06395nam 22008653u 450 991084178050332120170809172635.01-283-17783-897866131778341-119-97401-11-119-97400-3(CKB)2550000000041193(EBL)697607(OCoLC)747411905(SSID)ssj0000539756(PQKBManifestationID)11327619(PQKBTitleCode)TC0000539756(PQKBWorkID)10580065(PQKB)10298968(PPN)262117177(EXLCZ)99255000000004119320130418d2011|||| u|| |engur|n|---|||||txtccrOptimal Design of Experiments[electronic resource] A Case Study ApproachChicester Wiley20111 online resource (305 p.)Description based upon print version of record.0-470-74461-8 Optimal 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 inflation2.3.7 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 designs3.4 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 regions4.4 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 box6.3.1 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 tests7.3.5 Optimal design of blocked experiments""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 notionExperimental design - Data processingExperimental design -- Data processingIndustrial engineeringIndustrial engineering -- Case studiesIndustrial engineering - Experiments - Computer-aided designIndustrial engineering -- Experiments -- Computer-aided designSCIENCE / Experiments & ProjectsIndustrial engineeringExperimentsComputer-aided designCase studiesExperimental designData processingIndustrial engineeringEngineering & Applied SciencesHILCCApplied MathematicsHILCCExperimental design - Data processing.Experimental design -- Data processing.Industrial engineering.Industrial engineering -- Case studies.Industrial engineering - Experiments - Computer-aided design.Industrial engineering -- Experiments -- Computer-aided design.SCIENCE / Experiments & Projects.Industrial engineeringExperimentsComputer-aided designExperimental designData processingIndustrial engineeringEngineering & Applied SciencesApplied Mathematics500620.00420285SCI028000bisacshGoos Peter1598894Jones Bradley42705AU-PeELAU-PeELAU-PeELBOOK9910841780503321Optimal Design of Experiments4045089UNINA