LEADER 04111nam 22006493u 450 001 9911019143203321 005 20260113153019.0 010 $a1-283-17783-8 010 $a9786613177834 010 $a1-119-97401-1 010 $a1-119-97400-3 035 $a(CKB)2550000000041193 035 $a(EBL)697607 035 $a(OCoLC)747411905 035 $a(SSID)ssj0000539756 035 $a(PQKBManifestationID)11327619 035 $a(PQKBTitleCode)TC0000539756 035 $a(PQKBWorkID)10580065 035 $a(PQKB)10298968 035 $a(PPN)262117177 035 $a(EXLCZ)992550000000041193 100 $a20130418d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOptimal design of experiments $ea case study approach /$fPeter Goos 210 1$aChicester :$cWiley,$d2011. 215 $a1 online resource (xiv, 287 pages) $cillustrations, charts 311 08$a0-470-74461-8 311 08$aPrint version: Goos, Peter. Optimal design of experiments. Hoboken, N.J. : Wiley, 2011 (DLC) 2011008381 320 $alncludes bibliographical references (pages 277-282) and index. 327 $aA Simple Comparative Experiment -- An Optimal Screening Experiment -- Adding Runs to a Screening Experiment -- A Response Surface Design with a Categorical Factor -- A Response Surface Design in an Irregularly Shaped Design Region -- A 'Mixture' Experiment with Process Variables -- A Response Surface Design in Blocks -- A Screening Experiment in Blocks -- Experimental Design in the Presence of Covariates -- A Split-Plot Design -- A Two-Way Split-Plot Design. 330 $a"This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities?While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain. The structure of the book is organized around the following chapters: 1) Introduction explaining the concept of tailored DOE. 2) Basics of optimal design. 3) Nine case studies dealing with the above questions using the flow: description-design-analysis-optimization or engineering interpretation. 4) Summary. 5) Technical appendices for the mathematically curious"--$cProvided by publisher. 517 3 $aCase study approach 606 $aIndustrial engineering$xExperiments$xComputer-aided design$vCase studies 606 $aExperimental design$xData processing 606 $aIndustrial engineering 606 $aComputer-aided design 608 $aCase studies.$2lcgft 615 0$aIndustrial engineering$xExperiments$xComputer-aided design 615 0$aExperimental design$xData processing. 615 0$aIndustrial engineering. 615 0$aComputer-aided design. 676 $a500 676 $a620.00420285 686 $aSCI028000$2bisacsh 700 $aGoos$b Peter$01598894 702 $aJones$b Bradley 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9911019143203321 996 $aOptimal design of experiments$94527342 997 $aUNINA