Quantitative Analyzes and Forecasting

Teachers

Included in study programs

Teaching results

The main educational goal of the course is:
• acquaint students with the principles, knowledge, main goals in the field of quantitative analysis and basic forecasting procedures,
• acquaint students with the stages of implementation of quantitative analysis, types of methods and approaches to quantitative analysis and prognosis,
• teach students to practically implement relevant analyzes and forecasting methods,
• get acquainted with primary and secondary data sources that are suitable for the implementation of analyzes and forecasting in the field of trade and marketing,
• gain knowledge about the possibilities of quantitative data processing using software products,
• to teach students to correctly draw conclusions from realized analyzes and forecasting methods,
• gain an analytical approach to solving economic problems that can applied to the field of economic practice.
Knowledge:
Successful graduates of the course will gain knowledge of quantitative analysis and basics of prognostic approaches in marketing, primary and secondary data sources for trade and marketing, how to evaluate qualitative and quantitative data, application of software products in processing analyzes, drawing conclusions from the implemented analytical procedures, which they can apply in decision-making in various areas of economic practice and will use them appropriately in the study of other subjects and the processing of final theses.
Skill:
The graduate is able to implement, take steps to perform a quantitative analysis in the field of trade and marketing, apply appropriate methods of analysis of qualitative and quantitative data of the primary resp. secondary research, uses a suitable statistical software product in solving analyzes, draws relevant conclusions from the applied quantitative procedures. The student applies the acquired theoretical knowledge to the practical solution of specific problems in the field of quantitative analysis and elementary forecasting.
Competence:
After completing the course the student is able to solve and analyze problems related to the application of one- and multivariate quantitative methods for analysis in marketing and business, assess the context of used quantitative methods, can think analytically, apply creative thinking in obtaining and processing relevant data, can orient in basic databases for the selection of secondary research indicators, to carry out quantitative analysis and evaluate its conclusions, to present conclusions and recommendations for the following periods in an appropriate manner.

Indicative content

Lectures:
1. Basic and advanced methods of quantitative analysis.
2. Empirical and graphical approaches to the analysis of categorical, ordinal and cardinal features.
3. Advanced approaches to data processing and analysis, problem solving in data processing.
4. Application of inductive statistics, software solutions.
5. Investigation of dependencies of qualitative and quantitative features, software solutions.
6. Linear and nonlinear regression models.
7. Multidimensional regression models.
8. Regression models and their use in forecasting.
9. One-factor and multi-factor analysis of variance.
10. Multifactor analysis of variance, software solutions.
11. Nonparametric analysis of variance.
12. Presentation of results, outputs of software solutions to the tasks of qualitative and quantitative analysis and forecasts.
13. Overview of other approaches to quantitative analysis, software products.
Seminars:
1. Application of appropriate basic and advanced methods of quantitative analysis.
2. Empirical and graphical approaches to the analysis of categorical, ordinal and cardinal features, solving practical cases.
3. Practical approaches to advanced data processing and analysis, problem solving in data processing.
4. Application of inductive statistics, software solutions, practical examples of application of inductive statistics.
5. Investigation of dependencies of qualitative and quantitative features in the software product environment.
6. Linear and nonlinear regression models, examples and possible solutions using a software product.
7. Multidimensional regression models, solving practical problems using a software product.
8. Regression models and their use in forecasting.
9. One-factor and multi-factor analysis of variance.
10. Multifactor analysis of variance, practical cases of application of methods in a software product environment.
11. Nonparametric analysis of variance, solution of practical cases in the software product environment.
12. Presentation of the semester assignment and discussion.
13. Presentation of the semester assignment and discussion.

Support literature

1. STOCKEMER, D. (2019). Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in SPSS and Stata. Springer, 2019. ISBN-13: 978-3319991177.
2. MOORE, D., McCABE, G., CRAIG, B., ALWAN, L. (2020). The Practice of Statistics for Business and Economics. WH Freeman, 2020. ISBN-13: 978-1319324810.
3. CHRISTENSEN, R. (2020). Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data. Routledge, 2020. ISBN-13: 978-0367737405.
4. MAXWELL, R. (1999). A Student´s Guide to Analysis of Variances. Routledge: Student edition, 1999. ISBN-13: 978-0415165655.
5. COHEN, J., WEST, S.G., AIKEN, L.S., COHEN, P. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge, 2002. ISBN-13-978-0805822236.

Syllabus

Lectures: 1. Basic and advanced methods of quantitative analysis. 2. Empirical and graphical approaches to the analysis of categorical, ordinal and cardinal features. 3. Advanced approaches to data processing and analysis, problem solving in data processing. 4. Application of inductive statistics, software solutions. 5. Investigation of dependencies of qualitative and quantitative features, software solutions. 6. Linear and nonlinear regression models. 7. Multidimensional regression models. 8. Regression models and their use in forecasting. 9. One-factor and multi-factor analysis of variance. 10. Multifactor analysis of variance, software solutions. 11. Nonparametric analysis of variance. 12. Presentation of results, outputs of software solutions to the tasks of qualitative and quantitative analysis and forecasts. 13. Overview of other approaches to quantitative analysis, software products. Seminars: 1. Application of appropriate basic and advanced methods of quantitative analysis. 2. Empirical and graphical approaches to the analysis of categorical, ordinal and cardinal features, solving practical cases. 3. Practical approaches to advanced data processing and analysis, problem solving in data processing. 4. Application of inductive statistics, software solutions, practical examples of application of inductive statistics. 5. Investigation of dependencies of qualitative and quantitative features in the software product environment. 6. Linear and nonlinear regression models, examples and possible solutions using a software product. 7. Multidimensional regression models, solving practical problems using a software product. 8. Regression models and their use in forecasting. 9. One-factor and multi-factor analysis of variance. 10. Multifactor analysis of variance, practical cases of application of methods in a software product environment. 11. Nonparametric analysis of variance, solution of practical cases in the software product environment. 12. Presentation of the semester assignment and discussion. 13. Presentation of the semester assignment and discussion.

Requirements to complete the course

individual work, written work
combined exam
• written examination – 30 %
• semester assignment – 10 %
• combined exam – 60 %

Student workload

• participation in lectures – 26 hours
• participation in exercises – 26 hours
• preparation for exercises – 13 hours
• preparation for the semester test – 26 hours
• preparation for the semester assignment – 13 hours
• preparation for the exam – 26 hours
Total: 130 hours

Language whose command is required to complete the course

English

Date of approval: 20.02.2023

Date of the latest change: 25.07.2022