Beyond Multiple Linear Regression: Applied Generalized Linear Models And Multilevel Models in R

✍ Scribed by Paul Roback, Julie Legler

• Publisher: Chapman and Hall/CRC
• Year: 2020
• Tongue: English
• Leaves: 437
• Series: Chapman & Hall/CRC Texts in Statistical Science
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Dedication
Contents
Preface
1 Review of Multiple Linear Regression
2 Beyond Least Squares: Using Likelihoods
3 Distribution Theory
4 Poisson Regression
5 Generalized Linear Models: 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
Bibliography
Index