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010 a| 2011008381;

020 a| 9780470744611 (hardback);

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042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| T57.5b| .G66 2011;

082 00 a| 670.2852| 22;

084 a| SCI0280002| bisacsh;

100 1 a| Goos, Peter.=| ^A537241;

245 10 a| Optimal design of experimentsh| [electronic resource] :b| a case study approach /c| Peter Goos and Bradley Jones.;

260 a| Hoboken, N.J. :b| Wiley,;

300 a| xiv, 287 p. :b| ill. ;c| 24 cm.;

504 a| Includes bibliographical references (p. [277]-282) and index.;

506 a| Available only to authorized users.;

520 a| "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"--c| Provided by publisher.;

520 a| "This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Industrial engineeringx| Experimentsx| Computer-aided design.=| ^A17108;

650 0 a| Experimental designx| Data processing.=| ^A23695;

650 0 a| Industrial engineeringv| Case studies.=| ^A17108;

650 7 a| SCIENCE / Experiments & Projects2| bisacsh.;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Jones, Bradley.=| ^A815792;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=697607;

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Optimal design of experiments a case study approach / Peter Goos and Bradley Jones.

Author/creator	Goos, Peter
Other author	Jones, Bradley.
Format	Electronic
Publication Info	Hoboken, N.J. : Wiley,
Description	xiv, 287 p. : ill. ; 24 cm.
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Industrial engineering--Experiments. --Computer-aided design. Experimental design--Data processing. Industrial engineering--Case studies.

Abstract	"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"-- Provided by publisher.
Abstract	"This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"-- Provided by publisher.
Bibliography note	Includes bibliographical references (p. [277]-282) and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2011008381
ISBN	9780470744611 (hardback)

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