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Multiple Regression
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Multiple Regression
A Practical Introduction



December 2020 | 280 pages | SAGE Publications, Inc
Multiple Regression: A Practical Introduction is a text for an advanced undergraduate or beginning graduate course in statistics for social science and related fields. Also, students preparing for more advanced courses can self-study the text to refresh and solidify their statistical background. Drawing on decades of teaching this material, the authors present the ideas in an approachable and nontechnical manner, with no expectation that readers have more than a standard introductory statistics course as background. Multiple regression asks how a dependent variable is related to, or predicted by, a set of independent variables. The book includes many interesting example analyses and interpretations, along with exercises. Each dataset used for the examples and exercises is small enough for readers to easily grasp the entire dataset and its analysis with respect to the specific statistical techniques covered.

A website for the book at https://edge.sagepub.com/roberts1e includes SPSS, Stata, SAS, and R code and commands for each type of analysis or recoding of variables in the book. Solutions to two of the end-of-chapter exercise types are also available for students to practice. The instructor side of the site contains editable PowerPoint slides, other solutions, and a test bank.

 
Chapter 1 Introduction
 
Chapter 2 Fundamentals of Multiple Regression
 
Chapter 3 Categorical Independent Variables in Multiple Regression: Dummy Variables
 
Chapter 4 Multiple Regression with Interaction
 
Chapter 5 Logged Variables in Multiple Regression
 
Chapter 6 Nonlinear Relationships in Multiple Regression
 
Chapter 7 Categorical Dependent Variables: Logistic Regression
 
Chapter 8 Count Dependent Variables: Poisson Regression
 
Chapter 9 A Brief Tour of Some Related Methods

Supplements

Instructor Site
SPSS, Stata, SAS, and R guidance and data sets for the examples in the book, and solutions to two of the end-of-chapter exercise types: the interpretation of results, and questions about concepts. The instructor site will contain the data sets, software guidance for and solutions to a third exercise type: data analysis using statistical software and interpretation of the results. Also available on the instructor site are editable PowerPoints slides and a test bank.
Student Study Site
SPSS, Stata, SAS, and R guidance and data sets for the examples in the book, and solutions to two of the end-of-chapter exercise types: the interpretation of results, and questions about concepts. 

This book gives students the practical knowledge and foundation of regression analysis. It is refreshing that the book includes two
chapters the extend past linear regression to other types of analysis.

Margaret Ralston
Mississippi State University

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