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SASInstitute Modeling Using SAS Visual Statistics Sample Questions (Q158-Q163):
NEW QUESTION # 158
Which statement is true about Decision Trees in SAS Visual Statistics
- A. Decision trees cannot handle missing values.
- B. Decision trees can be used for outlier detection.
- C. Decision trees can be used for classification and estimation.
- D. Decision trees cannot handle continuous dependent variables.
Answer: C
NEW QUESTION # 159
In linear regression, what does the coefficient of determination (R-squared) measure?
- A. The impact of outliers on the model
- B. The degree of multicollinearity among predictors
- C. The goodness of fit of the model to the data
- D. The p-value associated with the intercept term
Answer: C
NEW QUESTION # 160
When filtering data for a model in SAS Visual Analytics, what can you do to exclude selections and filter data?
- A. Use the exclude option when setting up a filter
- B. Apply a data filter on the visualization
- C. Specify exclusion criteria in the data source properties
- D. Define a filter condition using the interactive filter pane
Answer: A,B,C,D
NEW QUESTION # 161
In model assessment, why might you need to assess residuals and other model diagnostics to choose an appropriate distribution and link function?
- A. To calculate the p-values of predictor variables
- B. To ensure that predictor variables are not highly correlated
- C. To verify if the chosen model assumptions are met and make adjustments if necessary
- D. To determine the order of predictor variables in the model
Answer: C
NEW QUESTION # 162
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. RestPulse is a significant predictor of the response.
- B. For one one-unit increase in RunTime, there is an expected increase in the response of 2.6287.
- C. For a .03696 unit decrease in RunPulse, there is an expected one-unit increase in the response.
- D. The Intercept is an important predictor of the response.
Answer: B
NEW QUESTION # 163
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