This course equips learners with the knowledge and practical skills to analyze, construct, and evaluate statistical models for count data using SAS. Beginning with Poisson regression, learners will identify appropriate datasets, assess distributional assumptions, and build models using PROC GENMOD with the log link function. They will then examine model diagnostics to detect issues such as overdispersion and refine models for better accuracy.

Building on these foundations, learners will differentiate between Poisson and negative binomial regression approaches, interpret the role of the dispersion parameter, and compare models using statistical criteria like AIC and deviance. Real-world examples and guided SAS implementations will allow learners to apply these techniques effectively, justify model selection decisions, and optimize predictive performance for diverse count data scenarios. By the end of the course, participants will be able to select, implement, and critique regression models that best fit the characteristics of their datasets, enhancing their analytical capabilities in statistical modeling with SAS.