{"id":636,"date":"2025-07-28T15:59:39","date_gmt":"2025-07-28T19:59:39","guid":{"rendered":"https:\/\/literaciadigital.ufms.br\/?page_id=636"},"modified":"2025-10-10T12:09:05","modified_gmt":"2025-10-10T16:09:05","slug":"10-4","status":"publish","type":"page","link":"https:\/\/literaciadigital.ufms.br\/en\/data8\/10-0\/10-4\/","title":{"rendered":"Cap\u00edtulo 10.4"},"content":{"rendered":"<div style=\"position: relative\">\n<div style=\"float: left;width: 300px;background-color: #f5f5f5;border: 1px solid #ddd;border-radius: 5px;padding: 15px;margin-right: 20px;margin-bottom: 5px;overflow: hidden\">\n<h3 style=\"margin: 0 0 10px 0;padding-bottom: 8px;border-bottom: 1px solid #ddd\">\u00cdndice<\/h3>\n<ol style=\"margin: 0;padding-left: 0;list-style-type: none\">\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/\">1. O que \u00e9 Ci\u00eancia de Dados?<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-1\/\">1.1. Introdu\u00e7\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-1\/1-1\/\">1.1.1. Ferramentas Computacionais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-1\/1-2\/\">1.1.2. T\u00e9cnicas Estat\u00edsticas<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-2\/\">1.2. Por que Ci\u00eancia de Dados?<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-3\/\">1.3. Tra\u00e7ando os Cl\u00e1ssicos<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-3\/3-1\/\">1.3.1. Personagens Liter\u00e1rios<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/1-0\/1-3\/3-2\/\">1.3.2. Outro Tipo de Personagem<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/\">2. Causalidade e Experimentos<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-1\/\">2.1. John Snow e a Bomba da Broad Street<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-2\/\">2.2. O &#8220;Grande Experimento&#8221; de Snow<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-3\/\">2.3. Estabelecendo Causalidade<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-4\/\">2.4. Randomiza\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/2-0\/2-5\/\">2.5. Notas Finais<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/\">3. Progamando em Python<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-1\/\">3.1. Express\u00f5es<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-2\/\">3.2. Nomes<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-2\/2-1\/\">3.2.1. Exemplo: Taxas de Crescimento<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-3\/\">3.3. Chamadas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/3-0\/3-4\/\">3.4. Introdu\u00e7\u00e3o \u00e0s Tabelas<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/\">4. Tipos de Dados<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-1\/\">4.1. N\u00fameros<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-2\/\">4.2. Strings<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-2\/2-1\/\">4.2.1. M\u00e9todos de Strings<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/4-0\/4-3\/\">4.3. Compara\u00e7\u00f5es<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/\">5. Sequ\u00eancias<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/5-1\/\">5.1. Arrays<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/5-2\/\">5.2. Ranges<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/5-0\/5-3\/\">5.3. Mais sobre Arrays<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/\">6. Tabelas<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-1\/\">6.1. Ordenando Linhas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-2\/\">6.2. Selecionando Linhas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-3\/\">6.3. Exemplo: Tend\u00eancias Populacionais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/6-0\/6-4\/\">6.4. Examplo: Propor\u00e7\u00f5es de Sexos<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/\">7. Visualiza\u00e7\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/7-1\/\">7.1. Visualizando Distribui\u00e7\u00f5es<br \/>\nCateg\u00f3ricas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/7-2\/\">7.2. Visualizando Distribui\u00e7\u00f5es Num\u00e9ricas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/7-0\/7-3\/\">7.3. Gr\u00e1ficos Sobrepostos<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/\">8. Fun\u00e7\u00f5es e Tabelas<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-1\/\">8.1. Aplicando Fun\u00e7\u00e3o a uma Coluna<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-2\/\">8.2. Classificando por uma Vari\u00e1vel<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-3\/\">8.3. Classifica\u00e7\u00e3o Cruzada<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-4\/\">8.4. Unindo Tabelas por Colunas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/8-0\/8-5\/\">8.5. Compartilhamento de Bicicletas<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/\">9. Aleatoriedade<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-1\/\">9.1. Declara\u00e7\u00f5es Condicionais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-2\/\">9.2. Itera\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-3\/\">9.3. Simula\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-4\/\">9.4. O Problema de Monty Hall<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/9-0\/9-5\/\">9.5. Encontrando Probabilidades<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/\">10. Amostragem e Distribui\u00e7\u00f5es