{"id":714,"date":"2025-07-28T20:29:45","date_gmt":"2025-07-29T00:29:45","guid":{"rendered":"https:\/\/literaciadigital.ufms.br\/?page_id=714"},"modified":"2025-10-11T03:37:06","modified_gmt":"2025-10-11T07:37:06","slug":"13-4","status":"publish","type":"page","link":"https:\/\/literaciadigital.ufms.br\/en\/data8\/13-0\/13-4\/","title":{"rendered":"Cap\u00edtulo 13.4"},"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\n%matplotlib inline\r\npath_data = '..\/..\/..\/assets\/data\/'\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=\"usando-intervalos-de-confian-a\" style=\"text-align: center\">Usando Intervalos de Confian\u00e7a<\/h1>\n<p style=\"text-align: justify\">Um intervalo de confian\u00e7a tem um \u00fanico prop\u00f3sito &#8211; estimar um par\u00e2metro desconhecido com base em dados de uma amostra aleat\u00f3ria. Na \u00faltima se\u00e7\u00e3o, dissemos que o intervalo (36%, 42%) era um intervalo de confian\u00e7a aproximado de 95% para a porcentagem de fumantes entre as m\u00e3es da popula\u00e7\u00e3o Essa foi uma forma formal de dizer que, pela nossa estimativa, a porcentagem de fumantes entre as m\u00e3es da popula\u00e7\u00e3o estava entre 36% e 42%, e que nosso processo de estimativa est\u00e1 correto em cerca de<br \/>\n95% dos casos.<\/p>\n<p style=\"text-align: justify\">\u00c9 importante resistir ao impulso de usar intervalos de confian\u00e7a para outros fins. Por exemplo, lembre-se que calculamos o intervalo (26,9 anos, 27,6 anos) como um intervalo de confian\u00e7a aproximado de 95% para a idade m\u00e9dia das m\u00e3es na popula\u00e7\u00e3o. O uso indevido e assustadoramente comum do intervalo \u00e9 concluir que cerca de 95% das mulheres tinham entre 26,9 e 27,6 anos de idade. Voc\u00ea n\u00e3o precisa saber muito sobre intervalos de confian\u00e7a para ver que isso n\u00e3o pode estar certo \u2013 voc\u00ea n\u00e3o<br \/>\nestaria certo. Esperamos que 95% das m\u00e3es tenham idades pr\u00f3ximas de alguns meses. Na verdade, o histograma das idades amostradas mostra bastante varia\u00e7\u00e3o.<\/p>\n<pre><code><span style=\"color: black\">births = Table.read_table(path_data + 'baby.csv')<\/span><\/code><\/pre>\n<pre><code><span style=\"color: black\">births.select('Maternal Age').hist()<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-716\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-1.png\" alt=\"\" width=\"424\" height=\"284\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-1.png 424w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-1-300x201.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-1-350x233.png 350w\" sizes=\"(max-width: 424px) 100vw, 424px\" \/><\/p>\n<p style=\"text-align: justify\">Uma pequena percentagem das idades amostradas est\u00e1 no intervalo (26,9, 27,6), e esperado uma pequena percentagem semelhante na popula\u00e7\u00e3o. O intervalo apenas estima um n\u00famero: a <em>m\u00e9dia<\/em> de todas as idades da popula\u00e7\u00e3o.<\/p>\n<p style=\"text-align: justify\">No entanto, estimar um par\u00e2metro por intervalos de confian\u00e7a tem uma utilidade importante al\u00e9m de apenas nos dizer aproximadamente o tamanho do par\u00e2metro.<\/p>\n<h2 id=\"usando-um-intervalo-de-confian-a-para-testar-hip-teses\" style=\"text-align: justify\">Usando um Intervalo de Confian\u00e7a para Testar Hip\u00f3teses<\/h2>\n<p style=\"text-align: justify\">Nosso intervalo de confian\u00e7a aproximado de 95% para a idade m\u00e9dia na popula\u00e7\u00e3o vai de 26,9 anos a 27,6 anos. Suponha que algu\u00e9m queira testar as seguintes hip\u00f3teses:<\/p>\n<p style=\"text-align: justify\"><strong>Hip\u00f3tese nula:<\/strong> A idade m\u00e9dia na popula\u00e7\u00e3o \u00e9 de 30 anos.<\/p>\n<p style=\"text-align: justify\"><strong>Hip\u00f3tese alternativa:<\/strong> A idade m\u00e9dia na popula\u00e7\u00e3o n\u00e3o \u00e9 de 30 anos.<\/p>\n<p style=\"text-align: justify\">Ent\u00e3o, se voc\u00ea estivesse usando o limite de 5% para o p-valor, voc\u00ea rejeitaria a hip\u00f3tese nula. Isso porque 30 n\u00e3o est\u00e1 no intervalo de confian\u00e7a de 95% para a m\u00e9dia populacional. No n\u00edvel de signific\u00e2ncia de 5%, 30 n\u00e3o \u00e9 um valor plaus\u00edvel para a m\u00e9dia populacional.<\/p>\n<p style=\"text-align: justify\">Este uso dos intervalos de confian\u00e7a \u00e9 o resultado de uma <em>dualidade<\/em> entre intervalos de confian\u00e7a e testes: se voc\u00ea est\u00e1 testando se a m\u00e9dia populacional \u00e9 um valor particular <em>x<\/em>, e usa o limite de 5% para o p-valor, ent\u00e3o voc\u00ea rejeitar\u00e1 a hip\u00f3tese nula se <em>x<\/em> n\u00e3o estiver no seu intervalo de confian\u00e7a de 95% para a m\u00e9dia.<\/p>\n<p style=\"text-align: justify\">Isso pode ser estabelecido pela teoria estat\u00edstica. Na pr\u00e1tica, basta verificar se o valor especificado na hip\u00f3tese nula est\u00e1 no intervalo de confian\u00e7a.