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
            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