Interpretation of commonly used statistical regression models

Authors

  • Jessica Kasza,

    Corresponding author
    1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
    2. Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia
    • Correspondence: Jessica Kasza, Department of Epidemiology and Preventive Medicine, The Alfred Centre, Monash University, 99 Commercial Road, Melbourne, Vic. 3004, Australia. Email: jessica.kasza@monash.edu

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  • Rory Wolfe

    1. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
    2. Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia
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  • The Authors: Dr. Jessica Kasza, BSc, PhD, a research fellow in biostatistics at the Department of Epidemiology and Preventive Medicine at Monash University, has research interests that include healthcare provider comparison and the estimation of causal effects. Professor Rory Wolfe, BSc, PhD, Professor of Biostatistics at the School of Public Health and Preventive Medicine, has broad research interests in biostatistics.
  • Series Editors: Rory Wolfe and Michael Abramson

Abstract

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.

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