This book provides a comprehensive treatment of the design of blocked and split-plot experiments. The optimal design approach advocated in the book will help applied statisticians from industry, medicine, agriculture, chemistry and many other fields of study in setting up tailor-made experiments. The book also contains a theoretical background, a thorough review of the recent work in the area of blocked and split-plot experiments, and a number of interesting theoretical results.

"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.

Response Surfaces: Designs and Analyses; Second Edition presents techniques for designing experiments that yield adequate and reliable measurements of one or several responses of interest, fitting and testing the suitability of empirical models used for acquiring information from the experiments, and for utilizing the experimental results to make decisions concerning the system under investigation. This edition contains chapters on response surface models with block effects and on Taguchi's robust parameter design, additional details on transformation of response variable, more material on modified ridge analysis, and new design criteria, including rotatability for multiresponse experiments. It also presents an innovative technique for displaying correlation among several response. Numerical examples throughout the book plus exercises--with worked solutions to selected problems--complement the text.

The primary objetive of response surface methodology in to aid the statistician and other uers of statistics in applying response surface procedures to appropriate problems in many technical fields. Although methods are emphasized in the boock, a certain amount of theory is presented so that a reader with sufficient baclground in mathematics, especially in the algeba of matrices, can obtain an expourse to the theoretical development. While response surface techniques are widely used, it seems that a need existes for an exposition which contains a considerable amount of the basic material under a single cover. At the time it is felt hist book may create a continued awareness of the basic techniques amog the potential users.

Split-plot designs often arise in agriculture and industrial experimentation when some factors are harder to vary than others, leading to randomization restrictions. This has an effect on both the run order and analysis of the experiment. Response surface methodology (RSM) split-plot designs for experiments with quantitative factors have received a lot of coverage in the literature. These designs are not appropriate, however, if categorical factors are also present. Draper and John (1988) and Wu and Ding (1998) explore techniques for adding categorical factors to non-split-plot RSM designs. Building on their initial idea of adding the categorical factor sequentially after taking an initial base design in the quantitative factors, this thesis explores how to add a two-level categorical factor in the split-plot RSM setting. Due to the randomization restrictions, adding a categorical factor in the split-plot setting requires considerably more care, in order to meet basic feasibility requirements and to maintain the structure. We explore four techniques for adding categorical factors and present results on requirements for the feasibility of proposed assignments of the categorical factor. We find that not all methods are appropriate for every base design. Throughout the thesis, we expand upon an example of an RSM split-plot experiment in quantitative factors for a ceramic pipe experiment from Vining and Kowalski (2008), by introducing a hypothetical additional categorical factor at either the whole-plot (hard-to-vary) or split-plot (easy-to-vary) level. We discuss optimal strategies for assigning a factor, conduct some initial exploration of the different response surfaces after perturbations to the data using contour plots, and suggest further avenues for analysis. The thesis culminates in tables of D-optimal designs for the various assignment methods based on an algorithm and computer code written for the various assignment methods.