No Title Symmetric matrices can be factored in a way that gives more insight about their structure Take A to be a symmetric matrix. Eigenvectors give the direction of variability.
Eigenvalues quantify how much variability is in the direction of the corresponding eigenvector.
And the eigenvalue associated with this eigenvector is 3.571. This number is the variance of the data if it were projected onto the first eigenvalue. Compare this variance to the variance of X 2 . Its associated eigenvalue is .2250, that is, if we projected the data onto this eigenvector the variance would be .2250.