""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 by showing
how tailor-made, optimal designs can be effectively employed to
meet a client's actual needs. It should be required reading for
anyone interested in using the design of experiments in industrial
settings."
"--Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations
Management, Carlson School of Management, University of
Minnesota
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.
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