Understanding statistical principles in linear and logistic regression

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