Poisson Regression: Count Data Modeling for Beginners

Modeling count-based outcomes requires specialized statistical tools beyond standard linear regression. This comprehensive text-based guide teaches you how Poisson regression works and how to apply it effectively to real-world datasets involving discrete event counts. You will gain a solid foundation in generalized linear models, learn how to prepare your data, fit Poisson models, and interpret rate ratios accurately. What you'll learn: Understand core terminology and statistical foundations of Poisson distributions; Fit Poisson regression models and interpret log-linear coefficients; Evaluate model fit using deviance, log-likelihood, and residual analysis; Identify and address overdispersion with quasi-Poisson and negative binomial alternatives; Implement count models using modern data science libraries and clean coding workflows; Validate predictions and assess goodness-of-fit on count-based datasets. The course begins with foundational concepts and probability theory before moving into hands-on modeling scenarios, diagnostics, and interpretation strategies. This course is designed for beginners, data analysts, and researchers looking to expand their statistical toolkit without requiring advanced mathematical prerequisites. Start reading today to master count data modeling step by step.