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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. When reviewing Measure Details in SAS Visual Analytics, what information can you find related to measures?

A) Measure data type
B) Measure name and label
C) Measure calculation method
D) Measure format and informat

2. Which statement is correct about using a multinomial response variable (with 3 or more levels) in the logistic regression task in SAS Visual Statistics?

A) The resulting model is an ordinal logistic regression model with the lowest level of the response as the reference.
B) The resulting model is a binary logistic regression model with a selected level as the event.
C) The resulting model is a multinomial logistic regression model with a generalized logit link.
D) You cannot assign a response variable with 3 or more levels to the logistic regression visualization.

3. What is the primary purpose of assigning variable roles in linear regression?

A) To determine the order in which variables are entered into the model
B) To determine the level of significance for each variable
C) To specify which variables are categorical and which are continuous
D) To indicate which variables are predictors and which are responses

4. After a logistic regression has been created in SAS Visual Statistics, you discover that not all observations were used to create the model.
How would you run the model on all of the data?

A) Select Informative Missingness on the properties tab.
B) Include the correct interaction term.
C) Select Use Variable Selection on the properties tab.
D) Include the correct offset term.

5. What does adding an interaction effect in linear regression modeling allow you to capture?

A) The multicollinearity among predictors
B) The presence of outliers in the data
C) The effect of missing data on the model
D) The joint impact of two or more predictors on the response variable

Solutions: