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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. What is the primary purpose of reviewing the residual plot in linear regression analysis?

A) To detect outliers in the predictor variables
B) To determine the p-value associated with the response variable
C) To assess the linearity assumption and identify potential issues
D) To calculate the coefficient of determination (R-squared)

2. What properties can be assigned when comparing models in SAS Visual Statistics?

A) The number of observations in the dataset
B) The learning rate for gradient boosting models
C) The variables to include in the model comparison
D) The color scheme for visualization

3. When investigating leaf nodes in a decision tree, what does it reveal about the data?

A) The statistical significance of each leaf node
B) The decision rules leading to each leaf node
C) The feature values associated with each leaf node
D) The class labels assigned to each leaf node

4. Which statement is true regarding model comparison within SAS Visual Statistics?

A) A linear regression and a generalized linear model cannot be compared.
B) Two cluster analyses cannot be compared if they have different initial seed assignments.
C) A logistic regression and a decision tree cannot be compared.
D) Two generalized linear models cannot be compared if they use different underlying distributions.

5. Refer to the exhibits:


An analyst is evaluating which model to use.
What changed from Comparison 1 to Comparison 2 to select a different champion model?

A) Misclassification was used instead of Cumulative Lift
B) Analyst clicked on Model 2 to highlight it
C) Prediction Cutoff reduced from .5 to .19
D) Percentile changed from 5th percentile to 40th percentile

Solutions: