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  <titleInfo>
    <title>Optimal design of experiments</title>
    <subTitle>a case study approach</subTitle>
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  <name type="personal">
    <namePart>Goos, Peter.</namePart>
    <role>
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  <name type="personal">
    <namePart>Jones, Bradley.</namePart>
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  <name type="corporate">
    <namePart>ebrary, Inc</namePart>
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    <place>
      <placeTerm type="text">Hoboken, N.J</placeTerm>
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    <publisher>Wiley</publisher>
    <dateIssued>2011</dateIssued>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
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    <extent>xiv, 287 p. : ill.</extent>
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  <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 &amp;rarr; design &amp;rarr; analysis &amp;rarr; optimization or engineering interpretation. 4) Summary. 5) Technical appendices for the mathematically curious"--</abstract>
  <abstract>"This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples"--</abstract>
  <note type="statement of responsibility">Peter Goos, Bradley Jones.</note>
  <note>Includes bibliographical references and index.</note>
  <note>Electronic reproduction. Palo Alto, Calif. : ebrary, 2011. Available via World Wide Web. Access may be limited to ebrary affiliated libraries.</note>
  <subject authority="lcsh">
    <topic>Industrial engineering</topic>
    <topic>Experiments</topic>
    <topic>Computer-aided design</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Experimental design</topic>
    <topic>Data processing</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Industrial engineering</topic>
    <topic>Case studies</topic>
  </subject>
  <classification authority="lcc">T57.5 .G66 2011eb</classification>
  <classification authority="ddc" edition="22">670.285</classification>
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