{"id":28434,"date":"2023-06-20T18:17:37","date_gmt":"2023-06-20T21:17:37","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/psychedelic-medicine-copy\/"},"modified":"2023-07-03T18:36:10","modified_gmt":"2023-07-03T21:36:10","slug":"regression-analysis","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/pt\/analise-de-regressao\/","title":{"rendered":"Uso da an\u00e1lise de regress\u00e3o para entender relacionamentos complexos"},"content":{"rendered":"<p>A an\u00e1lise de regress\u00e3o \u00e9 uma abordagem para identificar e analisar a conex\u00e3o entre uma ou mais vari\u00e1veis independentes e uma vari\u00e1vel dependente. Esse m\u00e9todo \u00e9 amplamente utilizado em diversas disciplinas, incluindo sa\u00fade, ci\u00eancias sociais, engenharia, economia e neg\u00f3cios. Voc\u00ea pode usar a an\u00e1lise de regress\u00e3o para investigar as rela\u00e7\u00f5es fundamentais nos dados e desenvolver modelos preditivos que o ajudar\u00e3o a tomar decis\u00f5es informadas.<\/p>\n\n\n\n<p>Este artigo fornecer\u00e1 uma vis\u00e3o geral abrangente da an\u00e1lise de regress\u00e3o, incluindo como ela funciona, um exemplo f\u00e1cil de entender e explicar\u00e1 como ela difere da an\u00e1lise de correla\u00e7\u00e3o.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-regression-analysis\">O que \u00e9 an\u00e1lise de regress\u00e3o?<\/h2>\n\n\n\n<p>A an\u00e1lise de regress\u00e3o \u00e9 um m\u00e9todo estat\u00edstico para identificar e quantificar a conex\u00e3o entre uma vari\u00e1vel dependente e uma ou mais vari\u00e1veis independentes. Em poucas palavras, ela ajuda a compreender como as mudan\u00e7as em uma ou mais vari\u00e1veis independentes est\u00e3o relacionadas \u00e0s mudan\u00e7as na vari\u00e1vel dependente.<\/p>\n\n\n\n<p>Para obter um entendimento completo da an\u00e1lise de regress\u00e3o, voc\u00ea deve primeiro compreender os seguintes termos:<\/p>\n\n\n\n<ul>\n<li><strong>Vari\u00e1vel dependente: <\/strong>Essa \u00e9 a vari\u00e1vel que voc\u00ea est\u00e1 interessado em analisar ou prever. \u00c9 a vari\u00e1vel de resultado que voc\u00ea est\u00e1 tentando entender e explicar.<\/li>\n\n\n\n<li><strong>Vari\u00e1veis independentes: <\/strong>Essas s\u00e3o as vari\u00e1veis que voc\u00ea acredita terem efeito sobre a vari\u00e1vel dependente. Elas s\u00e3o frequentemente chamadas de vari\u00e1veis preditoras, pois s\u00e3o usadas para prever ou explicar mudan\u00e7as na vari\u00e1vel dependente.<\/li>\n<\/ul>\n\n\n\n<p>A an\u00e1lise de regress\u00e3o pode ser usada em uma s\u00e9rie de circunst\u00e2ncias, incluindo a previs\u00e3o de valores futuros da vari\u00e1vel dependente, a compreens\u00e3o do efeito de vari\u00e1veis independentes sobre a vari\u00e1vel dependente e a descoberta de valores discrepantes ou ocorr\u00eancias incomuns na coleta de dados.<\/p>\n\n\n\n<p>A an\u00e1lise de regress\u00e3o pode ser classificada em v\u00e1rios tipos, incluindo regress\u00e3o linear simples, regress\u00e3o log\u00edstica, regress\u00e3o polinomial e regress\u00e3o m\u00faltipla. O modelo de regress\u00e3o adequado \u00e9 determinado pela natureza dos dados e pelo tema da investiga\u00e7\u00e3o em quest\u00e3o.