GLZ Introductory Overview - Interpretation of Results and Diagnostics

Simple estimation and test statistics may not be sufficient for adequate interpretation of the effects in an analysis. Especially for higher order (e.g., interaction) effects, inspection of the observed and predicted means can be invaluable for understanding the nature of an effect. Plots of these means (with error bars) can be useful for quickly grasping the role of the effects in the model.

Inspection of the distributions of variables is critically important when using the generalized linear model. Histograms and probability plots for variables, and scatterplots showing the relationships between observed values, predicted values, and residuals (e.g., Pearson residuals, deviance residuals, studentized residuals, differential Chi-square statistics, differential deviance statistics, and generalized Cook's D) provide invaluable model-checking tools.