(Wiley: Hoboken, NJ) -- Optimal Design of Experiments: A Case Study Approach, by Peter Goos and Bradley Jones (Wiley, 2011), demonstrates the utility of the computer-aided optimal design approach using real industrial examples.

These examples address questions such as:
• 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 three 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 during 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?

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