Optimal Design of Experiments

Beschreibung

"This is an engaging and informative book on the modern practiceof experimental design. The authors' writing style is entertaining,the consulting dialogs are extremely enjoyable, and the technicalmaterial is presented brilliantly but not overwhelmingly. The bookis a joy to read. Everyone who practices or teaches DOE should readthis book." - Douglas C. Montgomery, RegentsProfessor, Department of Industrial Engineering, Arizona StateUniversity "It's been said: 'Design for the experiment, don't experimentfor the design.' This book ably demonstrates this notion by showinghow tailor-made, optimal designs can be effectively employed tomeet a client's actual needs. It should be required reading foranyone interested in using the design of experiments in industrialsettings." --Christopher J. Nachtsheim, Frank A DonaldsonChair in Operations Management, Carlson School of Management,University of Minnesota This book demonstrates the utility of the computer-aided optimaldesign approach using real industrial examples. These examplesaddress questions such as the following: * How can I do screening inexpensively if I have dozens offactors to investigate? * What can I do if I have day-to-day variability and I can onlyperform 3 runs a day? * How can I do RSM cost effectively if I have categoricalfactors? * How can I design and analyze experiments when there is a factorthat can only be changed a few times over the study? * How can I include both ingredients in a mixture and processingfactors in the same study? * How can I design an experiment if there are many factorcombinations that are impossible to run? * How can I make sure that a time trend due to warming up ofequipment does not affect the conclusions from a study? * How can I take into account batch information in when designingexperiments involving multiple batches? * How can I add runs to a botched experiment to resolveambiguities? While answering these questions the book also shows how toevaluate and compare designs. This allows researchers to makesensible trade-offs between the cost of experimentation and theamount of information they obtain.

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Peter Goos

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Computational & Graphical Statistics Engineering Statistics Experimental Design Rechnergestützte u. graphische Statistik Statistics Statistik Statistik in den Ingenieurwissenschaften Versuchsplanung

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