{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 01 Introduction to sampling distributions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html\n", "" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "import numpy as np\n", "import pandas as pd\n", "from pandas import Series, DataFrame\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy import stats, special" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[khanacademy](https://www.khanacademy.org/math/ap-statistics/sampling-distribution-ap/what-is-sampling-distribution/v/introduction-to-sampling-distributions?modal=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Introduction to sampling distributions fig 1](./imgs/10-01-01.png)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "df = DataFrame({\"#'s picks\": list(itertools.product([1, 2, 3], repeat=2))})" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "df['X'] = [np.mean(i) for i in df[\"#'s picks\"]]" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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#'s picksX
0(1, 1)1.0
1(1, 2)1.5
2(1, 3)2.0
3(2, 1)1.5
4(2, 2)2.0
5(2, 3)2.5
6(3, 1)2.0
7(3, 2)2.5
8(3, 3)3.0
\n", "
" ], "text/plain": [ " #'s picks X\n", "0 (1, 1) 1.0\n", "1 (1, 2) 1.5\n", "2 (1, 3) 2.0\n", "3 (2, 1) 1.5\n", "4 (2, 2) 2.0\n", "5 (2, 3) 2.5\n", "6 (3, 1) 2.0\n", "7 (3, 2) 2.5\n", "8 (3, 3) 3.0" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BparaDlwKXJlkF6MjgXOHqqfzmE8vPY7Yl8entdKXtdIPsC+rGuxisSRpPvjLYklqnEEgSY1bk0HQY2iL85IsJ7mpe711FnWuJsllSe7tfm8xaX2SfKTr5y1JTpl2jX316MvpSe5fsU8unHaNfSTZlORrSXYmuT3Jeya0mYv90rMv87Jfjk5yfZKbu7783oQ2czGkTc++HNnvsKpaUy9GF6a/CzwbeCJwM/C8sTbnARfNutYeffkF4BTgtgOsPxP4MqPfY5wGXDfrmh9DX04HvjTrOnv04zjglG76qcDfT/j3NRf7pWdf5mW/BHhKN70euA44bazNO4CLu+lzgU/Puu7H0Jcj+h22Fo8I+gxtMReq6usc/HcVZwNX1Mi3gGOTHDed6g5Nj77Mhaq6p6pu7KZ/AOwENo41m4v90rMvc6H7//qBbnZ99xq/E2YuhrTp2Zcjai0GwUbgrhXzu5n8j/t13WH7VUk2TVg/D/r2dV68tDsc/nKS58+6mNV0pxZOZvQX20pzt18O0heYk/2SZF2Sm4B7gWuq6oD7par2AvcDT59ulf306Ascwe+wtRgEfYat+CKwWFUvBK7l//5KmDe9huiYEzcyGgvlJOBPgS/MuJ6DSvIU4HPAe6tqz/jqCW953O6XVfoyN/ulqh6tqhcxGsXg1CQvGGsyN/ulR1+O6HfYWgyCVYe2qKr7qurhbvYS4MVTqu1I6zOMx1yoqj37DoeragewPsmGGZc1UZL1jL44P1lVn5/QZG72y2p9maf9sk9V/SfwN8AZY6vmbkibA/XlSH+HrcUgWHVoi7HztWcxOjc6j7YDb+ruUjkNuL+q7pl1UYcjyTP2na9Nciqjf5v3zbaq/XU1XgrsrKoPHaDZXOyXPn2Zo/2ykOTYbvoY4JXAnWPN5mJImz59OdLfYUOOPjoT1W9oi3cnOQvYy+gvgvNmVvBBJPkUo7s2NiTZDXyA0YUjqupiYAejO1R2AQ8C58+m0tX16Ms5wNuT7AUeAs59PP5HCrwMeCNwa3cOF+AC4ASYu/3Spy/zsl+OAy7P6IFYTwA+U1VfymyHtDlcffpyRL/DHGJCkhq3Fk8NSZIOgUEgSY0zCCSpcQaBJDXOIJCkxhkEktQ4g0CSGvc/tzdSnFE0TUQAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df['X'].plot(kind='hist',\n", " bins=np.arange(0.5, 4, 0.5),\n", " width=0.02)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "# np.random.sample, random.sample" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 4 }