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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

1. Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:

A) 25-49.99
B) 0-24.99
C) 50-74.99
D) 75 or more

2. For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
B) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
C) It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
D) It looks for a set of colinear inputs that correlate with the target.

3. Perform these tasks in SAS Enterprise Miner:
*Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:

The percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:

A) 5%-5.99%
B) under 4.99%
C) 7% or higher
D) 6%-6.99%

4. Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:

A) Ensemble
B) Regression
C) Decision Tree
D) Neural Network

5. Refer to the exhibit:

The SAS data set retail contains information on the count of retail store sales based on the following item types: bargain, essential, gourmet, and health. Based on the results from the Cluster Profile node, which statement is true?
Select one:
Response:

A) The overall distribution of bargain item sales is approximately normal and Segment 1 contains stores selling fewer than average bargain items.
B) The overall distribution of bargain item sales is left-skewed and Segment 4 contains stores selling fewer than average bargain items.
C) The overall distribution of essential item sales is right skewed and Segment 4 contains stores selling higher than average essential items.
D) The overall distribution of essential item sales is approximately normal and Segment 1 contains stores selling fewer than average essential items.

Solutions: