Syllabus
1. Introduction - Simple linear regression
2. Linear regression model, least squares method
3. Properties of LS estimates
4. Statistical inference in LR model
5. Predictions
6. Model Checking and Diagnostic Methods (residuals)
7. Transformation of the response
8. Parametrization of a single covariate
9. Interactions
10. Analysis of variance (ANOVA) models
11. Multiple tests and simultaneous confidence intervals
12. Regression model with multiple covariates
13. Regression Models With Heteroskedastic Data (weighted least squares, sandwich estimation)
14. Sources of Bias in Regression Estimation (Covariate measurement errors, sampling bias)
Annotation
Linear regression model, also without classical assumptions (normality, constant variance, uncorrelated errors), simultaneous testing, residual analysis and regression diagnostics.