Бакалавриат
2022/2023
R для решения прикладных задач
Статус:
Курс обязательный (Международный бизнес и менеджмент)
Направление:
38.03.02. Менеджмент
Кто читает:
Департамент экономики
Где читается:
Санкт-Петербургская школа экономики и менеджмента
Когда читается:
3-й курс, 1 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
14
Course Syllabus
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.
Bibliography
Recommended Core Bibliography
- 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
- Stowell, S. (2014). Using R for Statistics. Berkeley, CA: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174344
Recommended Additional Bibliography
- R Cookbook : Proven recipes for data analysis, statistics, and graphics, Teetor, P., 2011