<\/p>\n<p style=\"text-align: justify\">Se voc\u00ea estivesse usando o limite de 1% para o p-valor, teria que verificar se o valor especificado na hip\u00f3tese nula est\u00e1 em um intervalo de confian\u00e7a de 99% para a m\u00e9dia populacional.<\/p>\n<p style=\"text-align: justify\">Aproximadamente, essas afirma\u00e7\u00f5es tamb\u00e9m s\u00e3o verdadeiras para propor\u00e7\u00f5es populacionais, desde que a amostra seja grande.<\/p>\n<p style=\"text-align: justify\">Embora agora tenhamos uma maneira de usar intervalos de confian\u00e7a para testar um tipo espec\u00edfico de hip\u00f3tese, voc\u00ea pode se perguntar sobre o valor de testar se a idade m\u00e9dia em uma popula\u00e7\u00e3o \u00e9 igual a 30. De fato, o valor n\u00e3o \u00e9 claro. Mas existem algumas situa\u00e7\u00f5es em que um teste desse tipo de hip\u00f3tese \u00e9 tanto natural quanto \u00fatil.<\/p>\n<h2 id=\"comparando-pontua-es-de-linha-de-base-e-p-s-tratamento\" style=\"text-align: justify\">Comparando Pontua\u00e7\u00f5es de Linha de Base e P\u00f3s-Tratamento<\/h2>\n<p style=\"text-align: justify\">Estudaremos isso no contexto de dados que s\u00e3o um subconjunto das informa\u00e7\u00f5es coletadas em um ensaio controlado randomizado sobre tratamentos para a doen\u00e7a de Hodgkin. A doen\u00e7a de Hodgkin \u00e9 um c\u00e2ncer que tipicamente afeta jovens. A doen\u00e7a \u00e9 cur\u00e1vel, mas o tratamento pode ser muito severo. O objetivo do ensaio era encontrar uma dosagem que curasse o c\u00e2ncer, mas minimizasse os efeitos adversos nos pacientes.<\/p>\n<p style=\"text-align: justify\">Esta tabela <code>hodgkins<\/code> cont\u00e9m dados sobre o efeito que o tratamento teve nos pulm\u00f5es de 22 pacientes. As colunas s\u00e3o:<\/p>\n<ul style=\"text-align: justify\">\n<li>Altura em cm<\/li>\n<li>Uma medida de radia\u00e7\u00e3o para a regi\u00e3o do manto (pesco\u00e7o, t\u00f3rax, axilas)<\/li>\n<li>Uma medida de quimioterapia<\/li>\n<li>Uma pontua\u00e7\u00e3o da sa\u00fade dos pulm\u00f5es na linha de base, ou seja, no in\u00edcio do tratamento; pontua\u00e7\u00f5es mais altas correspondem a pulm\u00f5es mais saud\u00e1veis<\/li>\n<li>A mesma pontua\u00e7\u00e3o da sa\u00fade dos pulm\u00f5es, 15 meses ap\u00f3s o tratamento<\/li>\n<\/ul>\n<pre><code><span style=\"color: black\">hodgkins = Table.read_table(path_data + 'hodgkins.csv')<\/span><\/code><\/pre>\n<pre><code><span style=\"color: black\">hodgkins.show(3)<\/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\">height<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">rad<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">chemo<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">base<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">month15<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">164<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">679<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">180<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">160.57<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">87.77<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">168<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">311<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">180<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">98.24<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.62<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">173<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">388<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">239<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">129.04<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">133.33<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">Compararemos as pontua\u00e7\u00f5es iniciais e de 15 meses. Como cada linha corresponde a um paciente, dizemos que a amostra de pontua\u00e7\u00f5es iniciais e a amostra de pontua\u00e7\u00f5es de 15 meses est\u00e3o <em>pareadas<\/em> &#8211; n\u00e3o s\u00e3o apenas dois conjuntos de 22 valores cada, mas 22 pares de valores, um para cada paciente.<\/p>\n<p style=\"text-align: justify\">\u00c0 primeira vista, voc\u00ea pode ver que as pontua\u00e7\u00f5es de 15 meses tendem a ser mais baixas do que as pontua\u00e7\u00f5es iniciais \u2013 os pulm\u00f5es dos pacientes da amostra parecem estar piorando 15 meses ap\u00f3s o tratamento. Isto \u00e9 confirmado pelos valores em sua maioria positivos na coluna <code>drop<\/code>, o valor pelo qual a pontua\u00e7\u00e3o caiu da linha de base para 15 meses.