Delivered at:: Department of Economics

Course type:: Elective course

When:: 1 year, 1 module

Instructor

Kislitsyn, Dmitry

Full Syllabus

Abstract

Statistical processing of data and visualisation of the results of analysis is an inevitable stage of working with data obtained in various fields of natural sciences, sociology, psychology or economics. In this course we will go through the basics of statistics and learn the basics of the statistical programming language R. We will teach you how to use visualisation tools (charts, graphs, etc.) flexibly to make the results of analysis as accessible and understandable as possible. You will learn how to calculate basic descriptive statistics: median and quantiles, mean and standard deviation. You will be introduced to the principles of using theoretical distributions of statistics to construct confidence intervals and test hypotheses (using the t-criterion as an example). Finally, we will discuss the difficulties encountered in multiple hypothesis testing and teach you how to overcome them.

Learning Objectives

Expected Learning Outcomes

understand and apply basic statistical concepts relevant to business
utilize R for data manipulation, analysis, and visualization
interpret statistical outputs and communicate findings effectively
conduct hypothesis testing and regression analysis
make data-driven decisions based on statistical evidence

Course Contents

1. Origins of Data
2. Preparing Data for Analysis
3. Exploratory Data Analysis
4. Comparison and Correlation
5. Generalizing from Data
6. Testing Hypotheses
7. Simple Regression
8. Complicated Patterns and Messy Data
9. Generalizing Results of a Regression
10. Multiple Linear Regression
11. Modeling Probabilities
12. Regression with Time Series Data

Assessment Elements

Quizzes
Final Test

Interim Assessment

2024/2025 1st module
0.6 * Final Test + 0.4 * Quizzes

Bibliography

Recommended Core Bibliography

Fraser C. Business statistics for competitive advantage with Excel 2016: basics, model building, simulation and cases. New York, NY: Springer Science+Business Media, 2016. 475 с.

Recommended Additional Bibliography

Groebner, David, et al. Business Statistics, EBook, Global Edition, Pearson Education, Limited, 2018. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=5186156.

Authors

BRODSKAYA NATALYA NIKOLAEVNA
KISLITSYN DMITRIY VIKTOROVICH

Master’s Programme 'Data Analytics for Business and Economics'

Contacts

Introduction to Business Statistics Using R