linear regression practice

I wanted to check over my answers based on these questions


(1) True/False: Clustering is an unsupervised learning problem.

(2) True/False: Principal Components Analysis can be used to create a low dimensional projection of the data for use with clustering.

3) True/False: Common factors estimated using maximum likelihood estimation with a PROMAX rotation are orthogonal.

4) True/False: Common factors estimated using Iterated Principal Factor Analysis with a VARIMAX rotation are orthogonal.

5) True/False: In cluster analysis the choice of similarity measure will affect the cluster assignments.

6) True/False: When computing principal components the data should be standardized, i.e. the data should be centered and scaled to a (0,1) distribution.

7) True/False: Cluster analysis can only be performed on continuous variables.

8) True/False: Hierarchical clustering requires that the number of clusters be specified in advance.

9) True/False: Factor Analysis and Principal Components Analysis have the same objective of modeling the correlation structure in multivariate data.

10) True/False: Since cluster analysis is an unsupervised learning method, two different cluster partitions cannot be compared.

My answers:

1) True
2) false
3) true
4) false
5) true
6) true
7) false
8) false
9) false
10) true

and any explanations to 2 & 10 would also be helpful. Thanks!


Less is more. Stay pure. Stay poor.
I quickly skimmed these and I will note that I don't work with PCA, but the responses seem acceptable. #10 may be interpreted as False if you did a post hoc comparison of the groups after the clustering process, which I have seen.