Beyond Multiple Linear Regression

Applied Generalized Linear Models And Multilevel Models in R
1. Auflage 2020
ISBN: 978-1-4398-8538-3
Verlag: Chapman and Hall/CRC

Buch, Englisch, 436 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 819 g

Reihe: Chapman & Hall/CRC Texts in Statistical Science

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)

Zielgruppe

Graduate students in other disciplines, graduate students in statistics, practitioners.

Autoren/Hrsg.

Roback, Paul

Legler, Julie

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

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

Über Autor(innen)

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.