Mathematical Statistics for Economists

Course: Economic Cybernetics

Structural unit: Faculty of Economics

Title
Mathematical Statistics for Economists
Code
ОК 28
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
3 Semester
Number of ECTS credits allocated
5
Learning outcomes
PLO8. Apply appropriate economic-mathematical methods and models to solve economic problems. PLO12. Apply the acquired theoretical knowledge to solve practical problems and meaningfully interpret the results. PLO13. Identify sources and understand the methodology for determining and methods of obtaining socio-economic data, collect and analyze the necessary information, calculate economic and social indicators. PLO21. Be able to think abstractly, apply analysis and synthesis to identify key characteristics of economic systems at different levels, as well as the behavior of their subjects.
Form of study
Full-time form
Prerequisites and co-requisites
Know: basic concepts of mathematics and probability theory, including methods for calculating the boundaries of numerical sequences and functions, the concept of numerical series, methods for investigating the convergence of numerical series, methods for calculating integrals, set theory, distributions of random variables, limit theorems of probability theory. Possess: skills in calculating the boundaries of numerical sequences, calculating integrals, probabilities of random events.
Course content
The curriculum consists of two content modules: 1. "Elements of the theory of random processes - 2. Mathematical statistics: estimation of unknown parameters" which examines Markov chains with continuous time, elements of queuing theory, martingales, the concept of stochastic differential and integral, stationary processes, basic tasks of mathematical statistics, descriptive statistics, statistical evaluation of unknown parameters of distributions. 2. "Mathematical statistics: testing of statistical hypotheses, elements of regression, variance, discriminant analysis, time series analysis".
Recommended or required reading and other learning resources/tools
Черняк О.І., Обушна О.М.,Ставицький А.В. Теорія ймовірностей та математична статистика. Збірник задач. – К.:Знання, 2002. Черняк О.І., Ляшенко О.І., Кравець Т.В., Банна О.Л. Теорія ймовірностей та математична статистика. Практикум.- Тернопіль: Економічна думка, 2019. Барковський В. В., Барковська Н. В., Лопатін О. К. Теорія ймовірностей та математична статистика. Підручник– К. : Центр учбової літератури, 2019. Кармелюк Г.І. Теорія ймовірностей та математична статистика. Посібник з розв’язування задач: Навч. посібник.– К.: Центр учбової літератури, 2019. Жлуктенко В.І., Наконечний С.І. Теорія ймовірностей і математична статистика: Навч.-метод.посібник. У 2ч. – Ч.І. Теорія ймовірностей. – К.: КНЕУ, 2007.
Planned learning activities and teaching methods
Lectures, practical classes
Assessment methods and criteria
1. Modular test work (2 MTW 10 points max. each) – 40 points / 24 points. 2. Independent work - calculation individual task - 20 points / 12 points; The final assessment is conducted in the form of an exam with a maximum score of 40 points (marginal score of 24 points).
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Yurii Petrovych Tadeyev
Department of Economic Cybernetics
Faculty of Economics

Departments

The following departments are involved in teaching the above discipline

Department of Economic Cybernetics
Faculty of Economics