Teaching Logistic Regression

Abstract

Based on the experience of teaching logistic regression to non-mathematicians, a number of areas of possible confusion are identified that may arise particularly when the method is contrasted with multiple linear regression. The fact that the model is multiplicative in odds ratios means that the concept of interaction needs to be clearly defined. Confidence intervals for the estimates of the odds ratios are asymmetric about the estimate, in contrast to confidence intervals in multiple regression which are symmetric. The fact that including a covariate will often increase the standard error of an estimate, rather than decrease it, is somewhat counter-intuitive. Logistic regression must be clearly distinguished from logit, or log-linear modelling.