
"```\n",
"20\n",
"```\n",
"\n",
"This is a perfectly legitimate sample, even if it does have a sample size of $N=1$. It has a sample mean of 20, and because every observation in this sample is equal to the sample mean (obviously!) it has a sample standard deviation of 0. As a description of the *sample* this seems quite right: the sample contains a single observation and therefore there is no variation observed within the sample. A sample standard deviation of $s = 0$ is the right answer here. But as an estimate of the *population* standard deviation, it feels completely insane, right? Admittedly, you and I don't know anything at all about what \"cromulence\" is, but we know something about data: the only reason that we don't see any variability in the *sample* is that the sample is too small to display any variation! So, if you have a sample size of $N=1$, it *feels* like the right answer is just to say \"no idea at all\". \n",
"\n",
"Notice that you *don't* have the same intuition when it comes to the sample mean and the population mean. If forced to make a best guess about the population mean, it doesn't feel completely insane to guess that the population mean is 20. Sure, you probably wouldn't feel very confident in that guess, because you have only the one observation to work with, but it's still the best guess you can make. \n",
"\n",
"Let's extend this example a little. Suppose I now make a second observation. My data set now has $N=2$ observations of the cromulence of shoes, and the complete sample now looks like this:\n",
"```\n",
"20, 22\n",
"```\n",
"This time around, our sample is *just* large enough for us to be able to observe some variability: two observations is the bare minimum number needed for any variability to be observed! For our new data set, the sample mean is $\\bar{X}=21$, and the sample standard deviation is $s=1$. What intuitions do we have about the population? Again, as far as the population mean goes, the best guess we can possibly make is the sample mean: if forced to guess, we'd probably guess that the population mean cromulence is 21. What about the standard deviation? This is a little more complicated. The sample standard deviation is only based on two observations, and if you're at all like me you probably have the intuition that, with only two observations, we haven't given the population \"enough of a chance\" to reveal its true variability to us. It's not just that we suspect that the estimate is *wrong*: after all, with only two observations we expect it to be wrong to some degree. The worry is that the error is *systematic*. Specifically, we suspect that the sample standard deviation is likely to be smaller than the population standard deviation. "
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import statistics\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"# generate data from 10000 \"IQ\" studies, where each study consists of two scores\n",
"n = 2\n",
"sample_sds = []\n",
"for i in range(1,10000):\n",
" sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int))\n",
" sample_sds.append(sample_sd)\n",
"\n",
"\n",
"# plot a histogram of the distribution of sample standard deviations, together with dashed line indicating \n",
"# population standard deviation\n",
"fig, ax = plt.subplots()\n",
"sns.histplot(sample_sds, ax=ax, binwidth=4)\n",
"plt.axvline(15, color = 'black', linestyle = \"dashed\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fig.cap=\"The sampling distribution of the sample standard deviation for a \\\"two IQ scores\\\" experiment. The true population standard deviation is 15 (dashed line), but as you can see from the histogram, the vast majority of experiments will produce a much smaller sample standard deviation than this. On average, this experiment would produce a sample standard deviation of only 8.5, well below the true value! In other words, the sample standard deviation is a *biased* estimate of the population standard deviation.\n",
"\n",
