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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

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

2. Given an Influence Plot generated in a Linear Regression within SAS Visual Statistics, which diagnostic can be used to quantify leverage?

A) Concordance
B) Adjusted R-Squared
C) Likelihood Displacement
D) AIC

3. When performing decision tree analysis in SAS Visual Statistics, how does the property setting for
"include missing" modify the usage of missing values within categorical discrete predictors?

A) imputes the mean of the predictor's known values
B) assigns its own level
C) assigns the smallest available machine value
D) imputes the median of the predictor's known values

4. When combining multiple categories into fewer levels in SAS Visual Analytics, what technique is often used?

A) Principal Component Analysis (PCA)
B) Binning or grouping
C) Data imputation
D) Variable transformation

5. When reviewing outlier details in linear regression, what factors should be considered when deciding whether to exclude outliers?

A) The magnitude of the outlier and its impact on the model
B) The variable roles assigned to outliers
C) The number of outliers in the dataset
D) The p-values associated with the outliers

Solutions: