correlation matrix

When the covariance is normalized, one obtains the correlation matrix.

We provide detailed correlation matrices that can be used to detect and control for non-syn-tenic association.

The purpose is to simplify the correlation matrix by using hypothetical underlying factors to explain the patterns in it.

A g factor can be computed from a correlation matrix of test results using several different methods.

Orthogonality is guaranteed by the symmetry of the correlation matrices.

Compute the eigenvalues for the correlation matrix and plot the values from largest to smallest.

Obtaining the squared multiple correlations from a singular correlation matrix.

When factoring a correlation matrix, the estimated answers appear as the first set of factor scores.

On the basis of the correlation matrix, one would expect to identify three tumor classes in this dataset.

Another good example is random matrix theory, which can be used to identify the noise in financial correlation matrices.