Introduction to General and Generalized Linear Models

Introduction to General and Generalized Linear Models bridges the gap between statistical theory and practical data analysis. The text offers a clear and systematic introduction to likelihood-based methods for model building, providing a solid foundation in both general linear models and their generalized counterparts. Implementations using R are featured throughout, demonstrating step-by-step how diverse statistical problems are tackled. After establishing the necessary likelihood theory, the book first examines classical general linear models before gradually extending the discussion to more advanced generalized linear models. The authors delve into topics such as random and mixed effects within Gaussian frameworks, as well as non-Gaussian hierarchical models from the exponential family. Each chapter is enriched with examples and practical guidelines, making complex methods accessible and directly applicable. With its balanced approach to theory and practice, this work serves as a valuable resource for anyone looking to predict outcomes and explore flexible data modeling techniques using likelihood-based estimations. Ancillary materials complement the text and further support readers in mastering modern statistical methods.