# Beyond Multiple Linear Regression

## Applied Generalized Linear Models And Multilevel Models in R

## Paul Roback, Julie Legler

- 418 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android

# Beyond Multiple Linear Regression

## Applied Generalized Linear Models And Multilevel Models in R

## Paul Roback, Julie Legler

## About This Book

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)

## Frequently asked questions

## Information

# 1

## Review of Multiple Linear Regression

## 1.1 Learning Objectives

- Identify cases where linear least squares regression (LLSR) assumptions are violated.
- Generate exploratory data analysis (EDA) plots and summary statistics.
- Use residual diagnostics to examine LLSR assumptions.
- Interpret parameters and associated tests and intervals from multiple regression models.
- Understand the basic ideas behind bootstrapped confidence intervals.

## 1.2 Introduction to Beyond Multiple Linear Regression

**generalized linear models (GLMs)**as opposed to using

**linear least squares regression (LLSR)**models.

**Generalized Linear Models**in the book’s title extends least squares methods you may have seen in linear regression to handle responses that are non-normal. The

**Multilevel Models**in the book’s title will allow us to create models for situations where the observations are not independent of one another. Overall, these approaches will permit us to get much more out of data and may be more faithful to the actual data structure than models based on ordinary least squares. These models will allow you to expand

*beyond multiple linear regression*.