Optimum Experimental Designs, with SAS

About this book

Experiments on patients, processes or plants all have random error, making statistical methods essential for their efficient design and analysis. Optimum Experimental Designs, with SAS presents the theory and methods of optimum experimental design, making them available through the use of SAS programs. Little previous statistical knowledge is assumed. The first part of Optimum Experimental Designs, with SAS stresses the importance of models in the analysis of data and introduces least squares fitting and simple optimum experimental designs.

The second part presents a more detailed discussion of the general theory and of a wide variety of experiments. Optimum Experimental Designs, with SAS stresses the use of SAS to provide hands-on solutions for the construction of designs in both standard and non-standard situations. The mathematical theory of the designs is developed in parallel with their construction in SAS, so providing motivation for the development of the subject. Many chapters cover self-contained topics drawn from science, engineering and pharmaceutical investigations, such as response surface designs, blocking of experiments, designs for mixture experiments and for nonlinear and generalized linear models.

Understanding is aided by the provision of "SAS tasks" after most chapters as well as by more traditional exercises and a fully supported website. The authors are leading experts in key fields and Optimum Experimental Designs, with SAS is ideal for statisticians and scientists in academia, research and the process and pharmaceutical industries.

Contents

Preface

I BACKGROUND
1. Introduction
2. Some key ideas
3. Experimental strategies
4. The choice of a model
5. Models and least squares
6. Criteria for a good experiment
7. Standard designs
8. The analysis of experiments

II THEORY AND APPLICATIONS
9. Optimum design theory
10. Criteria of optimality
11. D-optimum designs
12. Algorithms for the construction of exact D-optimum designs
13. Optimum experimental design with SAS
14. Experiments with both qualitative and quantitative factors
15. Blocking response surface designs
16. Mixture experiments
17. Nonlinear models
18. Bayesian optimum designs
19. Design augmentation
20. Model checking and designs for discriminating between models
21. Compound design criteria
22. Generalized linear models
23. Response transformation and structured variances
24. Time-dependent models with correlated observations
25. Further topics
26. Exercises

Bibliography
Author index
Subject index

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