Analyzing Count Data in SAS: Poisson and Negative Binomial Models

Analyzing count data—like the number of website visits, daily transactions, or patient admissions—requires specialized statistical techniques. Traditional linear regression falls short for these scenarios, making Poisson and Negative Binomial models essential tools for any analyst. This text-based course guides you through the process of preparing count data, fitting models in SAS, and evaluating their performance. You will transition from understanding basic probability distributions to confidently writing SAS code and interpreting statistical outputs to make data-driven decisions. Through clear written explanations and practical code examples, you will learn how to handle real-world data challenges such as overdispersion. What you'll learn: - Understand the foundational theory of count data, Poisson distributions, and the assumption of equidispersion. - Identify overdispersion in datasets and determine when to transition to a Negative Binomial model. - Write clean SAS code to fit count regression models using standard statistical procedures. - Evaluate model fit using key diagnostic statistics like AIC, BIC, and deviance. - Interpret model coefficients and rate ratios to draw meaningful business or scientific conclusions. - Apply data preparation techniques within SAS to ensure your analysis is robust and reproducible. The course begins with foundational definitions of count distributions before moving into step-by-step SAS code implementations. You will read clear explanations, study practical code snippets, and practice evaluating model outputs through written exercises. This course is designed for beginning data analysts, researchers, and students who have a basic familiarity with SAS and want to expand their predictive modeling toolkit. No advanced statistical background is required. Start reading today to master count data modeling in SAS.