A data scientist wants to reduce the dimensionality of a dataset with 50 numeric variables before clustering. In SAS, which procedure is most commonly used to perform Principal Component Analysis?
Select an answer to reveal the explanation.
Short Explanation
You'd pick PROC PRINCOMP because it is the SAS procedure built specifically for principal component analysis. It gives you the component structure you need before clustering without treating the task as a factor-analysis problem.
Full Explanation
PROC PRINCOMP is the correct choice because it is the SAS procedure designed specifically for principal component analysis, making it the most common way to reduce many numeric variables into principal components before clustering. SAS also provides PROC FACTOR with METHOD=PRINCIPAL, which can compute principal components as part of factor analysis, but that procedure is primarily used for factor analysis and is not the most commonly used procedure for PCA. PROC PCA is wrong because PROC PCA does not exist as a standalone SAS procedure. PROC VARCLUS is wrong because it clusters variables into groups rather than producing principal components.