Multivariate statistical techniques are the application of statistics to simultaneous observations and can
include the analysis of more than one outcome (dependent) variable. Good multivariate analysis starts with
exploratory and graphical analyses to reveal potential relations in the data and to highlight potential outliers.
First, this presentation will discuss how to extend univariate and bivariate methods for graphical analysis to
multivariate data, as well as methods unique to multivariate data. Second, multivariate outlier detection will
be presented. Third, there will be a brief discussion of multivariate statistical analysis methods, such as
multiple regression, principal component analysis, and cluster analysis, including examples and suggestions as
to when one might want to use these techniques.