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Full Description
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.
Contents
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
