1. Introduction to econometrics ( subject of economterics, metodology of econometrics)
2. Simple linear regression function ( the nature of regression analysis, the concept of population and sample regression function ( deterministic and stochastic version), the method of ordinary least squares, coefficient of determination)
3. Statistical verification ( testing od regression coefficients, the overall of sample regression model)
4. Autocorrelation (the nature, the consequences of autocorrelation, detection, removing).
5. Heteroscedasticity ( the nature, its consequences, detection, removing, WOLS)
7. Multicollinearity ( ( the nature, its consequences, detection, removing).
8. Model specification (model selection criteria, types of specification errors, consequences, tests)
9. Prediction (ex-post and ex-ante, mean and individual prediction, point and interval prediction).
10. Functional form of regression models (exponencial regression model, semilog models, reciprocal models).
11. Dummy variable regression models.
2. Simple linear regression function ( the nature of regression analysis, the concept of population and sample regression function ( deterministic and stochastic version), the method of ordinary least squares, coefficient of determination)
3. Statistical verification ( testing od regression coefficients, the overall of sample regression model)
4. Autocorrelation (the nature, the consequences of autocorrelation, detection, removing).
5. Heteroscedasticity ( the nature, its consequences, detection, removing, WOLS)
7. Multicollinearity ( ( the nature, its consequences, detection, removing).
8. Model specification (model selection criteria, types of specification errors, consequences, tests)
9. Prediction (ex-post and ex-ante, mean and individual prediction, point and interval prediction).
10. Functional form of regression models (exponencial regression model, semilog models, reciprocal models).
11. Dummy variable regression models.