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