Emp\u00edricas<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-1\/\">10.1. Distribui\u00e7\u00f5es Emp\u00edricas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-2\/\">10.2. Amostragem de uma Popula\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-3\/\">10.3. Distribui\u00e7\u00e3o Emp\u00edrica de uma<br \/>\nEstat\u00edstica<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-4\/\">10.4. Amostragem Aleat\u00f3ria em Python <\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/\">11. Testando Hip\u00f3teses<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-1\/\">11.1. Avaliando um Modelo<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-2\/\">11.2. M\u00faltiplas Categorias<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-3\/\">11.3. Decis\u00f5es e Incertezas<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/11-4\/\">11.4. Probabilidades de Erro<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/\">12. Comparando Duas Amostras<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/12-1\/\">12.1. Teste A\/B<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/12-2\/\">12.2. Causalidade<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/12-0\/12-3\/\">12.3. Esvaziar<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/\">13. Estima\u00e7\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-1\/\">13.1. Percentis<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-2\/\">13.2. O Bootstrap<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-3\/\">13.3. Intervalos de Confian\u00e7a<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/13-0\/13-4\/\">13.4. Usando Intervalos de Confian\u00e7a<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/\">14. Por que a M\u00e9dia \u00e9 Importante<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-1\/\">14.1. Propriedades da M\u00e9dia<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-2\/\">14.2. Variabilidade<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-3\/\">14.3. O DP e a Curva Normal<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-4\/\">14.4. Teorema Central do Limite<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-5\/\">14.5. Variabilidade da M\u00e9dia da Amostra<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-6\/\">14.6. Escolhendo um Tamanho de Amostra<\/a><\/li>\n<\/ul>\n<\/li>\n<li style=\"margin-bottom: 5px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/\">15. Previs\u00e3o<\/a>\n<ul style=\"margin: 5px 0 5px 15px;padding-left: 10px;list-style-type: none;border-left: 1px solid #ddd\">\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-1\/\">15.1. Correla\u00e7\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-2\/\">15.2. Linha de Regress\u00e3o<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-3\/\">15.3. M\u00e9todo dos M\u00ednimos Quadrados<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-4\/\">15.4. Regress\u00e3o de M\u00ednimos Quadrados<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-5\/\">15.5. Diagn\u00f3sticos Visuais<\/a><\/li>\n<li style=\"margin-bottom: 3px\"><a style=\"padding: 2px 0\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/15-0\/15-6\/\">15.6. Diagn\u00f3stico Num\u00e9rico<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<\/div>\n<p><!-- Main Content --><\/p>\n<div style=\"overflow: hidden\">\n<p><!--###########################################################################################################################################################--><\/p>\n<pre><code><span style=\"color: black\">from datascience import *\r\npath_data = '..\/..\/..\/assets\/data\/'\r\nimport matplotlib\r\nmatplotlib.use('Agg')\r\n%matplotlib inline\r\nimport matplotlib.pyplot as plots\r\nplots.style.use('fivethirtyeight')\r\nimport numpy as np<\/span><\/code><\/pre>\n<p>&nbsp;<\/p>\n<h1 id=\"amostragem-aleat\u00f3ria-em-python\" style=\"text-align: center\">Amostragem Aleat\u00f3ria em Python<\/h1>\n<p style=\"text-align: justify\">Esta se\u00e7\u00e3o resume as formas que voc\u00ea aprendeu para amostrar aleatoriamente usando Python e introduz uma nova forma.<\/p>\n<h2>Revis\u00e3o: Amostragem de uma Popula\u00e7\u00e3o em uma Tabela<\/h2>\n<p style=\"text-align: justify\">Se voc\u00ea est\u00e1 amostrando de uma popula\u00e7\u00e3o cujos dados s\u00e3o representados nas linhas de uma tabela, ent\u00e3o voc\u00ea pode usar o m\u00e9todo <code>sample<\/code> da tabela para <a href=\"https:\/\/inferentialthinking.com\/chapters\/10\/1\/Empirical_Distributions.html#id1\">selecionar aleatoriamente as linhas<\/a> da tabela. Ou seja, voc\u00ea pode usar <code>sample<\/code> para selecionar uma amostra aleat\u00f3ria de indiv\u00edduos.<\/p>\n<p style=\"text-align: justify\">Por padr\u00e3o, <code>sample<\/code> realiza a amostragem aleat\u00f3ria com reposi\u00e7\u00e3o de forma uniforme. Este \u00e9 um modelo natural para experimentos de chance, como rolar um dado.<\/p>\n<pre><code><span style=\"color: black\">faces = np.arange(1, 7)\r\ndie = Table().with_columns('Face', faces)\r\ndie<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Face<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Execute a c\u00e9lula abaixo para simular 7 jogadas de um dado.<\/p>\n<pre><code><span style=\"color: black\">die.sample(7)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Face<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">5<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">5<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">\u00c0s vezes \u00e9 mais natural amostrar indiv\u00edduos aleatoriamente sem reposi\u00e7\u00e3o. Isso \u00e9 chamado de amostra aleat\u00f3ria simples. O argumento <code>with_replacement=False<\/code> permite que voc\u00ea fa\u00e7a isso.<\/p>\n<pre><code><span style=\"color: black\">actors = Table.read_table(path_data + 'actors.csv')\r\nactors<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Actor<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Total Gross<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Number of Movies<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Average per Movie<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">#1 Movie<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Gross<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Harrison Ford<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4871.7<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">41<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">118.8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Star Wars: The Force Awakens<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">936.7<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Samuel L. Jackson<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4772.8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">The Avengers<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">623.4<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Morgan Freeman<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4468.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">61<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">The Dark Knight<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">534.9<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Tom Hanks<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4340.8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">44<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">98.7<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Toy Story 3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">415<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Robert Downey, Jr.<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3947.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">53<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">74.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">The Avengers<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">623.4<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Eddie Murphy<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3810.4<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">38<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">100.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Shrek 2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">441.2<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Tom Cruise<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3587.2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">36<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">99.6<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">War of the Worlds<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">234.3<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Johnny Depp<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3368.6<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">45<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">74.9<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Dead Man&#8217;s Chest<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">423.3<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Michael Caine<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3351.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">58<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">57.8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">The Dark Knight<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">534.9<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Scarlett Johansson<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3341.2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">37<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">90.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">The Avengers<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">623.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\"># Amostra aleat\u00f3ria simples de 5 linhas\r\nactors.sample(5, with_replacement=False)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-collapse: collapse;width: auto;margin-left: 1em\" border=\"1\">\n<thead>\n<tr style=\"background-color: #f0f0f0;border-bottom: 2px solid #ddd\">\n<th style=\"text-align: left;padding: 4px 8px\">Actor<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Total Gross<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Number of Movies<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Average per Movie<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">#1 Movie<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">Gross<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Morgan Freeman<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4468.