Quantitive mathods in Management 2600-MSMz1MIZ
Lecture:
I. Models of decision making
1. Optimization issues in management.
Linear, integer and dynamic problems
2. Project management (web programming)
The CPM critical path method and the PERT method
II. Fundamentals of Econometrics
3. Statistical data
Types, probabilistic properties, data segmentation, inference
4. Cause-effect relationships and correlation relationships
Methods and interpretations; apparent dependence;
5. The analysis of causal relationships with the use of regression models
a. model building and methods of parameter estimation,
b. inference and interpretation;
c. verification of the model (considerably the assumptions for the NMK).
6. Forecasting with the use of a regression model
Exercises:
Classes will be held in a computer room in the MsExcel, Gretl, Eviews or R environment with the use of examples of empirical analysis of data sets in the field of management:
I. Models of decision making
1. Optimization issues in management.
Case study: (1) Optimizing the location of objects; (2) The transport issue; (3) The investment portfolio problem; (4) Multi-period production and inventory management
2. Project management
Case study: Time-cost analysis of a defined project
II. Data analysis for management
3. Statistical data.
Case study: (1) Obtaining statistical data from various available sources; (2) Statistical analysis of data combined with segmentation and inference
4. Cause-effect relationships and correlation relationships
Case study: Defining the problem, obtaining statistical data, analysis of correlation and causality;
5. Analysis of cause-effect relationships with the use of regression models.
Case study :, Building and estimating a regression model, model specification tests
Case study: Relevance testing and interpretation of the regression model;
Case study: Verification of the regression model
6. Forecasting with the use of a regression model
Case study: making forecasts and ex ante accuracy assessment
Type of course
Mode
Course coordinators
Learning outcomes
After completing the course, the student:
In terms of knowledge:
• EW_1:
correctly uses terminology related to quantitative research methodology in the field of econometrics. (K_W01)
• EW_2:
identifies the differences between causal relationships and correlational dependencies. (K_W01)
• EW_3:
identifies different types of statistical data, such as cross-sectional data, time series, and panel data. (K_W01)
• EW_4:
explains the basic probabilistic properties of statistical data. (K_W01)
• EW_5:
explains the basic assumptions and limitations related to the use of optimization models. (K_W01)
In terms of skills:
• EU_1:
applies appropriate statistical methods to analyze relationships between variables. (K_U01)
• EU_2:
interprets analysis results, distinguishing between spurious and real relationships. (K_U01)
• EU_3:
utilizes regression models to generate forecasts and assess ex ante accuracy in presented empirical examples. (K_U01)
• EU_4:
applies optimization methods in solving decision problems, e.g., in resource management. (K_U01)
• EU_5:
analyzes results in terms of statistical significance and the accuracy of the econometric model. (K_U01)
• EU_6:
solves problems using examples of business data with the use of information and communication tools, such as Excel, GRETL, EVIEWS, STATA, or R. (K_U06)
In terms of social competencies:
• EK_1:
assesses the quality of regression models built on real data related to a specific management problem. (K_K01)
• EK_2:
evaluates the accuracy of forecasts based on regression models by comparing them with real data. (K_K01)
• EK_3:
evaluates the effectiveness of applied optimization methods in the context of a specific management problem. (K_K01)
Assessment criteria
Receives more than 50% of the maximum number of points
Bibliography
Base:
Kukuła K. (red.), Badania operacyjne w przykładach i zadaniach, PWN, Warszawa, 2011
Gruszczyński M. i In., Ekonometria i badania operacyjne, PWN, Warszawa, 2009.
Borkowski B., Dudek H., Szczesny W., Ekonometria, wybrane zagadnienia. PWE, Warszawa 2003 i dalsze wydania.
Turynao B., Statystyka dla ekonomistów, Difin, Warszawa, 2011
Additional:
Rószkiewicz M., Metody ilościowe w badaniach marketingowych. PWN, Warszawa 2018.
Radzikowski W., Badania operacyjne w zarządzaniu przedsiębiorstwem. Wydawnictwo Uniwersytetu im. M. Kopernika w Toruniu, Toruń 1997.
Gajda J., Ekonometria praktyczna, Absolwent, Łódź 1996.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: