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1. ANOVA - 10%
2. Linear Regression - 20%
3. Logistic Regression - 25%
4. Prepare Inputs for Predictive Model Performance - 20%
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| Section | Objectives |
|---|---|
| Topic 1: Multiple Regression | - Interaction effects - Model building and selection - Multicollinearity diagnostics |
| Topic 2: Simple Linear Regression | - Estimation and inference - Model assumptions and interpretation - Residual analysis |
| Topic 3: Model Diagnostics and Validation | - Goodness-of-fit measures - Influence and leverage |
| Topic 4: Logistic Regression | - Binary logistic regression - Model fit and diagnostics |
| Topic 5: ANOVA and General Linear Models | - GLM procedures in SAS - One-way and multi-way ANOVA |
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