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Regular version of the site
Bachelor 2022/2023

# Statistics

Area of studies: Economics
When: 2 year, 1-4 module
Mode of studies: distance learning
Online hours: 16
Open to: students of one campus
Language: English
ECTS credits: 7
Contact hours: 116

### Course Syllabus

#### Abstract

Elements of statistics is a two-semesters course for second year ICEF students. This is a course for students specializing in economics. The course is taught in Russian and English. Basic ideas of statistics, such as descriptive statistics, population and sample, parameters estimation, testing statistical hypotheses etc, are studied in the course, as well as elements of probability theory which are necessary for understanding the course. Prerequisites Prerequisites are Calculus (functions of several variables, partial derivatives, integrals, maximum of functions), and elements of Linear algebra (vectors, matrices, linear equations).

#### Learning Objectives

• to give a sound and self-contained (in the sense that the necessary probability theory is included) description of classical or mainstream statistical theory and its applications
• The main objective of the course is to give a sound and self-contained (in the sense that the necessary probability theory is included) description of classical or mainstream statistical theory and its applications. The students should learn to carry out a simple analysis of data (to find mean, median, standard deviation and other descriptive statistics), to present the data graphically (histograms, stem plots). They should understand the differences between population and sample, and theoretical and sample characteristics. Since it is not worth while to teach Statistics without elements of probability theory, studying its basic notions and results is a part of the course. Students should understand what probability space, random event, probability of an event are. The should know how to calculate probabilities of complex events, solve elementary combinatorial problems, use the full-probability and Bayes formulas. The students should have a clear understanding of what a random variable and its distribution are. The students should learn to formulate and solve basic problems of statistics, such as pa-rameters estimation, statistical hypotheses testing, correlation analysis, analysis of variance, Spearman correlation, contingency tables. One of the course aims is to prepare students for fur-ther studying of Econometrics on the basis of studying simple and multiple regression models. The course is not mathematically rigorous. Proofs, and even exact statements of results, are often not given. The problems are an essential part of the course. A serious effort has been made in the problems to illustrate the variety of ways in which the theory may be applied.
• to prepare students for further studying of Econometrics on the basis of studying simple and multiple regression models
• to teach students how to carry out a simple analysis of data (to find mean, median, standard deviation and other descriptive statistics), to present the data graphically (histograms, stem plots)
• to explain differences between population and sample, and theoretical and sample characteristics
• to enable students to calculate probabilities of complex events, solve elementary combinatorial problems, use the full-probability and Bayes formulas
• to teach students to formulate and solve basic problems of statistics, such as parameters estimation, statistical hypotheses testing, correlation analysis, analysis of variance, Spearman correlation, contingency tables.

#### Expected Learning Outcomes

• be able to correctly use ANOVA in real life applications
• be able to design and understand the structure of data; use various visual and table presentations of data
• be able to use simple regression model and understand computer outputs
• be able to use the concept of mathematical statistics, its application to real life problems
• be able to use the concept of probability, as a model for real life; discrete and continuous random variables; joint and conditional distributions

#### Course Contents

• Primary data analysis
• Elements of Probability theory
• Elements of Mathematical Statistics
• Models of simple regression
• Analysis of variance (ANOVA)

#### Assessment Elements

• Final exam
• Home assignments and quizzes
• Midterm test

#### Interim Assessment

• 2022/2023 3rd module
See interim assessment for module 4 for final grade determination.
• 2022/2023 4th module
Home assignments and quizzes (HW) 0.18 Midterm test1 (fall) (test1) 0.14 Midterm test2 (winter) (test2) 0.14 Midterm test3 (spring) (test3) 0.14 Final exam (June) 0.40 Bonus points for the activity in class. In practice no more than 5 bonus points are added to the final grade.

#### Recommended Core Bibliography

• Newbold, P., Carlson, W. L., & Thorne, B. (2013). Statistics for Business and Economics: Global Edition (Vol. Eight edition). Boston, Massachusetts: Pearson Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1417883

• Гмурман, В. Е.  Руководство к решению задач по теории вероятностей и математической статистике : учебное пособие для вузов / В. Е. Гмурман. — 11-е изд., перераб. и доп. — Москва : Издательство Юрайт, 2020. — 406 с. — (Высшее образование). — ISBN 978-5-534-08389-7. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/449645 (дата обращения: 28.08.2023).
• Гмурман, В. Е.  Теория вероятностей и математическая статистика : учебник для среднего профессионального образования / В. Е. Гмурман. — 12-е изд. — Москва : Издательство Юрайт, 2020. — 479 с. — (Профессиональное образование). — ISBN 978-5-534-00859-3. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/450808 (дата обращения: 28.08.2023).

• Syllabus

#### Authors

• PERESETSKIY ANATOLIY ABRAMOVICH
• ZASORIN ALEKSANDR IGOREVICH