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| Section | Objectives |
|---|---|
| Topic 1: Predictive Modeling Fundamentals | - Regression modeling concepts
|
| Topic 2: Model Evaluation and Interpretation | - Assessing model performance - Interpreting model outputs and reports |
| Topic 3: Machine Learning Methods in Visual Statistics | - Decision trees and random forests concepts - Clustering techniques |
| Topic 4: Data Preparation and Exploration in SAS Viya | - Exploratory data analysis and visualization - Loading and preparing data for Visual Statistics |
1. Refer to the exhibit:
A cluster has been created with four variables selected on the roles tab.
Select the set of Parallel Coordinates Properties that would display the image in the exhibit.
A)
B)
C)
D) 
2. What are some features of model comparison in SAS Visual Statistics?
A) Comparing the performance of different models
B) Evaluating model stability over time
C) Identifying the best model based on a single metric
D) Assessing the impact of predictor variables on model outcomes
3. What is the primary purpose of modeling effects in linear regression?
A) To assess multicollinearity among predictors
B) To calculate the p-values of the predictors
C) To identify outliers in the data
D) To capture the relationship between predictors and the response variable
4. When creating a Linear Regression, Category Variables can be used for Classification Effects and
A) Continuous Effects
B) Response
C) Group-by
D) Frequency
5. What can you interpret from the Assessment panel in a decision tree analysis?
A) The tree's structural properties, such as depth and number of nodes
B) The feature importance for each variable in the tree
C) The decision boundaries between leaf nodes
D) The tree's training and validation performance metrics
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: D |
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