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

1. Which of the following is not true about results produced by the Regression node?
Response:

A) Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.
B) Model Information provides you with information that includes the number of target categories and the number of model parameters.
C) Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.
D) Variable Summary information identifies the roles of variables used by the Regression node.

2. Suppose your input variables have missing values. Before running a decision tree with these input variables, you should do which of the following?
Response:

A) impute all missing values using the Tree method
B) impute only class variables using the Tree method but do not impute the interval variables
C) impute only interval variables using the Tree method but do not impute the class variables
D) not impute any missing values because trees can handle them.

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

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

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) 9
C) 3
D) 6

5. 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) 75 or more
D) 50-74.99

Solutions: