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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)
List of contents
1. Review of Multiple Linear Regression 2. Beyond Least Squares: Using Likelihoods to Fit and Compare Models 3. Distribution Theory 4. Poisson Regression 5. Generalized Linear Models (GLMs): A Unifying Theory 6. Logistic Regression 7. Correlated Data 8. Introduction to Multilevel Models 9. Two Level Longitudinal Data 10. Multilevel Data With More Than Two Levels 11. Multilevel Generalized Linear Models
Summary
For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each chapter. Solutions manual for qualified instructors.
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"Overall, this is an excellent text that is highly appropriate for undergraduate students. I am a really big fan of Chapter 2. The authors introduce the concepts of likelihood and model comparisons via likelihood in a very gentle and intuitive way. It will be very useful for the wide audience anticipated for the course we are designing. In Chapter 4, the authors do an excellent job discussing some of the common 'extensions' of Poisson regression that are likely to be observed in practice (overdispersion and ZIP). In particular, they do an excellent job describing situations that might lead to zero-inflate Poissons. The use of case studies across all chapters is a major strength of the textbook."
-Jessica Chapman, St. Lawrence University
"This text would be ideal for statistics undergrad majors & minors as a 2nd or 3rd course in statistics...In particular, this book intuitively covers many topics without delving into technical proofs and details which are not needed for successful application of the methods described. It is a strength that it uses the software R. Use of R is a skill welcomed in any industry, and is not a burden for students to obtain. The book emphasizes methods as well as numerical literacy. For example, it guides the student in how to assess the appropriateness of methods (e.g. assumptions of linear model), not just the use and interpretation of the results. There is a strong focus on understanding and checking assumptions, as well as the effect violations of those assumptions will have on the result. I think this may be an effective way to train the reader to think like a statistician, without overwhelming the reader with technical details." ---Kirsten Eilertson, Colorado State University
