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)