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SASInstitute Modeling Using SAS Visual Statistics Sample Questions (Q88-Q93):

NEW QUESTION # 88
When evaluating a Misclassification plot, what does it typically show?

A. The p-values associated with the predictor variables
B. The trade-off between false positives and false negatives at different thresholds
C. The distribution of predictor variables in the dataset
D. The relationship between two predictor variables

Answer: B

NEW QUESTION # 89
What is the primary benefit of exporting score code for model deployment?

A. It allows for model performance assessment on a test dataset.
B. It enables scoring of new data without the need for SAS Visual Statistics.
C. It provides insights into the distribution of predictor variables.
D. It automatically generates new predictor variables.

Answer: B

NEW QUESTION # 90
Which of the following are common distributions used in generalized linear models?

A. Exponential
B. Normal
C. Poisson
D. Logistic

Answer: B,C,D

NEW QUESTION # 91
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 a .03696 unit decrease in RunPulse, there is an expected one-unit increase in the response.
D. For one one-unit increase in RunTime, there is an expected increase in the response of 2.6287.

Answer: D

NEW QUESTION # 92
What is the maximum number of response variables that SAS Visual Statistics allows for a decision tree?

A. 0
B. 1
C. 2
D. 3

Answer: D

NEW QUESTION # 93
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