A data scientist applies PCA via PROC PRINCOMP and retains components that explain at least 80% of total variance. The cumulative proportion output shows components 1-4 explain 82% of variance. How many components should be retained?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you retain four components because your rule is to keep enough components to explain at least 80% of total variance. The cumulative proportion hits 82% at four components, so four is the answer.
Full Explanation
Retaining four components is correct because the criterion is to keep components that cumulatively explain at least 80% of total variance. The cumulative proportion reaches 82% at four components, so four satisfy the rule. Retaining one component is wrong because it falls short of the 80% threshold. Retaining three components is also wrong because it still falls short. Retaining all components above the eigenvalue-1 threshold is wrong because it applies the Kaiser rule, a different criterion not specified.