The course is specifically designed to introduce students to multivariate techniques.  It helps students to handle multivariate data effectively. Specific areas include: Structure of multivariate data. Inferences about multivariate means - Hotelling’s  ; likelihood ratio tests, etc.  Comparisons of several multivariate means - paired comparisons; one-way MANOVA; profile analysis.  Principal component analysis - graphing; summarizing sample variation, etc.  Factor analysis.  Discriminant analysis -  separation and classification for two populations; Fisher’s discriminant function; Fisher’s method for discriminating among several populations  Cluster analysis - hierarchical clustering; non-hierarchical clustering; multi-dimensional scaling.