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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. When combining multiple categories into fewer levels in SAS Visual Analytics, what technique is often used?

A) Variable transformation
B) Principal Component Analysis (PCA)
C) Binning or grouping
D) Data imputation

2. Which assessment feature is available when modeling logistic regression models in SAS Visual Statistics?

A) English Rules
B) ROC Chart
C) Icicle Plot
D) Logworth

3. Refer to the exhibits:


An analyst is evaluating which model to use.
What changed from Comparison 1 to Comparison 2 to select a different champion model?

A) Misclassification was used instead of Cumulative Lift
B) Analyst clicked on Model 2 to highlight it
C) Prediction Cutoff reduced from .5 to .19
D) Percentile changed from 5th percentile to 40th percentile

4. Refer to the exhibit from SAS Visual Statistics:

What can be concluded from the lift plot shown in the exhibit?

A) The response rate for the best 20% is predicted to increase 5 times under the fitted model.
B) The 20% of cases with the lowest predicted probability of the modeled event level is predicted to capture 2.75 times as many events as a random sample of 20%.
C) The response rate for the best 20% is predicted to be 2.75 times greater than the sample fitted in the model.
D) The 20% of cases with the highest predicted probability of the modeled event level is expected to capture 2.75 times as many events as a random sample of the same size.

5. Refer to the exhibits:
Exhibit 1:

Exhibit 2:

An analyst has created a cluster model based on the settings in Exhibit 1 and chose to derive a cluster ID variable.
Why are there 5 cluster IDs in Exhibit 2 based on these settings?

A) There are observations with missing values.
B) Cluster Matrix Visible Roles setting is 5.
C) Parallel Coordinates Visible Roles setting is 5.
D) Variable Standardization has been chosen.

Solutions: