Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

Introduction to Linear Regression Analysis

• Gebundenes Buch

Produktbeschreibung

INTRODUCTION TO LINEAR REGRESSION ANALYSIS

A comprehensive and current introduction to the fundamentals of regression analysis

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.

The new edition focuses on four key areas of improvement over the fifth edition:
_ New exercises and data sets
_ New material on generalized regression techniques
_ The inclusion of JMP software in key areas
_ 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.

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Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119578720
• 6. Aufl.
• Seitenzahl: 704
• Erscheinungstermin: 16. März 2021
• Englisch
• Abmessung: 259mm x 187mm x 30mm
• Gewicht: 1232g
• ISBN-13: 9781119578727
• ISBN-10: 1119578728
• Artikelnr.: 59644682

Autorenporträt

DOUGLAS C. MONTGOMERY, PHD, is Regents Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery is the co-author of several Wiley books including Introduction to Linear Regression Analysis, 5th Edition. ELIZABETH A. PECK, PHD, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia. G. GEOFFREY VINING, PHD, is Professor in the Department of Statistics at Virginia Polytechnic and State University. Dr. Peck is co-author of Introduction to Linear Regression Analysis, 5th Edition.

Inhaltsangabe

Preface xiii

About the Companion Website xvi

1. Introduction 1

1.1 Regression and Model Building 1

1.2 Data Collection 5

1.3 Uses of Regression 9

1.4 Role of the Computer 10

2. Simple Linear Regression 12

2.1 Simple Linear Regression Model 12

2.2 Least-Squares Estimation of the Parameters 13

2.2.1 Estimation of ß0 and ß1 13

2.2.2 Properties of the Least-Squares Estimators and the Fitted Regression Model 18

2.2.3 Estimation of sigma² 20

2.2.4 Alternate Form of the Model 22

2.3 Hypothesis Testing on the Slope and Intercept 22

2.3.1 Use of t Tests 22

2.3.2 Testing Significance of Regression 24

2.3.3 Analysis of Variance 25

2.4 Interval Estimation in Simple Linear Regression 29

2.4.1 Confidence Intervals on ß0, ß1, and sigma² 29

2.4.2 Interval Estimation of the Mean Response 30

2.5 Prediction of New Observations 33

2.6 Coefficient of Determination 35

2.7 A Service Industry Application of Regression 37

2.8 Does Pitching Win Baseball Games? 39

2.9 Using SAS(r) and R for Simple Linear Regression 41

2.10 Some Considerations in the Use of Regression 44

2.11 Regression Through the Origin 46

2.12 Estimation by Maximum Likelihood 52

2.13 Case Where the Regressor x is Random 53

2.13.1 x and y Jointly Distributed 54

2.13.2 x and y Jointly Normally Distributed: Correlation Model 54

Problems 59

3. Multiple Linear Regression 69

3.1 Multiple Regression Models 69

3.2 Estimation of the Model Parameters 72

3.2.1 Least-Squares Estimation of the Regression Coefficients 72

3.2.2 Geometrical Interpretation of Least Squares 79

3.2.3 Properties of the Least-Squares Estimators 81

3.2.4 Estimation of sigma² 82

3.2.5 Inadequacy of Scatter Diagrams in Multiple Regression 84

3.2.6 Maximum-Likelihood Estimation 85

3.3 Hypothesis Testing in Multiple Linear Regression 86

3.3.1 Test for Significance of Regression 86

3.3.2 Tests on Individual Regression Coefficients and Subsets of Coefficients 90

3.3.3 Special Case of Orthogonal Columns in X 95

3.3.4 Testing the General Linear Hypothesis 97

3.4 Confidence Intervals in Multiple Regression 99

3.4.1 Confidence Intervals on the Regression Coefficients 100

3.4.2 CI Estimation of the Mean Response 101

3.4.3 Simultaneous Confidence Intervals on Regression Coefficients 102

3.5 Prediction of New Observations 106

3.6 A Multiple Regression Model for the Patient Satisfaction Data 106

3.7 Does Pitching and Defense Win Baseball Games? 108

3.8 Using SAS and R for Basic Multiple Linear Regression 110

3.9 Hidden Extrapolation in Multiple Regression 111

3.10 Standardized Regression Coefficients 115

3.11 Multicollinearity 121

3.12 Why Do Regression Coefficients Have the Wrong Sign? 123

Problems 125

4. Model Adequacy Checking 134

4.1 Introduction 134

4.2 Residual Analysis 135

4.2.1 Definition of Residuals 135

4.2.2 Methods for Scaling Residuals 135

4.2.3 Residual Plots 141

4.2.4 Partial Regression and Partial Residual Plots 148

4.2.5 Using Minitab(r), SAS, and R for Residual Analysis 151

4.2.6 Other Residual Plotting and Analysis Methods 154

4.3 PRESS Statistic 156

4.4 Detection and Treatment of Outliers 157

4.5 Lack of Fit of