SAS INSTITUTE -SAS Certified Statistical Business Analyst Using SAS 9 Regression and Modeling Credential

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

Given the following SAS data set TEST:
Which SAS program is NOT a correct way to create dummy variables?
 

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?

This question will ask you to provide a missing option. Given the following SAS program:
What option must be added to the program to obtain a data set containing Pearson statistics?

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

The standard form of a linear regression model is:
Which statement best summarizes the assumptions placed on the errors?

Refer to the REG procedure output:
Click on the calculator button to display a calculator if needed.

Refer to the REG procedure output:
An analyst has selected this model as a champion because it shows better model fit than a competing model with more predictors.
Which statistic justifies this rationale?

The selection criterion used in the forward selection method in the REG procedure is:

Refer to the REG procedure output:
The Intercept estimate is interpreted as: