# MC on Regression Analysis

#### shane414

##### New Member
I wanted to check over my answers based on these practice questions. Thanks

(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.

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

#### hlsmith

##### Not a robit
I just super quick skimmed these, though don't work with unstructured data or pca, and agree with your responses.

Though I was wondering if 10 maybe false given you can compare characteristics of clusters after assigning them to a group. So I create two cluster then examine unit characteristic between them afterwards. Not sure if they may be allu ding to that process.

#### Gloryhorse

##### New Member
shane414 , Can you elaborate more on 2, 3 & 4 !

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