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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, June 27, 2000}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Susan Holmes}
\centerline{\sl Department of Statistics}
\centerline{\sl Stanford University}
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\centerline{\bf How tree-like are these data}
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Large amounts of multivariate data are often simplified by building
hierarchical clusters. Examples include whether linguistic co-occurrence,
genetical micro-arrays or DNA sequence data. I will show how some
nonparametric multivariate techniques such as correspondence analysis and
its extensions can be used to map such data and find out whether trees are
relevant in a case by case analysis.

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