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E-raamat: Experiments: Planning, Analysis, and Optimization

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Experiments: Planning, Analysis, and OptimizationSuurem pilt
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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 improvementthe 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.
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(44)
1.1 Introduction and Historical Perspective
1(3)
1.2 A Systematic Approach to the Planning and Implementation of Experiments
4(4)
1.3 Fundamental Principles: Replication, Randomization, and Blocking
8(3)
1.4 Simple Linear Regression
11(3)
1.5 Testing of Hypothesis and Interval Estimation
14(6)
1.6 Multiple Linear Regression
20(6)
1.7 Variable Selection in Regression Analysis
26(2)
1.8 Analysis of Air Pollution Data
28(6)
1.9 Practical Summary
34(1)
Exercises
35(8)
References
43(2)
2 Experiments with a Single Factor
45(40)
2.1 One-Way Layout
45(7)
*2.1.1 Constraint on the Parameters
50(2)
2.2 Multiple Comparisons
52(4)
2.3 Quantitative Factors and Orthogonal Polynomials
56(5)
2.4 Expected Mean Squares and Sample Size Determination
61(7)
2.5 One-Way Random Effects Model
68(3)
2.6 Residual Analysis: Assessment of Model Assumptions
71(5)
2.7 Practical Summary
76(1)
Exercises
77(5)
References
82(3)
3 Experiments with More than One Factor
85(66)
3.1 Paired Comparison Designs
85(3)
3.2 Randomized Block Designs
88(4)
3.3 Two-Way Layout: Factors with Fixed Levels
92(6)
3.3.1 Two Qualitative Factors: A Regression Modeling Approach
95(3)
3.4 Two-Way Layout: Factors with Random Levels
98(7)
3.5 Multi-Way Layouts
105(3)
3.6 Latin Square Designs: Two Blocking Variables
108(4)
3.7 Graeco-Latin Square Designs
112(1)
*3.8 Balanced Incomplete Block Designs
113(5)
*3.9 Split-Plot Designs
118(8)
3.10 Analysis of Covariance: Incorporating Auxiliary Information
126(4)
*3.11 Transformation of the Response
130(4)
3.12 Practical Summary
134(1)
Exercises
135(12)
Appendix 3A Table of Latin Squares, Graeco-Latin Squares, and Hyper-Graeco-Latin Squares
147(1)
References
148(3)
4 Full Factorial Experiments at Two Levels
151(54)
4.1 An Epitaxial Layer Growth Experiment
151(2)
4.2 Full Factorial Designs at Two Levels: A General Discussion
153(4)
4.3 Factorial Effects and Plots
157(8)
4.3.1 Main Effects
158(1)
4.3.2 Interaction Effects
159(6)
4.4 Using Regression to Compute Factorial Effects
165(2)
*4.5 ANOVA Treatment of Factorial Effects
167(1)
4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity
168(1)
4.7 Comparisons with the "One-Factor-at-a-Time" Approach
169(3)
4.8 Normal and Half-Normal Plots for Judging Effect Significance
172(2)
4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates
174(4)
4.10 Nominal-the-Best Problem and Quadratic Loss Function
178(1)
4.11 Use of Log Sample Variance for Dispersion Analysis
179(2)
4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment
181(3)
*4.13 Test of Variance Homogeneity and Pooled Estimate of Variance
184(1)
*4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments With Variance Estimates
185(3)
4.15 Blocking and Optimal Arrangement of 2* Factorial Designs in 2q Blocks
188(5)
4.16 Practical Summary
193(2)
Exercises
195(6)
Appendix 4A: Table of 2k Factorial Designs in 2q Blocks
201(2)
References
203(2)
5 Fractional Factorial Experiments at Two Levels
205(60)
5.1 A Leaf Spring Experiment