"This intuition feels right, but it would be nice to demonstrate this somehow. There are in fact mathematical proofs that confirm this intuition, but unless you have the right mathematical background they don't help very much. Instead, what I'll do is use R to simulate the results of some experiments. With that in mind, let's return to our IQ studies. Suppose the true population mean IQ is 100 and the standard deviation is 15. I can use the `rnorm()` function to generate the the results of an experiment in which I measure $N=2$ IQ scores, and calculate the sample standard deviation. If I do this over and over again, and plot a histogram of these sample standard deviations, what I have is the *sampling distribution of the standard deviation*. I've plotted this distribution in Figure \\@ref(fig:sampdistsd). Even though the true population standard deviation is 15, the average of the *sample* standard deviations is only 8.5. Notice that this is a very different result to what we found in Figure \\@ref(fig:IQsampb) when we plotted the sampling distribution of the mean. If you look at that sampling distribution, what you see is that the population mean is 100, and the average of the sample means is also 100. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's extend the simulation. Instead of restricting ourselves to the situation where we have a sample size of $N=2$, let's repeat the exercise for sample sizes from 1 to 10. If we plot the average sample mean and average sample standard deviation as a function of sample size, you get the results shown in Figure \\@ref(fig:estimatorbias). On the left hand side (panel a), I've plotted the average sample mean and on the right hand side (panel b), I've plotted the average standard deviation. The two plots are quite different: *on average*, the average sample mean is equal to the population mean. It is an **_unbiased estimator_**, which is essentially the reason why your best estimate for the population mean is the sample mean.^[I should note that I'm hiding something here. Unbiasedness is a desirable characteristic for an estimator, but there are other things that matter besides bias. However, it's beyond the scope of this book to discuss this in any detail. I just want to draw your attention to the fact that there's some hidden complexity here.] The plot on the right is quite different: on average, the sample standard deviation $s$ is *smaller* than the population standard deviation $\\sigma$. It is a **_biased estimator_**. In other words, if we want to make a \"best guess\" $\\hat\\sigma$ about the value of the population standard deviation $\\sigma$, we should make sure our guess is a little bit larger than the sample standard deviation $s$. "
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Sample Standard Deviations')"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import statistics\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"\n",
"\n",
"\n",
"ns = range(2,11)\n",
"\n",
"\n",
"averageSampleSds = []\n",
"averageSampleMeans = []\n",
"\n",
"# Simulate data for N = 2 to 10\n",
"for n in ns:\n",
" sample_sds = []\n",
" sample_means = []\n",
" for i in range(1,10000):\n",
" sample_sd = statistics.stdev(np.random.normal(loc=100,scale=15,size=n).astype(int))\n",
" sample_sds.append(sample_sd)\n",
" sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int))\n",
" sample_means.append(sample_mean)\n",
" averageSampleSds.append(statistics.mean(sample_sds))\n",
" averageSampleMeans.append(statistics.mean(sample_means))\n",
"\n",
"# Simulate data for N = 1. This is not possible in the loop above, because Python can't calculate a SD\n",
"# from only one observation\n",
"n = 1\n",
"sample_mean_1 = []\n",
"for i in range(1,10000):\n",
" sample_mean = statistics.mean(np.random.normal(loc=100,scale=15,size=n).astype(int))\n",
" sample_mean_1.append(sample_mean)\n",
"\n",
"# Add in sample mean and SD for N=1 at the beginning of the lists\n",
"# For N = 1, the sample SD is simply 0\n",
"averageSampleSds.insert(0,0)\n",
"averageSampleMeans.insert(0,statistics.mean(sample_mean_1))\n",
"\n",
"# Collect simulated data in a dataframe, together with a vector from 1 to 10 representing N\n",
"df = pd.DataFrame(\n",
" {'N': range(1,11),\n",
" 'SampleMeans': averageSampleMeans,\n",
" 'SampleSDs': averageSampleSds\n",
" })\n",
"\n",
"# Plot the data\n",
"fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharey=False)\n",
"fig.suptitle('Simulated IQ Data')\n",
"\n",
"\n",
"sns.lineplot(data=df, x='N', y='SampleMeans',ax=axes[0], linestyle = \"dashdot\")\n",
"sns.lineplot(data=df, x='N', y='SampleSDs',ax=axes[1], linestyle = \"dashdot\")\n",
"axes[0].set(ylim=(0,110))\n",
"axes[1].set(ylim=(0,17))\n",
"axes[0].axhline(100, color = 'black', linestyle = \"dashed\")\n",
"axes[1].axhline(15, color = 'black', linestyle = \"dashed\")\n",
"axes[0].set_title(\"Sample Means\")\n",
