Бакалавриат
2022/2023
Теория вероятностей и математическая статистика
Статус:
Курс по выбору (Экономика)
Направление:
38.03.01. Экономика
Кто читает:
Департамент математики
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
2-й курс, 1, 2 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
6
Контактные часы:
98
Course Syllabus
Abstract
The goal of studying the discipline is learning the methods of computation of probabilities of random events and probability distributions of random variables, solving statistical estimation problems, notions of the theory of statistical hypotheses testing, that allow the student to apply this knowledge in the disciplines such as “Methods of Optimal Solution”, “Mathematical Models in Economics”, “Game Theory”, “Econometrics”. The course “Probability Theory and Mathematical Statistics” will be used in the theory and applications of multidimensional statistical analysis, mathematical economics, econometrics. The material of the course can be used for development and application of numerical methods of solving problems in various regions sciences and for creating and studying mathematical models of such problems.
Learning Objectives
- The goal of studying the discipline is learning the methods of computation of probabilities of random events and probability distributions of random variables, solving statistical estimation problems, notions of the theory of statistical hypotheses testing, that allow the student to apply this knowledge in the disciplines such as “Methods of Optimal Solution”, “Mathematical Models in Economics”, “Game Theory”, “Econometrics”. The course “Probability Theory and Mathematical Statistics” will be used in the theory and applications of multidimensional statistical analysis, mathematical economics, econometrics. The material of the course can be used for development and application of numerical methods of solving problems in various regions sciences and for creating and studying mathematical models of such problems.
Expected Learning Outcomes
- The student can define the relevant sample space, compute the probabilities of random
- Can solve problems about random variables and their characteristics
- Can use Chebyshev inequality and Markov inequality
- Can compute sample characteristics, construct the empirical distribution function, histogram and the frequency polygon
- Can solve problems about construction of confidence intervals for the parameters of the normal distribution, test the hypothesis about the mean for samples from the normal distribution
- Can find the estimates of the parameters of the distribution
- Can test parametric and nonparametric hypotheses
- Can solve problems about two dimensional random variables and their characteristics
Course Contents
- Events and Bernoulli trials
- One dimensional random variables
- Law of Large Numbers and Central Limit Theorem
- Two dimensional random variables.
- Basic notions of mathematical statistics
- Samples from normal distribution
- Statistical hypotheses testing
- Statistical theory of parameter estimation
Interim Assessment
- 2022/2023 2nd module0.14 * Test 3 + 0.44 * Exam + 0.14 * Test 2 + 0.14 * In class work + 0.14 * Test 1
Bibliography
Recommended Core Bibliography
- Ковалев, Е. А. Теория вероятностей и математическая статистика для экономистов : учебник и практикум для вузов / Е. А. Ковалев, Г. А. Медведев ; под общей редакцией Г. А. Медведева. — 2-е изд., испр. и доп. — Москва : Издательство Юрайт, 2020. — 284 с. — (Высшее образование). — ISBN 978-5-534-01082-4. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/450466 (дата обращения: 28.08.2023).
Recommended Additional Bibliography
- Кремер, Н. Ш. Теория вероятностей и математическая статистика : учебник и практикум для вузов / Н. Ш. Кремер. — 5-е изд., перераб. и доп. — Москва : Издательство Юрайт, 2019. — 538 с. — (Высшее образование). — ISBN 978-5-534-10004-4. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/431167 (дата обращения: 28.08.2023).