Modern Experimental Design / Edition 1

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Using current research and discussion of the topic along with clear applications, Modern Experimental Design highlights the guiding role of statistical principles in experimental design construction. This text can serve as both an applied introduction as well as a concise review of the essential types of experimental designs and their applications. Topical coverage includes designs containing one or multiple factors, designs with at least one blocking factor, split-unit designs and their variations as well as supersaturated and Plackett-Burman designs. In addition, the text contains extensive treatment of: Conditional effects analysis as a proposed general method of analysis, Multiresponse optimization, Space-filling designs, including Latin hypercube and uniform designs, Restricted regions of operability and debarred observations, Analysis of Means (ANOM) used to analyze data from various types of designs, The application of available software, including Design-Expert[Registered], JMP[Registered], and MINITAB[Registered].

This text provides thorough coverage of the topic while also introducing the reader to new approaches. Using a large number of references with detailed analyses of datasets, Modern Experimental Design works as a well-rounded learning tool for beginners as well as a valuable resource for practitioners.

About the Author:
Thomas P. Ryan, PhD, an elected Fellow of the American Statistical Association

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Editorial Reviews

Statistical Papers
A good introduction to statistical design of experiments, covering a wide variety of topics in a well readable and structured way.
Journal of the American Statistical Association
Modern Experimental Design is a must-have reference for anyone who will be designing experiments or for statisticians interested in remaining on the leading edge of this important area.
Computing Reviews
This attractive text is written in a precise style that interconnects and builds on discussion, examples, and methods from chapter to chapter. Especially pleasant are the care and attention devoted to details. The comprehensive and easy-to-read style of writing suggests that statistics is fun and exploratory.
... this book will prove to be a boon for advances in experimental design. --Zentralblatt
. . . the author's wealth of knowledge is immediately evident . . . an excellent expose concerning the actual statistical planning or 'design' of experiments.
A very interesting and useful book . . . highly recommended.
From the Publisher
"A good introduction to statistical design of experiments, covering a wide variety of topics in a  well readable and structured way." (Statistical Papers 2008)

"Modern Experimental Design is a must-have reference for anyone who will be designing experiments or for statisticians interested in remaining on the leading edge of this important area." (Journal of the American Statistical Association)

"This attractive text is written in a precise style that interconnects and builds on discussion, examples, and methods from chapter to chapter. Especially pleasant are the care and attention devoted to details. The comprehensive and easy-to-read style of writing suggests that statistics is fun and exploratory." (Computing Reviews, 2008)

"... this book will prove to be a boon for advances in experimental design." (Zentralblatt MATH, 2007)

"…the author's wealth of knowledge is immediately evident…an excellent expose concerning the actual statistical planning or 'design' of experiments." (Biometrics, September 2007)

"A very interesting and useful book…highly recommended." (CHOICE, August 2007)

"It is definitely nice to have such a book in the library." (International Statistical Review, 2007)

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Product Details

  • ISBN-13: 9780471210771
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 1/9/2007
  • Series: Wiley Series in Probability and Statistics Series , #401
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 593
  • Product dimensions: 6.56 (w) x 9.51 (h) x 1.39 (d)

Meet the Author

THOMAS P. RYAN, PhD, has served on the Editorial Review Board of the Journal of Quality Technology since 1990. He is the author of three other books published by Wiley, and is an elected Fellow of the American Statistical Association, the American Society for Quality, and the Royal Statistical Society. He is currently teaching advanced courses on design of experiments and engineering statistics at, and serves as a consultant to Cytel Software Corporation.
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Table of Contents

