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SAS Institute A00-240 Exam
SAS Institute Certified Statistical Business Analyst Using SAS 9: Regression and Modeling Credential Exam (Page 6 )

Updated On: 19-Jan-2026
Page 4 of 21
PDF with 99 Questions

Refer to the exhibit:


SAS output from the RSQUARE selection method, within the REG procedure, is shown. The top two models in each subset are given.

Based on the exhibit, which statement is true?

  1. The AIC champion model is more parsimonious than the SBC champion.
  2. The SBC champion model is more parsimonious than the AIC champion.
  3. The R-Square champion model is the most parsimonious.
  4. Adjusted R-Square and R-Square agree on the champion model.

Answer(s): B



The question will ask you to provide a missing statement. Given the following SAS program:


Which SAS statement will complete the program to correctly score the data set NEW_DATA?

  1. Score data data=MYDIR.NEW_DATA out=scores;
  2. Score data data=MYDIR.NEW_DATA output=scores;
  3. Score data=HYDIR.NEU_DATA output=scores;
  4. Score data=MYDIR, NEW DATA out=scores;

Answer(s): D



A financial services manager wants to assess the probability that certain clients will default on their Home Equity Line of Credit (HELOC). A former employee left the code listed below.


The training data set is named HELOC, while a similar data set of more recent clients is named RECENT_HELOC. Which SAS data steps will calculate the predicted probability of default on recent clients? (Choose two.)

  1. Option A
  2. Option B
  3. Option C
  4. Option D

Answer(s): A,B



One common approach for predicting rare events in the LOGISTIC procedure is to build a model that disproportionately over-re presents those cases with an event occurring (e.g. a 50-50 event/non-event split). What problem does this present?

  1. All parameter estimates are biased.
  2. Only the intercept estimate is biased.
  3. Only the non-intercept parameter estimates are biased.
  4. Sensitivity estimates are biased.

Answer(s): B



Refer to the exhibit:


An analyst examined logistic regression models for predicting whether a customer would make a purchase. The ROC curve displayed summarizes the models. Using the selected model and the analyst's decision rule, 25% of the customers who did not make a purchase are incorrectly classified as purchasers. What can be concluded from the graph?

  1. About 25% of the customers who did make a purchase are correctly classified as making a purchase.
  2. About 50% of the customers who did make a purchase are correctly classified as making a purchase.
  3. About 85% of the customers who did make a purchase are correctly classified as making a purchase.
  4. About 95% of the customers who did make a purchase are correctly classified as making a purchase.

Answer(s): C



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