**Course** Description: | An introduction to the methodology of multivariate statistics
for quantitatively-oriented students from various disciplines
who have training in regression and analysis of variance.
Topics include the multivariate normal distribution, one and
two population inference on population mean vectors, MANOVA,
principal component analysis, factor analysis, discrimination,
classification, and canonical correlation. |

**Course Objectives:** | 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. |

**Topical Outline:** | 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. |