Free SASInstitute A00-240 Exam Dumps Questions & Answers

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 too many observations in the data.
• D.There are missing values in the data.

A non-contributing predictor variable (Pr > |t| =0.658) is added to an existing multiple linear regression model.
What will be the result?

• A.No change in R-Square
• B.A decrease in R-Square
• C.An increase in R-Square
• D.A decrease in Mean Square Error

The Model SS in a multiple linear regression model is equal to:

• A.the sum of Type I SS of all model terms
• B.the sum of SSE and MSE
• C.the sum of Type II SS of all model terms
• D.the total SS- MSE

While building a predictive model, median imputations are performed while preparing the training data.
How should the imputations be addressed in the validation data?

• A.The imputed values must be recalculated using both the training and the validation data.
• B.The imputed values must be recalculated using the validation data.
• C.The imputed values must be applied directly to the validation data without recalculation.
• D.The imputed values are irrelevant to the validation data, and are not used.

There are missing values in the input variables for a regression application.
Which SAS procedure provides a viable solution?

• A.GLM
• B.VARCLUS
• C.CLUSTER
• D.STDI2E