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Обычная версия сайта
09
Апрель

R for solving applied problems

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс обязательный
Когда читается:
3-й курс, 1 модуль

Преподаватели

Нырка Оксана Викторовна

Course Syllabus

Abstract

This course is developed to instruct students to conduct a basic data analysis in R

Learning Objectives

• This course aims to show how to use R Studio to conduct basic statistical and econometric analysis.

Expected Learning Outcomes

• be able to estimate marginal effects
• be able to launch a project in R Studio
• be able to import data in csv, txt, xlsx, data; and from stock markets, World Bank databases
• be able to create, to delete a quantitative variable
• be able to create and to delete a qualitative variable
• be able to modify a variable with and without a condition(s)
• be able to create line chart
• be able to create histogram
• be able to create simple graphs by using ggplot
• be able to create a table with the descriptive statistics
• be able to create the descriptive statistics on subsamples or with a condition
• be able to estimate a simple and multivariable linear regression
• be able to estimate robust standard errors
• be able to perform basic F-tests
• be able to estimate logit and probit models
• be able to estimate robust standard errors in logit or probit models

Course Contents

• Introduction to R Studio
• Data Management
• Graphs
• Descriptive statistics
• Linear regression in R Studio
• Binary choice models in R Studio

Assessment Elements

• Lab work (exercises)
The students have to solve ten exercises within 70 minutes.
• Lab work (exam)
The students have to solve ten exercises within 70 minutes.

Interim Assessment

• 2022/2023 1st module
0.4 * Lab work (exercises) + 0.6 * Lab work (exam)

Recommended Core Bibliography

• Data mining for business analytics : concepts, techniques, and applications in R, , 2018
• Discovering statistics using R, Field, A., 2012
• Eric Goh Ming Hui. (2019). Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics. Apress.
• Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl, K. C. (2017). Data Mining for Business Analytics : Concepts, Techniques, and Applications in R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1585613