# Principal Components Analysis and Canonical Correlation Analysis

Principal Components 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.

Canonical Correlation Analysis (CCA) focuses on maximizing the correlation between a linear combination of one set of variables and a linear combination of another set of variables. 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.

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.

Topics:

- Background and concepts
- How to conduct PCA and CCA
- How to interpret the results

**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.