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SASInstitute A00-485 Exam Syllabus Topics:

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. When would you typically use a linear regression model instead of a generalized linear model (GLM)?

A) When dealing with count data
B) When there is no assumption of linearity in the data
C) When the dependent variable is binary
D) When the goal is to model categorical outcomes

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

3. What are the essentials of logistic regression?

A) Using the logit link function to model the probability of an event
B) Assuming a linear relationship between predictors and response
C) Estimating probabilities of categorical outcomes
D) Modeling binary or ordinal outcomes

4. Refer to the exhibits:


An analyst has created a cluster model based on the settings in Exhibit 1 and chose to derive a cluster ID variable.
Why are there 6 cluster IDs in Exhibit 2 based on these settings?

A) Cluster Diagram Visible Roles setting is 6.
B) There are observations with missing values.
C) Parallel Coordinates Visible Roles setting is 6.
D) Variable Standardization has been chosen.

5. Refer to the exhibit:

Prior to performing a decision tree analysis, you need to assess the default Values of Classification and Model Type Properties for Sale Amount. The variable represents product purchase amounts from an organization's e-commerce website.
How do you want the decision tree algorithm to treat this variable?

A) Classification = Category & Model Type = Continuous
B) Classification = Measure & Model Type = Continuous
C) Classification = Category & Model Type = Discrete
D) Classification = Measure & Model Type = Discrete

Solutions: