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SASInstitute A00-485 Exam Syllabus Topics:

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

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

2. What is one potential disadvantage of excessively deep decision trees?

A) Overfitting to the training data
B) Faster prediction times
C) Greater stability in the model
D) Increased interpretability

3. Given a scenario where the response variable represents the time until an event occurs, what distribution and link function might be appropriate for modeling?

A) Exponential distribution with a log link function
B) Normal distribution with an identity link function
C) Logistic distribution with a logit link function
D) Poisson distribution with a log link function

4. You want to build a model that predicts whether a customer will default on a loan assuming an underlying binomial distribution.
Which model within SAS Visual Statistics would you use?

A) logistic regression model using the Probit link function
B) logistic regression model using the Logit link function
C) logistic regression model using the Tobit link function
D) generalized linear model using the binomial distribution and the log link function

5. Which statement is true for the Informative Missingness property in SAS Visual Statistics?

A) Missing values are flagged on diagnostic plots.
B) The user is prompted to fill in a value to impute for missing predictor variables.
C) Missing values are imputed and indicator variables are created for missing predictors.
D) A table of missingness patterns for the predictor variables is displayed in the model output.

Solutions: