Optimal design of experiments : a case study approach 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 ambiguitiesWhile 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. The structure of the book is organized around the following chapters: 1) Introduction explaining the concept of tailored DOE. 2) Basics of optimal design. 3) Nine case studies dealing with the above questions using the flow: description &rarr; design &rarr; analysis &rarr; optimization or engineering interpretation. 4) Summary. 5) Technical appendices for the mathematically curious