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-how-does-regression-analysis-work\">Como funciona a an\u00e1lise de regress\u00e3o?<\/h2>\n\n\n\n<p>O objetivo da an\u00e1lise de regress\u00e3o \u00e9 identificar a linha ou curva de melhor ajuste que reflete a conex\u00e3o entre as vari\u00e1veis independentes e a vari\u00e1vel dependente. Essa linha ou curva de melhor ajuste \u00e9 gerada usando m\u00e9todos estat\u00edsticos que reduzem as disparidades entre os valores esperados e reais na coleta de dados.<\/p>\n\n\n\n<p>Aqui est\u00e3o as f\u00f3rmulas para os dois tipos mais comuns de an\u00e1lise de regress\u00e3o:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-single-linear-regression\">Regress\u00e3o linear simples<\/h3>\n\n\n\n<p>Na Regress\u00e3o Linear Simples, voc\u00ea usa uma linha de melhor ajuste para mostrar a rela\u00e7\u00e3o entre duas vari\u00e1veis: a vari\u00e1vel independente (x) e a vari\u00e1vel dependente (y).<\/p>\n\n\n\n<p>A linha de melhor ajuste pode ser representada pela equa\u00e7\u00e3o: y = a + bx.<\/p>\n\n\n\n<p>Aqui, a \u00e9 a intercepta\u00e7\u00e3o e b \u00e9 a inclina\u00e7\u00e3o da linha. Para calcular a inclina\u00e7\u00e3o, voc\u00ea usa a f\u00f3rmula: b = (n\u03a3(xy) - \u03a3x\u03a3y) \/ (n\u03a3(x<sup>2<\/sup>) - (\u03a3x)<sup>2<\/sup>), em que n \u00e9 o n\u00famero de observa\u00e7\u00f5es, \u03a3xy \u00e9 a soma do produto de x e y, \u03a3x e \u03a3y s\u00e3o as somas de x e y, respectivamente, e \u03a3(x<sup>2<\/sup>) \u00e9 a soma dos quadrados de x.<\/p>\n\n\n\n<p>Para calcular o intercepto, use a f\u00f3rmula: a = (\u03a3y - b\u03a3x) \/ n.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-multiple-regression\">Regress\u00e3o m\u00faltipla&nbsp;<\/h3>\n\n\n\n<p>Regress\u00e3o linear m\u00faltipla:<\/p>\n\n\n\n<p>A f\u00f3rmula para a equa\u00e7\u00e3o do modelo de regress\u00e3o linear m\u00faltipla \u00e9:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p><strong>y = b<sub>0<\/sub> + b<sub>1<\/sub>x<sub>1<\/sub> + b<sub>2<\/sub>x<sub>2<\/sub> + ... + b<sub>n<\/sub>x<sub>n<\/sub><\/strong><\/p>\n<\/blockquote>\n\n\n\n<p>em que y \u00e9 a vari\u00e1vel dependente, x<sub>1<\/sub>, x<sub>2<\/sub>, ..., x<sub>n<\/sub> s\u00e3o as vari\u00e1veis independentes, e b<sub>0<\/sub>, b<sub>1<\/sub>, b<sub>2<\/sub>..., bn s\u00e3o os coeficientes das vari\u00e1veis independentes.<\/p>\n\n\n\n<p>A f\u00f3rmula para estimar os coeficientes usando m\u00ednimos quadrados comuns \u00e9:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p><strong>\u03b2 = (X'X)<sup>(-1)<\/sup>X'y<\/strong><\/p>\n<\/blockquote>\n\n\n\n<p>em que \u03b2 \u00e9 um vetor de coluna de coeficientes, X \u00e9 a matriz de projeto de vari\u00e1veis independentes, X' \u00e9 a transposi\u00e7\u00e3o de X e y \u00e9 o vetor de observa\u00e7\u00f5es da vari\u00e1vel dependente.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-regression-analysis-example\">Exemplo de an\u00e1lise de regress\u00e3o<\/h2>\n\n\n\n<p>Suponha que voc\u00ea queira analisar a rela\u00e7\u00e3o entre a m\u00e9dia de notas (GPA) de um indiv\u00edduo e o n\u00famero de horas que ele estuda por semana. Voc\u00ea coleta informa\u00e7\u00f5es de um conjunto de alunos, incluindo o n\u00famero de horas de estudo e a m\u00e9dia de notas.