LEADER 06398nam 22008653u 450 001 9910830036603321 005 20170809172635.0 010 $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|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aOptimal Design of Experiments$b[electronic resource] $eA Case Study Approach 210 $aChicester $cWiley$d2011 215 $a1 online resource (305 p.) 300 $aDescription based upon print version of record. 311 $a0-470-74461-8 327 $aOptimal Design of Experiments : A Case Study Approach; Contents; Preface; Acknowledgments; 1 A simple comparative experiment; 1.1 Key concepts; 1.2 The setup of a comparative experiment; 1.3 Summary; 2 An optimal screening experiment; 2.1 Key concepts; 2.2 Case: an extraction experiment; 2.2.1 Problem and design; 2.2.2 Data analysis; 2.3 Peek into the black box; 2.3.1 Main-effects models; 2.3.2 Models with two-factor interaction effects; 2.3.3 Factor scaling; 2.3.4 Ordinary least squares estimation; 2.3.5 Significance tests and statistical power calculations; 2.3.6 Variance inflation 327 $a2.3.7 Aliasing2.3.8 Optimal design; 2.3.9 Generating optimal experimental designs; 2.3.10 The extraction experiment revisited; 2.3.11 Principles of successful screening: sparsity, hierarchy, and heredity; 2.4 Background reading; 2.4.1 Screening; 2.4.2 Algorithms for finding optimal designs; 2.5 Summary; 3 Adding runs to a screening experiment; 3.1 Key concepts; 3.2 Case: an augmented extraction experiment; 3.2.1 Problem and design; 3.2.2 Data analysis; 3.3 Peek into the black box; 3.3.1 Optimal selection of a follow-up design; 3.3.2 Design construction algorithm; 3.3.3 Foldover designs 327 $a3.4 Background reading3.5 Summary; 4 A response surface design with a categorical factor; 4.1 Key concepts; 4.2 Case: a robust and optimal process experiment; 4.2.1 Problem and design; 4.2.2 Data analysis; 4.3 Peek into the black box; 4.3.1 Quadratic effects; 4.3.2 Dummy variables for multilevel categorical factors; 4.3.3 Computing D-efficiencies; 4.3.4 Constructing Fraction of Design Space plots; 4.3.5 Calculating the average relative variance of prediction; 4.3.6 Computing I-efficiencies; 4.3.7 Ensuring the validity of inference based on ordinary least squares; 4.3.8 Design regions 327 $a4.4 Background reading4.5 Summary; 5 A response surface design in an irregularly shaped design region; 5.1 Key concepts; 5.2 Case: the yield maximization experiment; 5.2.1 Problem and design; 5.2.2 Data analysis; 5.3 Peek into the black box; 5.3.1 Cubic factor effects; 5.3.2 Lack-of-fit test; 5.3.3 Incorporating factor constraints in the design construction algorithm; 5.4 Background reading; 5.5 Summary; 6 A "mixture" experiment with process variables; 6.1 Key concepts; 6.2 Case: the rolling mill experiment; 6.2.1 Problem and design; 6.2.2 Data analysis; 6.3 Peek into the black box 327 $a6.3.1 The mixture constraint6.3.2 The effect of the mixture constraint on the model; 6.3.3 Commonly used models for data from mixture experiments; 6.3.4 Optimal designs for mixture experiments; 6.3.5 Design construction algorithms for mixture experiments; 6.4 Background reading; 6.5 Summary; 7 A response surface design in blocks; 7.1 Key concepts; 7.2 Case: the pastry dough experiment; 7.2.1 Problem and design; 7.2.2 Data analysis; 7.3 Peek into the black box; 7.3.1 Model; 7.3.2 Generalized least squares estimation; 7.3.3 Estimation of variance components; 7.3.4 Significance tests 327 $a7.3.5 Optimal design of blocked experiments 330 $a""This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book."" - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University ""It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion 606 $aExperimental design - Data processing 606 $aExperimental design -- Data processing 606 $aIndustrial engineering 606 $aIndustrial engineering -- Case studies 606 $aIndustrial engineering - Experiments - Computer-aided design 606 $aIndustrial engineering -- Experiments -- Computer-aided design 606 $aSCIENCE / Experiments & Projects 606 $aIndustrial engineering$xExperiments$xComputer-aided design$vCase studies 606 $aExperimental design$xData processing 606 $aIndustrial engineering 606 $aEngineering & Applied Sciences$2HILCC 606 $aApplied Mathematics$2HILCC 615 4$aExperimental design - Data processing. 615 4$aExperimental design -- Data processing. 615 4$aIndustrial engineering. 615 4$aIndustrial engineering -- Case studies. 615 4$aIndustrial engineering - Experiments - Computer-aided design. 615 4$aIndustrial engineering -- Experiments -- Computer-aided design. 615 4$aSCIENCE / Experiments & Projects. 615 0$aIndustrial engineering$xExperiments$xComputer-aided design 615 0$aExperimental design$xData processing 615 0$aIndustrial engineering 615 7$aEngineering & Applied Sciences 615 7$aApplied Mathematics 676 $a500 676 $a620.00420285 686 $aSCI028000$2bisacsh 700 $aGoos$b Peter$01598894 701 $aJones$b Bradley$042705 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910830036603321 996 $aOptimal Design of Experiments$94045089 997 $aUNINA