ABSTRACT

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, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case 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.

A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)

chapter Chapter 1|38 pages

Review of Multiple Linear Regression

chapter Chapter 2|32 pages

Beyond Least Squares: Using Likelihoods

chapter Chapter 3|22 pages

Distribution Theory

chapter Chapter 4|52 pages

Poisson Regression

chapter Chapter 5|6 pages

Generalized Linear Models: A Unifying Theory

chapter Chapter 6|42 pages

Logistic Regression

chapter Chapter 7|18 pages

Correlated Data

chapter Chapter 8|52 pages

Introduction to Multilevel Models

chapter Chapter 9|58 pages

Two-Level Longitudinal Data

chapter Chapter 10|52 pages

Multilevel Data With More Than Two Levels

chapter Chapter 11|36 pages

Multilevel Generalized Linear Models