Preface     xv
Introduction     1
Experiments All Around Us     2
Objectives for Experimental Designs     3
Planned Experimentation versus Use of Observational Data     5
Basic Design Concepts     6
Randomization     6
Replication versus Repeated Measurements     7
Example     8
Size of an Effect That Can be Detected     11
Terminology     12
Steps for the Design of Experiments     13
Recognition and Statement of the Problem     14
Selection of Factors and Levels     14
Choice of Factors     14
Choice of Levels     15
Processes Should Ideally be in a State of Statistical Control     18
Types of Experimental Designs     20
Analysis of Means     20
Missing Data     22
Experimental Designs and Six Sigma     22
Quasi-Experimental Design     23
Summary     23
References     23
Exercises     26
Completely Randomized Design     31
Completely Randomized Design     31
Model     32
Example: One Factor, Two Levels     33
Assumptions     33
Examples: One Factor, More Than Two Levels     35
Multiple Comparisons     36
Unbalanced and Missing Data     39
Computations     40
Example Showing the Effect of Unequal Variances     41
Analysis of Means     42
ANOM for a Completely Randomized Design     43
Example     44
ANOM with Unequal Variances     45
Applications     47
Nonparametric ANOM     47
ANOM for Attributes Data     47
Software for Experimental Design     48
Missing Values     48
Summary     48
Appendix     49
References     49
Exercises     51
Designs that Incorporate Extraneous (Blocking) Factors     56
Randomized Block Design     56
Assumption     57
Blocking an Out-of-Control Process     60
Efficiency of a Randomized Block Design     61
Example     61
Critique     63
ANOM     64
Incomplete Block Designs     65
Balanced Incomplete Block Designs     65
Analysis      66
Recovery of Interblock Information     68
ANOM     68
Partially Balanced Incomplete Block Designs     69
Lattice Design     70
Nonparametric Analysis for Incomplete Block Designs     70
Other Incomplete Block Designs     70
Latin Square Design     71
Assumptions     72
Model     74
Example     74
Efficiency of a Latin Square Design     77
Using Multiple Latin Squares     77
ANOM     79
Graeco-Latin Square Design     80
Model     80
Degrees of Freedom Limitations on the Design Construction     81
Sets of Graeco-Latin Square Designs     82
Application     82
ANOM     83
Youden Squares     84
Model     85
Lists of Youden Designs     86
Using Replicated Youden Designs     86
Analysis     86
Missing Values     86
Software     89
Summary     90
References     91
Exercises     93
Full Factorial Designs with Two Levels     101
The Nature of Factorial Designs      101
The Deleterious Effects of Interactions     106
Conditional Effects     107
Sample Sizes for Conditional Effects Estimation     113
Can We "Transform Away" Interactions?     114
Effect Estimates     114
Why Not One-Factor-at-a-Time Designs?     115
ANOVA Table for Unreplicated Two-Factor Design?     116
The 2 3 Design     119
Built-in Replication     122
Multiple Readings versus Replicates     123
Reality versus Textbook Examples     124
Factorial Design but not "Factorial Model"     124
Bad Data in Factorial Designs     127
ANOM Display     134
Normal Probability Plot Methods     136
Missing Data in Factorial Designs     138
Resulting from Bad Data     139
Proposed Solutions     140
Inaccurate Levels in Factorial Designs     140
Checking for Statistical Control     141
Blocking 2k Designs     142
The Role of Expected Mean Squares in Experimental Design     144
Hypothesis Tests with Only Random Factors in 2k Designs? Avoid Them!     146
Hierarchical versus Nonhierarchical Models     147
Hard-to-Change Factors      148
Software for Designs with Hard-to-Change Factors     150
Factors Not Reset     150
Detecting Dispersion Effects     150
Software     151
Summary     151
Derivation of Conditional Main Effects     152
Relationship Between Effect Estimates and Regression Coefficients     153
Precision of the Effect Estimates     153
Expected Mean Squares for the Replicated 22 Design     153
Expected Mean Squares, in General     155
References     157
Exercises     162
Fractional Factorial Designs with Two Levels     169
2k-1 Designs     170
Which Fraction?     176
Effect Estimates and Regression Coefficients     177
Alias Structure     177
What if I Had Used the Other Fraction?     179
2k-1 Designs     181
Basic Concepts     185
Designs with k - p = 16     187
Normal Probability Plot Methods when k - p = 16     187
Other Graphical Methods     188
Utility of Small Fractional Factorials vis-a-vis Normal Probability Plots     188
Design Efficiency     190
Retrieving a Lost Defining Relation      190
Minimum Aberration Designs and Minimum Confounded Effects Designs     192
Blocking Factorial Designs     194
Blocking Fractional Factorial Designs     195
Blocks of Size 2     200
Foldover Designs     201
Semifolding     203
Conditional Effects     208
Semifolding a 2k-1 Design     210
General Strategy?     215
Semifolding with Software     215
John's 3/4 Designs     216
Projective Properties of 2k-p Designs     219
Small Fractions and Irregular Designs     220
An Example of Sequential Experimentation     222
Critique of Example     224
Inadvertent Nonorthogonality-Case Study     225
Fractional Factorial Designs for Natural Subsets of Factors     226
Relationship Between Fractional Factorials and Latin Squares     228
Alternatives to Fractional Factorials     229
Designs Attributed to Genichi Taguchi     229
Missing and Bad Data     230
Plackett-Burman Designs     230
Software     230
Summary     233
References     234
Exercises     238
Designs With More Than Two Levels     248
3k Designs     248
Decomposing the A*B Interaction     251
Inference with Unreplicated 3k Designs     252
Conditional Effects     255
3k-p Designs     257
Understanding 3k-p Designs     259
Constructing 3k-p Designs     260
Alias Structure     262
Constructing a 3 3-1 Design     262
Need for Mixed Number of Levels     263
Replication of 3k-1 Designs?     264
Mixed Factorials     264
Constructing Mixed Factorials     265
Additional Examples     266
Mixed Fractional Factorials     274
Orthogonal Arrays with Mixed Levels     275
Minimum Aberration Designs and Minimum Confounded Effects Designs     277
Four or More Levels     278
Software     280
Catalog of Designs     284
Summary     284
References     284
Exercises     286
Nested Designs     291
Various Examples     294
Software Shortcomings     295
A Workaround     295
Staggered Nested Designs     298
Nested and Staggered Nested Designs with Factorial Structure     300
Estimating Variance Components     300
ANOM for Nested Designs?     302
Summary     302
References     302
Exercises     304
Robust Designs     311
"Taguchi Designs?"     312
Identification of Dispersion Effects     314
Designs with Noise Factors     316
Product Array, Combined Array, or Compound Array?     318
Software     320
Further Reading     322
Summary     322
References     323
Exercises     326
Split-Unit, Split-Lot, and Related Designs     330
Split-Unit Design     331
Split-Plot Mirror Image Pairs Designs     336
Split-Unit Designs in Industry     336
Split-Unit Designs with Fractional Factorials     340
Blocking Split-Plot Designs     342
Split-Unit Plackett-Burman Designs     343
Examples of Split-Plot Designs for Hard-to-Change Factors     343
Split-Split-Plot Designs     345
Split-Lot Design     345
Strip-Plot Design     346
Applications of Strip-Block (Strip-Plot) Designs      347
Commonalities and Differences Between these Designs     349
Software     350
Summary     351
References     351
Exercises     354
Response Surface Designs     360
Response Surface Experimentation: One Design or More Than One?     362
Which Designs?     364
Classical Response Surface Designs versus Alternatives     364
Effect Estimates?     369
Method of Steepest Ascent (Descent)     370
Central Composite Designs     373
CCD Variations     377
Small Composite Designs     377
Draper-Lin Designs     378
Additional Applications     383
Properties of Space-Filling Designs     384
Applications of Uniform Designs     386
Box-Behnken Designs     386
Application     388
Conditional Effects?     389
Other Response Surface Designs     390
Hybrid Designs     390
Uniform Shell Designs     393
Koshal Designs     393
Hoke Designs     394
Blocking Response Surface Designs     394
Blocking Central Composite Designs     394
Blocking Box-Behnken Designs     396
Blocking Other Response Surface Designs     396
Comparison of Designs     397
Analyzing the Fitted Surface     398
Characterization of Stationary Points     401
Confidence Regions on Stationary Points     402
Ridge Analysis     403
Ridge Analysis with Noise Factors     404
Optimum Conditions and Regions of Operability     404
Response Surface Designs for Computer Simulations     404
ANOM with Response Surface Designs?     405
Further Reading     405
The Present and Future Direction of Response Surface Designs     406
Software     406
Catalogs of Designs     408
Summary     408
References     409
Exercises     414
Repeated Measures Designs     425
One Factor     426
The Example in Section 2.1.2     428
More Than One Factor     428
Crossover Designs     429
Designs for Carryover Effects     432
How Many Repeated Measures?     437
Further Reading     438
Software     438
Summary     439
References      439
Exercises     444
Multiple Responses     447
Overlaying Contour Plots     448
Seeking Multiple Response Optimization with Desirability Functions     449
Weight and Importance     451
Dual Response Optimization     452
Designs Used with Multiple Responses     452
Applications     453
Multiple Response Optimization Variations     463
The Importance of Analysis     469
Software     469
Summary     471
References     472
Exercises     474
Miscellaneous Design Topics     483
One-Factor-at-a-Time Designs     483
Cotter Designs     487
Rotation Designs     488
Screening Designs     489
Plackett-Burman Designs     489
Projection Properties of Plackett-Burman Designs     493
Applications     494
Supersaturated Designs     498
Applications     499
Lesser-Known Screening Designs     500
Design of Experiments for Analytic Studies     500
Equileverage Designs     501
One Factor, Two Levels     502
Are Commonly Used Designs Equileverage?     502
Optimal Designs     503
Alphabetic Optimality     504
Applications of Optimal Designs     507
Designs for Restricted Regions of Operability     508
Space-Filling Designs     514
Uniform Designs     515
From Raw Form to Coded Form     518
Sphere-Packing Designs     518
Latin Hypercube Design     519
Trend-Free Designs     521
Cost-Minimizing Designs     522
Mixture Designs     522
Optimal Mixture Designs or Not?     523
ANOM     523
Design of Measurement Capability Studies     523
Design of Computer Experiments     523
Design of Experiments for Categorical Response Variables     524
Weighing Designs and Calibration Designs     524
Calibration Designs     525
Weighing Designs     526
Designs for Assessing the Capability of a System     528
Designs for Nonlinear Models     528
Model-Robust Designs     528
Designs and Analyses for Non-normal Responses     529
Design of Microarray Experiments     529
Multi-Vari Plot      530
Evolutionary Operation     531
Software     531
Summary     532
References     533
Exercises     542
Tying It All Together     544
Training for Experimental Design Use     544
References     545
Exercises     546
Answers to Selected Exercises     551
Statistical Tables     565
Author Index     575
Subject Index     587
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