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Question 1:

Which SAS program will divide the original data set into 60% training and 40% validation data sets, stratified by county?

A. Option A

B. Option B

C. Option C

D. Option D

Correct Answer: C

Question 2:

Suppose training data are oversampled in the event group to make the number of events and non-events roughly equal. A logistic regression is run and the probabilities are output to a data set NEW and given the variable name PE. A decision rule considered is, “Classify data as an event if probability is greater than 0.5.” Also the data set NEW contains a variable TG that indicates whether there is an event (1=Event, 0= No event).

The following SAS program was used.

What does this program calculate?

A. Depth

B. Sensitivity

C. Specificity

D. Positive predictive value

Correct Answer: B

Question 3:

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?

A. Model A. It is more complex with a higher accuracy than model B on training data.

B. Model A. It performs better on the boundary for the training data.

C. Model B. It is more complex with a higher accuracy than model A on validation data.

D. Model B. It is simpler with a higher accuracy than model A on validation data.

Correct Answer: D

Question 4:

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

A. Training: 50% Validation: 0% Testing: 50%

B. Training: 100% Validation: 0% Testing: 0%

C. Training: 0% Validation: 100% Testing: 0%

D. Training: 50% Validation: 50% Testing: 0%

Correct Answer: D

Question 5:

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?

A. More high value customers are found in some regions than others.

B. The difference between average purchases for medium and high value customers depends on the region.

C. Regions with higher average purchases have more high value customers.

D. Regions with higher average purchases have more medium value customers.

Correct Answer: B

A00-240 PDF DumpsA00-240 Study GuideA00-240 Exam Questions

Question 6:

Refer to the exhibit.

Based on the control plot, which conclusion is justified regarding the means of the response?

A. All groups are significantly different from each other.

B. 2XL is significantly different from all other groups.

C. Only XL and 2XL are not significantly different from each other.

D. No groups are significantly different from each other.

Correct Answer: C

Question 7:

Given the following GLM procedure output:

Which statement is correct at an alpha level of 0.05?

A. School*Gender should be removed because it is non-significant.

B. Gender should be removed because it is non-significant.

C. School should be removed because it is significant.

D. Gender should not be removed due to its involvement in the significant interaction.

Correct Answer: D

Question 8:

There are missing values in the input variables for a regression application. Which SAS procedure provides a viable solution?

A. GLM

B. VARCLUS

C. STDI2E

D. CLUSTER

Correct Answer: C

Question 9:

Given the following SAS data set TEST:

Which SAS program is NOT a correct way to create dummy variables?

A. Option A

B. Option B

C. Option C

D. Option D

Correct Answer: D

Question 10:

Including redundant input variables in a regression model can:

A. Stabilize parameter estimates and increase the risk of overfitting.

B. Destabilize parameter estimates and increase the risk of overfitting.

C. Stabilize parameter estimates and decrease the risk of overfitting.

D. Destabilize parameter estimates and decrease the risk of overfitting.

Correct Answer: B

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