Introduction to Poisson regression models for count data by Gabriele Durrant

This resource introduces Poisson regression, a form of regression analysis which is used to model count data, such as number of event occurrences during a particular time period. For example, researchers may be interested in modelling the variation in the number of traffic accidents across a 12 months period or the number of stressful events experienced during the last 18 months prior to interview. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modelled by a linear combination of unknown parameters. Poisson regression models are generalized linear models with the logarithm as the link function. This web resource introduces the basic principles of Poisson regression. First, it discusses a very simple model (the equiprobable model), a model without a covariate. Goodness of fit statistics for Poisson regression are presented. It then extends the basic principles to a Poisson regression model with one covariate, and illustrates this with a Poisson time trend model.