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SASInstitute A00-485 Exam Syllabus Topics:

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. Refer to the exhibit from SAS Visual Statistics:

What can be concluded from the lift plot shown in the exhibit?

A) The 20% of cases with the lowest predicted probability of the modeled event level is predicted to capture 2.75 times as many events as a random sample of 20%.
B) The 20% of cases with the highest predicted probability of the modeled event level is expected to capture 2.75 times as many events as a random sample of the same size.
C) The response rate for the best 20% is predicted to be 2.75 times greater than the sample fitted in the model.
D) The response rate for the best 20% is predicted to increase 5 times under the fitted model.

2. Refer to the fit summary from in the exhibit below.

What can be concluded from the fit summary?

A) Debt to income ratio is a significant predictor when Job Type = Mgr.
B) Debt to income ratio is a significant predictor when Job Type = Sales.
C) Job Type = Office has no important variables associated with it.
D) Job Type is not a significant predictor in this model.

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

4. Which statement is true about performing regressions in SAS Visual Statistics?

A) You can build neither a linear regression nor a logistic regression with a generalized linear model.
B) You can build both a linear regression or a logistic regression with a generalized linear model.
C) You can specify an ordinal classifier as the response variable with linear regression.
D) You can specify a multinomial classifier as the response variable with linear regression.

5. What does a Lift chart evaluate in terms of model performance?

A) The correlation between predictor variables
B) The interaction effects between predictor variables
C) The distribution of the response variable
D) The relative performance of the model compared to a random model

Solutions: