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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and Assessing Segmentation Models | 28% | - Assess and interpret decision tree performance - Perform unsupervised segmentation using cluster analysis - Perform supervised segmentation using decision trees - Analyze and interpret cluster results |
| Topic 2: Model Comparison and Scoring | 9% | - Apply models to score new data - Compare multiple models using fit statistics |
| Topic 3: Building and Assessing Regression-type Models | 41% | - Explain concepts of linear models - Perform linear regression modeling - Perform generalized linear regression modeling - Assess model fit and diagnostics - Perform generalized additive modeling - Perform nonparametric logistic regression modeling |
| Topic 4: SAS Visual Statistics Cross-functional Tasks | 22% | - Filter data used for a model - Perform model validation - Prepare data using SAS Visual Analytics - Use interactive group-by functionality |
Refer to the exhibit from SAS Visual Statistics:
What can be concluded from the lift plot shown in the exhibit?
Correct Answer: B 🗳️
Given a scenario where the response variable represents the number of accidents in a day, what distribution and link function might be appropriate for modeling?
Correct Answer: C 🗳️
What does it mean to prune a decision tree?
Correct Answer: A 🗳️
When performing a logistic regression in SAS Visual Statistics, the default prediction cutoff is .50. If you increase the cutoff to .80 then derive predicted values, which statement is the expected outcome?
Correct Answer: B 🗳️
What does a Lift chart evaluate in terms of model performance?
Correct Answer: D 🗳️
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