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Introduction to Linear Regression Analysis, 5th Editionby G. Geoffrey Vining, Elizabeth A. Peck, Douglas C. Montgomery
Introduction to Linear Regression Analysis.pdf
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Publisher: John Wiley & Sons
Release Date: April 2012
- 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
1.1 REGRESSION AND MODEL BUILDING
1.2 DATA COLLECTION
1.3 USES OF REGRESSION
1.4 ROLE OF THE COMPUTER
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
4.1 INTRODUCTION
4.2 RESIDUAL ANALYSIS
4.3 PRESS STATISTIC
4.4 DETECTION AND TREATMENT OF OUTLIERS
4.5 LACK OF FIT OF THE REGRESSION MODEL
PROBLEMS
CHAPTER 5: TRANSFORMATIONS AND WEIGHTING TO CORRECT MODEL INADEQUACIES
5.1 INTRODUCTION
5.2 VARIANCE-STABILIZING TRANSFORMATIONS
5.3 TRANSFORMATIONS TO LINEARIZE THE MODEL
5.4 ANALYTICAL METHODS FOR SELECTING A TRANSFORMATION
5.5 GENERALIZED AND WEIGHTED LEAST SQUARES
5.6 REGRESSION MODELS WITH RANDOM EFFECTS
PROBLEMS
CHAPTER 6: DIAGNOSTICS FOR LEVERAGE AND INFLUENCE
6.1 IMPORTANCE OF DETECTING INFLUENTIAL OBSERVATIONS
6.2 LEVERAGE
6.3 MEASURES OF INFLUENCE: COOK'S D
6.4 MEASURES OF INFLUENCE: DFFITS AND DFBETAS
6.5 A MEASURE OF MODEL PERFORMANCE
6.6 DETECTING GROUPS OF INFLUENTIAL OBSERVATIONS
6.7 TREATMENT OF INFLUENTIAL OBSERVATIONS
PROBLEMS
CHAPTER 7: POLYNOMIAL REGRESSION MODELS
7.1 INTRODUCTION
7.2 POLYNOMIAL MODELS IN ONE VARIABLE
7.3 NONPARAMETRIC REGRESSION
7.4 POLYNOMIAL MODELS IN TWO OR MORE VARIABLES
7.5 ORTHOGONAL POLYNOMIALS
PROBLEMS
CHAPTER 8: INDICATOR VARIABLES
8.1 GENERAL CONCEPT OF INDICATOR VARIABLES
8.2 COMMENTS ON THE USE OF INDICATOR VARIABLES
8.3 REGRESSION APPROACH TO ANALYSIS OF VARIANCE
PROBLEMS
CHAPTER 9: MULTICOLLINEARITY
9.1 INTRODUCTION
9.2 SOURCES OF MULTICOLLINEARITY
9.3 EFFECTS OF MULTICOLLINEARITY
9.4 MULTICOLLINEARITY DIAGNOSTICS
9.5 METHODS FOR DEALING WITH MULTICOLLINEARITY
9.6 USING SAS TO PERFORM RIDGE AND PRINCIPAL-COMPONENT REGRESSION
PROBLEMS
CHAPTER 10: VARIABLE SELECTION AND MODEL BUILDING
10.1 INTRODUCTION
10.2 COMPUTATIONAL TECHNIQUES FOR VARIABLE SELECTION
10.3 STRATEGY FOR VARIABLE SELECTION AND MODEL BUILDING
10.4 CASE STUDY: GORMAN AND TOMAN ASPHALT DATA USING SAS
PROBLEMS
CHAPTER 11: VALIDATION OF REGRESSION MODELS
11.1 INTRODUCTION
11.2 VALIDATION TECHNIQUES
11.3 DATA FROM PLANNED EXPERIMENTS
PROBLEMS
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
13.1 INTRODUCTION
13.2 LOGISTIC REGRESSION MODELS
13.3 POISSON REGRESSION
13.4 THE GENERALIZED LINEAR MODEL
PROBLEMS
CHAPTER 14: REGRESSION ANALYSIS OF TIME SERIES DATA
14.1 INTRODUCTION TO REGRESSION MODELS FOR TIME SERIES DATA
14.2 DETECTING AUTOCORRELATION: THE DURBIN–WATSON TEST
14.3 ESTIMATING THE PARAMETERS IN TIME SERIES REGRESSION MODELS
PROBLEMS
CHAPTER 15: OTHER TOPICS IN THE USE OF REGRESSION ANALYSIS
15.1 ROBUST REGRESSION
15.2 EFFECT OF MEASUREMENT ERRORS IN THE REGRESSORS
15.3 INVERSE ESTIMATION—THE CALIBRATION PROBLEM
15.4 BOOTSTRAPPING IN REGRESSION
15.5 CLASSIFICATION AND REGRESSION TREES (CART)
15.6 NEURAL NETWORKS
15.7 DESIGNED EXPERIMENTS FOR REGRESSION
PROBLEMS
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
D.1 BASIC DATA ENTRY
D.2 CREATING PERMANENT SAS DATA SETS
D.3 IMPORTING DATA FROM AN EXCEL FILE
D.4 OUTPUT COMMAND
D.5 LOG FILE
D.6 ADDING VARIABLES TO AN EXISTING SAS DATA SET
APPENDIX E: INTRODUCTION TO R TO PERFORM LINEAR REGRESSION ANALYSIS
E.1 Basic Background on R
E.2 Basic Data Entry
E.3 Brief Comments on Other Functionality in R
E.4 R Commander
REFERENCES
INDEX
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