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)

Linear regression model, also without classical assumptions (normality, constant variance, uncorrelated errors), simultaneous testing, residual analysis and regression diagnostics.