Interpretation of commonly used statistical regression models

被引：49

作者：

Kasza, Jessica ^{[1
,2
]}

Wolfe, Rory ^{[1
,2
]}

机构：

[1] Monash Univ, Dept Epidemiol & Prevent Med, Melbourne, Vic 3004, Australia

[2] Victorian Ctr Biostat ViCBiostat, Melbourne, Vic, Australia

来源：

RESPIROLOGY | 2014年 / 19卷 / 01期

基金：

澳大利亚国家健康与医学研究理事会;

关键词：

linear model; logistic model; ordinal logistic model; regression analysis; POISSON; RISK;

D O I：

10.1111/resp.12221

中图分类号：

R56 [呼吸系及胸部疾病];

学科分类号：

100201 [内科学];

摘要：

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study.

引用

页码：14 / 21

页数：8

共 28 条

[1]

Altman DG, 1996, BRIT MED J, V313, P486

[2]

Statistics notes - Standard deviations and standard errors [J].