How many components/factors should you retain? How much of the total variance (information) is retained by keeping N Components

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Question 1: Dimension Reduction (10 points)

Part I. Use the data Brand_Valuation.jmp.

1) Subset the data to Cars

2) Conduct a PCA on 48 ‘perceptual’ attributes.

How many components/factors should you retain? (eigenvalue > 1)?: _ How much of the total variance (information) is retained by keeping N Components (your answer to the previous question)?

3) Conduct a Factor Analysis using “Principle Component” method and retaining the number of factors you chose.

4) Interpret the Factors. Put your chosen names for the first 3 Factors:

Factor 1:

Factor 2:

Factor 3:

Also rename these in your data.

5) Save the “rotated components” to data.

Part II.

6) Conduct a Hierarchical Cluster Analysis of Brands using

a. the following variables: Rotated Components from Step 5 + Total User + Total Preference (in the “Outcome” folder)

b. Save 5 Clusters to your data

7) Create a Constellation Map similar to below:

8) Compute average of “Total_User_Pct” by Segment/Cluster. Which segment has the highest average “Total_User_Pct”?

Segment # _

Part III.

9) Run a regression with “Brand_Asset_C” as dependent variable (in the “BAV” folder) and the saved Factor Scores as predictors. What is the R-square of this regression?

R-square: _ _

10) Create a Perceptual Map. Use Factor 1 & Factor 2 as two axis and color brands by “Cluster”. Size the brands by “Total Preference Pct” and show the labels.

Attachments:

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