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SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Exam (A00-240) - SASI Exam Questions

Last updated on June 20, 2026

97% Exam Compliance
99 Total Questions
1
Question
Fill IN THE BLANK

A linear model has the following characteristics:

• A dependent variable (y)

• One continuous variable (x1)

• One categorical (с1 with 3 levels) predictor variable and an interaction term (с1 by x1)

How many parameters, including the intercept, will be estimated for this model? Enter your numeric answer in the space below.
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2
Question
A company has branch offices in eight regions. Customers within each region are classified as either "High Value" or "Medium Value" and are coded using the variable name VALUE. In the last year, the total amount of purchases per customer is used as the response variable. Suppose there is a significant interaction between REGION and VALUE. What can you conclude?
Options
A Regions with higher average purchases have more medium value customers.
B More high value customers are found in some regions than others.
C The difference between average purchases for medium and high value customers depends on the region.
D Regions with higher average purchases have more high value customers.
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3
Question
This question will ask you to provide a missing option. Complete the following syntax to test the homogeneity of variance assumption in the GLM procedure:
means Region / =levene ;
Options
A hovtest
B test
C adjust
D var
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4
Question
DRAG DROP Drag the adjustment formulas for oversamping from the left and place them into the correct location in the confusion matrix shown on the right.
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5
Question
An analyst has a sufficient volume of data to perform a 3-way partition of the data into training, validation, and test sets to perform honest assessment during the model building process.

What is the purpose of the training data set?
Options
A To build the predictive models.
B To provide an unbiased measure of assessment for the final model.
C To compare models and select and fine-tune the final model.
D To reduce total sample size to make computations more efficient.
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