Synopses & Reviews
Synopsis
Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, Beyond Multiple Linear Regression still introduces fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. Thecase studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling.
Beyond Multiple Linear Regression is organized as follows:
- Chapter 1 - review of multiple linear regression and introduction to our approach to exploratory data analysis and model building
- Chapter 2 - build intuition for likelihoods and their usefulness in testing and estimation
- Chapters 3-6 - Generalized Linear Models, featuring Poisson, binomial, logistic, and negative binomial regression.
- Chapter 7 - build intuition and vocabulary about correlated data through an extended simulation and a real case study
- Chapters 8-10 - Multilevel Models, with extensions to longitudinal data and more than two levels.
- Chapter 11 - Multilevel Generalized Linear Models, where everything is brought together: multilevel data with non-normal responses.
- Supplemental material - a solutions manual for all exercises, available to qualified instructors at our book's website (www.routledge.com), and data sets and Rmd files for all case studies and exercises, available at our GitHub repo (https: //github.com/proback/BYSH)