"axes[1].set_title(\"Sample Standard Deviations\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fig.cap: An illustration of the fact that the sample mean is an unbiased estimator of the population mean (panel a), but the sample standard deviation is a biased estimator of the population standard deviation (panel b). To generate the figure, I generated 10,000 simulated data sets with 1 observation each, 10,000 more with 2 observations, and so on up to a sample size of 10. Each data set consisted of fake IQ data: that is, the data were normally distributed with a true population mean of 100 and standard deviation 15. *On average*, the sample means turn out to be 100, regardless of sample size (panel a). However, the sample standard deviations turn out to be systematically too small (panel b), especially for small sample sizes.\n",
"\n",
"The fix to this systematic bias turns out to be very simple. Here's how it works. Before tackling the standard deviation, let's look at the variance. If you recall from Section \\@ref(var), the sample variance is defined to be the average of the squared deviations from the sample mean. That is:\n",
"\n",
"$s^2 = \\frac{1}{N} \\sum_{i=1}^N (X_i - \\bar{X})^2$ \n",
"\n",
"The sample variance $s^2$ is a biased estimator of the population variance $\\sigma^2$. But as it turns out, we only need to make a tiny tweak to transform this into an unbiased estimator. All we have to do is divide by $N-1$ rather than by $N$. If we do that, we obtain the following formula:\n",
"$$\n",
"\\hat\\sigma^2 = \\frac{1}{N-1} \\sum_{i=1}^N (X_i - \\bar{X})^2 \n",
"$$\n",
"This is an unbiased estimator of the population variance $\\sigma$. Moreover, this finally answers the question we raised in Section \\@ref(var). Why did R give us slightly different answers when we used the `var()` function? Because the `var()` function calculates $\\hat\\sigma^2$ not $s^2$, that's why. A similar story applies for the standard deviation. If we divide by $N-1$ rather than $N$, our estimate of the population standard deviation becomes:\n",
"$$\n",
"\\hat\\sigma = \\sqrt{\\frac{1}{N-1} \\sum_{i=1}^N (X_i - \\bar{X})^2} \n",
"$$\n",
"and when we use R's built in standard deviation function `sd()`, what it's doing is calculating $\\hat\\sigma$, not $s$.^[Okay, I'm hiding something else here. In a bizarre and counterintuitive twist, since $\\hat\\sigma^2$ is an unbiased estimator of $\\sigma^2$, you'd assume that taking the square root would be fine, and $\\hat\\sigma$ would be an unbiased estimator of $\\sigma$. Right? Weirdly, it's not. There's actually a subtle, tiny bias in $\\hat\\sigma$. This is just bizarre: $\\hat\\sigma^2$ is and unbiased estimate of the population variance $\\sigma^2$, but when you take the square root, it turns out that $\\hat\\sigma$ is a biased estimator of the population standard deviation $\\sigma$. Weird, weird, weird, right? So, why is $\\hat\\sigma$ biased? The technical answer is \"because non-linear transformations (e.g., the square root) don't commute with expectation\", but that just sounds like gibberish to everyone who hasn't taken a course in mathematical statistics. Fortunately, it doesn't matter for practical purposes. The bias is small, and in real life everyone uses $\\hat\\sigma$ and it works just fine. Sometimes mathematics is just annoying.] \n",
"\n",
"One final point: in practice, a lot of people tend to refer to $\\hat{\\sigma}$ (i.e., the formula where we divide by $N-1$) as the *sample* standard deviation. Technically, this is incorrect: the *sample* standard deviation should be equal to $s$ (i.e., the formula where we divide by $N$). These aren't the same thing, either conceptually or numerically. One is a property of the sample, the other is an estimated characteristic of the population. However, in almost every real life application, what we actually care about is the estimate of the population parameter, and so people always report $\\hat\\sigma$ rather than $s$. This is the right number to report, of course, it's that people tend to get a little bit imprecise about terminology when they write it up, because \"sample standard deviation\" is shorter than \"estimated population standard deviation\". It's no big deal, and in practice I do the same thing everyone else does. Nevertheless, I think it's important to keep the two *concepts* separate: it's never a good idea to confuse \"known properties of your sample\" with \"guesses about the population from which it came\". The moment you start thinking that $s$ and $\\hat\\sigma$ are the same thing, you start doing exactly that. \n",
"\n",
"To finish this section off, here's another couple of tables to help keep things clear:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|Symbol |What it's called |Do we know what it is? |\n",
"|:----------------|:---------------------------------------------|:-------------------------------------------------------|\n",
"|$s$ |Sample standard deviation |Yes - calculated from the raw data |\n",