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">61<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">The Dark Knight<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">534.9<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Orlando Bloom<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2815.8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">17<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">165.6<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Dead Man&#8217;s Chest<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">423.3<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Cameron Diaz<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3031.7<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">34<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">89.2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Shrek 2<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">441.2<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Michael Caine<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">3351.5<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">58<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">57.8<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">The Dark Knight<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">534.9<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Leonardo DiCaprio<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">2518.3<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">25<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">100.7<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">Titanic<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">658.7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Como <code>amostra<\/code> fornece a amostra inteira na ordem em que as linhas foram selecionadas, voc\u00ea pode usar m\u00e9todos de tabela na tabela amostrada para responder a muitas perguntas sobre a amostra. Por exemplo, voc\u00ea pode encontrar o n\u00famero de vezes que o dado apareceu seis spots, ou o n\u00famero m\u00e9dio de filmes em que os atores da amostra apareceram, ou se dois atores especificados apareceram na amostra. Voc\u00ea pode precisar de v\u00e1rias linhas de c\u00f3digo para obter algumas dessas informa\u00e7\u00f5es.<\/p>\n<h2>Revis\u00e3o: Amostragem de uma Popula\u00e7\u00e3o em uma Matriz<\/h2>\n<p style=\"text-align: justify\">Se voc\u00ea est\u00e1 amostrando de uma popula\u00e7\u00e3o de indiv\u00edduos cujos dados s\u00e3o representados como uma matriz, voc\u00ea pode usar a fun\u00e7\u00e3o do NumPy <code>np.random.choice<\/code> para <a href=\"https:\/\/inferentialthinking.com\/chapters\/09\/3\/Simulation.html#example-number-of-heads-in-100-tosses\">selecionar aleatoriamente elementos da matriz<\/a>.<\/p>\n<p style=\"text-align: justify\">Por padr\u00e3o, <code>np.random.choice<\/code> amostra aleatoriamente com reposi\u00e7\u00e3o.<\/p>\n<pre><code><span style=\"color: black\"># As faces de um dado, como um array\r\nfaces<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[1]:<\/td>\n<td style=\"text-align: left\">array([1, 2, 3, 4, 5, 6])<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\"># 7 rolagens do dado\r\nnp.random.choice(faces, 7)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[2]:<\/td>\n<td style=\"text-align: left\">array([4, 1, 6, 3, 5, 4, 6])<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">O argumento <code>replace=False<\/code> permite obter uma amostra aleat\u00f3ria simples, ou seja, uma amostra sorteada aleatoriamente sem reposi\u00e7\u00e3o.<\/p>\n<pre><code><span style=\"color: black\"># Array de nomes dos atores\r\nactor_names = actors.column('Actor')<\/span><\/code><\/pre>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\"># Amostra simples dos nomes de 5 atores\r\nnp.random.choice(actor_names, 5, replace=False)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[3]:<\/td>\n<td style=\"text-align: left\">array([&#8216;Jonah Hill&#8217;, &#8216;Julia Roberts&#8217;, &#8216;Bruce Willis&#8217;, &#8216;Eddie Murphy&#8217;,<br \/>\n&#8216;Matt Damon&#8217;], dtype='&lt;U18&#8242;)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Assim como <code>sample<\/code>, <code>np.random.choice<\/code> tamb\u00e9m fornece toda a sequ\u00eancia de elementos amostrados. Voc\u00ea pode usar opera\u00e7\u00f5es de array para responder a muitas perguntas sobre a amostra. Por exemplo, voc\u00ea pode descobrir qual ator foi o segundo a ser sorteado ou o n\u00famero de faces do dado que apareceram mais de uma vez. Algumas respostas podem precisar de v\u00e1rias linhas de c\u00f3digo.<\/p>\n<h2>Amostragem de uma Distribui\u00e7\u00e3o Categ\u00f3rica<\/h2>\n<p style=\"text-align: justify\">\u00c0s vezes estamos interessados em um atributo categ\u00f3rico de nossos indiv\u00edduos amostrados. Por exemplo, podemos estar observando se uma moeda cai com cara ou coroa. Ou podemos estar interessados nos partidos pol\u00edticos dos eleitores selecionados aleatoriamente.<\/p>\n<p style=\"text-align: justify\">Nesses casos, frequentemente precisamos das propor\u00e7\u00f5es de eleitores amostrados nas diferentes categorias. Se tivermos toda a amostra, podemos calcular essas propor\u00e7\u00f5es. A fun\u00e7\u00e3o <code>sample_proportions<\/code> na biblioteca <code>datascience<\/code> faz esse trabalho para n\u00f3s. Ela \u00e9 feita sob medida para amostragem aleat\u00f3ria com reposi\u00e7\u00e3o a partir de uma distribui\u00e7\u00e3o categ\u00f3rica e retorna as propor\u00e7\u00f5es de elementos amostrados em cada categoria.