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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, April 22, 2003}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}


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\centerline{\sl William Heavlin}
\centerline{\sl Advanced Micro Devices}
\centerline{\sl Sunnyvale, CA}
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\centerline{\bf Designing Experiments for Causal Networks}
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Causal networks are directed graphs representing cause-effect 
relationships and are multiple-response generalizations of Ishikawa's 
cause-effect diagrams.  Emphasizing tolerance design applications, I 
describe an algorithm for designing suitable experiments when the 
factors and responses are organized in a causal network.  The causal 
network is transformed into a so-called causal map, which represents 
all factors and responses as points in a common D-dimensional metric 
space.  The design approach is algorithmic, optimizing the entropy 
criterion due to Wynn.  This criterion is applied to maximize 
dispersion among the multiple responses, using a distance-in-space 
coefficients model.  A key constraint is for the blocks to be self- 
contained; this implies that each block can be analyzed without 
reference to other blocks.  This is to be complemented by a unified, 
all-block analysis.  The resulting designs are evaluated for 
efficiency, response dispersion, and resolution V column rank.  
Particular attention is given to skewing each block by shifting one or 
a few factors off-center. 

Key Words: blocking, cause-effect diagram, directed graph,
multidimensional  scaling, optimal design, tolerance design. 

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