"|$\\sigma$ |Population standard deviation |Almost never known for sure |\n",
"|$\\hat{\\sigma}$ |Estimate of the population standard deviation |Yes - but not the same as the sample standard deviation |\n",
"|$s^2$ |Sample variance |Yes - calculated from the raw data |\n",
"|$\\sigma^2$ |Population variance |Almost never known for sure |\n",
"|$\\hat{\\sigma}^2$ |Estimate of the population variance |Yes - but not the same as the sample variance |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(ci)=\n",
"## Estimating a confidence interval\n",
"\n",
"> *Statistics means never having to say you're certain* -- Unknown origin^[This quote appears on a great many t-shirts and websites, and even gets a mention in a few academic papers (e.g., \\url{http://www.amstat.org/publications/jse/v10n3/friedman.html] but I've never found the original source.\n",
"\n",
"Up to this point in this chapter, I've outlined the basics of sampling theory which statisticians rely on to make guesses about population parameters on the basis of a sample of data. As this discussion illustrates, one of the reasons we need all this sampling theory is that every data set leaves us with some degree of uncertainty, so our estimates are never going to be perfectly accurate. The thing that has been missing from this discussion is an attempt to *quantify* the amount of uncertainty that attaches to our estimate. It's not enough to be able guess that, say, the mean IQ of undergraduate psychology students is 115 (yes, I just made that number up). We also want to be able to say something that expresses the degree of certainty that we have in our guess. For example, it would be nice to be able to say that there is a 95\\% chance that the true mean lies between 109 and 121. The name for this is a **_confidence interval_** for the mean.\n",
"\n",
"Armed with an understanding of sampling distributions, constructing a confidence interval for the mean is actually pretty easy. Here's how it works. Suppose the true population mean is $\\mu$ and the standard deviation is $\\sigma$. I've just finished running my study that has $N$ participants, and the mean IQ among those participants is $\\bar{X}$. We know from our discussion of the central limit theorem (Section \\@ref(clt) that the sampling distribution of the mean is approximately normal. We also know from our discussion of the normal distribution Section \\@ref(normal) that there is a 95\\% chance that a normally-distributed quantity will fall within two standard deviations of the true mean. To be more precise, we can use the `norm.ppf()` function from `scipy.stats` to compute the 2.5th and 97.5th percentiles of the normal distribution"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1.95996398, 1.95996398])"
]
},
"execution_count": 215,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.stats import norm\n",
"norm.ppf([.025, 0.975])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so I lied earlier on. The more correct answer is that there is a 95\\% chance that a normally-distributed quantity will fall within 1.96 standard deviations of the true mean. Next, recall that the standard deviation of the sampling distribution is referred to as the standard error, and the standard error of the mean is written as SEM. When we put all these pieces together, we learn that there is a 95\\% probability that the sample mean $\\bar{X}$ that we have actually observed lies within 1.96 standard errors of the population mean. Mathematically, we write this as:\n",
"\n",
"$$\n",
"\\mu - \\left( 1.96 \\times \\mbox{SEM} \\right) \\ \\leq \\ \\bar{X}\\ \\leq \\ \\mu + \\left( 1.96 \\times \\mbox{SEM} \\right) \n",
"$$\n",
"\n",
"where the SEM is equal to $\\sigma / \\sqrt{N}$, and we can be 95\\% confident that this is true. However, that's not answering the question that we're actually interested in. The equation above tells us what we should expect about the sample mean, given that we know what the population parameters are. What we *want* is to have this work the other way around: we want to know what we should believe about the population parameters, given that we have observed a particular sample. However, it's not too difficult to do this. Using a little high school algebra, a sneaky way to rewrite our equation is like this:\n",
"\n",
"$$\n",
"\\bar{X} - \\left( 1.96 \\times \\mbox{SEM} \\right) \\ \\leq \\ \\mu \\ \\leq \\ \\bar{X} + \\left( 1.96 \\times \\mbox{SEM}\\right)\n",
"$$\n",
"\n",
