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[Oct 26, 2026] Today Updated A00-240 Exam Dumps Actual Questions

A00-240 exam dumps with real SASInstitute questions and answers

SASInstitute A00-240 Exam Syllabus Topics:

Section Objectives
Topic 1: ANOVA and General Linear Models – One-way and multi-way ANOVA
– GLM procedures in SAS
Topic 2: Logistic Regression – Model fit and diagnostics
– Binary logistic regression
Topic 3: Multiple Regression – Multicollinearity diagnostics
– Interaction effects
– Model building and selection
Topic 4: Simple Linear Regression – Residual analysis
– Model assumptions and interpretation
– Estimation and inference
Topic 5: Model Diagnostics and Validation – Influence and leverage
– Goodness-of-fit measures

 

QUESTION 53
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?

 
 
 
 

QUESTION 54
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?

 
 
 
 

QUESTION 55
Refer to the exhibit:

Which SAS program produced the graph?

 
 
 
 

QUESTION 56
This question will ask you to provide a segment of missing code.
The following code is used to create missing value indicator variables for input variables, fred1 to fred7.

Which segment of code would complete the task?

 
 
 
 

QUESTION 57
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?

 
 
 
 

QUESTION 58
Refer to the exhibit.

These graphs were created using the GLM procedure with the plots(only)=diagnostics option.
Which plot do you use to identify influential observations?

 
 
 
 

QUESTION 59
Which SAS program will correctly use backward elimination selection criterion within the REG procedure?

 
 
 
 

QUESTION 60
Refer to the REG procedure output:

Click on the calculator button to display a calculator if needed.

 
 
 
 

QUESTION 61
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 AIC statistic, which model is the champion model?

 
 
 
 

QUESTION 62
A marketing manager attempts to determine those customers most likely to purchase additional products as the result of a nation-wide marketing campaign.
The manager possesses a historical dataset (CAMPAIGN) of a similar campaign from last year.
It has the following characteristics:
-Target variable Respond (0,1) -Continuous predictor Income -Categorical predictor Homeowner(Y,N)
Which SAS program performs this analysis?

 
 
 
 

QUESTION 63
Refer to the confusion matrix:

Calculate the sensitivity. (0 – negative outcome, 1 – positive outcome)
Click the calculator button to display a calculator if needed.

 
 
 
 

QUESTION 64
Screening for non-linearity in binary logistic regression can be achieved by visualizing:

 
 
 
 

QUESTION 65
Refer to the exhibit:

The plots represent two models, A and B, being fit to the same two data sets, training and validation.
Model A is 90.5% accurate at distinguishing blue from red on the training data and 75.5% accurate at doing the same on validation data. Model B is 83% accurate at distinguishing blue from red on the training data and 78.3% accurate at doing the same on the validation data.
Which of the two models should be selected and why?

 
 
 
 

QUESTION 66
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?

 
 
 
 

QUESTION 67
Screening for non-linearity in binary logistic regression can be achieved by visualizing:

 
 
 
 

QUESTION 68
Refer to the REG procedure output:

The Intercept estimate is interpreted as:

 
 
 
 

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