Kaiser's rule states that only those components in principal components analysis whose eigenvalues are greater than 1 should be retained.

Name: Kaiser's rule states that only those components in principal components analysis whose eigenvalues are greater than 1 should be retained.
Brand: Quizplus
Rating: 4.9 (41 reviews)

Question

Quizplus · Accepted Answer

Kaiser's rule is a widely used criterion in principal components analysis (PCA) that suggests retaining only those components whose eigenvalues are greater than 1, as they explain more variance than a single variable.

Kaiser's Rule States That Only Those Components in Principal Components