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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. Refer to the exhibit:

Based on the Validation Data in the Model Comparison output which conclusions are valid? (Choose two.)

A) Logistic Regression model would be selected at lift=5
B) Decision Tree model would be selected at lift=5
C) Decision Tree model would be selected at lift=20
D) Logistic Regression model would be selected at lift=20

2. What is meant by "informative missingness" in the context of linear regression?

A) Missing data that provides valuable information about the outcome
B) Missing data that leads to multicollinearity among predictors
C) Missing data that is unrelated to the outcome variable
D) Missing data that is randomly distributed across observations

3. Which of the following criteria are commonly used for pruning a decision tree?

A) Minimum split count
B) Maximum depth
C) Maximum leaf nodes
D) Maximum tree height

4. Refer to the exhibit from a linear regression model in SAS Visual Statistics.

Based on the table above and assuming a significance level of 0.05, what can be concluded about the linear regression model?

A) The Intercept is an important predictor of the response.
B) RestPulse is a significant predictor of the response.
C) For one one-unit increase in RunTime, there is an expected increase in the response of 2.6287.
D) For a .03696 unit decrease in RunPulse, there is an expected one-unit increase in the response.

5. You want to filter out certain observations to analyze their effect on the model.

Which windows will achieve this? (Choose two.)

A) Residual Plot
B) Influence Plot
C) Assessment
D) Fit Summary

Solutions: