SASInstitute A00-255 : SAS Predictive Modeling Using SAS Enterprise Miner 14

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SASInstitute A00-255 Exam Syllabus Topics:

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

1. Multicollinearity in regression refers to which of the following?
Response:

A) non-constant variance of the target variable
B) non-normality of the target variable
C) high skewness in distributions of input variables
D) high correlations among input variables

2. 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.
In the validation data, the lift corresponding to the fourth decile is in which of the following ranges?
Response:

A) 1.5-1.74999
B) 0-1.24999
C) 1.75 or more
D) 1.25-.49999

3. Refer to the graphs shown below. The graphs are from a study of response rate to a marketing campaign.

How much more likely are the top 20% of targeted respondents to purchase the product than a randomly selected sample?
Select one:
Response:

A) 30%
B) 140%
C) 25%
D) 60%

4. 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) Backward
B) Forward
C) Stepwise
D) Simple

5. 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 data. 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 median of the predicted probabilities of TARGET=1 in the scoring data is in which of the following ranges?
Response:

A) less than 0.149999
B) 0.85 or more
C) 0.50-0.849999
D) 0.15-0.499999

Solutions: