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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. When should you replace dirty data with missing values in SAS Visual Analytics?

A) When the data is incorrect or invalid
B) When you want to remove missing values
C) When you want to create a new variable
D) When the data contains outliers

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

3. A champion model has been selected and you want to export the score code.
Score code is exported as:

A) SAS Data Step
B) C
C) PMML
D) SAS DS2

4. What does the Variable Importance feature help with when choosing the best fitting group-by model?

A) Identifying the most influential predictors
B) Assessing the significance of each predictor
C) Evaluating the multicollinearity among predictors
D) Understanding the distribution of the target variable

5. Prior to fitting a regression model, data binning of a continuous effect should be used for what purpose?

A) Reduce the impact of minor observation errors.
B) Reduce the cardinality of a continuous effect.
C) Improve the accuracy of your predictions.
D) Gain information due to discretization.

Solutions: