Modern Experimental Design
Published on 20. August 2007
640 pages
978-0-470-07434-3 (ISBN)
Description
A complete and well-balanced introduction to modern experimentaldesign
Using current research and discussion of the topic along withclear applications, Modern Experimental Design highlightsthe guiding role of statistical principles in experimental designconstruction. This text can serve as both an applied introductionas well as a concise review of the essential types of experimentaldesigns and their applications.
Topical coverage includes designs containing one or multiplefactors, designs with at least one blocking factor, split-unitdesigns and their variations as well as supersaturated andPlackett-Burman designs. In addition, the text contains extensivetreatment of:
* Conditional effects analysis as a proposed general method ofanalysis
* Multiresponse optimization
* Space-filling designs, including Latin hypercube and uniformdesigns
* Restricted regions of operability and debarredobservations
* Analysis of Means (ANOM) used to analyze data from varioustypes of designs
* The application of available software, including Design-Expert,JMP, and MINITAB
This text provides thorough coverage of the topic while alsointroducing the reader to new approaches. Using a large number ofreferences with detailed analyses of datasets, ModernExperimental Design works as a well-rounded learning tool forbeginners as well as a valuable resource for practitioners.
Reviews / Votes
"A good introduction to statistical design of experiments, coveringa wide variety of topics in a well readable and structuredway." (Statistical Papers 2008) "Modern Experimental Design is a must-have reference for anyonewho will be designing experiments or for statisticians interestedin remaining on the leading edge of this important area."(Journal of the American Statistical Association) "This attractive text is written in a precise style thatinterconnects and builds on discussion, examples, and methods fromchapter to chapter. Especially pleasant are the care and attentiondevoted to details. The comprehensive and easy-to-read style ofwriting suggests that statistics is fun and exploratory."(Computing Reviews, 2008) "... this book will prove to be a boon for advances inexperimental design." (Zentralblatt MATH, 2007) "...the author's wealth of knowledge is immediatelyevident...an excellent expose concerning the actual statisticalplanning or 'design' of experiments." (Biometrics, September2007) "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)More details
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Thomas P. Ryan
Modern Experimental Design
Book
02/2007
Wiley
€182.50
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Thomas P. Ryan
Modern Experimental Design
Software
04/2006
Wiley
€176.79
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Person
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 statistics.com, and serves as a consultant to Cytel Software Corporation.
Content
Preface.
1. Introduction.
1.1 Experiments All Around Us.
1.2 Objectives for Experimental Designs.
1.3 Planned Experimentation versus use of Observational Data.
1.4 Basic Design Concepts.
1.5 Terminology.
1.6 Steps for the Design of Experiments.
1.7 Processes Should Ideally be in a State of Statistical Control.
1.8 Types of Experimental Designs.
1.9 Analysis of Means.
1.10 Missing Data.
1.11 Experimental Designs and Six Sigma.
1.12 Quasi-Experimental Design.
1.13 Summary.
2. Completely Randomized Design.
2.1 completely Randomized Design.
2.2 Analysis of Means.
2.3 Software for Experimental Design.
2.4 Missing Values.
2.5 Summary.
3. Designs that Incorporate Extraneous (Blocking) Factors.
3.1 Randomized Block Design.
3.2 Incomplete Block Designs.
Graeco-Latin Square Design.
3.5 Youden Squares.
3.6 Missing Values.
3.7 Software.
3.8 Summary.
4. Full Factorial Designs with Two Levels.
4.1 The nature of Factorial Designs.
4.2 The Deleterious Effects of Interactions.
4.3 Effect Estimates.
4.4 Why Not One-Factor-at-a-Time Designs?
4.5 ANOVA Table for Unreplicated Two-factor Design?
4.6 The 23 Design.
4.7 Built-in Replication.
4.8 Multiple Readings versus Replicates.
4.9 Reality versus Textbook Examples.
4.10 Bad data in Factorial Designs.
4.11 Normal Probability Plot Methods.
4.12 Missing Data in Factorial Designs.
4.13 Inaccurate Levels in Factorial Designs.
4.14 Checking for Statistical Control.
4.15 Blocking 2k Designs.
4.16 The Role of Expected Mean Squares in Experimental Design.
4.17 Hypothesis Tests with Only Random Factors in 2k Designs? Avoid Them!
4.18 Hierarchical versus Nonhierarchical Models.
4.19 Hard-to-Change factors.
4.20 Factors Not reset.
4.21 Detecting Dispersion Effects.
4.22 Software.
4.23 Summary.
5. Fractional factorial Designs with Two-Levels.
5.1 2k-1 Designs.
5.2 2k-2 Designs.
