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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Measure and Evaluate Model Performance | 25% | - Create and interpret ROC, lift, and gains charts - Apply honest assessment principles - Determine optimal decision cut-off values - Use confusion matrices and classification metrics - Validate models with training/validation data |
| Topic 3: Logistic Regression | 25% | - Interpret odds ratios and model coefficients - Score new datasets with LOGISTIC and PLM - Select and optimize input variables - Build models using PROC LOGISTIC |
| Topic 4: Analysis of Variance (ANOVA) | 10% | - Use PROC GLM for ANOVA - Assess ANOVA assumptions - Analyze factor interactions - Perform post-hoc tests |
| Topic 5: Prepare Inputs for Predictive Modeling | 20% | - Identify data preparation challenges - Screen variables with CORR procedure and empirical logit plots - Manipulate data using DATA step - Transform categorical variables |
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