"What this is telling is is that the range of values has a 95\\% probability of containing the population mean $\\mu$. We refer to this range as a **_95\\% confidence interval_**, denoted $\\mbox{CI}_{95}$. In short, as long as $N$ is sufficiently large -- large enough for us to believe that the sampling distribution of the mean is normal -- then we can write this as our formula for the 95\\% confidence interval:\n",
"\n",
"$$\n",
"\\mbox{CI}_{95} = \\bar{X} \\pm \\left( 1.96 \\times \\frac{\\sigma}{\\sqrt{N}} \\right)\n",
"$$\n",
"\n",
"Of course, there's nothing special about the number 1.96: it just happens to be the multiplier you need to use if you want a 95\\% confidence interval. If I'd wanted a 70\\% confidence interval, I could have used the `norm.ppf` function to calculate the 15th and 85th quantiles:"
]
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1.03643339, 1.03643339])"
]
},
"execution_count": 216,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"norm.ppf([.15, .85])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and so the formula for $\\mbox{CI}_{70}$ would be the same as the formula for $\\mbox{CI}_{95}$ except that we'd use 1.04 as our magic number rather than 1.96.\n",
"\n",
"### A slight mistake in the formula\n",
"\n",
"As usual, I lied. The formula that I've given above for the 95\\% confidence interval is approximately correct, but I glossed over an important detail in the discussion. Notice my formula requires you to use the standard error of the mean, SEM, which in turn requires you to use the true population standard deviation $\\sigma$. Yet, in Section \\@ref(pointestimates I stressed the fact that we don't actually *know* the true population parameters. Because we don't know the true value of $\\sigma$, we have to use an estimate of the population standard deviation $\\hat{\\sigma}$ instead. This is pretty straightforward to do, but this has the consequence that we need to use the quantiles of the $t$-distribution rather than the normal distribution to calculate our magic number; and the answer depends on the sample size. When $N$ is very large, we get pretty much the same value using `t.ppf()` that we would if we used `norm.ppf()`..."
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-1.96020126, 1.96020126])"
]
},
"execution_count": 217,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.stats import t\n",
"N = 10000 # suppose our sample size is 10,000\n",
"t.ppf([.025, 0.975], df = N-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But when $N$ is small, we get a much bigger number when we use the $t$ distribution:"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-2.26215716, 2.26215716])"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from scipy.stats import t\n",
"N = 10 # suppose our sample size is 10\n",
"t.ppf([.025, 0.975], df = N-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There's nothing too mysterious about what's happening here. Bigger values mean that the confidence interval is wider, indicating that we're more uncertain about what the true value of $\\mu$ actually is. When we use the $t$ distribution instead of the normal distribution, we get bigger numbers, indicating that we have more uncertainty. And why do we have that extra uncertainty? Well, because our estimate of the population standard deviation $\\hat\\sigma$ might be wrong! If it's wrong, it implies that we're a bit less sure about what our sampling distribution of the mean actually looks like... and this uncertainty ends up getting reflected in a wider confidence interval. \n",
"\n",
"\n",
"### Interpreting a confidence interval\n",
"\n",
"The hardest thing about confidence intervals is understanding what they *mean*. Whenever people first encounter confidence intervals, the first instinct is almost always to say that \"there is a 95\\% probabaility that the true mean lies inside the confidence interval\". It's simple, and it seems to capture the common sense idea of what it means to say that I am \"95\\% confident\". Unfortunately, it's not quite right. The intuitive definition relies very heavily on your own personal *beliefs* about the value of the population mean. I say that I am 95\\% confident because those are my beliefs. In everyday life that's perfectly okay, but if you remember back to Section \\@ref(probmeaning), you'll notice that talking about personal belief and confidence is a Bayesian idea. Personally (speaking as a Bayesian) I have no problem with the idea that the phrase \"95\\% probability\" is allowed to refer to a personal belief. However, confidence intervals are *not* Bayesian tools. Like everything else in this chapter, confidence intervals are *frequentist* tools, and if you are going to use frequentist methods then it's not appropriate to attach a Bayesian interpretation to them. If you use frequentist methods, you must adopt frequentist interpretations!\n",
"\n",
"Okay, so if that's not the right answer, what is? Remember what we said about frequentist probability: the only way we are allowed to make \"probability statements\" is to talk about a sequence of events, and to count up the frequencies of different kinds of events. From that perspective, the interpretation of a 95\\% confidence interval must have something to do with replication. Specifically: if we replicated the experiment over and over again and computed a 95\\% confidence interval for each replication, then 95\\% of those *intervals* would contain the true mean. More generally, 95\\% of all confidence intervals constructed using this procedure should contain the true population mean. This idea is illustrated in Figure \\@ref(fig:cirep), which shows 50 confidence intervals constructed for a \"measure 10 IQ scores\" experiment (top panel) and another 50 confidence intervals for a \"measure 25 IQ scores\" experiment (bottom panel). A bit fortuitously, across the 100 replications that I simulated, it turned out that exactly 95 of them contained the true mean."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```{r cirep, fig.cap=\"95% confidence intervals. The top (panel a) shows 50 simulated replications of an experiment in which we measure the IQs of 10 people. The dot marks the location of the sample mean, and the line shows the 95% confidence interval. In total 47 of the 50 confidence intervals do contain the true mean (i.e., 100), but the three intervals marked with asterisks do not. The lower graph (panel b) shows a similar simulation, but this time we simulate replications of an experiment that measures the IQs of 25 people.\", echo=FALSE}\n",
"knitr::include_graphics(file.path(projecthome, \"img/estimation/confIntReplicated.png\"))\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 319,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 319,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"

"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import t, sem\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"n = 10\n",
"\n",
"population_mean = [0]*50\n",
"\n",
"uppers = []\n",
"lowers = []\n",
"\n",
"for i in range(1,51):\n",
" simdata = np.random.normal(loc=100,scale=15,size=n).astype(int)\n",
" sample_mean = statistics.mean(simdata)\n",
" sample_means.append(sample_mean)\n",
" ci_int = t.interval(alpha=0.95, df=len(simdata)-1, loc=np.mean(simdata), scale=sem(simdata))\n",
" uppers.append(ci_int[1])\n",
" lowers.append(ci_int[0])\n",
"\n",
"x = range(1,51)\n",
" \n",
"fig, axes = plt.subplots(1, 2, figsize=(15, 5), sharey=False, sharex=False)\n",
"fig.suptitle('Simulated IQ Data')\n",
"\n",
"\n",
"too_high = [x[s] for s, val in enumerate(lowers) if val > 100]\n",
"too_low = [x[s] for s, val in enumerate(uppers) if val < 100]\n",
"no_mean = too_high + too_low\n",
"highlight = ['blue']*50\n",
"for s, val in enumerate(no_mean):\n",
" highlight[val-1] = 'red'\n",
"\n",
"\n",
"axes[0].vlines(x=range(1,51), ymin=lowers, ymax=uppers, color = highlight)\n",
"axes[0].axhline(y=100, linestyle = \"dashed\")\n",
"axes[0].plot()\n",
"\n",
"\n",
"n = 25\n",
"\n",
"uppers = []\n",
"lowers = []\n",
"\n",
"for i in range(1,51):\n",
" simdata = np.random.normal(loc=100,scale=15,size=n).astype(int)\n",
" sample_mean = statistics.mean(simdata)\n",
" sample_means.append(sample_mean)\n",
" ci_int = t.interval(alpha=0.95, df=len(simdata)-1, loc=np.mean(simdata), scale=sem(simdata))\n",
" uppers.append(ci_int[1])\n",
" lowers.append(ci_int[0])\n",
"\n",
"too_high = [x[s] for s, val in enumerate(lowers) if val > 100]\n",
"too_low = [x[s] for s, val in enumerate(uppers) if val < 100]\n",
"no_mean = too_high + too_low\n",
"highlight = ['blue']*50\n",
"for s, val in enumerate(no_mean):\n",
" highlight[val-1] = 'red' \n",
"\n",
"axes[1].vlines(x=range(1,51), ymin=lowers, ymax=uppers, color = highlight)\n",
"axes[1].axhline(y=100, linestyle = \"dashed\")\n",
"axes[1].plot()"
]
},
{
"cell_type": "code",
"execution_count": 312,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[49, 1, 18]\n",
"[79.77520514258639, 96.42422560091191, 79.8469811400032]\n",
"[96.82479485741361, 111.3757743990881, 111.3530188599968]\n"
]
}
],
"source": [
"\n",
"nomean_up = []\n",
"nomean_low =[]\n",
"for s, val in enumerate(no_mean):\n",
" nomean_up.append(uppers[s])\n",
" nomean_low.append(lowers[s])\n",
"print(no_mean)\n",
"print(nomean_low)\n",