5.3 Designs with k & p = 16.
5.4 Utility of Small Fractional factorials vis-à-vis Normal Probability Plots.
5.5 Design Efficiency.
5.6 Retrieving a Lost Defining Relation.
5.7 Minimum Aberration Designs and Minimum Confounded Effects Designs.
5.8 Blocking Factorial Design.
5.9 Foldover Designs.
5.10 John's ¿ Designs.
5.11 Projective Properties of 2k-p Designs.
5.12 Small Fractions and Irregular Designs.
5.13 An Example of Sequential Experimentation.
5.14 Inadvertent Nonorthogonality-Case Study.
5.15 Fractional factorial Designs for Natural Subsets of factors.
5.16 Relationship Between Fractional Factorials and Latin Squares.
5.17 Alternatives to Fractional Factorials.
5.18 Missing and Bad data.
5.19 Plackett-Burman Designs.
5.20 Software.
5.21 Summary.
6. Designs With More Than Two Levels.
6.1 3k Designs.
6.2 Conditional Effects.
6.3 3k-p Designs.
6.4 Mixed factorials.
6.5 Mixed Fractional Factorials.
6.6 Orthogonal Arrays with Mixed levels.
6.7 Minimum Aberration Designs and Minimum Confounded Effects Designs.
6.8 Four or More Levels.
6.9 Software.
6.10 Catalog of Designs.
6.11 Summary.
7. Nested Designs.
7.1 Various Examples.
7.2 Software Shortcomings.
7.3 Staggered Nested Designs.
7.4 Nested and Staggered Nested Designs with factorial Structure.
7.5 Estimating Variance Components.
7.6 ANOM for Nested Designs?
7.7 Summary.
8. Robust Designs.
8.1 "Taguchi Designs?"
8.2 Identification of Dispersion Effects.
8.3 Designs with Noise factors.
8.4 Product Array, Combined Array, or Compound Array?
8.5 Software.
8.6 Further Reading.
8.7 Summary.
9. Split-Unit, Split-Lot, and Related Designs.
9.1 Split-Unit Design.
9.2 Split-Lot Design.
9.3 Commonalities and Differences Between these Designs.
9.4 Software.
9.5 Summary.
10. Response Surface Designs.
10.1 Response Surface Experimentation: One Design or More Than One?
10.2 Which Designs?
10.3 Classical Response Surface Designs versus Alternatives.
10.4 Methods of Steepest Ascent (Descent).
10.5 Central Composite Designs.
10.6 Properties of Space-Filling Designs.
10.7 Applications of Uniform Designs.
10.8 Box-Behnken Designs.
10.9 Conditional Effects?
10.10 Other Response Surface Designs.
10.11 Blocking Response Surface Designs.
10.12 Comparison of Designs.
10.13 Analyzing the Fitted Surface.
10.14 Response Surface Designs for Computer Simulations.
10.15 ANOM with Response Surface Designs?
10.16 Further reading.
10.17 The Present and Future Direction of response Surface Designs.
10.18 Software.
10.19 Catalogs of Designs.
10.20 Summary.
11. Repeated Measure Designs.
11.1 One factor.
11.2 More Than One Factor.
11.3 Crossover Designs.
11.4 Designs for Carryover Effects.
11.5 How Many Repeated Measures?
11.6 Further Reading.
11.7 Software.
11.8 Summary.
12. Multiple Responses.
12.1 Overlaying Contour Plots.
12.2 Seeking Multiple Response Optimization with Desirability Functions.
12.3 Dual response Optimization.
12.4 Designs Used with Multiple Responses.
12.5 Applications.
12.6 Multiple Response Optimization variations.
12.7 The Importance of Analysis.
12.8 Software.
12.9 Summary.
13. Miscellaneous Design Topics.
13.1 One-Factor-at-a-Time Designs.
13.2 Cotter Designs.
13.3 Rotation Designs.
13.4 Screening Designs.
13.5 Design of Experiments for Analytic Studies.
13.6 Equileverage Designs.
13.7 Optimal Designs.
13.8 Designs for Restricted Regions of Operability.
13.9 Space-Filling, Designs.
13.10 Trend-Free Designs.
13.11 Cost-Minimizing Designs.
13.12 Mixture Designs.
13.13 Design of Measurement Capability Studies.
13.14 Design of Computer Experiments.
13.15 Design of Experiments for Categorical Response variables.
13.16 Weighing Designs and Calibration Designs.
13.17 Designs for Assessing the Capability of a System.
13.18 Designs for Nonlinear Models.
13.19 Model-Robust Designs.
13.20 Designs and Analyses for Non-normal Responses.
13.21 Design of Microarray Experiments.
13.22 Multi-Vari Plot.
13.23 Evolutionary Operation.
13.24 Software.
13.25 Summary.
14. Tying It All Together.
14.1 Training for Experimental Design Use.
Answers to Selected Exercises.
Appendix: Statistical Tables.
Author Inde.
Subject Index.
