There are missing values in the input variables for a regression application.Which SAS procedure provides a viable solution?
Screening for non-linearity in binary logistic regression can be achieved by visualizing:
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?
An analyst knows that the categorical predictor, storeId, is an important predictor of the target.However, store_Id has too many levels to be a feasible predictor in the model. The analyst wants to combine stores and treat them as members of the same class level.What are the two most effective ways to address the problem? (Choose two.)
SIMULATION -A linear model has the following characteristics:*A dependent variable (y)*One continuous variable (xl), including a quadratic term (x12)*One categorical (d with 3 levels) predictor variable and an interaction term (d by x1)How many parameters, including the intercept, are associated with this model?Enter your numeric answer in the space below. Do not add leading or trailing spaces to your answer.
