{"id":750,"date":"2025-07-29T16:18:17","date_gmt":"2025-07-29T20:18:17","guid":{"rendered":"https:\/\/literaciadigital.ufms.br\/?page_id=750"},"modified":"2025-10-11T15:00:26","modified_gmt":"2025-10-11T19:00:26","slug":"14-3","status":"publish","type":"page","link":"https:\/\/literaciadigital.ufms.br\/en\/data8\/14-0\/14-3\/","title":{"rendered":"Cap\u00edtulo 14.3"},"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 math\r\nimport numpy as np<\/span><\/code><\/pre>\n<p>&nbsp;<\/p>\n<h1 id=\"o-dp-e-a-curva-normal\" style=\"text-align: center\">O Desvio Padr\u00e3o (SD) e a Curva Normal<\/h1>\n<p style=\"text-align: justify\">Sabemos que a m\u00e9dia \u00e9 o ponto de equil\u00edbrio do histograma. Ao contr\u00e1rio da m\u00e9dia, o SD geralmente n\u00e3o \u00e9 f\u00e1cil de identificar apenas olhando para o histograma.<\/p>\n<p style=\"text-align: justify\">No entanto, h\u00e1 um formato de distribui\u00e7\u00e3o para o qual o SD \u00e9 quase t\u00e3o claramente identific\u00e1vel quanto a m\u00e9dia. Esse \u00e9 o formato de sino. Esta se\u00e7\u00e3o examina esse formato, pois ele aparece frequentemente em histogramas de probabilidade e tamb\u00e9m em alguns histogramas de dados.<\/p>\n<h2 id=\"um-histograma-aproximadamente-em-forma-de-sino-de-dados\" style=\"text-align: justify\">Um Histograma Aproximadamente em Forma de Sino de Dados<\/h2>\n<p style=\"text-align: justify\">Vamos olhar para a distribui\u00e7\u00e3o das alturas das m\u00e3es em nossa amostra familiar de 1.174 pares m\u00e3e-rec\u00e9m-nascido. As alturas das m\u00e3es t\u00eam uma m\u00e9dia de 64 polegadas e um SD de 2,5 polegadas. Ao contr\u00e1rio das alturas dos jogadores de basquete, as alturas das m\u00e3es s\u00e3o distribu\u00eddas de forma bastante sim\u00e9trica em torno da m\u00e9dia em uma curva em forma de sino.<\/p>\n<pre><code><span style=\"color: black\">baby = Table.read_table(path_data + 'baby.csv')<\/span><\/code><\/pre>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\">heights = baby.column('Maternal Height')\r\nmean_height = np.round(np.mean(heights), 1)\r\nmean_height<\/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\">64.0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\">sd_height = np.round(np.std(heights), 1)\r\nsd_height<\/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\">2.5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\">baby.hist('Maternal Height', bins=np.arange(55.5, 72.5, 1), unit='inch')\r\npositions = np.arange(-3, 3.1, 1)*sd_height + mean_height\r\nplots.xticks(positions);<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-751\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-1.png\" alt=\"\" width=\"446\" height=\"284\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-1.png 446w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-1-300x191.png 300w\" sizes=\"(max-width: 446px) 100vw, 446px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">As duas \u00faltimas linhas de c\u00f3digo na c\u00e9lula acima alteram a rotulagem do eixo horizontal. Agora, os r\u00f3tulos correspondem a &#8220;m\u00e9dia \u00b1 z SDs&#8221; para z = 0, \u00b1 1, \u00b1 2, e \u00b1 3. Devido ao formato da distribui\u00e7\u00e3o, o &#8220;centro&#8221; tem um significado inequ\u00edvoco e \u00e9 claramente vis\u00edvel em 64.<\/p>\n<h2 id=\"como-identificar-o-sd-em-uma-curva-em-forma-de-sino\" style=\"text-align: justify\">Como identificar o SD em uma curva em forma de sino<\/h2>\n<p style=\"text-align: justify\">Para ver como o SD est\u00e1 relacionado \u00e0 curva, comece no topo da curva e olhe para a direita. Note que h\u00e1 um ponto onde a curva muda de um formato de &#8216;U&#8217; invertido para um formato de &#8216;U&#8217; normal&#8221;; formalmente, a curva tem um ponto de inflex\u00e3o. Esse ponto est\u00e1 um desvio padr\u00e3o acima da m\u00e9dia. \u00c9 o ponto z=1, que \u00e9 &#8220;m\u00e9dia mais 1 SD&#8221; = 66.5 polegadas.