<\/p>\n<pre><code><span style=\"color: black\">hodgkins = hodgkins.with_columns(\r\n    'drop', hodgkins.column('base') - hodgkins.column('month15')\r\n)<\/span><\/code><\/pre>\n<pre><code><span style=\"color: black\">hodgkins<\/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\">height<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">rad<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">chemo<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">base<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">month15<\/th>\n<th style=\"text-align: left;padding: 4px 8px\">drop<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">164<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">679<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">180<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">160.57<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">87.77<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">72.8<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">168<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">311<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">180<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">98.24<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.62<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">30.62<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">173<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">388<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">239<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">129.04<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">133.33<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">-4.29<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">157<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">370<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">168<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">85.41<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">81.28<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">4.13<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">160<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">468<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">151<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">67.94<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">79.26<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">-11.32<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">170<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">341<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">96<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">150.51<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">80.97<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.54<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">163<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">453<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">134<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">129.88<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">69.24<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">60.64<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">175<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">529<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">264<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">87.45<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">56.48<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">30.97<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">185<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">392<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">240<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">149.84<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">106.99<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">42.85<\/td>\n<\/tr>\n<tr style=\"background-color: #f8f8f8\">\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">178<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">479<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">216<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">92.24<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">73.43<\/td>\n<td style=\"padding: 4px 8px;border: 1px solid #ddd\">18.81<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<pre><code><span style=\"color: black\">hodgkins.select('drop').hist(bins=np.arange(-20, 81, 20))<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img decoding=\"async\" class=\"alignnone size-full wp-image-717\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-2.png\" alt=\"\" width=\"437\" height=\"284\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-2.png 437w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-2-300x195.png 300w\" sizes=\"(max-width: 437px) 100vw, 437px\" \/><\/p>\n<pre><code><span style=\"color: black\">np.average(hodgkins.column('drop'))<\/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\">28.615909090909096<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify\">No exemplo, a queda m\u00e9dia \u00e9 de cerca de 28,6. Mas isso poderia ser resultado de uma varia\u00e7\u00e3o ao acaso? Os dados s\u00e3o de uma amostra aleat\u00f3ria. Poderia ser que em toda a popula\u00e7\u00e3o de pacientes, a queda m\u00e9dia seja apenas 0?<\/p>\n<p style=\"text-align: justify\">Para responder a isso, podemos estabelecer duas hip\u00f3teses:<\/p>\n<p style=\"text-align: justify\"><strong>Hip\u00f3tese nula:<\/strong> Na popula\u00e7\u00e3o, a queda m\u00e9dia \u00e9 0.