Beyond Multiple Linear Regression - Roback, Paul;Legler, Julie
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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…mehr

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Produktbeschreibung
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 thewider 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)

Autorenporträt
Authors Paul Roback is the Kenneth O. Bjork Distinguished Professor of Statistics and Data Science and Julie Legler is Professor Emeritus of Statistics at St. Olaf College in Northfield, MN. Both are Fellows of the American Statistical Association and are founders of the Center for Interdisciplinary Research at St. Olaf. Dr. Roback is the past Chair of the ASA Section on Statistics and Data Science Education, conducts applied research using multilevel modeling, text analysis, and Bayesian methods, and has been a statistical consultant in the pharmaceutical, health care, and food processing industries. Dr. Legler is past Chair of the ASA/MAA Joint Committee on Undergraduate Statistics, is a co-author of Stat2: Modelling with Regression and ANOVA, and was a biostatistician at the National Cancer Institute.
Rezensionen
"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

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