C. F. Jeff Wu, Michael S. Hamada

Experiments

Planning, Analysis, and Optimization

• Gebundenes Buch

Produktbeschreibung

Praise for the First Edition:

"If you ... want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library."
--Journal of the American Statistical Association

A COMPREHENSIVE REVIEW OF MODERN EXPERIMENTAL DESIGN

Experiments: Planning, Analysis, and Optimization, Third Edition provides a complete discussion of modern experimental design for product and process improvement--the design and analysis of experiments and their applications for system optimization, robustness, and treatment comparison. While maintaining the same easy-to-follow style as the previous editions, this book continues to present an integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. New chapters provide modern updates on practical optimal design and computer experiments, an explanation of computer simulations as an alternative to physical experiments. Each chapter begins with a real-world example of an experiment followed by the methods required to design that type of experiment. The chapters conclude with an application of the methods to the experiment, bridging the gap between theory and practice.

The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays.

The third edition includes:
_ Information on the design and analysis of computer experiments
_ A discussion of practical optimal design of experiments
_ An introduction to conditional main effect (CME) analysis and definitive screening designs (DSDs)
_ New exercise problems

This book includes valuable exercises and problems, allowing the reader to gauge their progress and retention of the book's subject matter as they complete each chapter.

Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study.

Experiments: Planning, Analysis, and Optimization, Third Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.

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Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119470100
• 3. Aufl.
• Seitenzahl: 736
• Erscheinungstermin: 30. März 2021
• Englisch
• Abmessung: 231mm x 160mm x 33mm
• Gewicht: 962g
• ISBN-13: 9781119470106
• ISBN-10: 1119470102
• Artikelnr.: 58457486

Autorenporträt

C. F. JEFF WU, PHD, is Coca-Cola Professor in Engineering Statistics at the Georgia Institute of Technology. Dr. Wu has published more than 180 papers and is the recipient of numerous accolades, including the National Academy of Engineering membership and the COPSS Presidents' Award. MICHAEL S. HAMADA, PHD, is Senior Scientist at Los Alamos National Laboratory (LANL) in New Mexico. Dr. Hamada is a Fellow of the American Statistical Association, a LANL Fellow, and has published more than 160 papers.

Inhaltsangabe

Preface to the Third Edition xvii

Preface to the Second Edition xix

Preface to the First Edition xxi

Suggestions of Topics for Instructors xxv

List of Experiments and Data Sets xxvii

About the Companion Website xxxiii

1 Basic Concepts for Experimental Design and Introductory Regression Analysis 1

1.1 Introduction and Historical Perspective 1

1.2 A Systematic Approach to the Planning and Implementation of Experiments 4

1.3 Fundamental Principles: Replication, Randomization, and Blocking 8

1.4 Simple Linear Regression 11

1.5 Testing of Hypothesis and Interval Estimation 14

1.6 Multiple Linear Regression 20

1.7 Variable Selection in Regression Analysis 26

1.8 Analysis of Air Pollution Data 28

1.9 Practical Summary 34

Exercises 35

References 43

2 Experiments with a Single Factor 45

2.1 One-Way Layout 45

*2.1.1 Constraint on the Parameters 50

2.2 Multiple Comparisons 52

2.3 Quantitative Factors and Orthogonal Polynomials 56

2.4 Expected Mean Squares and Sample Size Determination 61

2.5 One-Way Random Effects Model 68

2.6 Residual Analysis: Assessment of Model Assumptions 71

2.7 Practical Summary 76

Exercises 77

References 82

3 Experiments with More than One Factor 85

3.1 Paired Comparison Designs 85

3.2 Randomized Block Designs 88

3.3 Two-Way Layout: Factors with Fixed Levels 92

3.3.1 Two Qualitative Factors: A Regression Modeling Approach 95

*3.4 Two-Way Layout: Factors with Random Levels 98

3.5 Multi-Way Layouts 105

3.6 Latin Square Designs: Two Blocking Variables 108

3.7 Graeco-Latin Square Designs 112

*3.8 Balanced Incomplete Block Designs 113

*3.9 Split-Plot Designs 118

3.10 Analysis of Covariance: Incorporating Auxiliary Information 126

*3.11 Transformation of the Response 130

3.12 Practical Summary 134

Exercises 135

Appendix 3A: Table of Latin Squares, Graeco-Latin Squares, and Hyper-Graeco-Latin Squares 147

References 148

4 Full Factorial Experiments at Two Levels 151

4.1 An Epitaxial Layer Growth Experiment 151

4.2 Full Factorial Designs at Two Levels: A General Discussion 153

4.3 Factorial Effects and Plots 157

4.3.1 Main Effects 158

4.3.2 Interaction Effects 159

4.4 Using Regression to Compute Factorial Effects 165

*4.5 ANOVA Treatment of Factorial Effects 167

4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity 168

4.7 Comparisons with the "One-Factor-at-a-Time" Approach 169

4.8 Normal and Half-Normal Plots for Judging Effect Significance 172

4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates 174

4.10 Nominal-the-Best Problem and Quadratic Loss Function 178

4.11 Use of Log Sample Variance for Dispersion Analysis 179

4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment 181

*4.13 Test of Variance Homogeneity and Pooled Estimate of Variance 184

*4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments With Variance Estimates 185

4.15 Blocking and Optimal Arrangement of 2¯k Factorial Designs in 2¯q Blocks 188

4.16 Practical Summary 193

Exercises 195

Appendix 4A: Table of 2¯k Factorial Designs in 2¯q Blocks 201

Refere