Most Recent SAS A00-240 Exam Questions & Answers

Prepare for the SAS Statistical Business Analysis SAS9: Regression and Model exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

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The questions for A00-240 were last updated on Dec 22, 2024.

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Question No. 1

Refer to the exhibit:

The box plot was used to analyze daily sales data following three different ad campaigns.

The business analyst concludes that one of the assumptions of ANOVA was violated.

Which assumption has been violated and why?

ANormality, because Prob > F < .0001.

BNormality, because the interquartile ranges are different in different ad campaigns.

CConstant variance, because Prob > F < .0001.

DConstant variance, because the interquartile ranges are different in different ad campaigns.

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Correct Answer: D

Question No. 2

In order to perform honest assessment on a predictive model, what is an acceptable division between training, validation, and testing data?

ATraining: 50% Validation: 0% Testing: 50%

BTraining: 100% Validation: 0% Testing: 0%

CTraining: 0% Validation: 100% Testing: 0%

DTraining: 50% Validation: 50% Testing: 0%

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Correct Answer: D

Question No. 3

An analyst investigates Region (A, B, or C) as an input variable in a logistic regression model.

The analyst discovers that the probability of purchasing a certain item when Region = A is 1.

What problem does this illustrate?

ACollinearity

BInfluential observations

CQuasi-complete separation

DProblems that arise due to missing values

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Correct Answer: C

Question No. 4

A confusion matrix is created for data that were oversampled due to a rare target.

What values are not affected by this oversampling?

ASensitivity and PV+

BSpecificity and PV-

CPV+ and PV-

DSensitivity and Specificity

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Correct Answer: D

Question No. 5

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?

AClustering the levels using the target proportion for each zip_code as input.

BClustering the levels using the zip_code values as input.

CClustering the levels using the number of cases in each zip_code as input.

DClustering the levels using dummy coded zip_code levels as inputs.

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Correct Answer: A

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