A variance-covariance matrix is a square matrix that contains the variances and covariances associated with several variables. The diagonal elements of the matrix contain the variances of the variables and the off-diagonal elements contain the covariances between all possible pairs of variables.
The variance-covariance matrix is symmetric because the covariance between X and Y is the same as the covariance between Y and X. Therefore, the covariance for each pair of variables is displayed twice in the matrix: the covariance between the ith and jth variables is displayed at positions (i, j) and (j, i).
For most statistical analyses, if a missing value exists in any column, Minitab ignores the entire row when it calculates the correlation or covariance matrix. However, when you calculate the covariance matrix by itself, Minitab does not ignore entire rows in its calculations when there are missing values. To obtain only the covariance matrix, choose .