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
        
        
            2023/2024
        
    
            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
        
        
            1. Know: the 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 studying the convergence of numerical series, methods for calculating integrals, set theory, distributions of random variables, limit theorems of Probability Theory.
2. Have the skills to calculate the boundaries of numerical sequences, calculating integrals and probabilities of random events.
        
    
            Course content
        
        
            The course structure has two modules:
Content module 1. "Basic concepts of mathematical statistics, estimation of unknown parameters", that discusses the main problems of mathematical statistics, descriptive statistics, sampling and its characteristics, statistical estimation of unknown parameters of distributions.
Content module 2. "Statistical hypothesis testing, elements of regression, variance, discriminant analysis, time series analysis", which includes the main methods of statistical hypothesis testing, sequential analysis, elements of correlation and regression analyses, linear regression, elements of variance analysis, elements of discriminant analysis , basic methods of time series analysis.
        
    
            Recommended or required reading and other learning resources/tools
        
        
            1.	Черняк О.І., Кравець Т.В., Ляшенко О.І. Буяк Л.М., Банна О.Л., Башуцька О.С. Теорія ймовірностей та математична статистика. Практикум: навчальний посібник. – Т.: ТНЕУ, 2019. – 251 с.
2.	Черняк О.І., Кравець Т.В., Банна О.Л., Полосьмак О.Л. Теорія ймовірностей та математична статистика: навчально-методичний комплекс для студентів економічних спеціальностей денної  та заочної форми навчання. Ч.1. Теорія ймовірностей.- К.: ВПЦ „Київський університет”, 2013. –71 с.
3.	Васильків І.М. Основи теорії ймовірностей і математичної статистики: навчальний посібник. –Львів: ЛНУ імені Івана Франка, 2020. – 184 с.
4.	Алілуйко А. М., Дзюбановська Н. В., Єрьоменко В. О., Мартинюк О. М., Шинкарик М. І. Практикум з теорії імовірностей та математичної статистики. Навчальний посібник для студентів економічних спеціальностей. – Тернопіль: Підручники і посібники, 2018. – 352 с. 
5.	Holmes A., Illowsky B., Dean S. Introductory Business Statistics, OpenStax, 2017. – 642p.
        
    
            Planned learning activities and teaching methods
        
        
            Lecture, practical lesson, student’s self-study
        
    
            Assessment methods and criteria
        
        
            1. Module test (2 Module tests, 20 points max. for each) (LO 1.1-1.4; 2.1-2.2; 4.1) - 40 points / 24 points;
2. Self-study assignment, which includes the search for economic information in the form of time series, analysis of this information, construction of a linear regression model based on the obtained data, estimation of regression parameters, economic conclusions (LO 1.1, 1.4; 2.1-2.2; 4.1) - 20 points / 12 points.
        
    
            Language of instruction
        
        
            Ukrainian 
        
    Lecturers
This discipline is taught by the following teachers
                    Viktor
                    Vasylovych
                    Shpyrko
                
                
                    Department of Economic Cybernetics 
Faculty of Economics
            Faculty of Economics
Departments
The following departments are involved in teaching the above discipline
                        Department of Economic Cybernetics
                    
                    
                        Faculty of Economics