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

1. Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

A) 67409.93
B) 5862.76
C) 14.69
D) 5.00

2. What is the average squared error in the training data?
Response:

A) 0.131208
B) 0.131583
C) 0.133665
D) 0.131709

3. 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) 94% or higher
B) 90-93.99%-
C) less than 83.99%
D) 84-89.99%

4. 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.

5. Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:

A) 11-15
B) 6-10
C) less than or equal to 5
D) 16 or more

Solutions: