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Canonical Correlation Analysis

The goal of a canonical correlation analysis (CCA) is to find the linear combinations in two sets of variables that maximize the correlations. By analyzing the variance-covariance structure of these two sets of variables, uncorrelated pairs of linear combinations from the two sets of variables (called canonical variables) are created and their correlations (called canonical correlations) are estimated. CCA serves as a multivariate extension to bivariate correlation analysis and can be used to test for the existence of relationships between two sets of variables.

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.