Free SAS Institute A00-240 Practice Exam with Questions & Answers | Set: 2

A researcher has several variables that could be possible predictors for the final model. There is interest in checking all 2-way interactions for possible entry to the model. The researcher has decided to use forward selection within PROC LOGISTIC. Fill in the missing code option that will ensure that all 2-way interactions will be considered for entry.

Refer to the following odds ratio table:

What is a correct interpretation of the estimate?

A predictive model uses a data set that has several variables with missing values.

What two problems can arise with this model? (Choose two.)

The SAS data set RESULT contains the following variables:

• Region (GrpA or GrpB)
• Sales (dollars per year)

Which SAS programs can be used to find the p-value for comparing GrpA sales with GrpB sales? (Choose two.)

Which characteristic of Studentized residuals indicate potential outliers?

Refer to the exhibit:

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

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?

Refer to the REG procedure output:

The Intercept estimate is interpreted as:

The PROC LOGISTIC options SELECTION=SCORE and BEST=2 are used in a MODEL statement to generate a series of predictive models. The models are assigned numbers in order from 1 to 99 reflecting the fact that there are 50 candidate input variables. Results from the collection of derived models are used to generate the following plot of overall average profit by model number. Results are restricted to models with at least 9 inputs and at most 40 inputs.

The maximum value for the training data occurs for model number 46, and the maximum value for the validation data occurs for model number 43.

If you base model selection solely on overall average profit, what is the correct choice?

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?