Кто читает:: Департамент экономики

Статус:: Курс обязательный

Когда читается:: 2-й курс, 4 модуль

Преподаватели

Афанасьев Кирилл Олегович

Гарипова Фарида Габдулхаевна

Журавлева Татьяна Леонидовна

Родионова Татьяна Игоревна

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Abstract

This discipline belongs to the professional cycle (Major) disciplines (B.PC), its basic part (B.PC.B). The study of this discipline is based on the following disciplines:•economic theory,•mathematics,•theory of probability and mathematical statistics.To master the discipline students must have the following knowledge and competencies:•Able to learn, acquire new knowledge, skills, including in a field other than professional (MC-1).•Is able to solve problems in professional activity on the basis of analysis and synthesis (MC-3).•Is able to work with information: find, evaluate and use information from different sources necessary for solving scientific and professional tasks (including systematic approach) (CB-5).•Is able to choose tools for information processing in accordance with the scientific task, analyze the results of calculations and justify the obtained conclusions (PC-32).The main provisions of the discipline should be used in the further study of the disciplines:•econometrics,•institutional economics, •economics of the public sector,•international economics,•labor economics.

Learning Objectives

The objectives of the discipline "Applied Statistics" are: • gaining students' knowledge of the properties of statistical data; • to acquire students' knowledge of the methods of descriptive statistical analysis; • to study the basic methods of regression analysis, their advantages and disad-vantages.

Expected Learning Outcomes

Justifies the plan of data search for statistical research of real economic situation, forms the system of initial indicators, prepares the data matrix according to the set analytical task, masters the skills of material structuring, checks information from different sources for methodological comparability
Can find data in open databases
Can meet the requirements for written work
Demonstrates the ability to identify the cause-and-effect relations of indicators on the basis of firm reports, and to build a suitable model on this basis.
Can make cross-country comparisons
Can use different application data pro-cessing packages
Can collect, process, and aggregate statistical data
Explain the basic concepts in statistics: population, sample, systematic error, parameters, sample generation and sampling error, random sampling.
Recognize the impact of distribution asymmetry and distinguish between symmetric and asymmetric distributions. Analyze measures of dispersion, including range, interquartile range (IQR), variance, and standard deviation. Calculate and interpret measures of central tendency, such as the mean, median, and mode.
Apply data visualization techniques, including scatter plots, histograms, and box plots, to effectively analyze data distributions. Distinguish between variable types (quantitative and categorical) and their properties.
Analyze the advantages and disadvantages of various probability sampling methods. Distinguish between types of sampling errors, including sampling and non-sampling errors.
Explain various non-probability sampling methods, including convenience sampling, judgmental sampling, and quota sampling. Explain the advantages and limitations of quota sampling. Explain the principles of probability sampling and its five main types: simple random, systematic, stratified, cluster, and multistage sampling.
Identify different types of sample surveys and their applications in fields such as demography, public administration, and marketing research. Compare and contrast population censuses and sample surveys, explaining when each method is preferable. Distinguish between probability and non-probability sampling methods.
Use Venn diagrams to visualize probability problems, which aids statistical reasoning. Calculate probabilities using the total probability formula. Interpret and apply relative frequency as an approach to estimating probability, a key concept in the analysis of empirical data.
Define and explain fundamental concepts of probability, including the space of elementary outcomes, events, and probability measures, which form the basis of inferential statistics. Distinguish between mutually exclusive and independent events, which is essential for understanding statistical relationships. Calculate conditional probabilities and apply Bayes' theorem to real-world problems.
Standardize a normally distributed variable. Use a standard normal distribution table to calculate probabilities. Explain the concept of sampling distribution and sampling variability. Explain the central limit theorem (CLT).
Explain the importance of sampling and why using population data is inappropriate. Define random variables, mean, and variance. Recognize the key properties of the normal distribution and its role in statistics.
Compare proportions of two populations. Explain the effect of sample size on the precision of an estimate and determine the required sample size. Determine when to assume equal or unequal variances in two-sample comparisons. Apply methods for constructing CIs to compare dependent samples. Interpret real-world statistical results using confidence intervals.
Explain the concept of confidence intervals and their role in statistical analysis. Calculate and interpret confidence intervals for means and proportions. Distinguish between the standard normal distribution and the Student's t-distribution. Construct and interpret confidence intervals.
Conduct hypothesis tests for the mean (σ known and unknown) and proportion. Compare two population means using statistical tests for independent and dependent samples. Analyze and test differences between two population proportions. Interpret hypothesis test results and draw conclusions based on statistical significance.
Distinguish between the null hypothesis (H0) and the alternative hypothesis (H1). Define and interpret type I and type II errors, and explain their consequences. Explain the concept of significance levels (α) and their role in hypothesis testing. Calculate and interpret p-values and use them to make decisions when testing hypotheses. Distinguish between one-sided and two-sided alternatives and determine when to use each.
Construct and interpret simple linear regression models. Calculate and interpret parameter estimates obtained using the least squares method: the constant and slope of the regression line. Explain the assumptions and limitations of linear regression analysis.
Distinguish between correlation and causation in empirical data. Explain the limitations of surveys when establishing causal relationships. Explain key elements of experimental research: control groups and randomization. Interpret correlation coefficients (Pearson and Spearman).
Determine degrees of freedom for a table of random variables and use the chi-square distribution to assess significance. Draw conclusions about relationships based on critical values and p-values.
Distinguish between correlation testing and association testing. Explain the structure and purpose of contingency tables. Formulate null and alternative hypotheses to test the relationship between two categorical variables. Calculate expected frequencies under the assumption of independence. Calculate and interpret the chi-square test statistic.

Course Contents

Introduction to Statistics
Descriptive Statistics
Sample Design
The Role of Probability Theory in Statistics
Normal Distribution
Construction of Confidence Intervals
Statistical Hypothesis Testing
Statistical Analysis of Relationships
Correlation and Linear Regression

Assessment Elements

Midterm exam
Homeworks
Project
Final exam

Interim Assessment

2025/2026 4th module
0.25 * Midterm exam + 0.45 * Final exam + 0.15 * Homeworks + 0.15 * Project

Bibliography

Recommended Core Bibliography

Introduction to Statistics and Data Analysis, With Exercises, Solutions and Applications in R, Christian Heumann, Michael Schomaker, Shalabh, Springer Nature Switzerland AG 2022, 978-3-031-11833-3, published: 30 January 2023

Recommended Additional Bibliography

Linear Regression Using R - An Introduction to Data Modeling - CCBY4_059 - David Lilja - 2022 - Open Educational Resources: libretexts.org - https://ibooks.ru/products/390845 - 390845 - iBOOKS

Authors

Garipova Farida Gabdulkhaevna
Zhuravleva Tatiana Leonidovna
RODIONOVA TATYANA IGOREVNA
AFANASEV KIRILL OLEGOVICH

Бакалаврская программа «Международная программа по бизнесу и экономике»

Контакты

Applied Statistics