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Calculating Principal Components

Saving Principal Components

This completes the principal component analysis. You began with a high dimensional set of data (six variables) and reduced it to two dimensions (two variables representing principal component scores) that accounted for over 95% of the variation. You were then able to plot the principal component scores against the variable of interest, SALARY.

At this point, you may want to save the principal component scores for use in subsequent analyses.

Choose Vars:Principal Components:2.


Figure 19.11: Vars Menu

This causes the two variables, PCR1 and PCR2, to be retained in the data window even after you delete the multivariate window. You can then include these variables in later analyses.

Principal Components, Chapter 40.

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