Students will learn some basic matrix algebra for statistical 
use and will become familiar with the multivariate normal
distribution, its properties and applications. Students will
learn multivariate analogs of one and two population inference 
on means, and the multivariate analysis of variance. Students 
will also learn multivariate methods for dimension reduction,
classification, clustering, and other purposes. For each method,
students will learn the statistical logic that explains why it
works, the underlying assumptions and conditions under which the
methodology can be expected to perform well, the type of
questions it addresses, the results it yields, and the proper
interpretation of those results. Students will learn how to
implement the methods covered in the course using appropriate
statistical software.

Relevant vector and matrix algebra, basic multivariate
statistical concepts, the multivariate normal distribution,
multivariate inference for means and variance-covariance 
matrices in one and two population settings, multivariate 
analysis of variance (MANOVA), principal components, factor 
analysis, discriminant analysis, and canonical correlation 
analysis. Additional topics may be covered at the discretion of 
the instructor.