diff --git a/10_teorema_central_limite.ipynb b/10_teorema_central_limite.ipynb new file mode 100644 index 0000000..7352747 --- /dev/null +++ b/10_teorema_central_limite.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy\n", + "import pandas\n", + "import random\n", + "import scipy\n", + "import seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generación de una población con distribución Poisson\n", + "\n", + "Docs: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.poisson.html" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000,)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 8, 10, 7, 10, 12, 9, 9, 11, 15, 11, 8, 11, 8, 12, 9, 8, 8,\n", + " 8, 13, 14, 8, 9, 4, 8, 12, 8, 9, 11, 12, 12, 11, 8, 15, 11,\n", + " 11, 11, 6, 5, 7, 9])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "population_mu = 10\n", + "population = scipy.stats.poisson.rvs(mu=population_mu, size=10000)\n", + "print(population.shape)\n", + "population[:40]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "seaborn.countplot(population, color='steelblue')\n", + "seaborn.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sampleo aleatorio con y sin reposición\n", + "\n", + "https://pynative.com/python-random-sample/" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "sample_size = 500" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "new_sample = random.sample(population.tolist(), sample_size) # Sin reemplazo\n", + "seaborn.countplot(new_sample, color='steelblue')\n", + "seaborn.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "new_sample = random.choices(population.tolist(), k=sample_size) # Con reemplazo\n", + "seaborn.countplot(new_sample, color='steelblue')\n", + "seaborn.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### La verdad del Teorema Central del Límite\n", + "\n", + "Por el teorema central del límite, esperamos que el conjunto de medias tenga una distribución aproximadamente normal con la misma media poblacional y $\\sigma = s/\\sqrt{n}$, donde n es la cantidad de muestras." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1000 500\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 1000\n", + "print(n_samples, sample_size)\n", + "sample_means = []\n", + "for _ in range(n_samples):\n", + " new_sample = random.sample(population.tolist(), k=sample_size) # Sin reemplazo\n", + " sample_means.append(numpy.mean(new_sample)) # media muestral\n", + " \n", + "# Calculate expected normal distribution\n", + "seaborn.distplot(sample_means, label=\"sample_means\", color=\"steelblue\")\n", + "plt.legend()\n", + "seaborn.despine()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate expected normal distribution\n", + "mean_distribution_sigma = numpy.sqrt(population_mu) / numpy.sqrt(sample_size)\n", + "expected_normal = numpy.random.normal(population_mu, mean_distribution_sigma, 1000)\n", + "seaborn.distplot(expected_normal, label=\"expected_normal\", color=\"red\")\n", + "seaborn.distplot(sample_means, label=\"sample_means\", color=\"steelblue\")\n", + "plt.legend()\n", + "seaborn.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intervalos de confianza (del promedio como estimador de la distribución de medias poblacionales)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Confidence interval with equal areas around the median.\n", + "\n", + "Parameters\n", + "----------\n", + "alpha : array_like of float\n", + " Probability that an rv will be drawn from the returned range.\n", + " Each value should be in the range [0, 1].\n", + "arg1, arg2, ... : array_like\n", + " The shape parameter(s) for the distribution (see docstring of the\n", + " instance object for more information).\n", + "loc : array_like, optional\n", + " location parameter, Default is 0.\n", + "scale : array_like, optional\n", + " scale parameter, Default is 1.\n", + "\n", + "Returns\n", + "-------\n", + "a, b : ndarray of float\n", + " end-points of range that contain ``100 * alpha %`` of the rv's\n", + " possible values.\n", + "\u001b[0;31mFile:\u001b[0m ~/anaconda3/envs/diplodatos-ayvd/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py\n", + "\u001b[0;31mType:\u001b[0m method\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scipy.stats.norm.interval?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.20018007729973064, 19.79981992270027)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scipy.stats.norm.interval(0.95, loc=10, scale=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:diplodatos-ayvd] *", + "language": "python", + "name": "conda-env-diplodatos-ayvd-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}