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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analysis of Variance (ANOVA) | 10% | - Assess ANOVA assumptions - Perform post-hoc tests - Analyze factor interactions - Use PROC GLM for ANOVA |
| Topic 2: Logistic Regression | 25% | - Interpret odds ratios and model coefficients - Select and optimize input variables - Score new datasets with LOGISTIC and PLM - Build models using PROC LOGISTIC |
| Topic 3: Prepare Inputs for Predictive Modeling | 20% | - Transform categorical variables - Screen variables with CORR procedure and empirical logit plots - Manipulate data using DATA step - Identify data preparation challenges |
| Topic 4: Linear Regression | 20% | - Interpret REG, GLM, and PLM procedure output - Conduct residual and diagnostic analysis - Fit multiple linear regression models - Perform model selection with GLMSELECT |
| Topic 5: Measure and Evaluate Model Performance | 25% | - Use confusion matrices and classification metrics - Determine optimal decision cut-off values - Apply honest assessment principles - Create and interpret ROC, lift, and gains charts - Validate models with training/validation data |
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