"print(nomean_up)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The critical difference here is that the Bayesian claim makes a probability statement about the population mean (i.e., it refers to our uncertainty about the population mean), which is not allowed under the frequentist interpretation of probability because you can't \"replicate\" a population! In the frequentist claim, the population mean is fixed and no probabilistic claims can be made about it. Confidence intervals, however, are repeatable so we can replicate experiments. Therefore a frequentist is allowed to talk about the probability that the *confidence interval* (a random variable) contains the true mean; but is not allowed to talk about the probability that the *true population mean* (not a repeatable event) falls within the confidence interval. \n",
"\n",
"I know that this seems a little pedantic, but it does matter. It matters because the difference in interpretation leads to a difference in the mathematics. There is a Bayesian alternative to confidence intervals, known as *credible intervals*. In most situations credible intervals are quite similar to confidence intervals, but in other cases they are drastically different. As promised, though, I'll talk more about the Bayesian perspective in Chapter \\@ref(bayes).\n",
"\n",
"\n",
"### Calculating confidence intervals in Python\n",
"\n",
"\n",
"To produce the confidence intervals for the plots of simulated IQ data above, I used the ``t``, ``sem``, and ``mean``functions available in the ``scipy.stats``package. Another option is to use the ``tconfint_mean `` function from the ``statsmodels`` package. As you can see, both methods give nearly identical results. Method 1 is good insofar is at requires you to explicitly specify the desired confidence interval, the degrees of freedom, and the standard error of the mean. Method takes care of all of this for us, which makes it easier, but a bit more of a black box."
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Method 1: (2.8949158625700564, 7.105084137429944)\n",
"Method 2: (2.894915862570057, 7.105084137429943)\n"
]
}
],
"source": [
"# Sample data:\n",
"data = range(1,10)\n",
"\n",
"# Method 1:\n",
"import numpy as np\n",
"import scipy.stats as st\n",
"ci_1 = t.interval(alpha=0.95, \n",
" df=len(data)-1, \n",
" loc=np.mean(data), \n",
" scale=sem(data))\n",
"\n",
"\n",
"# Method 2:\n",
"import statsmodels.stats.api as sms\n",
"ci_2 = sms.DescrStatsW(data).tconfint_mean()\n",
"\n",
"# Compare the methods\n",
"print(\"Method 1: \", ci_1)\n",
"print(\"Method 2: \", ci_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting confidence intervals in Python\n",
"\n",
"There are many different ways you can draw graphs that show confidence intervals as error bars, and the method you select will depend on what you are trying to achieve. However, ``seaborn``offers some good, off-the-shelf methods for plotting confidence intervals, which should cover most of the common cases. More in-depth information about these can be found in the seaborn documentation, but here are a few common cases, using seaborn's built-in \"tips\" dataset."
]
},
{
"cell_type": "code",
"execution_count": 341,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"0 16.99 1.01 Female No Sun Dinner 2\n",
"1 10.34 1.66 Male No Sun Dinner 3\n",
"2 21.01 3.50 Male No Sun Dinner 3\n",
"3 23.68 3.31 Male No Sun Dinner 2\n",
"4 24.59 3.61 Female No Sun Dinner 4"
]
},
"execution_count": 341,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import seaborn as sns\n",
"tips = sns.load_dataset(\"tips\")\n",
"tips.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To compare the mean total bill for lunches and dinners for smokers and non-smokers, we can use ``sns.pointplot``. Notice that you can specifiy the size of desired confidence interval. By convention, people tend to use a 95% confidence interval, and this is the default in seaborn, but it is possible to specify a different one. Just make sure you report what size confidence interval you are showing! In the figure to the right, below, I have used a 40% confidence interval, but I probably wouldn't do that in a paper, because it is likely to confuse or mislead readers who expect a 95% CI."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '40% Confidence Interval')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"total_bill | tip | sex | smoker | day | time | size | |
---|---|---|---|---|---|---|---|

0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |

1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |

2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |

3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |

4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |