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Understanding statistical principles in linear and logistic regression

Alice M Richardson, Grace Joshy and Catherine A D'Este
Med J Aust 2018; 208 (8): 332-334. || doi: 10.5694/mja17.00222
Published online: 7 May 2018

A previous article in this series assessed the association between two variables.1 Here, we introduce the concept of multivariable regression.2-4 A regression model establishes the relationship between one or more exposure, or explanatory, variables (such as height, weight and sex) and an outcome (such as body mass index or smoking status). The resulting model describes the nature of the relationship between explanatory variables and outcome, and can be used to predict an unknown outcome value based on given values of the explanatory variables. The term “multivariate” indicates more than one outcome being analysed concurrently, and “multivariable” indicates more than one explanatory variable being analysed. This article concentrates on one outcome and multiple explanatory variables.

  • Alice M Richardson
  • Grace Joshy
  • Catherine A D'Este

  • National Centre for Epidemiology and Population Health, Australian National University, Canberra, ACT

Series Editors

John R Attia 

Michael P Jones


Competing interests:

No relevant disclosures.

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