Standard syllabus
Regression analysis · Undergraduate · Statistics
Topics
Simple linear regression
- Least squares estimation of slope and intercept
- Interpretation of coefficients and R²
- Inference for regression parameters
- Prediction intervals and confidence bands
- Assumptions: linearity, homoscedasticity, normality of errors
Multiple regression
- Matrix formulation of the linear model
- Interpretation of partial coefficients
- F-tests for nested models
- Categorical predictors and dummy coding
- Interaction terms and effect modification
- Multicollinearity: detection and remedies
Model diagnostics
- Residual analysis and influence measures
- Leverage, Cook's distance, and outliers
- Transformations and weighted least squares (intro)
- Variable selection: stepwise and information criteria
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