– For the on Correlations option, the eigenvalues are scaled to sum to the number of variables.
 – For the on Covariances options, the eigenvalues are not scaled.
 – For the on Unscaled option, the eigenvalues are divided by the total number of observations.
If you select the Bartlett Test option from the red triangle menu, hypothesis tests (Bartlett Test) are given for each eigenvalue (Jackson, 2003).
Eigenvalues
Bartlett Test
 – For the on Correlations option, the ith column of loadings is the ith eigenvector multiplied by the square root of the ith eigenvalue. The i,jth loading is the correlation between the ith variable and the jth principal component.
 – For the on Covariances option, the jth entry in the ith column of loadings is the ith eigenvector multiplied by the square root of the ith eigenvalue and divided by the standard deviation of the jth variable. The i,jth loading is the correlation between the ith variable and the jth principal component.
 – For the on Unscaled option, the jth entry in the ith column of loadings is the ith eigenvector multiplied by the square root of the ith eigenvalue and divided by the standard error of the jth variable. The standard error of the jth variable is the jth diagonal entry of the sum of squares and cross products matrix divided by the number of rows (X’X/n).
Note: When you are analyzing the unscaled data, the i,jth loading is not the correlation between the ith variable and the jth principal component.
Formatted Loading Matrix
Biplot
Scatterplot Matrix
Scatterplot 3D Score Plot
The variables show as rays in the plot. These rays, called biplot rays, approximate the variables as a function of the principal components on the axes. If there are only two or three variables, the rays represent the variables exactly. The length of the ray corresponds to the eigenvalue or variance of the principal component.
 – For the on Correlations option, the ith principal component is a linear combination of the centered and scaled observations using the entries of the ith eigenvector as coefficients.
 – For the on Covariances options, the ith principal component is a linear combination of the centered observations using the entries of the ith eigenvector as coefficients.
 – For the on Unscaled option, the ith principal component is a linear combination of the raw observations using the entries of the ith eigenvector as coefficients.
See Local Data Filter, Redo Menus, and Save Script Menusin the Using JMP book for more information about the following options:

Help created on 9/19/2017