Understanding Regression Analysis
An Introductory Guide

Second Edition

Larry D. Schroeder - Syracuse University, USA
David L. Sjoquist - Georgia State University, USA
Paula E. Stephan - Georgia State University, USA

Volume: 57

Series:
Quantitative Applications in the Social Sciences

Courses:
Methods & Statistics in Public Administration | Regression & Correlation | Regression & Correlation | Statistics in Sociology

November 2016 | 120 pages | SAGE Publications, Inc

Understanding Regression Analysis: An Introductory Guide presents the fundamentals of regression analysis, from its meaning to uses, in a concise, easy-to-read, and non-technical style. It illustrates how regression coefficients are estimated, interpreted, and used in a variety of settings within the social sciences, business, law, and public policy. Packed with applied examples and using few equations, the book walks readers through elementary material using a verbal, intuitive interpretation of regression coefficients, associated statistics, and hypothesis tests. The Second Edition features updated examples and new references to modern software output.

Available Formats

ISBN: 9781506332888	Paperback	Suggested Retail Price: $46.00	Bookstore Price: $36.80
ISBN: 9781506361604	Electronic Version	Suggested Retail Price: $38.00	Bookstore Price: $30.40

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Series Editor’s Introduction

Preface

Acknowledgments

About the Authors

1. Linear Regression

Introduction

Hypothesized Relationships

A Numerical Example

Estimating a Linear Relationship

Least Squares Regression

Examples

The Linear Correlation Coefficient

The Coefficient of Determination

Regression and Correlation

Summary

2. Multiple Linear Regression

Introduction

Estimating Regression Coefficients

Standardized Coefficients

Associated Statistics

Examples

Summary

3. Hypothesis Testing

Introduction

Concepts Underlying Hypothesis Testing

The Standard Error of the Regression Coefficient

The Student’s t Distribution

Left-Tail Tests

Two-Tail Tests

Confidence Intervals

F Statistic

What Tests of Significance Can and Cannot Do

Summary

4. Extensions to the Multiple Regression Model

Introduction

Types of Data

Dummy Variables

Interaction Variables

Transformations

Prediction

Examples

Summary

5. Problems and Issues Associated With Regression

Introduction

Specification of the Model

Variables Used in Regression Equations and Measurement of Variables

Violations of Assumptions Regarding Residual Errors

Additional Topics

Conclusions

Appendix A: Derivation of a and b

Appendix B: Critical Values for Student’s t Distribution

Appendix C: Regression Output From SAS, Stata, SPSS, R, and EXCEL

Appendix D: Suggested Textbooks

References

Index

“This is an excellent update; a clear and accessible introduction to a complex, yet very important, statistical method: regression analysis. The book can serve as a perfect supplement or stand-alone book in introductory social statistics courses.”

Grigoris Argeros

Eastern Michigan University

“Understanding Regression Analysis provides students at all levels a foundational understanding of multiple linear regression analysis through intuitive explanations and interdisciplinary examples aimed at elucidating concepts, approaches, and interpretations.”

Andrea Hetling

Rutgers University – New Brunswick

“This monograph provides a clear and concise introduction to regression analysis concepts and procedures, with a problem-solving approach toward addressing common maladies of regression modeling.”

Ross E. Burkhart

Boise State University

“The authors do a top-notch job of competently presenting a plethora of topics regarding regression analysis.”

Wyatt Brown

University of South Florida

Key features

NEW TO THIS EDITION:

A revised explanation of hypothesis testing in Chapter 3 is more in line with current approaches.
New examples have been taken from current books and articles.
Recent changes in the approaches to and applications of regression analysis are reflected in the book’s two final chapters.
Expanded coverage of prominent techniques, such as experiments and instrumental variables, enhance student understanding.
New chapter introductions and summaries help students master key concepts.

KEY FEATURES:

No background in statistics and only limited background in mathematics is needed to understand the material.
An intuitive, non-technical explanation of linear regression analysis makes the book ideal for anyone who needs to understand the principles of regression analysis.
Discussions of problems that can arise in conducting regression analysis are included.

Sample Materials & Chapters

Chapter 1

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Understanding Regression Analysis
An Introductory Guide

Sample Materials & Chapters

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