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Optimal Design of Experiments A Case Study Approach

ISBN-10: 0470744618
ISBN-13: 9780470744611
Edition: 2011
List price: $61.50 Buy it from $49.73 Rent it from $33.45
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Description: This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate?  More...

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Study Briefs

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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.

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Book details

List price: $61.50
Copyright year: 2011
Publisher: John Wiley & Sons, Limited
Publication date: 7/1/2011
Binding: Hardcover
Pages: 304
Size: 6.50" wide x 9.50" long x 1.00" tall
Weight: 1.474

This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain. The structure of the book is organized around the following chapters: 1) Introduction explaining the concept of tailored DOE. 2) Basics of optimal design. 3) Nine case studies dealing with the above questions using the flow: description → design → analysis → optimization or engineering interpretation. 4) Summary. 5) Technical appendices for the mathematically curious.

Preface
Acknowledgments
A simple comparative experiment
Key concepts
The setup of a comparative experiment
Summary
An optimal screening experiment
Key concepts
Case: an extraction experiment
Problem and design
Data analysis
Peek into the black box
Main-effects models
Models with two-factor interaction effects
Factor scaling
Ordinary least squares estimation
Significance tests and statistical power calculations
Variance inflation
Aliasing
Optimal design
Generating optimal experimental designs
The extraction experiment revisited
Principles of successful screening: sparsity, hierarchy, and heredity
Background reading
Screening
Algorithms for finding optimal designs
Summary
Adding runs to a screening experiment
Key concepts
Case: an augmented extraction experiment
Problem and design
Data analysis
Peek into the black box
Optimal selection of a follow-up design
Design construction algorithm
Foldover designs
Background reading
Summary
A response surface design with a categorical factor
Key concepts
Case: a robust and optimal process experiment
Problem and design
Data analysis
Peek into the black box
Quadratic effects
Dummy variables for multilevel categorical factors
Computing D-efficiencies
Constructing Fraction of Design Space plots
Calculating the average relative variance of prediction
Computing I-efficiencies
Ensuring the validity of inference based on ordinary least squares
Design regions
Background reading
Summary
A response surface design in an irregularly shaped design region
Key concepts
Case: the yield maximization experiment
Problem and design
Data analysis
Peek into the black box
Cubic factor effects
Lack-of-fit test
Incorporating factor constraints in the design construction algorithm
Background reading
Summary
A "mixture" experiment with process variables
Key concepts
Case: the rolling mill experiment
Problem and design
Data analysis
Peek into the black box
The mixture constraint
The effect of the mixture constraint on the model
Commonly used models for data from mixture experiments
Optimal designs for mixture experiments
Design construction algorithms for mixture experiments
Background reading
Summary
A response surface design in blocks
Key concepts
Case: the pastry dough experiment
Problem and design
Data analysis
Peek into the black box
Model
Generalized least squares estimation
Estimation of variance components
Significance tests
Optimal design of blocked experiments
Orthogonal blocking
Optimal versus orthogonal blocking
Background reading
Summary
A screening experiment in blocks
Key concepts
Case: the stability improvement experiment
Problem and design
Afterthoughts about the design problem
Data analysis
Peek into the black box
Models involving block effects
Fixed block effects
Background reading
Summary
Experimental design in the presence of covariates
Key concepts
Case: the polypropylene experiment
Problem and design
Data analysis
Peek into the black box
Covariates or concomitant variables
Models and design criteria in the presence of covariates
Designs robust to time trends
Design construction algorithms
To randomize or not to randomize
Final thoughts
Background reading
Summary
A split-plot design
Key concepts
Case: the wind tunnel experiment
Problem and design
Data analysis
Peek into the black box
Split-plot terminology
Model
Inference from a split-plot design
Disguises of a split-plot design
Required number of whole plots and runs
Optimal design of split-plot experiments
A design construction algorithm for optimal split-plot designs
Difficulties when analyzing data from split-plot experiments
Background reading
Summary
A two-way split-plot design
Key concepts
Case: the battery cell experiment
Problem and design
Data analysis
Peek into the black box
The two-way split-plot model
Generalized least squares estimation
Optimal design of two-way split-plot experiments
A design construction algorithm for D-optimal two-way split-plot designs
Extensions and related designs
Background reading
Summary
Bibliography
Index

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