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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.
Which of the following variable(s) is (are) statistically significant at the 5% level in the selected model?
Response:

A) all of the above
B) IMP_TLOpen24Pct
C) TLDel3060Cnt24
D) TLTimeFirst

2. Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

A) The model has too few input variables.
B) All of the above
C) The misclassification error for the test data is 0.154255.
D) The optimization for the model has not been completed.

3. Assume a variable is coded as follows: 1=unmarried, 2=married, 3=divorced, and 4=widowed. Then which of the following measurement levels should be selected in SAS Enterprise Miner for this variable?
Response:

A) Interval
B) Ordinal
C) Unary
D) Nominal

4. 1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.
What is the mean credit card balance (CCBal) of the customers with a variable annuity?
Response:

A) $9,586.55
B) $8,711.65
C) $0.00
D) $11,142.45

5. 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.
The percentage of observations correctly predicted in the validation data by the Ensemble model is in which of the following ranges?
Response:

A) less than 83.99%
B) 84-89.99%
C) 94% or higher
D) 90-93.99%-

Solutions: