Statistical Inference
- Credits: 6
- Ending: Examination
- Range: 2P + 2C
- Semester: summer
- Year: 2
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
At the end of the semester, students will have a good overview of inference methods used in statistics, more specifically:
In particular, students acquire the following abilities:
- Students will acquire knowledge about the principles of individual methods as well as about the contents between different methods so that they can be properly decided in the real situation. They will be able to interpret the methods results correctly.
Students acquire in particular the following skills:
- Students will be able to apply methods of statistical inference in appropriate situations and verify the assumptions of their use
Students will acquire the following competencies:
- Students will be able to realize a qualified analysis of data from the selection survey, creatively approaching the absent prerequisite for some methods, qualified to interpret the results in the necessary contexts
Indicative content
The course provides comprehensive knowledge of the theoretical principle, assumptions and procedures for inference methods so that students will adequately use them in practice. In addition to points and interval estimates, a great emphasis is given on testing hypotheses that are part of various statistical procedures (mainly for verification of assumptions and to verify statistical significance). The course deals also with non-parametric tests that may be widely used if the assumptions of numeric variables distribution are not met.
Support literature
1. Kotlebová a kol. (2015). Štatistická indukcia v príkladoch. Bratislava: Ekonóm.
2. Malá, I. (2013). Statistické úsudky. Praha: Professional Publishing.
3. Garthwaite, P. H., Jolliffe, I. T. (1995). Statistical Inference. Prentice-Hall International, Inc.
4. Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J. (2016). Statistics for business and economics. Nelson Education.
5. Pacáková, V. a kol. (2012). Štatistická indukcia pre ekonómov (1. vyd.). Bratislava: Ekonóm.
6. Pacáková, V. a kol. (2015). Štatistické indukcia pre ekonómov a manažérov. Bratislava: Wolters Kluwer.
7. Liu, H. (2015). Comparing Welch ANOVA, a Kruskal-Wallis test, and traditional ANOVA in case of heterogeneity of variance. Richmond, Virginia: Virginia Commonwealth University.
8. Blatná, D. (1996). Neparametrické metody. Praha: VŠE.
Literature will be continuously updated with the latest scientific and professional titles.
Syllabus
1. Introduction: Random variable – basic concepts, properties and characteristics. 2. Discrete and continuous random variables. 3. Point estimation of the population parameters – principle and methods of the point estimation. 4. Interval estimation of the population parameters. 5. Hypothesis testing. 6. Inference conclusions of two populations parameters. 7. Analysis of variance. 8. Analysis of categorical data independency. 9. Goodness of fit-tests. 10. Nonparametric tests – the principle, comparing with parametric tests, randomity tests, tests of population parameters. 11. Nonparametric tests comparing two populations. 12. Nonparametric tests comparing more than two populations. 13. Summary.
Requirements to complete the course
30 % assignments (2 assignments)
70 % final exam (35% theoretical part, 35% practical – examples solution)
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: 26 hours
Final exam preparation: 52 hours
Language whose command is required to complete the course
Slovak
Date of approval: 11.03.2024
Date of the latest change: 03.02.2022