Principal Component Analysis

Principal Component Analysis (PCA) is a popular multivariate technique used for data reduction. By analyzing the variance-covariance structure of a set of variables, uncorrelated linear combinations of the variables (called principal components) are calculated that maximize the amount of variance explained. The resulting principal components can then be used in further analyses such as regression and ordination.

The workshop is intended for participants who have the equivalent of one semester of statistics and some previous experience doing data analysis. It is appropriate for faculty, research staff, and graduate students.