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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. In model assessment, why might you need to assess residuals and other model diagnostics to choose an appropriate distribution and link function?

A) To determine the order of predictor variables in the model
B) To ensure that predictor variables are not highly correlated
C) To verify if the chosen model assumptions are met and make adjustments if necessary
D) To calculate the p-values of predictor variables

2. What does the Variable Importance feature help with when choosing the best fitting group-by model?

A) Evaluating the multicollinearity among predictors
B) Identifying the most influential predictors
C) Assessing the significance of each predictor
D) Understanding the distribution of the target variable

3. What information is conveyed by a Summary bar in model assessment?

A) The standard deviation of the predictor variables
B) The performance metrics of the model, such as AUC and KS Statistic
C) The summary statistics of the response variable
D) The distribution of residuals in the model

4. What does a ROC chart help assess in model comparison?

A) The correlation between predictor variables
B) The stability of model predictions
C) The distribution of residuals in the models
D) The trade-off between true positives and false positives at different thresholds

5. Which statement is true for negative binomial and Poisson regression models?

A) Negative binomial models accommodate negative integers while Poisson regression does not.
B) The canonical link function for Poisson regression is the log, while for negative binomial it is the logit.
C) Poisson regression is a special case of negative binomial regression.
D) Poisson regression models are used for count data, and negative binomial models are used for binary responses.

Solutions: