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Last Updated: Sep 09, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Analysis of Variance (ANOVA) | 10% | - Analyze factor interactions - Assess ANOVA assumptions - Perform post-hoc tests - Use PROC GLM for ANOVA |
| Prepare Inputs for Predictive Modeling | 20% | - Transform categorical variables - Screen variables with CORR procedure and empirical logit plots - Identify data preparation challenges - Manipulate data using DATA step |
| Measure and Evaluate Model Performance | 25% | - Determine optimal decision cut-off values - Use confusion matrices and classification metrics - Create and interpret ROC, lift, and gains charts - Validate models with training/validation data - Apply honest assessment principles |
| Logistic Regression | 25% | - Interpret odds ratios and model coefficients - Select and optimize input variables - Build models using PROC LOGISTIC - Score new datasets with LOGISTIC and PLM |
| Linear Regression | 20% | - Perform model selection with GLMSELECT - Conduct residual and diagnostic analysis - Interpret REG, GLM, and PLM procedure output - Fit multiple linear regression models |
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