Sample correlation coefficient ($r_{XY}$)

For paired observations $(x_i,y_i)$, the sample correlation coefficient isIt is the normalized inner product of the centered data vectors, or the sample covariance divided by the product of the corresponding sample standard deviations. The choice of divisor $n$ or $n-1$ cancels when used consistently in all three quantities. It is undefined if either centered data vector is zero, which occurs when that variable is constant in the observed statistical sample.

For $x=(1,2,3,4)$ and $y=(2,1,4,3)$, both sample means are $2.5$. The sum of centered products is $3$, and each sum of centered squares is $5$, hence the sample correlation coefficient is $3/5=0.6$. This is a summary of these four observed pairs; it is not automatically the population Pearson correlation coefficient. Philip Stark, Computing the correlation coefficient explains the centered and standardized calculation.

A sample correlation matrix rescales a sample covariance matrix $S$ to unit diagonal, for variables with positive sample variance. Its off-diagonal entries are sample correlation coefficients. Strong correlations between predictors can indicate multicollinearity, but pairwise correlations alone do not exclude higher-order linear dependence.