Skip to main content



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.

Fee: None to members of the Cornell community, but registration is required. Since space is limited, early registration is encouraged.

For times and locations of upcoming workshops, please see the Workshop Schedule.