<\/p>\n<p style=\"text-align: justify\">A fun\u00e7\u00e3o <code>sample_proportions<\/code> leva dois argumentos:<\/p>\n<ul>\n<li>o tamanho da amostra<\/li>\n<li>a distribui\u00e7\u00e3o das categorias na popula\u00e7\u00e3o, como uma lista ou matriz de propor\u00e7\u00f5es que somam 1<\/li>\n<\/ul>\n<p style=\"text-align: justify\">Ela retorna uma matriz contendo a distribui\u00e7\u00e3o das categorias em uma amostra aleat\u00f3ria do tamanho fornecido, retirada da popula\u00e7\u00e3o. Essa \u00e9 uma matriz que consiste nas propor\u00e7\u00f5es da amostra em todas as diferentes categorias, na mesma ordem em que apareceram na distribui\u00e7\u00e3o da popula\u00e7\u00e3o.<\/p>\n<p style=\"text-align: justify\">Por exemplo, suponha que cada planta de uma esp\u00e9cie tenha flores vermelhas com chance de 25%, flores rosa com chance de 50% e flores brancas com chance de 25%, independentemente das cores das flores de todas as outras plantas. Voc\u00ea pode usar <code>sample_proportions<\/code> para ver as propor\u00e7\u00f5es das diferentes cores entre 300 plantas da esp\u00e9cie.<\/p>\n<pre><code><span style=\"color: black\"># Distribui\u00e7\u00e3o de esp\u00e9cies de cores de flores:\r\n# As propor\u00e7\u00f5es est\u00e3o na ordem Vermelho, Rosa, Branco\r\nspecies_proportions = [0.25, 0.5, .25]\r\n\r\nsample_size = 300\r\n\r\n# Distribui\u00e7\u00e3o da amostra\r\nsample_distribution = sample_proportions(sample_size, species_proportions)\r\nsample_distribution<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[4]:<\/td>\n<td style=\"text-align: left\">array([0.24333333, 0.50333333, 0.25333333])<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Como voc\u00ea espera, as propor\u00e7\u00f5es na amostra somam 1.<\/p>\n<pre><code><span style=\"color: black\">sum(sample_distribution)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[5]:<\/td>\n<td style=\"text-align: left\">1.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">As categorias em <code>species_proportions<\/code> est\u00e3o na ordem Vermelho, Rosa, Branco. Essa ordem \u00e9 preservada por <code>sample_proportions<\/code>. Se voc\u00ea quiser apenas a propor\u00e7\u00e3o de plantas com flores rosas na amostra, voc\u00ea pode usar <code>item<\/code>:<\/p>\n<pre><code><span style=\"color: black\"># Propor\u00e7\u00e3o de falores rosas\r\nsample_distribution.item(1)<\/span><\/code><\/pre>\n<table style=\"font-family: monospace;border-spacing: 0;border-collapse: collapse;width: auto;margin-left: 1em\">\n<tbody>\n<tr>\n<td style=\"text-align: right;color: #888;padding-right: 0.5em\">Out[6]:<\/td>\n<td style=\"text-align: left\">0.5033333333333333<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Voc\u00ea pode usar <code>sample_proportions<\/code> e opera\u00e7\u00f5es de array para responder perguntas baseadas apenas nas propor\u00e7\u00f5es de indiv\u00edduos amostrados nas diferentes categorias. Voc\u00ea n\u00e3o poder\u00e1 responder perguntas que exijam informa\u00e7\u00f5es mais detalhadas sobre a amostra, como quais das plantas amostradas tinha cada uma das cores diferentes.<\/p>\n<p><!--###########################################################################################################################################################--><\/p>\n<table width=\"100%\">\n<tbody>\n<tr>\n<td align=\"left\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/10-0\/10-3\/\">\u2190 Cap\u00edtulo 10.3 &#8211; Distribui\u00e7\u00e3o Emp\u00edrica de uma Estat\u00edstica<\/a><\/td>\n<td align=\"right\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/11-0\/\">Cap\u00edtulo 11 &#8211; Testando Hip\u00f3teses \u2192<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><!--###########################################################################################################################################################--><\/p>\n<\/div>\n<\/div>\n<div style=\"clear: both;height: 1px;margin-top: -1px\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u00cdndice 1. O que \u00e9 Ci\u00eancia de Dados? 1.1. Introdu\u00e7\u00e3o 1.1.1. Ferramentas Computacionais 1.1.2. T\u00e9cnicas Estat\u00edsticas 1.2. Por que Ci\u00eancia de Dados? 1.3. Tra\u00e7ando os Cl\u00e1ssicos 1.3.1. Personagens Liter\u00e1rios 1.3.2. Outro Tipo de Personagem 2. Causalidade e Experimentos 2.1. John Snow e a Bomba da Broad Street 2.2. O &#8220;Grande Experimento&#8221; de Snow 2.3. Estabelecendo [&hellip;]<\/p>\n","protected":false},"author":21894,"featured_media":0,"parent":611,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"coauthors":[14],"class_list":["post-636","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/636","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/users\/21894"}],"replies":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/comments?post=636"}],"version-history":[{"count":6,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/636\/revisions"}],"predecessor-version":[{"id":1001,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/636\/revisions\/1001"}],"up":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/611"}],"wp:attachment":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/media?parent=636"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/coauthors?post=636"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}