Free SAS Institute A00-225 Exam Questions

When using SAS Visual Statistics to build a generalized linear model for count data, which model settings should you choose to properly account for overdispersion in the data if the initial model assessment indicates that the variance is greater than the mean?

You need to implement a logistic regression model in SAS to predict customer churn. During model development, you have to ensure the model accounts for the effects of multicollinearity. Which PROC LOGISTIC option should you use to diagnose multicollinearity and provide a measure to identify problematic variables?

A data analyst is using PROC LOGISTIC to perform logistic regression on a dataset where the response variable is binary. The analyst wants to ensure that the output dataset contains the predicted probabilities for the event of interest along with the total number of events and non-events for each group defined by the CLASS variable. Which combination of statements and options should the analyst use?

You are building an artificial neural network model to predict the probability that a customer will respond to a marketing campaign. The target variable is binary, indicating a '1' for a response and '0' for no response. Which combination of activation function and error function would be most appropriate for the output layer of this neural network?

You are analyzing a dataset with a linear regression model to predict sales revenue based on multiple input variables. To prevent overfitting, you decide to include a penalty for including too many variables in the model. Which property adjustment are you most likely to use?