Mathematical statistics

Course: Applied Mathematics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Mathematical statistics
Code
ОК.23
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
5 Semester
Number of ECTS credits allocated
3
Learning outcomes
Know and understand the basic formulas, models, concepts and problems of mathematical statistics. Be able to prove basic limit theorems; construct point and interval estimates and examine them for unbiasedness, efficiency and consistency; check the main ones statistical hypotheses. To justify one's own view on the problem, to communicate with colleagues on issues of formalization of problems and the choice of solution methods; make written reports. Demonstrate skills of interaction with other people, ability to work in teams. Organize your independent work to achieve results. Be responsible for the work performed, bear responsibility for their quality.
Form of study
Prerequisites and co-requisites
Know: basics of probability theory, mathematical analysis and algebra Be able to: apply knowledge of probability theory Possess elementary skills: solve problems in probability theory
Course content
The discipline ""Mathematical statistics"" has the following sections: laws of large numbers, central limit theorems, random vectors, basic problems of mathematical statistics, parametric estimation, classification of estimates, confidence intervals, testing of non-parametric and parametric hypotheses. The main task is to provide students with basic knowledge about stochastic experiments, to develop the ability to work with basic statistical models, to develop the skills of applying the acquired knowledge to applied problems that require probabilistic statistical analysis. Discipline is a must. Uses concepts from probability theory, mathematical analysis, discrete mathematics and algebra. Acts as a base for disciplines: actuarial mathematics, econometrics, financial mathematics, economic-mathematical modeling, decision-making methods. It is taught in the 5th semester, the volume is 90 hours. (3 ECTS credits), of which lectures – 20 hours, practical – 22 hours, consultations – 2 hours, independent work – 46 hours. There are 2 content parts, 2 test papers and an exam.
Recommended or required reading and other learning resources/tools
1. I. Gikhman, A. Skorokhod, M. Yadrenko "Probability theory and mathematical statistics". 2. A.V. Skorokhod ""Elements of the theory of probabilities and the theory of random processes"", K. 1975. 3. Lebedev E.O., Sharapov M.M. A course of lectures on probability theory. - K.: Norita-plus, 2007. - 168 p. 4. E.O. Lebedev, O.A. Chechelnytskyi, M.M. Sharapov, M.S. Bratiychuk Collection of problems on the theory of probabilities, KNU named after T. Shevchenko, 2006. Online program to check practical knowledge Index http://indexator.pp.ua All available author's methodical materials and electronic tables on the website http://teorver.pp.ua/ukr/ukr.php
Planned learning activities and teaching methods
Lecture, practical classes, independent work
Assessment methods and criteria
Control work 1 and current evaluation (RN.1, RN.2): 30 points/15 points. Control work 2 and current evaluation (RN.1, RN.2): 30 points/15 points. final evaluation (in the form of an exam): - the maximum number of points that can be obtained by a student: 40; - learning outcomes that are evaluated: PH.1, PH.2, PH.3; - form of conduct: written - types of tasks: two theoretical questions (40%), three tasks (60%). A student is admitted to the exam if he scored at least 20 points in the semester.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Mykhailo Mykhailovich Sharapov
Applied Statistics
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Applied Statistics
Faculty of Computer Science and Cybernetics