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

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

2. Which method of input selection for regression analysis evaluates the statistical significance of the total model to see if it improves on the baseline as the variables are added and once no further improvement is made then variable selection ends?
Select one:
Response:

A) Simple
B) Forward
C) Backward
D) Stepwise

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

A) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
B) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant 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.

4. 1. Define a new data source, PatternData, in SAS Enterprise Miner (SAS data set Patterndata.sas7bdat in the zip file distributed with this practice exam).
2. Set the role of all variables to Input, with the exception set the ID variable role to ID.
3. Set the measurement level for all variables to Interval, except:
- Set DemHomeOwner and StatusCatStarAll to Binary.
- Set DemCluster, DemGender, ID, and StatusCat96NK to Nominal.
4. Create a new diagram (name it Section6) within the project labeled Test.
5. Add the data source, PatternData, to this diagram. Make sure the variable roles and measurements are the same as in the table below. (Check the highlighted rows carefully and reset roles/levels as needed.)
6. Connect a Cluster node to the data source.
7. Modify the Cluster node to exclude nominal and binary input variables.
8. Run the Cluster node.

How many clusters are created by the Cluster node?
Response:

A) 8
B) 6
C) 3
D) 9

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

Solutions: