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Question 1:

When mean imputation is performed on data after the data is partitioned for honest assessment, what is the most appropriate method for handling the mean imputation?

A. The sample means from the validation data set are applied to the training and test data sets.

B. The sample means from the training data set are applied to the validation and test data sets.

C. The sample means from the test data set are applied to the training and validation data sets.

D. The sample means from each partition of the data are applied to their own partition.

Correct Answer: B

Question 2:

In partitioning data for model assessment, which sampling methods are acceptable? (Choose two.)

A. Simple random sampling without replacement

B. Simple random sampling with replacement

C. Stratified random sampling without replacement

D. Sequential random sampling with replacement

Correct Answer: AC

Question 3:

Assume a $10 cost for soliciting a non-responder and a $200 profit for soliciting a responder. The logistic regression model gives a probability score named P_R on a SAS data set called VALID. The VALID data set contains the responder variable Pinch, a 1/0 variable coded as 1 for responder. Customers will be solicited when their probability score is more than 0.05.

Which SAS program computes the profit for each customer in the data set VALID?

A. Option A

B. Option B

C. Option C

D. Option D

Correct Answer: A

Question 4:

An analyst has a sufficient volume of data to perform a 3-way partition of the data into training, validation, and test sets to perform honest assessment during the model building process.

What is the purpose of the test data set?

A. To provide a unbiased measure of assessment for the final model.

B. To compare models and select and fine-tune the final model.

C. To reduce total sample size to make computations more efficient.

D. To build the predictive models.

Correct Answer: A

Question 5:

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

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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:

Refer to the exhibit.

Given alpha=0.02, which conclusion is justified regarding percentage of body fat, comparing small (S), medium (M), and large (L) wrist sizes?

A. Medium wrist size is significantly different than small wrist size.

B. Large wrist size is significantly different than medium wrist size.

C. Large wrist size is significantly different than small wrist size.

D. There is no significant difference due to wrist size.

Correct Answer: C

Question 8:

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 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:

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

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