205(1)
5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria of Resolution and Minimum Aberration
206(6)
5.3 Analysis of Fractional Factorial Experiments
212(5)
5.4 Techniques for Resolving the Ambiguities in Aliased Effects
217(10)
5.4.1 Fold-Over Technique for Follow-Up Experiments
218(4)
5.4.2 Optimal Design Approach for Follow-Up Experiments
222(5)
5.5 Conditional Main Effect (CME) Analysis: A Method to Unravel Aliased Interactions
227(5)
5.6 Selection of 2k~p Designs Using Minimum Aberration and Related Criteria
232(4)
5.7 Blocking in Fractional Factorial Designs
236(2)
5.8 Practical Summary
238(2)
Exercises
240(12)
Appendix 5A Tables of 2k-p Fractional Factorial Designs
252(6)
Appendix 5B Tables of 2k-p Fractional Factorial Designs in 2q Blocks
258(4)
References
262(3)
6 Full Factorial and Fractional Factorial Experiments at Three Levels
265(50)
6.1 A Seat-Belt Experiment
265(2)
6.2 Larger-the-Better and Smaller-the-Better Problems
267(1)
6.3 3k Full Factorial Designs
268(5)
6.4 3k-p Fractional Factorial Designs
273(4)
6.5 Simple Analysis Methods: Plots and Analysis of Variance
277(5)
6.6 An Alternative Analysis Method
282(9)
6.7 Analysis Strategies for Multiple Responses I: Out-Of-Spec Probabilities
291(8)
6.8 Blocking in 3k and 3k-p Designs
299(2)
6.9 Practical Summary
301(2)
Exercises
303(6)
Appendix 6A Tables of 3k-p Fractional Factorial Designs
309(1)
Appendix 6B Tables of 3k-p Fractional Factorial Designs in 3k Blocks
310(4)
References
314(1)
7 Other Design and Analysis Techniques for Experiments at More than Two Levels
315(54)
7.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design
315(3)
7.2 Method of Replacement and Construction of 2m4" Designs
318(3)
7.3 Minimum Aberration 2m4" Designs with n = 1, 2
321(3)
7.4 An Analysis Strategy for 2m4" Experiments
324(2)
7.5 Analysis of the Router Bit Experiment
326(3)
7.6 A Paint Experiment Based on a Mixed Two-Level and Three-Level Design
329(3)
7.7 Design and Analysis of 36-Run Experiments at Two And Three Levels
332(5)
7.8 Rk-p Fractional Factorial Designs for any Prime Number r
337(4)
7.8.1 25-Run Fractional Factorial Designs at Five Levels
337(3)
7.8.2 49-Run Fractional Factorial Designs at Seven Levels
340(1)
7.8.3 General Construction
340(1)
7.9 Definitive Screening Designs
341(2)
*7.10 Related Factors: Method of Sliding Levels, Nested Effects Analysis, and Response Surface Modeling
343(9)
7.10.1 Nested Effects Modeling
346(1)
7.10.2 Analysis of Light Bulb Experiment
347(2)
7.10.3 Response Surface Modeling
349(3)
7.10.4 Symmetric and Asymmetric Relationships Between Related Factors
352(1)
7.11 Practical Summary
352(1)
Exercises
353(8)
Appendix 7A Tables of 2m4' Minimum Aberration Designs
361(1)
Appendix 7B Tables of 2m42 Minimum Aberration Designs
362(2)
Appendix 7C OA(25, 56)
364(1)
Appendix 7D OA(49, 78)
364(2)
Appendix 7E Conference Matrices C6, C8, CIO, C12, C14, and C16
366(2)
References
368(1)
8 Nonregular Designs: Construction and Properties
369(48)
8.1 Two Experiments: Weld-Repaired Castings and Blood Glucose Testing
369(1)
8.2 Some Advantages of Nonregular Designs Over the 2k-p AND 3k-p Series of Designs
370(2)
8.3 A Lemma on Orthogonal Arrays
372(1)
8.4 Plackett-Burman Designs and Hall's Designs
373(4)
8.5 A Collection of Useful Mixed-Level Orthogonal Arrays
377(2)
*8.6 Construction of Mixed-Level Orthogonal Arrays Based on Difference Matrices
379(3)
8.6.1 General Method for Constructing Asymmetrical Orthogonal Arrays
380(2)
*8.7 Construction of Mixed-Level Orthogonal Arrays Through the Method of Replacement
382(2)
8.8 Orthogonal Main-Effect Plans Through Collapsing Factors
384(4)
8.9 Practical Summary
388(1)
Exercises
389(5)
Appendix 8A Plackett-Burman Designs OA(N, 2N-1) with 12 ≤ N ≤ 48 and N = 4 k but not a Power of 2
394(3)
Appendix 8B Hall's 16-Run Orthogonal Arrays of Types II to V
397(2)
Appendix 8C Some Useful Mixed-Level Orthogonal Arrays
399(12)
Appendix 8D Some Useful Difference Matrices
411(2)
Appendix 8E Some Useful Orthogonal Main-Effect Plans
413(1)
References
414(3)
9 Experiments with Complex Aliasing
417(38)
9.1 Partial Aliasing of Effects and the Alias Matrix
417(3)
9.2 Traditional Analysis Strategy: Screening Design and Main Effect Analysis
420(1)
9.3 Simplification of Complex Aliasing via Effect Sparsity
421(1)
9.4 An Analysis Strategy for Designs with Complex Aliasing
422(7)
9.4.1 Some Limitations
428(1)
*9.5 A Bayesian Variable Selection Strategy for Designs with Complex Aliasing
429(8)
9.5.1 Bayesian Model Priors
431(1)
9.5.2 Gibbs Sampling
432(2)
9.5.3 Choice of Prior Tuning Constants
434(1)
9.5.4 Blood Glucose Experiment Revisited
435(2)
9.5.5 Other Applications
437(1)
*9.6 Supersaturated Designs: Design Construction and Analysis
437(4)
9.7 Practical Summary
441(1)
Exercises
442(9)
Appendix 9A Further Details for the Full Conditional Distributions
451(2)
References
453(2)
10 Response Surface Methodology
455(48)
10.1 A Ranitidine Separation Experiment
455(2)
10.2 Sequential Nature of Response Surface Methodology
457(3)
10.3 From First-Order Experiments to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid Search
460(9)
10.3.1 Curvature Check
460(1)
10.3.2 Steepest Ascent Search
461(5)
10.3.3 Rectangular Grid Search
466(3)
10.4 Analysis of Second-Order Response Surfaces
469(3)
10.4.1 Ridge Systems
470(2)
10.5 Analysis of the Ranitidine Experiment
472(3)
10.6 Analysis Strategies for Multiple Responses II: Contour Plots and the Use of Desirability Functions
475(3)
10.7 Central Composite Designs
478(5)
10.8 Box-Behnken Designs and Uniform Shell Designs
483(3)
10.9 Practical Summary
486(2)
Exercises
488(10)
Appendix 10A Table of Central Composite Designs
498(2)
Appendix 10B Table of Box-Behnken Designs
500(1)
Appendix 10C Table of Uniform Shell Designs
501(1)
References
502(1)
11 Introduction to Robust Parameter Design
503(50)
11.1 A Robust Parameter Design Perspective of the Layer Growth and Leaf Spring Experiments
503(3)
11.1.1 Layer Growth Experiment Revisited
503(1)
11.1.2 Leaf Spring Experiment Revisited
504(2)
11.2 Strategies for Reducing Variation
506(2)
11.3 Noise (Hard-to-Control) Factors
508(2)
11.4 Variation Reduction Through Robust Parameter Design
510(2)
11.5 Experimentation and Modeling Strategies I: Cross Array
512(11)
11.5.1 Location and Dispersion Modeling
513(5)
11.5.2 Response Modeling
518(5)
11.6 Experimentation and Modeling Strategies II: Single Array and Response Modeling
523(3)
11.7 Cross Arrays: Estimation Capacity and Optimal Selection
526(3)
11.8 Choosing Between Cross Arrays and Single Arrays
529(5)
*11.8.1 Compound Noise Factor
533(1)
11.9 Signal-to-Noise Ratio and Its Limitations for Parameter Design Optimization
534(5)
11.9.1 SN Ratio Analysis of Layer Growth Experiment
536(1)
*11.10 Further Topics
537(2)
11.11 Practical Summary
539(2)
Exercises
541(9)
References
550(3)
