Using Poisson Regression to Analyze Ship Damage Rates (Generalized Linear Models)
A generalized linear model can be used to fit a Poisson regression for the analysis of count data. For example, a dataset presented and analyzed elsewhere 1 concerns damage to cargo ships caused by waves. The incident counts can be modeled as occurring at a Poisson rate given the values of the predictors, and the resulting model can help you determine which ship types are most prone to damage.
Data for the incident counts is collected in ships.sav. See the topic Sample Files for more information.
Modeling the raw cell counts can be misleading in this situation because the Aggregate months of service varies by ship type. Variables like this that measure the amount of "exposure" to risk are handled within the generalized linear model as offset variables. Moreover, a Poisson regression assumes that the log of the dependent variable is linear in the predictors. Thus, to use generalized linear models to fit a Poisson regression to the accident rates, you need to use Logarithm of aggregate months of service.