<\/p>\n<p style=\"text-align: justify\">Simetricamente, no lado esquerdo da m\u00e9dia, o ponto de inflex\u00e3o est\u00e1 em z=-1, ou seja, &#8220;m\u00e9dia menos 1 SD&#8221; = 61.5 polegadas.<\/p>\n<p style=\"text-align: justify\">Em geral, <strong>para distribui\u00e7\u00f5es em forma de sino, o SD \u00e9 a dist\u00e2ncia entre a m\u00e9dia e os pontos de inflex\u00e3o de cada lado.<\/strong><\/p>\n<h2 id=\"a-curva-normal-padr-o\" style=\"text-align: justify\">A curva normal padr\u00e3o<\/h2>\n<p style=\"text-align: justify\">Todos os histogramas em forma de sino que vimos t\u00eam essencialmente a mesma apar\u00eancia, exceto pelos r\u00f3tulos nos eixos. De fato, h\u00e1 apenas uma curva b\u00e1sica da qual todas essas curvas podem ser desenhadas apenas rotulando os eixos de maneira apropriada.<\/p>\n<p style=\"text-align: justify\">Para desenhar essa curva b\u00e1sica, usaremos as unidades nas quais podemos converter cada lista: unidades padr\u00e3o. A curva resultante \u00e9, portanto, chamada de <em>curva normal padr\u00e3o<\/em>.<\/p>\n<p style=\"text-align: justify\">A curva normal padr\u00e3o tem uma equa\u00e7\u00e3o impressionante. Mas, por enquanto, \u00e9 melhor pens\u00e1-la como um contorno suavizado de um histograma de uma vari\u00e1vel que foi medida em unidades padr\u00e3o e tem uma distribui\u00e7\u00e3o em forma de sino.<\/p>\n<p>&nbsp;<\/p>\n<div style=\"font-family: serif;font-size: 1.8em;text-align: center\"><span style=\"font-size: 14pt\">\u03c6(z) = <sup>1<\/sup>\u2044<sub>\u221a2\u03c0<\/sub> e<sup>-1\u20442 z<sup>2<\/sup><\/sup>, -\u221e &lt; z &lt; \u221e<\/span><\/div>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\"># A curva normal padr\u00e3o\r\n\r\nplot_normal_cdf()<\/span><\/code><\/pre>\n<p style=\"text-align: left\"><img decoding=\"async\" class=\"alignnone size-full wp-image-752\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-2.png\" alt=\"\" width=\"441\" height=\"307\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-2.png 441w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-2-300x209.png 300w\" sizes=\"(max-width: 441px) 100vw, 441px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Como sempre ao examinar um novo histograma, comece olhando para o eixo horizontal. No eixo horizontal da curva normal padr\u00e3o, os valores s\u00e3o unidades padr\u00e3o.<\/p>\n<p style=\"text-align: justify\">Aqui est\u00e3o algumas propriedades da curva. Algumas s\u00e3o aparentes por observa\u00e7\u00e3o, e outras requerem uma quantidade consider\u00e1vel de matem\u00e1tica para serem estabelecidas.<\/p>\n<ul style=\"text-align: justify\">\n<li>A \u00e1rea total sob a curva \u00e9 1. Ent\u00e3o, voc\u00ea pode pensar nela como um histograma desenhado na escala de densidade.<\/li>\n<li>A curva \u00e9 sim\u00e9trica em torno de 0. Portanto, se uma vari\u00e1vel tem essa distribui\u00e7\u00e3o, sua m\u00e9dia e mediana s\u00e3o ambas 0.<\/li>\n<li>Os pontos de inflex\u00e3o da curva est\u00e3o em -1 e +1.<\/li>\n<li>Se uma vari\u00e1vel tem essa distribui\u00e7\u00e3o, seu desvio padr\u00e3o \u00e9 1. A curva normal \u00e9 uma das poucas distribui\u00e7\u00f5es que tem um desvio padr\u00e3o t\u00e3o claramente identific\u00e1vel no histograma.<\/li>\n<\/ul>\n<p style=\"text-align: justify\">Como estamos pensando na curva como um histograma suavizado, queremos representar propor\u00e7\u00f5es do total de dados por \u00e1reas sob a curva.<\/p>\n<p style=\"text-align: justify\">\u00c1reas sob curvas suaves s\u00e3o frequentemente encontradas por c\u00e1lculo, usando um m\u00e9todo chamado integra\u00e7\u00e3o. No entanto, \u00e9 um fato da matem\u00e1tica que a curva normal padr\u00e3o n\u00e3o pode ser integrada de nenhuma das maneiras usuais do c\u00e1lculo.<\/p>\n<p style=\"text-align: justify\">Portanto, as \u00e1reas sob a curva devem ser aproximadas. \u00c9 por isso que quase todos os livros de estat\u00edstica carregam tabelas de \u00e1reas sob a curva normal. Tamb\u00e9m \u00e9 por isso que todos os sistemas estat\u00edsticos, incluindo um m\u00f3dulo do Python, incluem m\u00e9todos que fornecem excelentes aproxima\u00e7\u00f5es dessas \u00e1reas.