1. Ungraded quiz
Unit 8

2. Show me your fingers
Do not shout out the answer, or your classmates will follow what you said.
Use your fingers
One finger (the right finger) = A
Two fingers = B
Three fingers = C
Four fingers = D
No finger = I don’t know. I didn’t study

3. What is the difference between discriminant analysis (DA) and cluster analysis (CA)?
In DA the analyst needs to assign the number of clusters in advance while CA is entirely exploratory..
DA aims to assign observations into existing groups while CA is intended to discover new grouping patterns.
DA starts with a centroid but CA can start anywhere.
All of the above.

4. Which of the following statements is true?
In normal mixtures the data must be categorical.
In latent class analysis the data must be continuous.
In K-mean clustering the data can be continuous and categorical.
Two-step clustering accepts both continuous and categorical data.

5. Which of the following about DBSCAN is untrue?
DBSCAN is available in SAS.
K-mean clustering might fail to detect a string-like clustering pattern but DBSCAN can detect any shape of clustering.
DBSCAN separates concentrated data from sparse data.
DBSCAN uses the elbow method to find the optimal K.

6. Which of the following about the elbow method is untrue?
The elbow method is available in Python.
The elbow plot in cluster analysis is similar to the scree plot in factor analysis.
The elbow method uses the raw distances to find the optimal k..
All of the above.

7. Which of the following about hierarchical clustering (HC) is true?
HC can be top-down or bottom-up.
HC and MDS can work hand in hand if the row items and the column items are the same in a symmetrical data matrix (e.g. R1 = C1, R2 = C2…etc.).
Besides grouping observations into a few clusters for data reduction, HC can also be used for matching two individuals into a pair.
All of the above

8. Which of the following about two-step clustering is true?
In two-step clustering a pre-cluster is created in step 1.
In two-step clustering if the model quality index is less than 0.5, then it is considered a poor model.
Two-step clustering tends to create asymmetrical clusters.
All of the above.