C. F. Jeff Wu, Michael S. Hamada

Experiments (eBook, ePUB)

Planning, Analysis, and Optimization

• Format: ePub

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 Fully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, Experiments, Second Edition introduces some of the newest discoveries--and sheds further light on existing ones--on the design and analysis of experiments and their applications in system optimization, robustness, and treatment comparison. Maintaining the same easy-to-follow style as the previous edition while also including modern updates, this book continues to present a new and integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, reliability improvement, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Along with a new chapter that focuses on regression analysis, the Second Edition features expanded and new coverage of additional topics, including: * Expected mean squares and sample size determination * One-way and two-way ANOVA with random effects * Split-plot designs * ANOVA treatment of factorial effects * Response surface modeling for related factors 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, Second 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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 760
• Erscheinungstermin: 20. September 2011
• Englisch
• ISBN-13: 9781118211533
• Artikelnr.: 37358715

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 130 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 Statistical Scientist at Los Alamos National Laboratory in New Mexico. Dr. Hamada is a Fellow of the American Statistical Association and the author of more than seventy published papers.

Inhaltsangabe

Preface to the Second Edition. Preface to the First Edition. Suggestions of
Topics for Instructors. List of Experiments and Data Sets. 1 Basic Concepts
for Experimental Design and Introductory Regression Analysis. 1.1
Introduction and Historical Perspective. 1.2 A Systematic Approach to the
Planning and Implementation of Experiments. 1.3 Fundamental Principles:
Replication, Randomization, and Blocking. 1.4 Simple Linear Regression. 1.5
Testing of Hypothesis and Interval Estimation. 1.6 Multiple Linear
Regression. 1.7 Variable Selection in Regression Analysis. 1.8 Analysis of
Air Pollution Data. 1.9 Practical Summary. 2 Experiments with a Single
Factor. 2.1 One-Way Layout. 2.2 Multiple Comparisons. 2.3 Quantitative
Factors and Orthogonal Polynomials. 2.4 Expected Mean Squares and Sample
Size Determination. 2.5 One-Way Random Effects Model. 2.6 Residual
Analysis: Assessment of Model Assumptions. 2.7 Practical Summary. 3
Experiments with More Than One Factor. 3.1 Paired Comparison Designs. 3.2
Randomized Block Designs. 3.3 Two-Way Layout: Factors With Fixed Levels.
3.4 Two-Way Layout: Factors With Random Levels. 3.5 Multi-Way Layouts. 3.6
Latin Square Designs: Two Blocking Variables. 3.7 Graeco-Latin Square
Designs. 3.8 Balanced Incomplete Block Designs. 3.9 Split-Plot Designs.
3.10 Analysis of Covariance: Incorporating Auxiliary Information. 3.11
Transformation of the Response. 3.12 Practical Summary. 4 Full Factorial
Experiments at Two Levels. 4.1 An Epitaxial Layer Growth Experiment. 4.2
Full Factorial Designs at Two Levels: A General Discussion. 4.3 Factorial
Effects and Plots. 4.4 Using Regression to Compute Factorial Effects. 4.5
ANOVA Treatment of Factorial Effects. 4.6 Fundamental Principles for
Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity.
4.7 Comparisons with the "One-Factor-at-a-Time" Approach. 4.8 Normal and
Half-Normal Plots for Judging Effect Significance. 4.9 Lenth's Method:
Testing Effect Significance for Experiments Without Variance Estimates.
4.10 Nominal-the-Best Problem and Quadratic Loss Function. 4.11 Use of Log
Sample Variance for Dispersion Analysis. 4.12 Analysis of Location and
Dispersion: Revisiting the Epitaxial Layer Growth Experiment. 4.13 Test of
Variance Homogeneity and Pooled Estimate of Variance. 4.14 Studentized
Maximum Modulus Test: Testing Effect Significance for Experiments with
Variance Estimates. 4.15 Blocking and Optimal Arrangement of 2^k Factorial
Designs in 2^q Blocks. 4.16 Practical Summary. 5 Fractional Factorial
Experiments at Two Levels. 5.1 A Leaf Spring Experiment. 5.2 Fractional
Factorial Designs: Effect Aliasing and the Criteria Of Resolution and
Minimum Aberration. 5.3 Analysis of Fractional Factorial Experiments. 5.4
Techniques for Resolving the Ambiguities in Aliased Effects. 5.5 Selection
of 2^k-p Designs Using Minimum Aberration and Related Criteria. 5.6
Blocking in Fractional Factorial Designs. 5.7 Practical Summary. 6 Full
Factorial and Fractional Factorial Experiments at Three Levels. 6.1 A
Seat-Belt Experiment. 6.2 Larger-the-Better and Smaller-the-Better
Problems. 6.3 3^k Full Factorial Designs. 6.4 3^k-p Fractional Factorial
Designs. 6.5 Simple Analysis Methods: Plots and Analysis of Variance. 6.6
An Alternative Analysis Method. 6.7 Analysis Strategies for Multiple
