Fitting an "overdispersed" Poisson regression

Add a Statistics File source node pointing to ships.sav in the Demos folder.
Figure 1. Sample stream to analyze damage rates
On the Filter tab of the source node, exclude the field months_service. The log-transformed values of this variable are contained in log_months_service, which will be used in the analysis.
Figure 2. Filtering an unneeded field

(Alternatively, you could change the role to None for this field on the Types tab rather than exclude it, or select the fields you want to use in the modeling node.)
On the Types tab of the source node, set the role for the damage_incidents field to Target. All other fields should have their role set to Input.
Click Read Values to instantiate the data.
Figure 3. Setting field role
Attach a Genlin node to the source node; on the Genlin node, click the Model tab.
Select log_months_service as the offset variable.
Figure 4. Choosing model options
Click the Expert tab and select Expert to activate the expert modeling options.
Figure 5. Choosing expert options
Select Poisson as the distribution for the response and Log as the link function.
Select Pearson Chi-Square as the method for estimating the scale parameter. The scale parameter is usually assumed to be 1 in a Poisson regression, but McCullagh and Nelder use the Pearson chi-square estimate to obtain more conservative variance estimates and significance levels.
Select Descending as the category order for factors. This indicates that the first category of each factor will be its reference category; the effect of this selection on the model is in the interpretation of parameter estimates.
Click Run to create the model nugget, which is added to the stream canvas, and also to the Models palette in the upper right corner. To view the model details, right-click the nugget and choose Edit or Browse, then click the Advanced tab.