Book Profile
Beyond Multiple Linear Regression Applied Generalized Linear Models And Multilevel Models in R
Paul Roback, Julie Legler
An applied textbook that teaches statisticians and data analysts how to move beyond standard linear regression to effectively model non-normal and correlated data using Generalized Linear Models and Multilevel Models in R.
Get the book →For students and analysts who have mastered multiple linear regression, this book serves as the essential next step for tackling the complexities of real-world data. It provides an accessible, case-study-driven guide to statistical modeling when the core assumptions of linear regression don't hold. Through intuitive explanations and practical R code, readers will learn to model count data with Poisson regression, binary outcomes with logistic regression, and handle correlated data structures like repeated measures or nested groups with multilevel models. By grounding advanced topics like likelihood theory, overdispersion, and random effects in tangible examples, the book empowers readers to expand their analytical toolkit and conduct more appropriate, robust, and insightful analyses.
What it argues
Beyond Multiple Linear Regression Applied Generalized Linear Models And Multilevel Models in R
Key ideas it contributes
- Data-Model Mismatch Conditions — Characteristics of the data, such as a non-normally distributed response variable (e.g., counts, binary) or a correlated observation structure (e.g., nested, longitudinal), that violate the core assumptions of standard Multiple Linear Regression.
- Appropriate Model Selection — The analyst's choice to use a statistical model whose assumptions are consistent with the data's characteristics, such as selecting a Generalized Linear Model for non-normal data or a Multilevel Model for correlated data. This is the core skill taught by the book.
- Model-Assumption Alignment — The degree to which the assumptions of the selected statistical model (e.g., about the response distribution, variance structure, and independence of errors) are met by the actual characteristics of the data.
- Inferential Validity — The correctness and reliability of the statistical inferences drawn from the model, including the accuracy of parameter estimates, standard errors, confidence intervals, and p-values.