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 uptodate 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 cuttingedge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression modelbuilding 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 DurbinWatson 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 SPLUS, 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 upperundergraduate 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 LEASTSQUARES 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 RSTUDENT
 C.10 INDEPENDENCE OF RESIDUALS AND FITTED VALUES
 C.11 GAUSSMARKOV 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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