Understanding Regression Analysis
An Introductory Guide
Second Edition
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Go to College Publishing WebsiteDescription
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
Series Editor’s Introduction
Preface
Preface
Acknowledgments
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
References
Index
Index
Additional materials
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
Series Editor’s Introduction
Preface
Preface
Acknowledgments
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
References
Index
Index
Additional materials
Reviews
Understanding Regression Analysis
An Introductory Guide
October 2016 | 120 pages | Sage US
| Format | Published Date | ISBN | Price |
|---|
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.
Table Of 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
Recent Product Reviews:
“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