Бакалавриат 2019/2020

Статистический анализ в социально-экономической сфере

Статус: Курс обязательный (Программа двух дипломов НИУ ВШЭ и Университета Кёнхи «Экономика и политика в Азии»)

Направление: 41.03.01. Зарубежное регионоведение

Кто читает: Департамент зарубежного регионоведения

Где читается: Факультет мировой экономики и мировой политики

Когда читается: 2-й курс, 1-3 модуль

Формат изучения: с онлайн-курсом

Преподаватели: Зайцев Дмитрий Геннадьевич

Язык: английский

Кредиты: 4

Контактные часы: 52

Course Syllabus

Abstract

This course serves as an introduction to fundamental and advanced concepts in statistics and probability and will be instrumental in teaching students how to effectively collect, analyze, and draw inferences from data in order to answer their own research questions and understand the analyses by others. The emphasis will be placed on statistical reasoning, problem solving, computer applications, and interpretation of the results. It is desired that you brush up your high school algebra to solve problem sets, yet most of the complex calculations will be performed using computers.

Learning Objectives

• understand fundamental concepts and important terminology in statistics and probability
• develop an understanding of principles of data collection, data analysis, and data visualization
• be able to perform basic statistical operations using R software
• present data in tables and charts, summarize and describe numerical data
• be able to apply statistical reasoning, perform statistical analysis and interpret the results

Expected Learning Outcomes

• After this session, students should be able to: Understand the focus of statistics as a subject; Learn key statistical terminology; Understand statistical applications in social science and business
• After this session, students should be able to: - Understand the concept of a random variable - Differentiate between types of variables - Be able to work with variables in R environment
• After this session, students should be able to: - Become familiar with a glossary of chart types - Learn graphical techniques to describe interval and categorical data - Show a relationship between numerical and nominal variables - Organize data using a frequency distribution - Represent data using charts
• After this session, students should be able to: - Apply numerical techniques for describing and summarizing data - Identify, compute, and interpret descriptive statistical summary measures - Differentiate between the measures of central tendency, dispersion, and relative standing
• After this session, students should be able to: - Understand the methodologies underlying data collection - Become acquainted with the concepts of random sampling and sample bias - Differentiate between sampling strategies
• After this session, students should be able to: - Learn probability concepts and rules - Identify components of probability - Assess probabilities and apply probability formulas
• After this session, students should be able to: - Learn the concept of a random variable - Become acquainted with discrete distributions of random variables
• After this session, students should be able to: - Become familiar with continuous distributions - Learn the 68-95-99.7 rule - Learn the concept of probability density functions - Calculate Z-scores and use distribution tables
• After this session, students should be able to: - Learn the concept of a sampling distribution of the mean - Learn the concept of a standard error of the mean - Apply the Central Limit Theorem - Use the sampling distribution for inference
• After this session, students should be able to: - Develop an understanding of statistical concepts behind parametric estimation - Calculate point estimates and interpret confidence intervals - Compute standard error for the sample mean - Select the sample size - Use the sampling distribution for inference
• After this session, students should be able to: - Develop an understanding of hypothesis testing framework - Conduct one-tailed and two-tailed hypothesis tests - Define decision errors - Explain the relationship between classical hypothesis test and p-values
• After this session, students should be able to: - Learn the concept of experimental design - Define degrees of freedom - Test and estimate a population variance - Test the equality of means - Test the equality of count data and proportions
• After this session, students should be able to: - Conduct F-tests - Decompose variance - Conduct one-way and two-way analysis of variance - Understand the properties of ANOVA tables
• After this session, students should be able to: - Run a regression model - Interpret the linear regression’s coefficients - Compute the coefficient of determination - Predict values using regression techniques

Course Contents

• Introduction
  1. Short history of the development of modern statistics 2. The subject of statistics as a scientific discipline 3. Applications of statistics in the social sciences and business 4. Overview of key terminology in statistics
• Data Basics
  1. Concept of a random variable 2. Variable types 3. Variable transformations 4. Observations, variables, and data matrices 5. Variables in R environment
• Graphical Descriptive Techniques
  1. Frequency and relative frequency distributions 2. Shapes of frequency distributions 3. Contingency tables and bar plots 4. Examining numerical data 5. Cross-sectional and time-series data 6. Misleading graphs and charts to be avoided 7. Alternatives to pie charts
• Numerical Descriptive Techniques
  1. Types of distributions (symmetrical, left-skewed, and right-skewed) 2. Measures of central tendency for numerical data 3. Comparing the mean, mode, and median 4. Measures of dispersion 5. Measures of relative standing 6. The Empirical Rule and Chebyshev’s Theorem
• Data Collection and Sampling Theory
  1. Methods of collecting data 2. The concepts of population and sample 3. Sampling from a population 4. Sampling strategies 5. Sampling bias
• Probability
  1. Defining probability 2. Joint, marginal, and conditional probability 3. Addition, multiplication, and complement rules 4. Marginal and joint probabilities 5. Defining conditional probabilities
• Discrete Probability Distributions
  1. Defining random variable 2. A family of discrete distributions 3. Bivariate Distributions 4. Binomial Distribution 5. Poisson Distribution
• Continuous Probability Distributions
  1. A family of continuous distributions 2. Normal distribution 3. Standardizing with Z-scores 4. Student’s t distribution 5. χ2 distribution
• Sampling Distributions
  1. The logic of parametric estimation 2. Sampling distribution 3. Standard error of an estimate 4. Sampling distribution of the difference between two means 5. Standard error of the difference between two means 6. Principles of A/B tests in business
• Estimation
  1. Basic properties of point estimates 2. Capturing the population parameters 3. Selecting the sample size 4. Calculations of point estimates and standard errors for the sample mean 5. Interpretation of confidence intervals
• Hypothesis Testing Framework
  1.Hypothesis testing methodology 2. Null and alternative hypotheses 3. Significance levels (α) and decision errors (Type I and Type II) 4. One-sided and two-sided hypothesis tests
• Inference for Numerical Data
  1. What is experimental design? 2. The meaning of degrees of freedom 3. One-sample mean tests 4. Inference for paired data 5. χ2-tests and goodness-of-fit
• Analysis of Variance
  1. Familywise error and the limitations of a t-test 2. The F-test and F-distribution 3. Variance decomposition 4. The within-groups and between-groups estimates of population variance 5. The F-ratio 6. Hypothesis testing with the analysis of variance 7. Diagnostics for an ANOVA analysis
• Regression Analysis
  1. Covariance and correlation 2. Assumptions of ordinary least squares (OLS) 3. Simple bivariate regression 4. Multivariate regression 5. Multicollinearity 6. Control variables in multiple regression

Assessment Elements

• Homeworks
• Class problem sets
• Midterm exam
• Final exam
  Exam was

Interim Assessment

• Interim assessment (3 module)
  0.25 * Class problem sets + 0.3 * Final exam + 0.25 * Homeworks + 0.2 * Midterm exam

Bibliography

Recommended Core Bibliography

• Stowell, Sarah (2014). Using R for Statistics. Apress. https://link.springer.com/book/10.1007%2F978-1-4842-0139-8
• Салин В.Н., Нарбут В.В., Шпаковская Е.П. - ECONOMIC STATISTICS. Бакалавриат - SCIENTIFIC WORLD - 2019 - 224с. - ISBN: 978-9934-8833-3-0 - Текст электронный // ЭБС BOOKRU - URL: https://book.ru/book/933492

Recommended Additional Bibliography

• Bruce, P. C., & Bruce, A. (2017). Practical Statistics for Data Scientists : 50 Essential Concepts (Vol. First edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1517577