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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and Assessing Regression-Type Models | 40% | - Perform logistic regression modeling - Assess model assumptions and results - Perform generalized linear regression modeling - Explain linear model concepts - Perform linear regression modeling |
| Topic 2: Model Comparison and Scoring | 10% | - Compare multiple models using fit statistics - Generate and apply model score code - Interpret scoring outputs |
| Topic 3: SAS Visual Statistics Cross-Functional Tasks | 18% | - Prepare data using SAS Visual Analytics - Use interactive group-by and variable transformation - Filter and subset data for modeling - Perform model validation and goodness-of-fit assessment |
| Topic 4: Building and Assessing Segmentation Models | 32% | - Assess decision tree performance and splits - Supervised segmentation using decision trees - Unsupervised segmentation using cluster analysis - Interpret and evaluate cluster results |
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