# Statistics

- Credits: 7
- Ending: Examination
- Range: 2P + 2C
- Semester: summer
- Year: 1
- Faculty of Business Economics with seat in Košice

## Teachers

## Included in study programs

**Teaching results**

The main educational goal of the course is:

• acquaint students with the principles of basic, elementary statistical methods,

• teach students to apply appropriate statistical methods in solving practical problems in the field of economic practice,

• to support students' awareness in the selection, evaluation, identification and interpretation of the results of the quantitative methods used.

Knowledge:

The successful graduate of the course will gain knowledge from the application of basic, elementary statistical methods, which can be applied in decision-making in various areas of economic practice and will use them appropriately in the study of other economic subjects, processing of theses.

Skills:

The graduate can implement, perform basic, elementary statistical analysis, construct hypotheses, solve associations between indicators, draw relevant conclusions from applied statistical procedures. The student applies the acquired theoretical knowledge to solve specific economic problems.

Competences:

After completing the course, the student is able to solve and analyze the problems of economic practice by applying appropriate statistical methods and procedures, interprets the results in a suitable way and draws conclusions based on empirical results.

**Indicative content**

Lectures:

1. Basic concepts, steps of statistical analysis.

2. Presentation of statistical features. Classification.

3. Statistical characteristics. Graphic presentation.

4. Random phenomena, random selection, statistical induction.

5. Point and interval estimation of basic parameters.

6. Testing statistical hypotheses.

7. Goodness-of-fit tests.

8. Normality tests. Tests of extreme values.

9. Elementary methods of dependency description.

10. Correlation.

11. Simple linear regression function.

12. Contingency coefficients.

13. Analysis of variance.

Seminars:

1. Descriptive statistics (unsorted statistical set - characteristics of position and variability).

2. Descriptive statistics (unsorted statistical set - moments of the statistical set, histogram, Box-plot).

3. Descriptive statistics (variational classification - characteristics of position and variability).

4. Descriptive statistics (variational classification - moments of the statistical set, histogram, Box-plot).

5. Point and interval estimation of statistical file parameters.

6. Testing statistical hypotheses – parameter tests.

7. Pearson's goodness-of-fit test.

8. Kolmogorov and Kolmogorov-Smirnov goodness-of-fit test.

9. Normality tests using skewness and kurtosis.

10. Grubbs and Dixon test of extreme values.

11. Written examination.

12. Pearson correlation coefficient. Spearman's order correlation coefficient. Simple linear regression. Tests of statistical significance and confidence intervals of estimates.

13. Contingency coefficients. ANOVA models.

**Support literature**

1. PACÁKOVÁ, V. a kol.: Štatistické metódy pre ekonómov. Bratislava: IURA EDITION, 2009.

2. KOTLEBOVÁ, E. a kol.: Štatistika pre bakalárov v praxi. Bratislava: Ekonóm, 2017.

3. PACÁKOVÁ, V. a kol.: Štatistika pre ekonómov. Zbierka príkladov A. Bratislava: Iura Edition, 2005.

4. TKÁČ, M.: Štatistické riadenie kvality. Bratislava: Ekonóm, 2001.

5. HINDLS, R. – HRONOVÁ, S. – SEGER, J.: Statistika pro ekonomy. Praha: Profesional Publishing, 2004.

6. ŠOLTÉS, E. a kol.: Štatistické metódy pre ekonómov. Zbierka príkladov. Bratislava: Wolters Kluwer, 2018.

7. MCCLAVE, J. T. – BENSON, P. G. – SINCICH, T.: Statistics For Business and economics (13th ed.). Pearson Education, UK, 2018.

8. WONNACOTT, T. H. – WONNACOTT, R. J.: Statistics for Business and Economics. New York : J. Wiley, 1984.

**Syllabus**

Lectures: 1. Basic concepts, steps of statistical analysis. 2. Presentation of statistical features. Classification. 3. Statistical characteristics. Graphic presentation. 4. Random phenomena, random selection, statistical induction. 5. Point and interval estimation of basic parameters. 6. Testing statistical hypotheses. 7. Goodness-of-fit tests. 8. Normality tests. Tests of extreme values. 9. Elementary methods of dependency description. 10. Correlation. 11. Simple linear regression function. 12. Contingency coefficients. 13. Analysis of variance. Seminars: 1. Descriptive statistics (unsorted statistical set - characteristics of position and variability). 2. Descriptive statistics (unsorted statistical set - moments of the statistical set, histogram, Box-plot). 3. Descriptive statistics (variational classification - characteristics of position and variability). 4. Descriptive statistics (variational classification - moments of the statistical set, histogram, Box-plot). 5. Point and interval estimation of statistical file parameters. 6. Testing statistical hypotheses – parameter tests. 7. Pearson's goodness-of-fit test. 8. Kolmogorov and Kolmogorov-Smirnov goodness-of-fit test. 9. Normality tests using skewness and kurtosis. 10. Grubbs and Dixon test of extreme values. 11. Written examination. 12. Pearson correlation coefficient. Spearman's order correlation coefficient. Simple linear regression. Tests of statistical significance and confidence intervals of estimates. 13. Contingency coefficients. ANOVA models.

**Requirements to complete the course**

individual work, written work

combined exam

• written examination - 40 %

• combined exam - 60 %

**Student workload**

• participation in lectures - 26 hours

• participation in exercises - 26 hours

• preparation for exercises - 26 hours

• preparation for the semester test - 26 hours

• preparation for the exam - 78 hours

Total: 182 hours

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

Slovak

Date of approval: 14.02.2023

Date of the latest change: 26.01.2022