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Q1. The selection criterion used in the forward selection method in the REG procedure is:

A. Adjusted R-Square

B. SLE

C. Mallows' Cp

D. AIC

Answer: B


Q2. Refer to the exhibit:

 

Based upon the comparative ROC plot for two competing models, which is the champion model and why?

A. Candidate 1, because the area outside the curve is greater

B. Candidate 2, because the area under the curve is greater

C. Candidate 1, because it is closer to the diagonal reference curve

D. Candidate 2, because it shows less over fit than Candidate 1

Answer: B


Q3. A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression model.

What will be the result?

A. An increase in R-Square

B. A decrease in R-Square

C. A decrease in Mean Square Error

D. No change in R-Square

Answer: A


Q4. The question will ask you to provide a missing statement. Given the following SAS program:

 

Which SAS statement will complete the program to correctly score the data set NEW_DATA?

A. Scoredata data=MYDIR.NEW_DATA out=scores;

B. Scoredata data=MYDIR.NEW_DATA output=scores;

C. Scoredata=HYDIR.NEU_DATA output=scores;

D. Scoredata=MYDIR,NEW DATA out=scores;

Answer: D


Q5. 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.

Answer: B,C


Q6. An analyst fits a logistic regression model to predict whether or not a client will default on a loan. One of the predictors in the model is agent, and each agent serves 15-20 clients each. The model fails to converge. The analyst prints the summarized data, showing the number of defaulted loans per agent. See the partial output below:

 

What is the most likely reason that the model fails to converge?

A. There is quasi-complete separation in the data.

B. There is collinearity among the predictors.

C. There are missing values in the data.

D. There are too many observations in the data.

Answer: A


Q7. 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

Answer: A


Q8. A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression model.

What will be the result?

A. An increase in R-Square

B. A decrease in R-Square

C. A decrease in Mean Square Error

D. No change in R-Square

Answer: A


Q9. A confusion matrix is created for data that were oversampled due to a rare target. What values are not affected by this oversampling?

A. Sensitivity and PV+

B. Specificity and PV-

C. PV+ and PV-

D. Sensitivity and Specificity

Answer: D


Q10. Which method is NOT an appropriate way to score new observations with a known target in a logistic regression model?

A. Use the SCORE statement in the LOGISTIC procedure.

B. Augment the training data set with new observations and set their responses to missing.

C. Augment the training data set with new observations and rerun the LOGISTIC procedure.

D. Use the saved parameter estimates from the LOGISTIC procedure and score new observations in the SCORE procedure.

Answer: C