{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SME0822 Análise Multivariada e Aprendizado não-Supervisionado\n", "\n", "Por Cibele Russo - ICMC USP\n", "\n", "- Teste Shapiro-Wilk multivariado (usando rpy2)\n", "- Teorema Limite Central - Ilustração em Python\n", "- Um teste de hipóteses para ($\\mu$ multidimensional)\n", "\n", "$$\\begin{array}{l}H_0:{\\mu}=\\mu_0\\mbox{ contra }\\\\H_1:\\mu\\neq\\mu_0,\\end{array}$$\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "mean = [0, 0, 0]\n", "\n", "cov = [[2,1,0],[1,3,1],[0,1,4]] \n", "\n", "x1, x2, x3 = np.random.multivariate_normal(mean, cov, 100).T\n", "\n", "x4 = np.random.poisson(1, 100)\n", "\n", "dados = np.array([x1,x2,x3,x4])\n", "\n", "df = pd.DataFrame(data=dados.T)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Outro exemplo que fizemos em aula\n", "#mean = [0, 0, 0]\n", "#cov = [[2,0,0],[0,3,0],[0,0,4]] \n", "#x1, x2, x3 = np.random.multivariate_normal(mean, cov, 100).T\n", "#dados = np.array([x1,x2,x3])\n", "#df = pd.DataFrame(data=dados.T)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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