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 and Infographic
Here's the deal — c is correct because the data scientist's criterion is to retain components that cumulatively explain at least 80% of total variance. The cumulative proportion reaches 82% at component 4, so exactly 4 components satisfy the criterion.
Full explanation below image
Full Explanation
C is correct because the data scientist's criterion is to retain components that cumulatively explain at least 80% of total variance. The cumulative proportion reaches 82% at component 4, so exactly 4 components satisfy the criterion. A and B retain fewer components than needed to reach the 80% threshold. D applies a different criterion (Kaiser rule: eigenvalue > 1) which was not specified in the question.