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A well-designed experiment is an efficient method of learning about the world. Because experiments in the field and in the laboratory cannot avoid random error, statistical methods are essential for their efficient design and analysis. In Optimum Experimental Designs, the fundamentals of optimum experimental design theory are presented. In the first part of Optimum Experimental Designs, the advantages of a statistical approach to the design of experiments are discussed, and the ideas of models, least squares fitting, and optimum experimental designs are introduced.
The second part presents a more detailed discussion of the general theory of optimum design and an evaluation of various criteria that may be appropriate for designing experiments. Specific experiments are detailed and algorithms for the construction of designs are given. Each chapter is a self-contained topic, illustrated with examples drawn from science and engineering. Little previous statistical knowledge is assumed, and the derivation of mathematical results has been avoided. Optimum Experimental Designs should be of interest to everyone concerned with designing efficient experiments in the laboratory or in the industry.
- Introduction
Part I. Fundamentals
- Some key ideas
- Experimental strategies
- The choice of a model
- Models and least squares
- Criteria for a good experiment
- Standard designs
- The analysis of experiments
- Optimum design theory
Part II. Theory and applications
- Criteria of optimality
- Experiments with both qualitative and quantitative factors
- Blocking response surface designs
- Restricted region designs
- Failure of the experiment and design augmentation
- Non-linear models
- OptimumBayesian design
- Discrimination between models
- Composite design criteria
- Further topics
"The book is well laid out and is as beautifully produced as we have come to expect from the Oxford Statistical Science Series, in which this is the eighth volume. [...] a thought-provoking reminder always to consider the objectives when designing experiments."
- The Times Higher Education Supplement
"A very interesting book. It should be read by every graduate student and by every statistician who designs or intends to design experiments."
- Technometrics
"If you are a believer in D-optimality, or at least wish to act like one, you will find this book excellent [...] Provides a good basis for a semester's course..."
- Journal of the American Statistical Association
