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