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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

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

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

2. Why are decision trees a useful technique for segmentation?

A) Trees split data into smaller cells which are decreasingly "pure".
B) Trees treat each split cell dependently based on earlier split decisions.
C) Trees split the response into homogeneous groups, while maximizing the difference in the response of the groups.
D) Trees split the response into heterogeneous groups, while minimizing the difference in the response of the groups.

3. What do cluster properties refer to in the context of cluster analysis?

A) The number of clusters in the analysis
B) A measure of data point centrality
C) Characteristics shared by data points within a cluster
D) Descriptive statistics of individual data points

4. What can be interpreted from the Summary Table for model comparison, including statistics and variable importance?

A) The correlation matrix of predictor variables
B) Model performance metrics and their relative importance
C) The distribution of predictor variables
D) The number of iterations performed by each model

5. In SAS Visual Analytics, what can you use to choose the best fitting group-by model?

A) Fit statistics such as AIC and BIC
B) Visualization of model residuals
C) Variable Importance
D) Group-by variable with the highest p-value

Solutions: