A data scientist uses PROC PRINCOMP in SAS with OUTSTAT= to save the component structure. Later, they want to score new data by projecting it onto the saved principal components. Which step applies the saved PCA transformation to new data?
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Short Explanation and Infographic
Here's the deal — d is correct because PROC SCORE is used to apply saved scoring coefficients (from PROC PRINCOMP's OUTSTAT= dataset) to new data. The TYPE=SCORE option tells PROC SCORE to look for scoring coefficient rows in the OUTSTAT dataset and compute linear combinations (principal component scores) for the new observations.
Full explanation below image
Full Explanation
D is correct because PROC SCORE is used to apply saved scoring coefficients (from PROC PRINCOMP's OUTSTAT= dataset) to new data. The TYPE=SCORE option tells PROC SCORE to look for scoring coefficient rows in the OUTSTAT dataset and compute linear combinations (principal component scores) for the new observations. A uses TYPE=PARMS which is for regression parameter datasets, not PCA scoring. B is wrong because PROC PRINCOMP does not have a LOAD= option for reading saved components. C is wrong because PROC FACTOR does not have a LOAD= option equivalent and OUTSCORE= is not a valid option name.