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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. This second edition features updated examples and new references to modern software output. "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 "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 "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 "The authors do a top-notch job of competently presenting a plethora of topics regarding regression analysis." -- Wyatt Brown

Understanding Regression Analysis: An Introductory Guide

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