Responses I: Out-of-Spec Probabilities. 6.8 Blocking in 3^k and 3^k-p
Designs. 6.9 Practical Summary. 7 Other Design and Analysis Techniques for
Experiments at More Than Two Levels. 7.1 A Router Bit Experiment Based on a
Mixed Two-Level and Four-Level Design. 7.2 Method of Replacement and
Construction of 2m4n Designs. 7.3 Minimum Aberration 2m4n Designs with n =
1, 2. 7.4 An Analysis Strategy for 2m4n Experiments. 7.5 Analysis of the
Router Bit Experiment. 7.6 A Paint Experiment Based on a Mixed Two-Level
and Three-Level Design. 7.7 Design and Analysis of 36-Run Experiments at
Two And Three Levels. 7.8 r^k-p Fractional Factorial Designs for any Prime
Number r. 7.9 Related Factors: Method of Sliding Levels, Nested Effects
Analysis, and Response Surface Modeling. 7.10 Practical Summary. 8
Nonregular Designs: Construction and Properties. 8.1 Two Experiments:
Weld-Repaired Castings and Blood Glucose Testing. 8.2 Some Advantages of
Nonregular Designs Over the 2^k-p and 3^k-p Series of Designs. 8.3 A Lemma
on Orthogonal Arrays. 8.4 Plackett-Burman Designs and Hall's Designs. 8.5 A
Collection of Useful Mixed-Level Orthogonal Arrays. 8.6 Construction of
Mixed-Level Orthogonal Arrays Based on Difference Matrices. 8.7
Construction of Mixed-Level Orthogonal Arrays Through the Method of
Replacement. 8.8 Orthogonal Main-Effect Plans Through Collapsing Factors.
8.9 Practical Summary. 9 Experiments with Complex Aliasing. 9.1 Partial
Aliasing of Effects and the Alias Matrix. 9.2 Traditional Analysis
Strategy: Screening Design and Main Effect Analysis. 9.3 Simplification of
Complex Aliasing via Effect Sparsity. 9.4 An Analysis Strategy for Designs
with Complex Aliasing. 9.5 A Bayesian Variable Selection Strategy for
Designs with Complex Aliasing. 9.6 Supersaturated Designs: Design
Construction and Analysis. 9.7 Practical Summary. 10 Response Surface
Methodology. 10.1 A Ranitidine Separation Experiment. 10.2 Sequential
Nature of Response Surface Methodology. 10.3 From First-Order Experiments
to Second-Order Experiments: Steepest Ascent Search and Rectangular Grid
Search. 10.4 Analysis of Second-Order Response Surfaces. 10.5 Analysis of
the Ranitidine Experiment. 10.6 Analysis Strategies for Multiple Responses
II: Contour Plots and the Use of Desirability Functions. 10.7 Central
Composite Designs. 10.8 Box-Behnken Designs and Uniform Shell Designs. 10.9
Practical Summary. 11 Introduction to Robust Parameter Design. 11.1 A
Robust Parameter Design Perspective of the Layer Growth and Leaf Spring
Experiments. 11.2 Strategies for Reducing Variation. 11.3 Noise
(Hard-to-Control) Factors. 11.4 Variation Reduction Through Robust
Parameter Design. 11.5 Experimentation and Modeling Strategies I: Cross
Array. 11.6 Experimentation and Modeling Strategies II: Single Array and
Response Modeling. 11.7 Cross Arrays: Estimation Capacity and Optimal
Selection. 11.8 Choosing Between Cross Arrays and Single Arrays. 11.9
Signal-to-Noise Ratio and Its Limitations for Parameter Design
Optimization. 11.10 Further Topics. 11.11 Practical Summary. 12 Robust
Parameter Design for Signal-Response Systems. 12.1 An Injection Molding
Experiment. 12.2 Signal-Response Systems and their Classification. 12.3
Performance Measures for Parameter Design Optimization. 12.4 Modeling and
Analysis Strategies. 12.5 Analysis of the Injection Molding Experiment.
12.6 Choice of Experimental Plans. 12.7 Practical Summary. 13 Experiments
for Improving Reliability. 13.1 Experiments with Failure Time Data. 13.2
Regression Model for Failure Time Data. 13.3 A Likelihood Approach for
Handling Failure Time Data with Censoring. 13.4 Design-Dependent Model
Selection Strategies. 13.5 A Bayesian Approach to Estimation and Model
Selection for Failure Time Data. 13.6 Analysis of Reliability Experiments
with Failure Time Data. 13.7 Other Types of Reliability Data. 13.8
Practical Summary. 14 Analysis of Experiments with Nonnormal Data. 14.1 A
Wave Soldering Experiment with Count Data. 14.2 Generalized Linear Models.
14.3 Likelihood-Based Analysis of Generalized Linear Models. 14.4
Likelihood-Based Analysis of the Wave Soldering Experiment. 14.5 Bayesian
Analysis of Generalized Linear Models. 14.6 Bayesian Analysis of the Wave
Soldering Experiment. 14.7 Other Uses and Extensions of Generalized Linear
Models and Regression Models for Nonnormal Data. 14.8 Modeling and Analysis
for Ordinal Data. 14.9 Analysis of Foam Molding Experiment. 14.10 Scoring:
A Simple Method for Analyzing Ordinal Data. 14.11 Practical Summary.
Appendix A Upper Tail Probabilities of the Standard Normal Distribution.
Appendix B Upper Percentiles of the t Distribution. Appendix C Upper
Percentiles of the Dz Distribution. Appendix D Upper Percentiles of the F
Distribution. Appendix E Upper Percentiles of the Studentized Range
Distribution. Appendix F Upper Percentiles of the Studentized Maximum
Modulus Distribution. Appendix G Coefficients of Orthogonal Contrast
Vectors. Appendix H Critical Values for Lenth's Method. Author Index.
Subject Index.