Probability and probability theory in business 2600-ABdz1PRPkf
This course will discuss methods for modelling random phenomena using discrete and continuous random variables. In both cases, the expected values and variances of the relevant random variables will be calculated.
When modelling random phenomena with a finite number of possible outcomes, discrete random variables and the probability distribution of the variable will be introduced, and combinatorial formulas will be used in the calculations. The course will discuss examples of basic distributions: uniform discrete, binomial, and Poisson.
For random phenomena with a potentially infinite number of outcomes, continuous random variables and probability density will be defined, and methods of integral calculus will be used in the calculations. Properties of example distributions will be discussed: uniform, exponential, normal, Student's t-value, and chi-square.
The course will conclude with the central limit theorem, which enables statistical inference in business analytics.
Type of course
Course coordinators
Learning outcomes
K_W01 – The student has a thorough knowledge and understanding of research methodology and terminology within the discipline of management and quality science, particularly in the field of business data analysis and complementary disciplines (economics and finance).
K_W02 – The student has a thorough knowledge and understanding of complex processes and phenomena occurring in various types of organizations and in the world around them. He/she uses management theory to identify, diagnose, and solve problems related to the functioning of the organization and their integration within the organizational strategy based on analytical results.
K_U01 – The student is able to use the theory of management and quality science, particularly in the field of numerical data analysis, to identify, diagnose, and solve complex and unusual problems related to key functions within the organization, including inference, strategy development, and business decision-making.
K_U04 – The student is able to formulate and test hypotheses related to simple research problems.
K_U05 – The student is able to propose solutions to tasks set in unpredictable conditions. K_U06 – The student is able to communicatively present the results of management analyses to diverse audiences using specialized terminology and lead debates, also in English.
K_U09 – The student is able to improve acquired skills and support others in this area, and has the capacity for self-education.
K_K01 – The student is ready to evaluate and critically approach complex situations and phenomena related to data analysis and their impact on the functioning of the organization.
Assessment criteria
Written test consisting of open-ended tasks, passing: minimum 60% of possible points.
Practical placement
Professional practice is not required
Bibliography
1. William Feller, Wstęp do rachunku prawdopodobieństwa, Wydawnictwo Naukowe PWN, Warszawa 2012
2. Jacek Jakubowski, Rafał Sztencel, Rachunek prawdopodobieństwa dla (prawie) każdego, Script, Warszawa 2006
3. Dorota Witkowska, Podstawy ekonometyrii i teorii prognozowania, Oficyna a Wolter Kluwer business, Warszawa 2012
4. Ronald L. Graham, Donald E. Knuth, Oren Patashnik, Matematyka konkretna, Wydawnictwa Naukowe PWN, Warszawa 2012
5. William Mendenhall, Robert J. Beaver, Introduction To Probability And Statistics, Duxbury Press, Belmont 1994
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: