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

1. A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:

A) 2*log(b1)
B) 0.69/b1
C) 2*b1
D) 2/log(b1)

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) Stepwise
B) Simple
C) Forward
D) Backward

3. Which method of input selection for regression analysis evaluates the statistical significance of all included inputs after each input is added?
Select one:
Response:

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

4. 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.
How many leaves are there in the decision tree?
Response:

A) 21 or more
B) 16-20
C) 1-10
D) 11-15

5. The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:

A) the average of the absolute values of parameter estimates between the input and all of the hidden neurons
B) none of the above
C) the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron
D) the highest absolute value of the parameter estimate between the input and any of the hidden neurons

Solutions: