1. Introduction to econometrics (definition of econometrics, relation to other scientific disciplines, clarification of basic concepts, origin of econometrics, process of econometric modeling).
2. Time series analysis (types of time series, methods of time series, decomposition of time series, regression analysis, model verification).
3. Simple linear regression model (meaning of regression analysis, population versus selective regression line).
4. Least Squares Method (LSM, fit of regression line to data, assumptions of classical simple regression model and their verification).
5. Multiple regression model (definition of classical multivariate linear regression model, assumptions, matrix notation, corrected determination coefficient).
6. Statistical verification (regression coefficients, model as a whole).
7. Econometrical verification - autocorrelation, heteroskedasticity, multicolinearity, normality, model specification.
8. Functional forms (exponential model, LIN-LOG model, LOG-LIN model, reciprocal model) + economic interpretation.
9. Prediction (prediction error, point or interval prediction, ex-post and ex-ante prediction).
10. Techniques of artificial variables - dummy variables.
11. Panel data (definition of panel models, fixed effects (time or space, level constants or slope coefficient), random component effect).