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

• A.It allows for the determination of the effectiveness of the cleansing method.
• B.It is computationally easier and requires less time.
• C.It makes inference on the model possible.
• D.It omits the training (and test) data sets from the benefits of the cleansing methods.

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Refer to the exhibit:

An analyst examined logistic regression models for predicting whether a customer would make a
purchase. The ROC curve displayed summarizes the models. Using the selected model and the analyst's
decision rule, 25% of the customers who did not make a purchase are incorrectly classified as purchasers.
What can be concluded from the graph?

• A.About 85% of the customers who did make a purchase are correctly classified as making a purchase.
• B.About 95% of the customers who did make a purchase are correctly classified as making a purchase.
• C.About 50% of the customers who did make a purchase are correctly classified as making a purchase.
• D.About 25% of the customers who did make a purchase are correctly classified as making a purchase.

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Which SAS program will best identify influential observations in a multiple regression application?

• A.Option B
• B.Option D
• C.Option A
• D.Option C

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Select the equivalent LOGISTIC procedure model statements. (Choose two.)

• A.Mode1 Purchase * Gender|Age|Region @2;
• B.Mode1 Purchase * Gender Age Region;
• C.Mode1 Purchase * Gender | Age | Region;
• D.Mode1 Purchase * Gender|Age|Region @1;

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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.The difference between average purchases for medium and high value customers depends on the region.
• B.More high value customers are found in some regions than others.
• C.Regions with higher average purchases have more high value customers.
• D.Regions with higher average purchases have more medium value customers.

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