Introduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book.
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The new edition focuses on four key areas of improvement over the fifth edition:
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New exercises and data sets
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New material on generalized regression techniques
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The inclusion of JMP software in key areas
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Carefully condensing the text where possible
Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today’s cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
Preface
About the Companion Website
Chapter 1 : Introduction
Chapter 2 : Simple Linear Regression
Chapter 3 : Multiple Linear Regression
Chapter 4 : Model Adequacy Checking
Chapter 5 : Transformations and Weighting to Correct Model Inadequacies
Chapter 6 : Diagnostics for Leverage and Influence
Chapter 7 : Polynomial Regression Models
Chapter 8 : Indicator Variables
Chapter 9 : Multicollinearity
Chapter 10 : Variable Selection and Model Building
Chapter 11 : Validation of Regression Models
Chapter 12 : Introduction to Nonlinear Regression
Chapter 13 : Generalized Linear Models
Chapter 14 : Regression Analysis of Time Series Data
Chapter 15 : Other Topics in the Use of Regression Analysis
Appendix A : Statistical Tables
Appendix B : Data Sets for Exercises
Appendix C : Supplemental Technical Material
Appendix D : Introduction to SAS
Appendix E : Introduction to R to Perform Linear Regression Analysis
Index
