# Statistical Methods II

- Credits: 6
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 2
- Faculty of Economic Informatics

## Teachers

## Included in study programs

**Teaching results**

After successful completion of this class, students will be able to analyze relationship between 2 statistical variables by means of simple linear regression, correlation analysis and categorical data analysis. Moreover, students will be able to do analyses of economic indicators based on time series analysis and index numbers.

In particular, students will acquire the following abilities:

− Students will acquire knowledge about the terms, principles and methods used in the mentioned areas of statistics.

Students will acquire in particular the following skills:

− Students will be able to perform calculations for the relevant statistical procedures (simple linear regression analysis, correlation analysis, analysis of contingency table, time series analysis, index numbers), both by their own calculations as well as with the use of a statistical software (e.g. Statgraphics, SAS).

− Students will learn to adequately interpret the results.

Students will acquire the following competencies:

− Students will be able to use the above stated knowledge and skills in solving practical tasks from economic practice.

**Indicative content**

The course Statistical Methods II provides students with basic knowledge of 4 areas of statistics, namely regression and correlation analysis, analysis of categorical data, time series analysis, comparison in statistics (index numbers). This knowledge is necessary for the analysis of relationships of 2 statistical variables and for the analysis of changes and development of 1 statistical variable over time. The whole course Statistical Methods (I and II) provides the knowledge and skills necessary for the acquisition of other statistical and econometric methods and procedures.

**Support literature**

Labudová, V., Pacáková, V., Sipková, Ľ., Šoltés, E., Vojtková, M. (2021). Štatistické metódy pre ekonómov a manažérov. Bratislava: Iura Edition.

Šoltés, E. a kol. (2018). Štatistické metódy pre ekonómov – zbierka príkladov. Bratislava: Iura Edition.

Marek, L. a kol. (2007). Statistika pro ekonomy. Praha: Professional Publishing.

Marek, L. a kol. (2015). Statistika v příkladech (2. vyd.). Praha: Kamil Mařík – Professional Publishing.

Johnson, R. A., Bhattacharyya, G. K. (2019). Statistics: principles and methods. John Wiley & Sons.

Literature will be continuously updated with the latest scientific and professional titles.

**Syllabus**

Syllabus: 1. Introduction to simple linear regression. Least squares method. Model assumptions. 2. Overall significance of a regression. Statistical inference for parameters of regression model. 3. Prediction. Confidence interval for an individual prediction and confidence interval for the expected value (mean) of the dependent variable. 4. Correlation analysis. Pearson correlation coefficient and coefficient of determination (including statistical inference). 5. Assumptions of the classical linear regression model. Graphical analysis of residuals. Nonlinear models that are intrinsically linear. Choice of regression model. 6. Analysis of contingency tables. Chi-square test of independence. 7. Introduction to time series analysis. Elementary characteristics. Components of time series. 8. Regression models for time trends. Forecasting. Forecast accuracy measures. 9. Moving averages. Time series decomposition. 10. Regression approaches to the seasonal component of time series. 11. Simple index numbers. 12. Aggregate index numbers. 13. Summary.

**Requirements to complete the course**

30% assignments (2 assignments)

70% final exam (30% theoretical part, 40% practical part)

**Student workload**

Total study load (in hours): 156 hours

Distribution of study load

Lectures participation: 26 hours

Seminar participation: 26 hours

Preparation for seminars: 26 hours

Preparation for assignments: 39 hours

Preparation for final exam: 39 hours

**Language whose command is required to complete the course**

Slovak

Date of approval: 10.02.2023

Date of the latest change: 17.05.2022