Publication date: 10/01/2019

Principal Components

The Principal Components report contain the following information:

Eigenvalue

Eigenvalues for the covariance matrix.

Percent

Percent variation explained by the corresponding eigenvector. Also shows an accompanying bar chart.

Cum Percent

Cumulative percent variation explained by eigenvectors corresponding to the eigenvalues.

ChiSquare

Provides a test of whether the correlation remaining in the data is of a random nature. This is a Bartlett test of sphericity. When this test rejects the null hypothesis, this implies that there is structure remaining in the data that is associated with this eigenvalue.

DF

Degrees of freedom associated with the Chi-square test.

Prob > ChiSq

p-value for the test.

Eigenvectors

Table of eigenvectors corresponding to the eigenvalues. Note that each eigenvector is divided by the square root of its corresponding eigenvalue.

For more information about principal components, see Principal Components in Multivariate Methods.

Want more information? Have questions? Get answers in the JMP User Community.