<\/p>\n<pre><code><span style=\"color: black\">from scipy import stats<\/span><\/code><\/pre>\n<p>&nbsp;<\/p>\n<h2 id=\"a-cdf-normal-padr-o\" style=\"text-align: justify\">A &#8220;cdf&#8221; normal padr\u00e3o<\/h2>\n<p style=\"text-align: justify\">A fun\u00e7\u00e3o fundamental para encontrar \u00e1reas sob a curva normal \u00e9 <code>stats.norm.cdf<\/code>. Ela recebe um argumento num\u00e9rico e retorna toda a \u00e1rea sob a curva \u00e0 esquerda desse n\u00famero. Formalmente, \u00e9 chamada de &#8220;fun\u00e7\u00e3o de distribui\u00e7\u00e3o cumulativa&#8221; da curva normal padr\u00e3o. Esse termo bastante complicado \u00e9 abreviado como cdf.<\/p>\n<p style=\"text-align: justify\">Vamos usar essa fun\u00e7\u00e3o para encontrar a \u00e1rea \u00e0 esquerda de z=1 sob a curva normal padr\u00e3o.<\/p>\n<pre><code><span style=\"color: black\"># \u00c1rea sob a curva normal padr\u00e3o, abaixo de 1\r\n\r\nplot_normal_cdf(1)<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img decoding=\"async\" class=\"alignnone size-full wp-image-753\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-3.png\" alt=\"\" width=\"441\" height=\"307\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-3.png 441w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-3-300x209.png 300w\" sizes=\"(max-width: 441px) 100vw, 441px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">O valor num\u00e9rico da \u00e1rea sombreada pode ser encontrado chamando <code>stats.norm.cdf<\/code>.<\/p>\n<pre><code><span style=\"color: black\">stats.norm.cdf(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[3]:<\/td>\n<td style=\"text-align: left\">0.8413447460685429<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Isso \u00e9 cerca de 84%. Agora podemos usar a simetria da curva e o fato de que a \u00e1rea total sob a curva \u00e9 1 para encontrar outras \u00e1reas.<\/p>\n<p style=\"text-align: justify\">A \u00e1rea \u00e0 direita de z=1 \u00e9 cerca de 100% &#8211; 84% = 16%.<\/p>\n<pre><code><span style=\"color: black\"># \u00c1rea sob a curva normal padr\u00e3o, acima de 1\r\n\r\nplot_normal_cdf(lbound=1)<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-754\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-4.png\" alt=\"\" width=\"441\" height=\"307\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-4.png 441w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-4-300x209.png 300w\" sizes=\"(max-width: 441px) 100vw, 441px\" \/><\/p>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\">1 - stats.norm.cdf(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[4]:<\/td>\n<td style=\"text-align: left\">0.15865525393145707<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">A \u00e1rea entre z=-1 e z=1 pode ser calculada de v\u00e1rias maneiras diferentes. \u00c9 a \u00e1rea de ouro sob a curva abaixo.<\/p>\n<pre><code><span style=\"color: black\"># \u00c1rea sob a curva normal padr\u00e3o, entre -1 e 1\r\n\r\nplot_normal_cdf(1, lbound=-1)<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-755\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-5.png\" alt=\"\" width=\"441\" height=\"307\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-5.png 441w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-5-300x209.png 300w\" sizes=\"(max-width: 441px) 100vw, 441px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Por exemplo, poder\u00edamos calcular a \u00e1rea como &#8220;100% &#8211; duas caudas iguais&#8221;, o que resulta em aproximadamente 100% &#8211; 2&#215;16% = 68%.<\/p>\n<p style=\"text-align: justify\">Ou poder\u00edamos notar que a \u00e1rea entre z=1 e z=-1 \u00e9 igual a toda a \u00e1rea \u00e0 esquerda de z=1, menos toda a \u00e1rea \u00e0 esquerda de z=-1.