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Predictive Modeling Using Logistic Regression Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
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- Stastical Analyst, or
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SASInstitute A00-240 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Measure and Evaluate Model Performance | 25% | - Apply honest assessment principles - Create and interpret ROC, lift, and gains charts - Use confusion matrices and classification metrics - Validate models with training/validation data - Determine optimal decision cut-off values |
| 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% | - Manipulate data using DATA step - Identify data preparation challenges - Transform categorical variables - Screen variables with CORR procedure and empirical logit plots |
| Topic 4: Linear Regression | 20% | - Fit multiple linear regression models - Conduct residual and diagnostic analysis - Interpret REG, GLM, and PLM procedure output - Perform model selection with GLMSELECT |
| Topic 5: Analysis of Variance (ANOVA) | 10% | - Use PROC GLM for ANOVA - Analyze factor interactions - Assess ANOVA assumptions - Perform post-hoc tests |
