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Question 1:

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?

A. Scoredata=valid1 out=roc;

B. Scoredata=valid1 outroc=roc;

C. mode1resp(event= \’1\’) = gender region/outroc=roc;

D. mode1resp(event”1″) = gender region/ out=roc;

Correct Answer: B

Question 2:

Refer to the lift chart:

At a depth of 0.1, Lift = 3.14. What does this mean?

A. Selecting the top 10% of the population scored by the model should result in 3.14 times more events than a random draw of 10%.

B. Selecting the observations with a response probability of at least 10% should result in 3.14 times more events than a random draw of 10%.

C. Selecting the top 10% of the population scored by the model should result in 3.14 timesgreater accuracy than a random draw of 10%.

D. Selecting the observations with a response probability of atleast 10% should result in 3.14times greater accuracy than a random draw of 10%.

Correct Answer: A

Question 3:

Refer to the lift chart:

What does the reference line at lift = 1 corresponds to?

A. The predicted lift for the best 50% of validation data cases

B. The predicted lift if the entire population is scored as event cases

C. The predicted lift if none of the population are scored as event cases

D. The predicted lift if 50% of the population are randomly scored as event cases

Correct Answer: B

Question 4:

What is a drawback to performing data cleansing (imputation, transformations, etc.) on raw data prior to partitioning the data for honest assessment as opposed to performing the data cleansing after partitioning the data?

A. It violates assumptions of the model.

B. It requires extra computational effort and time.

C. It omits the training (and test) data sets from the benefits of the cleansing methods.

D. There is no ability to compare the effectiveness of different cleansing methods.

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 VCE DumpsA00-240 Practice TestA00-240 Study Guide

Question 6:

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?

A. Normality, because Prob > F < .0001.

B. Normality, because the interquartile ranges are different in different ad campaigns.

C. Constant variance, because Prob > F < .0001.

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

Correct Answer: D

Question 7:

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 8:

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 9:

An analyst knows that the categorical predictor, storeId, is an important predictor of the target. However, store_Id has too many levels to be a feasible predictor in the model. The analyst wants to combine stores and treat them as members of the same class level.

What are the two most effective ways to address the problem? (Choose two.)

A. Eliminate store_id as a predictor in the model because it has too many levels to be feasible.

B. Cluster by using Greenacre\’s method to combine stores that are similar.

C. Use subject matter expertise to combine stores that are similar.

D. Randomly combine the stores into five groups to keep the stochastic variation among the observations intact.

Correct Answer: BC

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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