SASInstitute Modeling Using SAS Visual Statistics : A00-485

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SASInstitute A00-485 Exam Syllabus Topics:

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. What are some properties that can be defined in a generalized linear regression model?

A) Number of predictors
B) Coefficients and p-values
C) Residuals and intercept
D) Distribution and link function

2. 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

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) Age is a significant predictor of the response.
B) The Intercept is an important predictor of the response.
C) For a 1371.5 unit decrease in Total Promos, there is an expected one-unit increase in the response.
D) For one one-unit increase in Average Sales, there is an expected increase in the response of
4475.443.

4. Refer to the fit summary from SAS Visual Statisitcs in the exhibit below.

What can be concluded from the fit summary?

A) Customer Value Level C has no important variables associated with it.
B) Average Sales is an important predictor when Customer Value Level = C.
C) Average Sales is a significant predictor when Customer Value Level = E.
D) Customer Value Level is not a significant predictor in this model.

5. How is prediction typically performed in logistic regression?

A) By maximizing the likelihood of the response variable
B) By calculating the mean of the response variable
C) By estimating the probability of an event for each observation
D) By fitting a straight line through the data points

Solutions: