 Partial correlation

The partial correlation is the correlation of residuals after linearly projecting each of two variables on the conditioning variables and an intercept. With nonsingular covariance matrix, subtracting the two least-squares predictors gives residual covariance ${\sigma }_{12}-{\sigma }_{13}{\sigma }_{23}/{\sigma }_{33}$ and residual variances ${\sigma }_{ii}-{\sigma }_{i3}^2/{\sigma }_{33}$. Dividing gives the displayed formula. For a multivariate normal distribution, it is also the conditional correlation, independent of the conditioning value; this equality need not hold for general distributions.