Generalized Linear Regression Foundations and Practice

Generalized Linear Models (GLMs) are the backbone of modern statistical analysis, allowing you to model real-world data that does not follow a simple normal distribution. If you want to move beyond standard linear regression and confidently work with binary outcomes, counts, and rate data, understanding GLMs is essential. This text-based course guides you from foundational probability concepts to practical model evaluation, ensuring you build a robust mathematical and practical intuition. By reading our structured explanations and completing targeted self-assessments, you will gain the confidence to select, fit, and interpret the right model for any dataset. What you'll learn: - Understand the core components of a GLM, including the random component, systematic component, and link functions - Differentiate between classical linear regression and generalized linear models - Master logistic regression for binary outcomes and Poisson regression for count data - Apply maximum likelihood estimation concepts to understand how model parameters are solved - Evaluate model fit using deviance, residuals, and information criteria like AIC - Practice interpreting model coefficients and exponentiated estimates for real-world reporting This course begins with essential terminology and the mathematical foundations of the exponential family of distributions, ensuring you have a solid conceptual starting point. You will then progress through detailed breakdowns of logistic and Poisson models, complete with structured written scenarios and step-by-step conceptual quizzes to test your understanding. This course is designed for aspiring data analysts, researchers, and beginners in statistics who have a basic familiarity with standard linear regression but no prior experience with generalized linear models. No advanced programming or calculus prerequisites are required. Start reading today to unlock the full potential of generalized linear regression for your data analysis projects.