Measurement Error in Nonlinear Models: A Modern Perspective

describes some of the aspects of analysis for designs where multiple responses are collected. Because most experiments have this feature, understanding the opportunities and challenges for this situation is essential reading for practitioners. Chapter 13 is a collection of short sections on a number of other specialized designs, including screening, equileverage, optimal, space-filling, trend-free, and mixture designs. Chapter 14, “Tying It All Together,” briefly discusses the difficult task of choosing between different designs when planning an experiment. This chapter reinforces my belief that learning this aspect of design of experiments is most challenging, both because of how we teach design (cleanly compartmentalized into tidy chunks with questions posed to fit precisely into a category) and because of the ever-increasing breadth of tools available. The exercises at the conclusion of this chapter help the reader gain experience with design selection and pose many thought-provoking questions that will challenge even the most seasoned statistician. Already an extensive volume on the subject, this book contains a wealth of information. However, on my wish list for additional topics would be more discussion about the different roles of experimentation, from exploration, to screening for important factors, to response surface methods for characterizing and optimizing the relationship between the key factors and the response, to confirmatory experiments near the chosen optimum. Matching types of designs to their intended purposes is another area that is difficult for those studying design of experiment, and direct discussion can greatly help accelerate this understanding. In addition, it would be beneficial to present some of the criteria and graphical tools that are available to compare different potential designs. This could help formalize the numerous trade-offs between the many aspects of a good design. Overall, Modern Experimental Design is a must-have reference for anyone who will be designing experiments or for statisticians interested in remaining on the leading edge of this important area. I thoroughly enjoyed reading this well-written and comprehensive book, both for the careful and clear synthesis of the new research in this area and for the many insightful comments that help connect the details of the methods to the big picture.