Presenter:
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Douglas C. Montgomery
Regents' Professor Industrial Engineering and Statistics
ASU Foundation Professor of Statistics
Arizona State University |
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Abstract |
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Designed experiments are perhaps the most powerful statistical tool for product and process design and improvement. With the widespread use of six sigma and design for six sigma the use of designed experiments in non-traditional areas has grown dramatically in recent years. This has led to many new developments in the field. The availability of software for implementing optimal designs has made it possible to deploy custom designs that are tailored to specific problems rather than relying on tales of standard designs that a problem must be molded to fit. This course gives an introduction to optimal designs and illustrates many of the new developments that this methodology makes possible.
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Course Titles |
1. Why should we design experiments?
2. Optimal designs versus standard designs.
3. The basics of optimal designs; the D, G, and I criteria.
4. Many basic designs are optimal designs.
5. Applications: Constraints, unusual blocking or sample size requirements, non-standard models.
6. No-confounding fractional factorial designs.
7. Minimal aliasing designs
8. Definitive screening designs
9. Experiments for computer models
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