SAS A00-240 practice materials Questions Answers

Exam Code:
A00-240
Exam Name:
SAS Statistical Business Analysis SAS9: Regression and Model Exam
Last Update:
Jun 01,2026

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SAS A00-240 Questions for SAS Certified Statistical Business Analyst Certification Exam 2026

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Page: 1 / 20
Total Questions: 99

Questions & Answers PDF

• Question 1

  

  A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression model.What will be the result?

  • An increase in R-Square
  • A decrease in R-Square
  • A decrease in Mean Square Error
  • No change in R-Square

  Reveal Answer Answer: A Next Question

• Question 2

  

  The selection criterion used in the forward selection method in the REG procedure is:

  • Adjusted R-Square
  • SLE
  • Mallows' Cp
  • AIC

  Reveal Answer Answer: B Next Question

• Question 3

  

  When working with smaller data sets (N<200), which method is preferred to perform honest assessment?

  • Training: 40% Validation: 30% Testing: 30%
  • K-fold cross validation
  • Cross validation using 4th quartile observations
  • Use the AIC goodness of fit statistic

  Reveal Answer Answer: C Next Question

• Question 4

  

  This question will ask you to provide a missing option. Given the following SAS program:What option must be added to the program to obtain a data set containing Spearman statistics?

  • OUTCORR=estimates
  • OUTS=estimates
  • OUT=estimates
  • OUTPUT=estimates

  Reveal Answer Answer: D Next Question

• Question 5

  

  An analyst generates a model using the LOGISTIC procedure. They are now interested in getting the sensitivity and specificity statistics on a validation data set for a variety of cutoff values.Which statement and option combination will generate these statistics?

  • Score data=valid1 out=roc;
  • Score data=valid1 outroc=roc;
  • mode1 resp(event= '1') = gender region/outroc=roc;
  • mode1 resp(event"1") = gender region/ out=roc;

  Reveal Answer Answer: B Next Question

• Question 6

  

  An analyst knows that the categorical predictor, zip_code, is an important predictor of a binary target. However, zip_code has too many levels to be a feasible predictor in a model. The analyst uses PROC CLUSTER to implement Greenacre's method to reduce the number of categorical levels.What is the correct application of Greenacre's method in this situation?

  • Clustering the levels using the target proportion for each zip_code as input.
  • Clustering the levels using the zip_code values as input.
  • Clustering the levels using the number of cases in each zip_code as input.
  • Clustering the levels using dummy coded zip_code levels as inputs.

  Reveal Answer Answer: A Next Question

• Question 7

  

  Within PROC GLM, the interaction between the two categorical predictors, Income and Gender, was shown to be significant. An item store was saved from the GLM analysis.Which statement from PROC PLM would test the significance of Gender within each level of Income and adjust for multiple tests?

  • sliceby Gender / adjust=tukey;
  • slice Income*Gender / sliceby=Gender adjust=tukey;
  • slice Income*Gender / sliceby=Income adjust=tukey;
  • sliceby Income / adjust=tukey;

  Reveal Answer Answer: D Next Question

• Question 8

  

  Fill IN THE BLANKA 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.

  Reveal Answer Answer: 7 Next Question

• Question 9

  

  Which SAS program will best identify influential observations in a multiple regression application?

  • Option A
  • Option B
  • Option C
  • Option D

  Reveal Answer Answer: C Next Question

• Question 10

  

  Including redundant input variables in a regression model can:

  • Stabilize parameter estimates and increase the risk of overfitting.
  • Destabilize parameter estimates and increase the risk of overfitting.
  • Stabilize parameter estimates and decrease the risk of overfitting.
  • Destabilize parameter estimates and decrease the risk of overfitting.

  Reveal Answer Answer: B Next Question

Page: 1 / 20
Total Questions: 99

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