Objectives and competences
Students:
1. nhance their theoretical knowledge in the field of statistical methods.
2. Gain the ability to apply their theoretical knowledge in practice.
3. Acquire advanced knowledge of basic theoretical approaches in the field of business decision making using statistical methods.
4. Acquire statistical approach to analysis of business problems and business decision making.
5. They are aware of ethical and sustainable aspects in business decision-making, both in obtaining data and in their analysis and in evaluating proposed solutions.
Content (Syllabus outline)
- Basics of important probability distributions
- Sampling and interval estimates
- sampling and the quality of sampling data
- survey design, sampling methods
- analysis of research: weighting, standard error of estimation, sample variance, sampling frame, non-responses;
- number of sampling units
- Hypothesis testing: one variable, one sample:
- Basics of hypothesis testing: estimation of parameters, hypothesis testing – two or more samples – independent and paired, related samples, ANOVA; association and dependencies
- Analysis of variance
- Multiple regression
- All discussed quantitative methods are related to business decision-making based on cases that address the sustainability aspects and goals of sustainable development.
- The use of critical analysis in solving specific professional problems so that the solution of business problem cases can be considered through the aspects of achieving the goals of sustainable development.
- The content also includes reviews of secondary databases that address the goals of sustainable development and their objectivity in different ways (for example, GEM, which includes an analysis of the consideration of social and environmental responsibility goals when making business decisions by entrepreneurs).
- Use of SPSS in all chapters.
Learning and teaching methods
- lectures
- AV presentations
- case studies
- active individual and group work
- flipped learning using AI tools
Intended learning outcomes - knowledge and understanding
Development of knowledge and understanding:
Students:
1. Acquire specific knowledge in the field of mathematical and statistical methods for business problem solving.
2. Develop the skills to interpret the gained results by mathematical and statistical methods.
3. Learn how to analyse and synthesise different approaches in decision making.
4. Can demonstrate awareness of wider ethical, social and sustainable development issues in the area of statistical data analysis.
Intended learning outcomes - transferable/key skills and other attributes
Development of knowledge and understanding:
Students:
1. Acquire specific knowledge in the field of mathematical and statistical methods for business problem solving.
2. Develop the skills to interpret the gained results by mathematical and statistical methods.
3. Learn how to analyse and synthesise different approaches in decision making.
4. Can demonstrate awareness of wider ethical, social and sustainable development issues in the area of statistical data analysis.
Cognitive/Intellectual skills:
Students:
1. Understand and apply critical analysis and theory development and their usability in solving real professional problems, with minimum guidance.
2. Synthesize different knowledge and procedures and are aware of importance of use of professional literature.
3. Can select appropriate techniques for problem solving and are able to evaluate the importance and significance of data.
4. They know how to identify a problem from different points of view, making a deep assessment of aspects of sustainability and social responsibility.
Key/Transferable skills
Students:
1. Further develop skills and expertise in the use of knowledge in a specific working area.
2. Upgrade the ability to become an autonomous learner.
3. Upgrade the ability to apply information technology.
4. Further develop their communication skills in an effective manner to effectively and professionally communicate.
5. Develop the ability to critically evaluate the use of AI tools.
Practical skills:
Students:
1. Are able to act autonomously with defined guidelines and certain level of supervision, in the field of statistical data analysis and interpretation of results.
Readings
Tominc, P. , Rožman, M. (2021). Statistična poslovna analiza, Univerza v Mariboru, Univerzitetna založba.
https://press.um.si/index.php/ump/catalog/book/572
Tominc, P. (2016). Poslovna statistika II, Maribor: EPF.
Izbrana poglavja iz učbenikov
McClave, J.T., Benson, P.G., Sincich, T. (2011). Statistics for Business and Economics, Pearson Prentice Hall.
Additional information on implementation and assessment - 20 % active participation in class discussions;
- 30 % seminar research work;
- 50 % written examination
The exam is passed when 28 out of 50 points are obtained by written axam AND at the same time, the sum together with points obtained by the active participation in class, equals at least 56 points (out of all 100).
