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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. What are decision tree properties that can be defined during model construction?

A) The learning rate for gradient boosting
B) The number of neighbors in a k-nearest neighbors model
C) The minimum number of data points in a leaf node
D) The maximum depth of the tree

2. What can you interpret from the Assessment panel in a decision tree analysis?

A) The tree's training and validation performance metrics
B) The decision boundaries between leaf nodes
C) The feature importance for each variable in the tree
D) The tree's structural properties, such as depth and number of nodes

3. When evaluating a Misclassification plot, what does it typically show?

A) The trade-off between false positives and false negatives at different thresholds
B) The relationship between two predictor variables
C) The distribution of predictor variables in the dataset
D) The p-values associated with the predictor variables

4. Given a scenario where the response variable represents the number of accidents in a day, what distribution and link function might be appropriate for modeling?

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

5. Refer to the exhibit:

What does the Residual Plot from a linear regression model reveal?

A) The model is not capturing all of the available signals in the data.
B) It suggests the errors are uncorrelated.
C) There is an approximately equal number of positive and negative residuals.
D) It suggests the error variance is constant.

Solutions: