Buy Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R 1st Edition PDF ebook by author Paul Roback; Julie Legler – published by Chapman & Hall in 2021 and save up to 80% compared to the print version of this textbook. With PDF version of this textbook, not only save you money, you can also highlight, add text, underline add post-it notes, bookmarks to pages, instantly search for the major terms or chapter titles, etc.
You can search our site for other versions of the Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R 1st Edition PDF ebook. You can also search for others PDF ebooks from publisher Chapman & Hall, as well as from your favorite authors. We have thousands of online textbooks and course materials (mostly in PDF) that you can download immediately after purchase.
Note: e-textBooks do not come with access codes, CDs/DVDs, workbooks, and other supplemental items.
eBook Details:
Full title: Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R 1st Edition
Edition: 1st
Copyright year: 2021
Publisher: Chapman & Hall
Author: Paul Roback; Julie Legler
ISBN: 9780429066665, 9780429527333
Format: PDF
Description of Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R 1st Edition:
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)