<\/p>\n<p style=\"text-align: justify\"><strong>Hip\u00f3tese alternativa:<\/strong> Na popula\u00e7\u00e3o, a queda m\u00e9dia n\u00e3o \u00e9 0.<\/p>\n<p style=\"text-align: justify\">Para testar essa hip\u00f3tese com um limite de 1% para o p-valor, vamos construir um intervalo de confian\u00e7a aproximado de 99% para a queda m\u00e9dia na popula\u00e7\u00e3o.<\/p>\n<pre><code><span style=\"color: black\">def one_bootstrap_mean():\r\n    resample = hodgkins.sample()\r\n    return np.average(resample.column('drop'))<\/span><\/code><\/pre>\n<pre><code><span style=\"color: black\"># Gere 10.000 meios de bootstrap\r\nnum_repetitions = 10000\r\nbstrap_means = make_array()\r\nfor i in np.arange(num_repetitions):\r\n    bstrap_means = np.append(bstrap_means, one_bootstrap_mean())<\/span><\/code><\/pre>\n<pre><code><span style=\"color: black\"># Obtenha os pontos finais do intervalo de confian\u00e7a de 99%\r\nleft = percentile(0.5, bstrap_means)\r\nright = percentile(99.5, bstrap_means)\r\n\r\nmake_array(left, right)<\/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([17.46863636, 40.97681818])<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<pre><code><span style=\"color: black\">resampled_means = Table().with_columns(\r\n    'Bootstrap Sample Mean', bstrap_means\r\n)\r\nresampled_means.hist()\r\nplots.plot([left, right], [0, 0], color='yellow', lw=8);<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img decoding=\"async\" class=\"alignnone size-full wp-image-718\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-3.png\" alt=\"\" width=\"424\" height=\"284\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-3.png 424w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-3-300x201.png 300w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/13-4-3-350x233.png 350w\" sizes=\"(max-width: 424px) 100vw, 424px\" \/><\/p>\n<p style=\"text-align: justify\">O intervalo de confian\u00e7a de 99% para a queda m\u00e9dia na popula\u00e7\u00e3o vai de cerca de 17 a cerca de 40. O intervalo n\u00e3o cont\u00e9m 0. Portanto, rejeitamos a hip\u00f3tese nula.<\/p>\n<p style=\"text-align: justify\">Mas observe que fizemos mais do que simplesmente concluir que a queda m\u00e9dia na popula\u00e7\u00e3o n\u00e3o \u00e9 0. Estimamos o qu\u00e3o grande \u00e9 a queda m\u00e9dia. Isso \u00e9 um resultado mais \u00fatil do que apenas dizer: &#8220;N\u00e3o \u00e9 0.&#8221;<\/p>\n<p style=\"text-align: justify\"><strong>Uma nota sobre a precis\u00e3o:<\/strong> Nosso intervalo de confian\u00e7a \u00e9 bastante amplo, por dois motivos principais:<\/p>\n<ul style=\"text-align: justify\">\n<li>O n\u00edvel de confian\u00e7a \u00e9 alto (99%).<\/li>\n<li>O tamanho da amostra \u00e9 relativamente pequeno em compara\u00e7\u00e3o com os exemplos anteriores.<\/li>\n<\/ul>\n<p style=\"text-align: justify\">No pr\u00f3ximo cap\u00edtulo, examinaremos como o tamanho da amostra afeta a precis\u00e3o. Tamb\u00e9m examinaremos como as distribui\u00e7\u00f5es emp\u00edricas das m\u00e9dias amostrais frequentemente apresentam formato de sino, mesmo que as distribui\u00e7\u00f5es dos dados subjacentes n\u00e3o apresentem esse formato de forma alguma.<\/p>\n<h2 id=\"nota-final\" style=\"text-align: justify\">Nota final<\/h2>\n<p style=\"text-align: justify\">A terminologia de um campo geralmente vem dos principais pesquisadores desse campo. <a href=\"https:\/\/en.wikipedia.org\/wiki\/Bradley_Efron\">Brad Efron<\/a>, que prop\u00f4s pela primeira vez a t\u00e9cnica bootstrap, usou um termo que tem <a href=\"https:\/\/en.wikipedia.org\/wiki\/Bootstrapping\">origens americanas<\/a>. Para n\u00e3o ficar atr\u00e1s, estat\u00edsticos chineses <a href=\"http:\/\/econpapers.repec.org\/article\/eeestapro\/v_3a37_3ay_3a1998_3ai_3a4_3ap_3a321-329.htm\">propuseram seu pr\u00f3prio m\u00e9todo<\/a>.<\/p>\n<p>&nbsp;<\/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\/13-0\/13-3\/\">\u2190 Cap\u00edtulo 13.3 &#8211; Intervalos de Confian\u00e7a<\/a><\/td>\n<td align=\"right\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/\">Cap\u00edtulo 14 &#8211; Por que a M\u00e9dia \u00e9 Importante \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":687,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"coauthors":[14],"class_list":["post-714","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/714","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=714"}],"version-history":[{"count":5,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/714\/revisions"}],"predecessor-version":[{"id":1050,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/714\/revisions\/1050"}],"up":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/687"}],"wp:attachment":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/media?parent=714"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/coauthors?post=714"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}