Overview

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Learn how to build, evaluate, and compare regression models for count data using SAS in this hands-on course. You'll begin by exploring datasets to determine their suitability for Poisson regression, then construct models using PROC GENMOD with the log link function and interpret key outputs. As you progress, you'll evaluate model performance by identifying overdispersion, refining models, and applying statistical diagnostics to improve accuracy. The course then introduces negative binomial regression, helping you understand when it is more appropriate than Poisson regression, interpret the dispersion parameter, and compare models using criteria such as AIC and deviance. Through guided SAS implementations and practical examples, you'll develop the skills to analyze count data, justify model selection decisions, and evaluate model performance with confidence. This course is designed for learners seeking practical experience with statistical modeling in SAS and for those who want to strengthen their ability to select and assess regression models for count data. By the end of the course, you'll be able to implement, compare, and critique Poisson and negative binomial regression models using SAS to support informed analytical decisions.

Syllabus

Poisson Regression Fundamentals

This module introduces learners to the principles and application of Poisson regression using SAS. It covers the fundamentals of modeling count data, exploring datasets for suitability, fitting models using PROC GENMOD, and interpreting results. Learners will progress from understanding the problem context and dataset structure to building and refining Poisson regression models, diagnosing potential issues like overdispersion, and improving model accuracy through variable selection and statistical analysis techniques.

Negative Binomial Regression Techniques

This module builds on the foundations of Poisson regression by introducing the negative binomial model for count data exhibiting overdispersion. Learners will explore when and why to choose the negative binomial approach, understand the role of the dispersion parameter, and evaluate model outputs using statistical diagnostics and information criteria. Through practical SAS implementations, learners will gain the skills to refine models, address influential observations, and compare performance against Poisson regression to select the most appropriate modeling strategy.