12 Analysis of Experiments with Nonnormal Data
553(36)
12.1 A Wave Soldering Experiment with Count Data
553(1)
12.2 Generalized Linear Models
554(4)
12.2.1 The Distribution of the Response
555(2)
12.2.2 The Form of the Systematic Effects
557(1)
12.2.3 GLM versus Transforming the Response
558(1)
12.3 Likelihood-Based Analysis of Generalized Linear Models
558(4)
12.4 Likelihood-Based Analysis of the Wave Soldering Experiment
562(2)
12.5 Bayesian Analysis of Generalized Linear Models
564(1)
12.6 Bayesian Analysis of the Wave Soldering Experiment
565(2)
12.7 Other Uses and Extensions of Generalized Linear Models and Regression Models for Nonnormal Data
567(1)
*12.8 Modeling and Analysis for Ordinal Data
567(5)
12.8.1 The Gibbs Sampler for Ordinal Data
569(3)
*12.9 Analysis of Foam Molding Experiment
572(3)
12.10 Scoring: A Simple Method for Analyzing Ordinal Data
575(1)
12.11 Practical Summary
576(1)
Exercises
577(10)
References
587(2)
13 Practical Optimal Design
589(22)
13.1 Introduction
589(1)
13.2 A Design Criterion
590(1)
13.3 Continuous and Exact Design
590(2)
13.4 Some Design Criteria
592(3)
13.4.1 Nonlinear Regression Model, Generalized Linear Model, and Bayesian Criteria
593(2)
13.5 Design Algorithms
595(3)
13.5.1 Point Exchange Algorithm
595(1)
13.5.2 Coordinate Exchange Algorithm
596(1)
13.5.3 Point and Coordinate Exchange Algorithms for Bayesian Designs
596(1)
13.5.4 Some Design Software
597(1)
13.5.5 Some Practical Considerations
597(1)
13.6 Examples
598(8)
13.6.1 A Quadratic Regression Model in One Factor
598(1)
13.6.2 Handling a Constrained Design Region
598(1)
13.6.3 Augmenting an Existing Design
598(2)
13.6.4 Handling an Odd-Sized Run Size
600(1)
13.6.5 Blocking from Initially Running a Subset of a Designed Experiment
601(4)
13.6.6 A Nonlinear Regression Model
605(1)
13.6.7 A Generalized Linear Model
605(1)
13.7 Practical Summary
606(1)
Exercises
607(1)
References
608(3)
14 Computer Experiments
611(36)
14.1 An Airfoil Simulation Experiment
611(2)
14.2 Latin Hypercube Designs (LHDs)
613(6)
14.2.1 Orthogonal Array-Based Latin Hypercube Designs
617(2)
14.3 Latin Hypercube Designs with Maximin Distance or Maximum Projection Properties
619(3)
14.4 Kriging: The Gaussian Process Model
622(3)
14.5 Kriging: Prediction and Uncertainty Quantification
625(6)
14.5.1 Known Model Parameters
626(1)
14.5.2 Unknown Model Parameters
627(2)
14.5.3 Analysis of Airfoil Simulation Experiment
629(2)
14.6 Expected Improvement
631(3)
14.6.1 Optimization of Airfoil Simulation Experiment
633(1)
14.7 Further Topics
634(2)
14.8 Practical Summary
636(1)
Exercises
637(6)
Appendix 14A Derivation of the Kriging Equations (14.10) and (14.11)
643(1)
Appendix 14B Derivation of the EI Criterion (14.22)
644(1)
References
645(2)
Appendix A Upper Tail Probabilities of the Standard Normal Distribution, &info;∞z 1/√2πe-u2/'2du 647(2)
Appendix B Upper Percentiles of the t Distribution 649(2)
Appendix C Upper Percentiles of the Χ2 Distribution 651(2)
Appendix D Upper Percentiles of the F Distribution 653(8)
Appendix E Upper Percentiles of the Studentized Range Distribution 661(8)
Appendix F Upper Percentiles of the Studentized Maximum Modulus Distribution 669(14)
Appendix G Coefficients of Orthogonal Contrast Vectors 683(2)
Appendix H Critical Values for Lenth's Method 685(4)
Author Index 689(4)
Subject Index 693
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.
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