Since these extensions are based on the multivariate normal distribution, we will briefly explore properties of the multivariate normal distribution. We will explore ordination techniques for selecting low dimensional summaries of high dimensional data. These include principal component analysis, factor analysis, canonical correlations, correspondence analysis, projection pursuit, and related graphical techniques. We will explore a variety of classifying cases into pre-specified groups, including linear and quadratic discriminant analysis, logistic regression, neural nets, and classification trees. We will also explore methods for creating groups known as hierarchical and non-hierarchical clustering procedures.
Assignments largely focus on analyzing data and becoming familiar with software packages.
Examples will be presented in the lectures using both the SAS and S-PLUS packages. Links to files containing SAS and S-PLUS code will be made available on this web page as we present them in the lectures. This is a UNIX version of S-PLUS that requires you to enter commands as text. Copies of S-PLUS and SAS manuals are available in Snedecor 115, the Computation Center Library, and the Parks Library. On-line help is also available.