Objectives and competences
- to understand the basic concepts of descriptive and inferential statistics,
- to be able to prepare data for statistical analysis,
- to understand how to employ basic methods of descriptive and inferential statistics for the study of different phenomena,
- to develop the ability to evaluate the goodnes-of-fit of different models,
- to make convincing presentations of data, interpret results of analyses and take appropriate actions based on the obtained results,
- to be able to use Excel and SPSS (or R) for data analysis.
Content (Syllabus outline)
The content of the course covers the topics (listed below) from the visualization of data to statistical models, based on which we can assess the risk and make decisions in crisis situations.
- Specification, collection, preparation, and cleaning of data
- Presentation of data
- Descriptive statistics: measures of central tendency and of dispersion, frequency distributions, graphics representation of data
- Basic concepts of probability and random variables (discrete and continuous)
- Basic concepts of statistical inference: sampling, interval estimation, hypothesis testing
- Linear correlation and regression: correlation coefficient, regression line, coefficient of determination
- Nonlinear models
- Time series: liner trend, time indices
- Clustering
Learning and teaching methods
- lectures and e-lectures,
- data analysis and problem solving with professor/assistant explanation and student participation in discussion,
- application of programs Excel and R (or SPSS)
- e-learning
- problem-solving
Intended learning outcomes - knowledge and understanding
Students will be:
- able to understand and apply the basic concepts of descriptive statistics,
- able to prepare and evaluate data for statistical analysis,
- able to apply basic inferential statistical methods to concrete examples reflecting the situation before, during and after the crisis,
- familiar with the use of linear and non-linear regression models.
Readings
1. Jesenko, J. (2001). Statistika v organizaciji in managementu (str. 422). Moderna organizacija.
2. Košmelj, B., & Rovan, J. (2007). Statistično sklepanje (2. izd., str. 312). Ekonomska fakulteta.
3. Rovan, J., & Turk, T. (2012). Analiza podatkov s SPSS za Windows (3. izd., str. 280). Ekonomska fakulteta.
4. Andrašec, G., & Bren, M. (2003). Matematika, Naloge iz kombinatorike in verjetnostnega računa (str. 277). Moderna organizacija.
5. Curwin, J., & Slater, R. (2008). Quantitative methods for business decisions (6th ed., str. XIX, 790). Cengage.
6. Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the practice of statistics (7th ed., str. XXIX, 657, 62). W. H. Freeman.
7. Fligner, M. A. (2009). Study guide for Moore, McCabe, and Craig’s Introduction to the practice of statistics (6th ed., str. 280). W. H. Freeman and Company.
8. Levin, R. I., & Rubin, D. S. (1998). Statistics for management (7th ed., str. XVI, 1026 , pril.). Prentice-Hall.
Additional information on implementation and assessment – written exam (composed of theoretical and empirical part) 60%
• written exam can be substituted with two half term exams
– criteria for passing the exam: at least 1/2 of the maximum scores at the written exam and at least 1/2 of the maximum scores at the research project
