Question 1

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 training data set?

Options :

A : To provide an 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

Answer: A

Question 2

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.)

Options :

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 theobservations intact

Answer: B,C

Question 3

Refer to the exhibit.

Output from a multiple linear regression analysis is shown. What is the most appropriate statement concerning collinearity between the input variables?

Options :

A : Collinearity is a problem since all variance inflation values are less than 10

B : Collinearity is not a problem since all variance inflation values are less than 10

C : Collinearity is not a problem since all Pr>|t| values are less than 0.05

D : Collinearity is a problem since all Pr>|t| values are less than 0.05

Answer: C

Question 4

PROC GLMSELECT was used for building a model predicting the natural log of a baseball player's salary from certain performance and longevity statistics. The model used backward elimination using SBC as its selection criterion. The sequence of steps is summarized in the graphic shown below:

At Step 9 number of at bats (nAtBat) was removed from the model. Why was it removed?

Options :

A : Removing nAtBat had the largest effect on the parameter estimate of nHits

B : The p-Value for nAtBat was largest

C : Removing nAtBat yielded the largest improvement to SBC.

D : The p-Value for nAtBat was smallest

Answer: B

Question 5

When working with smaller data sets (N<200), which method is preferred to perform honest assessment?

Options :

A : Training: 40% Validation: 30% Testing: 30%

B : K-fold cross validation

C : Cross validation using 4th quartile observations

D : Use the AIC goodness of fit statistic

Answer: C

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