<\/p>\n<pre><code><span style=\"color: black\">stats.norm.cdf(1) - stats.norm.cdf(-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[5]:<\/td>\n<td style=\"text-align: left\">0.6826894921370859<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Por um c\u00e1lculo semelhante, vemos que a \u00e1rea entre -2 e 2 \u00e9 de cerca de 95%.<\/p>\n<pre><code><span style=\"color: black\"># \u00c1rea sob a curva normal padr\u00e3o, entre -2 e 2\r\n\r\nplot_normal_cdf(2, lbound=-2)<\/span><\/code><\/pre>\n<p style=\"text-align: justify\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-756\" src=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-6.png\" alt=\"\" width=\"441\" height=\"307\" srcset=\"https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-6.png 441w, https:\/\/literaciadigital.ufms.br\/files\/2025\/07\/14-3-6-300x209.png 300w\" sizes=\"(max-width: 441px) 100vw, 441px\" \/><\/p>\n<p>&nbsp;<\/p>\n<pre><code><span style=\"color: black\">stats.norm.cdf(2) - stats.norm.cdf(-2)<\/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\">0.9544997361036416<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p style=\"text-align: justify\">Em outras palavras, se um histograma tem uma forma aproximadamente de sino, a propor\u00e7\u00e3o de dados no intervalo &#8220;m\u00e9dia \u00b1 2 SDs&#8221; \u00e9 de cerca de 95%.<\/p>\n<p style=\"text-align: justify\">Isso \u00e9 consideravelmente maior do que o limite inferior de Chebychev de 75%. O limite de Chebychev \u00e9 mais fraco porque precisa funcionar para todas as distribui\u00e7\u00f5es. Se sabemos que uma distribui\u00e7\u00e3o \u00e9 normal, temos boas aproxima\u00e7\u00f5es para as propor\u00e7\u00f5es, n\u00e3o apenas limites.<\/p>\n<p style=\"text-align: justify\">A tabela abaixo compara o que sabemos sobre todas as distribui\u00e7\u00f5es e sobre distribui\u00e7\u00f5es normais. Note que quando z=1, o limite de Chebychev est\u00e1 correto, mas n\u00e3o \u00e9 esclarecedor.<\/p>\n<table style=\"width: 80%;margin-left: auto;margin-right: auto\">\n<thead>\n<tr>\n<th style=\"text-align: left\">Percentual no Intervalo<\/th>\n<th style=\"text-align: left\">Todas as Distribui\u00e7\u00f5es: Limite<\/th>\n<th style=\"text-align: left\">Distribui\u00e7\u00e3o Normal: Aproxima\u00e7\u00e3o<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align: left\">m\u00e9dia \u00b1 1 SD<\/td>\n<td style=\"text-align: left\">pelo menos 0%<\/td>\n<td style=\"text-align: left\">cerca de 68%<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left\">m\u00e9dia \u00b1 2 SDs<\/td>\n<td style=\"text-align: left\">pelo menos 75%<\/td>\n<td style=\"text-align: left\">cerca de 95%<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left\">m\u00e9dia \u00b1 3 SDs<\/td>\n<td style=\"text-align: left\">pelo menos 88.888&#8230;%<\/td>\n<td style=\"text-align: left\">cerca de 99.73%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\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\/14-0\/14-2\/\">\u2190 Cap\u00edtulo 14.2 &#8211; Variabilidade<\/a><\/td>\n<td align=\"right\"><a class=\"next-page-link\" href=\"https:\/\/literaciadigital.ufms.br\/data8\/14-0\/14-4\/\">Cap\u00edtulo 14.4 &#8211; O Teorema Central do Limite \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":722,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"coauthors":[14],"class_list":["post-750","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/750","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=750"}],"version-history":[{"count":9,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/750\/revisions"}],"predecessor-version":[{"id":1062,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/750\/revisions\/1062"}],"up":[{"embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/pages\/722"}],"wp:attachment":[{"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/media?parent=750"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/literaciadigital.ufms.br\/en\/wp-json\/wp\/v2\/coauthors?post=750"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}