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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and Assessing Segmentation Models | 28% | - Perform supervised segmentation using decision trees - Analyze and interpret cluster results - Perform unsupervised segmentation using cluster analysis - Assess and interpret decision tree performance |
| Topic 2: SAS Visual Statistics Cross-functional Tasks | 22% | - Perform model validation - Filter data used for a model - Prepare data using SAS Visual Analytics - Use interactive group-by functionality |
| Topic 3: Building and Assessing Regression-type Models | 41% | - Explain concepts of linear models - Perform nonparametric logistic regression modeling - Assess model fit and diagnostics - Perform generalized additive modeling - Perform generalized linear regression modeling - Perform linear regression modeling |
| Topic 4: Model Comparison and Scoring | 9% | - Compare multiple models using fit statistics - Apply models to score new data |
Question 1
When exporting score code, what information is typically included in the code?
A. The entire training dataset
B. The distribution of the response variable
C. The model parameters and coefficients
D. Instructions for preprocessing new data
Question 2
Which statement is true regarding the number of polylines created in a Parallel Coordinates plot during Cluster Analysis?
A. It is calculated using the number of variables used in the cluster analysis.
B. It is calculated using the number of variables visible in the plot.
C. It is calculated using the variable with the greatest number of bins.
D. It cannot exceed 3,000.
Question 3
What do cluster properties refer to in the context of cluster analysis?
A. Characteristics shared by data points within a cluster
B. The number of clusters in the analysis
C. Descriptive statistics of individual data points
D. A measure of data point centrality
Question 4
How can you interpret a Tree Map in the context of a decision tree?
A. It visualizes the distribution of data points in leaf nodes.
B. It displays the hierarchical structure of the decision tree.
C. It shows the decision boundaries between classes.
D. It summarizes the performance metrics of the decision tree.
Question 5
What are variable selection methods for decision trees used to determine the most important features?
A. F-test for feature significance
B. Principal Component Analysis (PCA)
C. Information gain or Gini impurity reduction
D. Recursive feature elimination
Solutions:
| Question 1 Answer: C,D | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: A,B | Question 5 Answer: C |
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