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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

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

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

2. How are predictions formulated for a decision tree when a new data point is presented to the model?

A) By averaging the values of all leaf nodes
B) By calculating the Euclidean distance to all tree nodes and selecting the closest one
C) By following a path through the tree to a leaf node and returning the majority class or mean value of that node
D) By using a weighted sum of all decision rules in the tree

3. How can you interpret a Tree Map in the context of a decision tree?

A) It shows the decision boundaries between classes.
B) It displays the hierarchical structure of the decision tree.
C) It visualizes the distribution of data points in leaf nodes.
D) It summarizes the performance metrics of the decision tree.

4. What is the purpose of implementing score code generated by SAS Visual Statistics?

A) To evaluate the performance of the scoring code
B) To calculate the p-values of predictor variables
C) To apply the trained model to new data for predictions
D) To assess the distribution of residuals in the model

5. In group-by modeling, when is the p-value for the group-by variable considered significant?

A) When the p-value is exactly 0.05
B) When the p-value is less than 0.01
C) When the p-value is less than 0.05
D) When the p-value is greater than 0.05

Solutions: