Converting a Covariance Matrix to a Correlation Matrix First, use the DIAG function to extract the variances from the diagonal elements of the covariance matrix. Then invert the matrix to form the diagonal matrix with diagonal elements that are the reciprocals of the standard deviations.

The covariance value can range from -∞ to +∞, with a negative value indicating a negative relationship and a positive value indicating a positive relationship. The greater this number, the more reliant the relationship. Positive covariance denotes a direct relationship and is represented by a positive number.

Similarly, covariance is frequently “de-scaled,” yielding the correlation between two random variables: Corr(X,Y) = Cov[X,Y] / ( StdDev(X) ∙ StdDev(Y) ) . The correlation between two random variables will always lie between -1 and 1, and is a measure of the strength of the linear relationship between the two variables.

Coefficient of variation is measured for a single variable. It is the mean divided by the standard deviation. It is only meaningful for ratio variables—that is metrics with natural zero points like height or length of time. Covariance is measured between two variables (or more than two in matrix form).