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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

Course Syllabus

Abstract

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

Learning Objectives

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

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

Course Contents

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

Assessment Elements

  • non-blocking Lab work (exercises)
    The students have to solve ten exercises within 70 minutes.
  • non-blocking Lab work (exam)
    The students have to solve ten exercises within 70 minutes.
Interim Assessment

Interim Assessment

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

Bibliography

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
  • 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

  • An introduction to statistical learning : with applications in R, , 2013
  • R Cookbook : Proven recipes for data analysis, statistics, and graphics, Teetor, P., 2011