<\/p>\n\n\n\n<p>Em seguida, use a an\u00e1lise de regress\u00e3o para ver se h\u00e1 uma conex\u00e3o linear entre as duas vari\u00e1veis e, em caso afirmativo, voc\u00ea pode criar um modelo que preveja o GPA de um aluno com base no n\u00famero de horas que ele estuda por semana.<\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/lh5.googleusercontent.com\/jY2vs2UsuRYMfVS7ZwPuk_epkVR-Yl7jnG8al1mDmUs6L8YsZ_X3WwNFy40jDCareFFtyOzL6b_DXIhO8FrJR1CMyVwg_rHyE1jycXX-LGWLsUf4LTzWV4L35ObUSidK1EsF136nqG-tHj_zjStgbbA\" alt=\"\" width=\"505\" height=\"263\"\/><figcaption class=\"wp-element-caption\"><em>Imagem dispon\u00edvel em <a href=\"https:\/\/www.alchemer.com\" target=\"_blank\" rel=\"noreferrer noopener\">alchemer.com<\/a><\/em><\/figcaption><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/www.alchemer.com\/wp-content\/uploads\/2019\/04\/regression-analysis-1.png\"><\/a><\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Quando os dados s\u00e3o plotados em um mapa de dispers\u00e3o, parece que h\u00e1 uma conex\u00e3o linear favor\u00e1vel entre as horas de estudo e o GPA. A inclina\u00e7\u00e3o e a intercepta\u00e7\u00e3o da linha de melhor ajuste s\u00e3o ent\u00e3o estimadas usando um modelo de regress\u00e3o linear simples. A solu\u00e7\u00e3o final poderia ter a seguinte apar\u00eancia:<\/p>\n\n\n\n<p>GPA = 2,0 + 0,3 (horas estudadas por semana)<\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/lh6.googleusercontent.com\/plMkcFRz9dE-xiHm7wkzhCBplbaGIBdvzy4y8LmGqBEaFAMV72IUx7DRx8uvaU_TVMkcOlwcgH_s12NMZFjni4gWrlANjcBH2RqyoFKzrks9q3SGUDpnd_ILZZ4ookIPxD-PJ2T5L-HS3GaWCJf8yEE\" alt=\"\" width=\"505\" height=\"263\"\/><figcaption class=\"wp-element-caption\"><em><em>Imagem dispon\u00edvel em <a href=\"https:\/\/www.alchemer.com\" target=\"_blank\" rel=\"noreferrer noopener\">alchemer.com<\/a><\/em><\/em><\/figcaption><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/www.alchemer.com\/wp-content\/uploads\/2019\/04\/regression-analysis-2.png\"><\/a><\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Essa equa\u00e7\u00e3o afirma que, para cada hora extra de estudo por semana, o GPA do aluno aumentar\u00e1 em 0,3 ponto, mantendo-se tudo o mais equivalente. Esse algoritmo pode ser usado para prever o GPA de um aluno com base em quantas horas ele estuda por semana, bem como para identificar quais alunos correm o risco de ter um desempenho inferior com base em suas rotinas de estudo.&nbsp;<\/p>\n\n\n\n<p>Usando os dados do exemplo, os valores para <strong>b<\/strong> e <strong>a<\/strong> s\u00e3o os seguintes:<\/p>\n\n\n\n<p>n = 10 (o n\u00famero de observa\u00e7\u00f5es)<\/p>\n\n\n\n<p>\u03a3x = 30 (a soma das horas de estudo)<\/p>\n\n\n\n<p>\u03a3y = 25 (a soma dos GPAs)<\/p>\n\n\n\n<p>\u03a3xy = 149 (a soma do produto das horas de estudo e dos GPAs)<\/p>\n\n\n\n<p>\u03a3(x)<sup>2<\/sup> = 102 (a soma dos quadrados das horas de estudo)<\/p>\n\n\n\n<p>Usando esses valores, calcule <strong>b<\/strong> como:<\/p>\n\n\n\n<p>b = (n\u03a3(xy) - \u03a3x\u03a3y) \/ (n\u03a3(x<sup>2<\/sup>) - (\u03a3x)<sup>2<\/sup>)<\/p>\n\n\n\n<p>= (10 * 149 &#8211; 30 * 25) \/ (10 * 102 &#8211; 30<sup>2<\/sup>)<\/p>\n\n\n\n<p>= 0.3<\/p>\n\n\n\n<p>E calcular <strong>a <\/strong>como:<\/p>\n\n\n\n<p>a = (\u03a3y - b\u03a3x) \/ n<\/p>\n\n\n\n<p>= (25 &#8211; 0.3 * 30) \/ 10<\/p>\n\n\n\n<p>= 2.0<\/p>\n\n\n\n<p>Portanto, a equa\u00e7\u00e3o da linha de melhor ajuste \u00e9:&nbsp;<\/p>\n\n\n\n<p>GPA = 2,0 + 0,3 (horas estudadas por semana)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-the-difference-between-correlation-and-regression\">Qual \u00e9 a diferen\u00e7a entre correla\u00e7\u00e3o e regress\u00e3o?