Methods and Applications of Linear Models: Regression and the Analysis of Variance / Edition 3

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Overview


Praise for the Second Edition
“An essential desktop reference book . . . it should definitely be on your bookshelf.” —Technometrics

A thoroughly updated book, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition features innovative approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for the methods being discussed.

The book presents a unique discussion that combines coverage of mathematical theory of linear models with analysis of variance models, providing readers with a comprehensive understanding of both the theoretical and technical aspects of linear models. With a new focus on fixed effects models, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition also features:

  • Newly added topics including least squares, the cell means model, and graphical inspection of data in the AVE method
  • Frequent conceptual and numerical examples for clarifying the statistical analyses and demonstrating potential pitfalls
  • Graphics and computations developed using JMP® software to accompany the concepts and techniques presented
  • Numerous exercises presented to test readers and deepen their understanding of the material

An ideal book for courses on linear models and linear regression at the undergraduate and graduate levels, the Third Edition of Methods and Applications of Linear Models: Regression and the Analysis of Variance is also a valuable reference for applied statisticians and researchers who utilize linear model methodology.

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Editorial Reviews

Booknews
A summary of the concepts and methodologies of linear models, containing chapter exercises and real-world examples. Offers a unified treatment of linear regression and the analysis of variance, focusing on the appropriate interpretation of results, with material on mixed models that has not previously appeared in the literature. Features a data-based approach to development and analysis, use of the cell means model, and an introduction to the AVE model. For upper-level undergraduate and graduate students . Annotation c. Book News, Inc., Portland, OR (booknews.com)
From the Publisher
“...presents a thorough treatment of the concepts and methods of linear model analysis and illustrates them with numerical and conceptual examples...” (Quarterly of Applied Mathematics, Vol. LXII, No. 1, March 2004)

"...an essential desktop reference book...it should definitely be on your bookshelf." (Technometrics, Vol. 45, No. 4, November 2003)

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Product Details

Meet the Author

RONALD R. HOCKING, PhD, is Professor Emeritus in the Department of Statistics and Founder of the Ronald R. Hocking Lecture Series at Texas A&M University. A Fellow of the American Statistical Association, Dr. Hocking is the recipient of numerous honors in the statistical community including the Shewell Award, the Youden Award, the Wilcoxon Award, the Snedecor Award, and the Owen Award.

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Table of Contents

Preface to the Third Edition xvii

Preface to the Second Edition xix

Preface to the First Edition xxi

Part I Regression 1

1 Introduction to Linear Models 3

1.1 Background Information 3

1.2 Mathematical and Statistical Models 5

1.3 Definition of the Linear Model 8

1.4 Examples of Regression Models 13

Exercises 22

2 Regression on Functions of One Variable 25

2.1 The Simple Linear Regression Model 25

2.2 Parameter Estimation 27

2.3 Properties of the Estimators and Test Statistics 36

2.4 Analysis of Simple Linear Regression Models 41

2.5 Examining the Data and the Model 52

2.6 Polynomial Regression Models 69

Exercises 78

3 Transforming the Data 87

3.1 The Need for Transformations 87

3.2 Weighted Least Squares 88

3.3 Variance Stabilizing Transformations 91

3.4 Transformations to Achieve a Linear Model 92

3.5 Analysis of the Transformed Model 98

Exercises 101

4 Regression on Functions of Several Variables 105

4.1 The Multiple Linear Regression Model 105

4.2 Preliminary Data Analysis 106

4.3 Analysis of the Multiple Linear Regression Model 109

4.4 Partial Correlation and Added-Variable Plots 120

4.5 Variable Selection 128

4.6 Model Specification 139

Exercises 146

5 Collinearity in Multiple Linear Regression 151

5.1 Collinearity Problem 151

5.2 An Example With Collinearity 160

5.3 Collinearity Diagnostics 166

5.4 Remedial Solutions: Biased Estimators 177

Exercises 188

6 Influential Observations in Multiple Linear Regression 193

6.1 Influential Data Problem 193

6.2 The Hat Matrix 194

6.3 The Effects of Deleting Observations 199

6.4 Numerical Measures of Influence 203

6.5 Dilemma Data 207

6.6 Plots for Identifying Unusual Cases 213

6.7 Robust/Resistant Methods in Regression Analysis 221

Exercises 225

7 Polynomial Models and Qualitative Predictors 229

7.1 Polynomial Models 229

7.2 The Analysis of Response Surfaces 234

7.3 Models with Qualitative Predictors 238

Exercises 263

8 Additional Topics 271

8.1 Non-Linear Regression Models 271

8.2 Non-Parametric Model-Fitting Methods 277

8.3 Generalized Linear Models 282

8.4 Random Input Variables 290

8.5 Errors in the Inputs 293

8.6 Calibration 294

Exercises 295

Part II Analysis of Variance 299

9 CLASSIFICATION MODELS I: INTRODUCTION 301

9.1 Background Information 301

9.2 The One-Way Classification Model 302

9.3 The Two-Way Classification Model: Balanced Data 320

9.4 The Two-Way Classification Model: Unbalanced Data 337

9.5 The Two-Way Classification Model: No Interaction 349

Exercises 362

10 The Mathematical Theory of Linear Models 375

10.1 The Distribution of Linear and Quadratic Forms 375

10.2 Estimation and Inference for Linear Models 383

10.3 Test of Linear Hypotheses on β 395

10.4 Confidence Regions and Intervals 407

Exercises 410

11 Classification Models II: Multiple Crossed and Nested Factors 419

11.1 The Three-Factor Cross-Classified Model 420

11.2 A General Structure for Balanced, Factorial Models 427

11.3 The Two-Fold Nested Model 432

11.4 A General Structure for Balanced, Nested Models 441

11.5 A Three-Factor, Nested Factorial Model 443

11.6 A General Structure for Balanced, Nested-Factorial Mod4e4ls8

Exercises 452

12 Mixed Models I: The AOV Method with Unbalanced Data 457

12.1 Introduction 457

12.2 Examples of the Analysis of Mixed Models 458

12.3 The General Analysis for Balanced, Mixed Models 478

12.4 Additional Examples 492

12.5 Alternative Developments for Mixed Models 500

Exercises 506

13 Mixed Models II: The AVE Method with Balanced Data 511

13.1 Introduction 511

13.2 The Two-Way Cross-Classification Model 512

13.3 The Three-Factor, Cross-Classification Model 524

13.4 Nested Models 529

13.5 Nested Factorial Models 532

13.6 A General Description of the AVE Table 537

13.7 Additional Examples 545

13.8 The Computational Procedure for the AVE Method 551

Exercises 552

14 Mixed Models III: Unbalanced Data 557

14.1 Introduction 557

14.2 Parameter Estimation: Likelihood Methods 559

14.3 ML and REML Estimates with Balanced Data 567

14.4 EM Algorithm for REML Estimation 572

14.5 Diagnostic Analysis with the EM Algorithm 584

14.6 Models with Covariates 593

Exercises 597

15 Simultaneous Inference: Tests and Confidence Intervals 603

15.1 Simultaneous Tests 603

15.2 Simultaneous Confidence Intervals 623

Exercises 625

Appendix A: Mathematics 627

A.I Matrix Algebra 627

A.II Optimization 642

Appendix B: Statistics 647

B.I Distributions 647

B.II The Distribution of Quadratic Forms 651

B.III Estimation 654

B.IV Tests of Hypotheses and Confidence Intervals 656

Appendix C: Data Tables 659

References 675

Index 685

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