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Applied Regression
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Applied Regression
An Introduction

Second Edition
Volume: 22
June 2015 | 120 pages | SAGE Publications, Inc
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Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. The authors then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.

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Series Editor's Introduction
 
Preface
 
Acknowledgments
 
About the Authors
 
1. Bivariate Regression: Fitting a Straight Line
 
2. Bivariate Regression: Assumptions and Inferences
 
3. Multiple Regression: The Basics
 
4. Multiple Regression: Special Topics
 
Appendix
 
References
 
Index

I teach advanced research methods in psychology at the undergraduate level. This text only applies to some of my students, depending upon their research project/ design. Therefore, I recommend it to those who should do regression analyses with their data.

Dr Angela Birt
Psychology, Mount St Vincent University
October 28, 2015
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Basic, uncomplicated introduction to regression for students in a professional degree program.

Dr Lizabeth Kay Kleintop
Economics Business Dept, Moravian College
October 29, 2015
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Key features

NEW TO THIS EDITION:

  • Updated and improved graphs and graphical examples have a more contemporary design, having been created in the R software.
  • New applied examples of transformations are included, with a focus on solving problems of nonlinearity or outliers.
  • Expanded coverage of statistical methods includes more on regression assumptions and model fit, additional material on residual analysis, and inclusion of the measures of tolerance and VIF in the discussion of collinearity.
  • More visual illustrations clarify discussion of the differences between linearity and nonlinearity.
  • A mathematical notation that conforms to more current style.

 KEY FEATURES:

  • The content is accessible, requiring no advanced math training (beyond high school level) for comprehension.
  • Information is organized logically in a way that students find it easy to follow.
  • The book is concise and can be digested quickly for the basics behind the OLS model.

Sample Materials & Chapters

Chapter 1

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