<\/h2>\n\n\n\n<p>Tanto a correla\u00e7\u00e3o quanto a regress\u00e3o s\u00e3o m\u00e9todos estat\u00edsticos para examinar a conex\u00e3o entre duas vari\u00e1veis. Eles t\u00eam finalidades diferentes e fornecem tipos diferentes de informa\u00e7\u00f5es.<\/p>\n\n\n\n<p>A correla\u00e7\u00e3o \u00e9 uma medida da for\u00e7a e do curso de uma conex\u00e3o entre duas vari\u00e1veis. Ela varia de -1 a +1, sendo que -1 representa uma correla\u00e7\u00e3o negativa perfeita, 0 representa nenhuma correla\u00e7\u00e3o e +1 representa uma correla\u00e7\u00e3o positiva perfeita. A correla\u00e7\u00e3o indica o grau em que duas vari\u00e1veis est\u00e3o conectadas, mas n\u00e3o indica causa ou previsibilidade.<\/p>\n\n\n\n<p>A regress\u00e3o, por outro lado, \u00e9 um m\u00e9todo para modelar a conex\u00e3o entre duas vari\u00e1veis, geralmente para prever ou explicar uma vari\u00e1vel com base na outra. A an\u00e1lise de regress\u00e3o pode fornecer estimativas do tamanho e da dire\u00e7\u00e3o da rela\u00e7\u00e3o, bem como testes de signific\u00e2ncia estat\u00edstica, intervalos de confian\u00e7a e previs\u00f5es de resultados futuros.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-your-creations-ready-within-minutes\">Suas cria\u00e7\u00f5es, prontas em minutos<\/h2>\n\n\n\n<p><a href=\"https:\/\/mindthegraph.com\" target=\"_blank\" rel=\"noreferrer noopener\">Mind the Graph<\/a> \u00e9 uma plataforma on-line que oferece uma ampla biblioteca de ilustra\u00e7\u00f5es cient\u00edficas e designs de infogr\u00e1ficos que podem ser simplesmente modificados para atender \u00e0s suas necessidades espec\u00edficas. Crie gr\u00e1ficos, p\u00f4steres e resumos gr\u00e1ficos com apar\u00eancia profissional em minutos, usando uma interface de arrastar e soltar e uma ampla variedade de ferramentas e recursos.&nbsp;<\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"800\" height=\"500\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2023\/05\/banco.gif\" alt=\"\" class=\"wp-image-28087\"\/><\/figure><\/div>\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"is-layout-flex wp-block-buttons\">\n<div class=\"wp-block-button aligncenter\"><a class=\"wp-block-button__link has-background wp-element-button\" href=\"https:\/\/mindthegraph.com\/\" style=\"border-radius:50px;background-color:#dc1866\" target=\"_blank\" rel=\"noreferrer noopener\">Comece a criar com o Mind the Graph<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:44px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Entenda como a an\u00e1lise de regress\u00e3o funciona com facilidade usando um exemplo abrangente e aprenda as f\u00f3rmulas mais comuns. <\/p>","protected":false},"author":28,"featured_media":28437,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[959,28],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.9 - 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