Canonical Correlation is an advanced statistical technique that can estimate the relationship between a set of dependent variables with a set of predictor or independent variables. Multiple regression techniques, on the other hand, look at the relationships between a single dependent variable and some predictor variables. Canonical correlation places fewer restrictions on the modeling structure.

Consider an example in the credit industry. Suppose you wanted to predict both the credit usage and the number of credit cards of individuals within a certain geographic market. You could build separate regression models to accomplish this, or you could estimate a model simulataneously to predict both number of credit cards as well as overall dollar usage. The canonical correlation is a measure of strength of the overall relationships between the linear composites of the predictor variables and the dependent variables. In general, it represents the bivariate correlation between the two linear composites.