Instructor: C. F. Jeff Wu, Ph.D., University of
Michigan
Course Outline:
This one-day course will be based on
the book by Jeff Wu and Mike Hamada which will be
published by John Wiley in March of 2000. The short
course notes will be made available to the participants.
This book contains many new methods not found in
existing textbooks, and covers more than 80 data sets
and 200 exercises. The new tools covered include robust
parameter design, use of minimum aberration criterion
for optimal factor assignment, orthogonal arrays of
economic run size, analysis strategies to exploit
interactions, experiments for reliability improvement,
and analysis of experiments with non-normal responses.
The course will cover basic tools and illustrate them with
data from real experiments. The course will be divided
into four units:
- Basic principles for design of experiments including
two-level full and fractional factorial designs and
basic analysis tools. Techniques for resolving
ambiguities in aliased effects and the minimum
aberration criterion for optimal factor assignment.
- Choice of optimal blocking schemes for two-level
designs. Three-level fractional factorial designs of
27 and 81 runs. Mixed 2- and 4-level designs. A
guide to the use of design tables.
- Nonregular designs including Plackett-Burman
designs and mixed-level orthogonal arrays. Their
basic statistical properties and practical use. Use of
design tables. Analysis of experiments based on
nonregular designs, including the exploitation of
interactions
- Robust parameter design for variation reduction.
Layout techniques: cross arrays and single arrays.
Modeling strategies. Choice of cross and single
arrays and use of design tables. Review on signal-to-noise
ratio.
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The course is targeting industrial or academic
statisticians who are interested in learning modern and
effective tools in the design and analysis of experiments.
Some basic knowledge on regression analysis and
analysis of variance are assumed. No prior background
in experimental design is required.
About the Instructor:
C. F. Jeff Wu is H. C. Carver
Professor of Statistics and also Professor of Industrial
and Operations Engineering at the University of
Michigan, Ann Arbor. He was formerly the
GM/NSERC Chair in Quality and Productivity at the
University of Waterloo, which was jointly funded by
General Motors of Canada and the Natural Sciences and
Engineering Research Council of Canada.
He taught at the Statistics Department at the University
of Wisconsin from 1977-1988 prior to join U. of
Waterloo. He is a Fellow of the IMS and the ASA. He
received many prestigious awards including COPSS
Award in 1987, Wilcoxon Prize in 1990, Brumbaugh
Award in 1992, and Jack Youden Prize in 1997. He gave
the 1998 P. C. Mahalanobis Memorial Lecturer at the
Indian Statistical Institutes.
He received his BS in Mathematics from National
Taiwan University in 1971 and Ph.D. in Statistics from
the University of California, Berkeley in 1976. His
research interests include experimental design, quality
and reliability improvement, biotech and preclinical
applications, robust product/process design, modeling of
complex systems, survey sampling and computer-intensive
statistical methods. He is the author or co-author
of over 100 research papers and a recent book
entitled "Experiments: Planning, Analysis, and
Parameter Design Optimization"(Wiley, 2000).
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