A00-240 SAS Statistical Business Analysis SAS9: Regression and Model Questions and Answers

The SCORE statement in the LOGISTIC procedure returns only predicted probabilities, whereas the SCORE procedure returns only predicted logits.

The SCORE statement in the LOGISTIC procedure returns only predicted logits, whereas the SCORE procedure returns only predicted probabilities.

Unlike the SCORE procedure, the SCORE statement in the LOGISTIC procedure produces both predicted probabilities and predicted logits.

The SCORE procedure and the SCORE statement in the LOGISTIC procedure produce the same output.

Option A

Option B

Option C

Option D

Collinearity

Influential observations

Quasi-complete separation

Problems that arise due to missing values

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

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

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

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

Use the SCORE statement in the LOGISTIC procedure.

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

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

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

Mode1 Purchase * Gender Age Region;

Mode1 Purchase * Gender | Age | Region;

Mode1 Purchase * Gender|Age|Region @1;

Mode1 Purchase * Gender|Age|Region @2;

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

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

Regions with higher average purchases have more high value customers.

Regions with higher average purchases have more medium value customers.

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%.

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%.

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

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

There is quasi-complete separation in the data.

There is collinearity among the predictors.

There are missing values in the data.

There are too many observations in the data.

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

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

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

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