Your browser doesn't support javascript.
loading
[Multiple linear regression models with natural logarithmic transformations of variables].
Yu, S C; Wang, Q Q; Long, X J; Hu, Y H; Li, J Q; Xiang, X L; Shi, J X.
Afiliação
  • Yu SC; Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Wang QQ; Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Long XJ; Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Hu YH; Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Li JQ; Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Xiang XL; Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
  • Shi JX; Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Zhonghua Yu Fang Yi Xue Za Zhi ; 54(4): 451-456, 2020 Apr 06.
Article em Zh | MEDLINE | ID: mdl-32268656
ABSTRACT
In general, the application conditions of linear regression models could be met after the natural logarithmic transformation of data. From the practical perspective, this paper introduced the linear regression models with natural logarithmic transformation of independent variable, dependent variable, and both independent and dependent variables in detail. The paper illustrated why the equation and coefficients could not be directly explained after the natural logarithmic transformation of data. The percentage changes of X and/or Y were applied to elaborate the principle and method for the explanation of the equation and coefficients. Three examples were used to fit simple linear models with natural logarithmic transformation of independent, dependent, and both independent and dependent variables and the results of theses models were explained in detail.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Lineares / Análise Multivariada Tipo de estudo: Prognostic_studies Idioma: Zh Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Lineares / Análise Multivariada Tipo de estudo: Prognostic_studies Idioma: Zh Ano de publicação: 2020 Tipo de documento: Article