Book description
Praise for the Fourth Edition
"As with previous editions, the authors have produced a leading textbook on regression."
—Journal of the American Statistical Association
A comprehensive and up-to-date introduction to the fundamentals of regression analysis
Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today's cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including:
A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models
Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model
Tests on individual regression coefficients and subsets of coefficients
Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data.
In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material, and a related FTP site features the presented data sets, extensive problem solutions, software hints, and PowerPoint slides to facilitate instructional use of the book.
Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.
Table of contents
- Cover Page
- Title Page
- Copyright
- Contents
- PREFACE
- CHAPTER 1: INTRODUCTION
-
CHAPTER 2: SIMPLE LINEAR REGRESSION
- 2.1 SIMPLE LINEAR REGRESSION MODEL
- 2.2 LEAST-SQUARES ESTIMATION OF THE PARAMETERS
- 2.3 HYPOTHESIS TESTING ON THE SLOPE AND INTERCEPT
- 2.4 INTERVAL ESTIMATION IN SIMPLE LINEAR REGRESSION
- 2.5 PREDICTION OF NEW OBSERVATIONS
- 2.6 COEFFICIENT OF DETERMINATION
- 2.7 A SERVICE INDUSTRY APPLICATION OF REGRESSION
- 2.8 USING SAS® AND R FOR SIMPLE LINEAR REGRESSION
- 2.9 SOME CONSIDERATIONS IN THE USE OF REGRESSION
- 2.10 REGRESSION THROUGH THE ORIGIN
- 2.11 ESTIMATION BY MAXIMUM LIKELIHOOD
- 2.12 CASE WHERE THE REGRESSOR x IS RANDOM
- PROBLEMS
-
CHAPTER 3: MULTIPLE LINEAR REGRESSION
- 3.1 MULTIPLE REGRESSION MODELS
- 3.2 ESTIMATION OF THE MODEL PARAMETERS
- 3.3 HYPOTHESIS TESTING IN MULTIPLE LINEAR REGRESSION
- 3.4 CONFIDENCE INTERVALS IN MULTIPLE REGRESSION
- 3.5 PREDICTION OF NEW OBSERVATIONS
- 3.6 A MULTIPLE REGRESSION MODEL FOR THE PATIENT SATISFACTION DATA
- 3.7 USING SAS AND R FOR BASIC MULTIPLE LINEAR REGRESSION
- 3.8 HIDDEN EXTRAPOLATION IN MULTIPLE REGRESSION
- 3.9 STANDARDIZED REGRESSION COEFFLCIENTS
- 3.10 MULTICOLLINEARITY
- 3.11 WHY DO REGRESSION COEFFICIENTS HAVE THE WRONG SIGN?
- PROBLEMS
- 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
- 12.1 LINEAR AND NONLINEAR REGRESSION MODELS
- 12.2 ORIGINS OF NONLINEAR MODELS
- 12.3 NONLINEAR LEAST SQUARES
- 12.4 TRANFORMATION TO A LINEAR MODEL
- 12.5 PARAMETER ESTIMATION IN A NONLINEAR SYSTEM
- 12.6 STATISTICAL INFERENCE IN NONLINEAR REGRESSION
- 12.7 EXAMPLES OF NONLINEAR REGRESSION MODELS
- 12.8 USING SAS AND R
- PROBLEMS
- 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
- C.1 BACKGROUND ON BASIC TEST STATISTICS
- C.2 BACKGROUND FROM THE THEORY OF LINEAR MODELS
- C.3 IMPORTANT RESULTS ON SS R AND SS RES
- C.4 GAUSS–MARKOV THEOREM, VAR(ε) = σ2I
- C.5 COMPUTATIONAL ASPECTS OF MULTIPLE REGRESSION
- C.6 RESULT ON THE INVERSE OF A MATRIX
- C.7 DEVELOPMENT OF THE PRESS STATISTIC
- C.8 DEVELOPMENT OF S2(i)
- C.9 OUTLIER TEST BASED ON R-STUDENT
- C.10 INDEPENDENCE OF RESIDUALS AND FITTED VALUES
- C.11 GAUSS-MARKOV THEOREM, VAR(ε) = V
- C.12 BIAS IN MS RES WHEN THE MODEL IS UNDERSPECIFIED
- C.13 COMPUTATION OF INFLUENCE DIAGNOSTICS
- C.14 GENERALIZED LINEAR MODELS
- APPENDIX D: INTRODUCTION TO SAS
- APPENDIX E: INTRODUCTION TO R TO PERFORM LINEAR REGRESSION ANALYSIS
- REFERENCES
- INDEX
Product information
- Title: Introduction to Linear Regression Analysis, 5th Edition
- Author(s):
- Release date: April 2012
- Publisher(s): Wiley
- ISBN: 9780470542811
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