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Course Syllabus

Course Syllabus


 

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Master 2024/2025

Statistical Analysis I

Type: Compulsory course (Population and Development)
Area of studies: Public Administration
Delivered by: Department of Higher Mathematics
Where: Faculty of Social Sciences
When: 1 year, 1, 2 module
Mode of studies: offline
Open to: students of one campus
Instructors: Nikita Kozlov, Daria Salnikova
Master’s programme: Population and Development
Language: English
ECTS credits: 6

Course Syllabus

Full SyllabusAsk Question

Abstract

This course is a gentle introduction to modern applied statistics and econometrics. The course is based on the following principle: first, idea and formal description of mathematical concepts are given, second, these concepts are applied to real-world problems. The course has three main chapters: probability theory, statistics, and econometrics. The statistics’ part explains principles of the basic applied statistical analysis and serves as a bridge between probability theory and the most applied part of the course, econometrics. Econometrics is a collection of mathematical tools which helps to forecast variables, find new dependences and test theories.
Learning Objectives

Learning Objectives

  • The aim of the course is to acquaint students with the main concepts and methods of applied statistical analysis and econometrics
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to identify overfitting
  • Be able to use the methods of descriptive statistics to summarize and visualize the raw data.
  • to be aware of the consequences of the omitted variable bias
  • makes a statistical inference by the significance level and by p-value
  • Be able to compute and interpret coefficient of determination.
  • Explain the relationship between confidence interval estimates and p-values in drawing inferences
  • Apply linear regression models in practice: identify situation where linear regression is appropriate; build and fit linear regression models with software; interpret estimates and diagnostic statistics; produce exploratory graphs
  • Understand potential outcome and directed acyclic graph approaches
  • to be aware of the difference between the theoretical and empirical estimand
  • to be able to explain the essence of the selection bias problem and be aware of the consequences of this problem
  • to be able to construct and interpret confidence intervals
  • to be able to test the equality of variances
  • to be able to test the equality of means
  • to be able to explain the essence of the post-treatment bias
  • to be able to detect and remedy multicollinearity
  • to be able to detect and remedy heteroskedasticity
Course Contents

Course Contents

  • Introduction: Bridging the Gap between Theory and Empirical Research
  • Statistical Inference: Parameter Estimation
  • Statistical Inference: Hypothesis Testing
  • Exploratory Data Analysis
  • Specifying the Relationship between Variables. Directed Acyclic Graphs
  • Linear Regression Models: Model Specification, Interpretation and Hypothesis Testing
  • Assessing Goodness-of-Fit in Linear Regression Models
  • Diagnostics in Linear Regression Models
Assessment Elements

Assessment Elements

  • non-blocking Seminar Activity
  • non-blocking Home Assignment 1
  • non-blocking Home Assignment 2
  • non-blocking Home Assignment 3
  • non-blocking Test 1
  • non-blocking Test 2
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • 2024/2025 2nd module
    0.3 * Exam + 0.1 * Home Assignment 1 + 0.1 * Home Assignment 2 + 0.1 * Home Assignment 3 + 0.1 * Seminar Activity + 0.15 * Test 1 + 0.15 * Test 2
Bibliography

Bibliography

Recommended Core Bibliography

  • Basic econometrics, Gujarati, D. N., 2009
  • Jeffrey M. Wooldridge. (2019). Introductory Econometrics: A Modern Approach, Edition 7. Cengage Learning.
  • Mastering 'Metrics : the path from cause to effect, Angrist, J. D., 2015
  • Mathematical statistics with applications, Wackerly, D. D., 2008
  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and Causal Inference : Methods and Principles for Social Research. New York: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=206937

Recommended Additional Bibliography

  • Core concepts in data analysis: summarization, correlation and visualization, Mirkin, B., 2011
  • Core data analysis : summarization, correlation, and visualization, Mirkin, B., 2019
  • Mostly harmless econometrics : an empiricist's companion, Angrist, J. D., 2009

Authors

  • Буваева Роксана Викторовна
  • SALNIKOVA DARIA VYACHESLAVOVNA