🗓️ Session 10: Linear models in R

Author

Affiliation

Claudius Gräbner-Radkowitsch

EUF and JKU

This session focuses on the practical implementation of linear regression models and on how to conduct the most common tasks associated with designing and implementing experiments in R.

This way it complements the more theoretical lectures. More precisely, we focus on the following topics:

• Implement simple and multiple linear regression in R
• Calculate and interpret \(R^2\) manually and using R functions
• Understand and demonstrate omitted variable bias
• Perform data transformations to linearize relationships
• Create effective visualizations using geom_smooth()
• Conduct basic model diagnostics
• Implement a power analysis in R

👨‍🏫 Lecture Slides

There are no slides for this lecture. The relevant information is given through the corresponding lecture and the mandatory readings.

📚 Mandatory Reading

• Linear Regression in R on Method and Inquiry (Gräbner-Radkowitsch 2026b)
  • Data sets used
• Experiments in R on Method and Inquiry (Gräbner-Radkowitsch 2026a)
  • Data sets used

Further Reading

• Chapter 5 and Chapter 6 in Ismay and Kim (2020)

In-class exercises

• Work sheet
• Data sets
• Cheat Sheet

✍️ Coursework

• Complete the in-class exercises at home
• Do the exercises LinearRegression1 and LinearRegression2 from the DataScienceExercises package

TipQuick code for starting the exercises I

learnr::run_tutorial(
  name = "LinearRegression1", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

TipQuick code for starting the exercises II

learnr::run_tutorial(
  name = "LinearRegression2", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

References

Gräbner-Radkowitsch, C. (2026a) “Experiments in R” C. Gräbner-Radkowitsch (ed.), Method and Inquiry, available at https://euf-methodology-hub.netlify.app/resources/experiments-in-r.

Gräbner-Radkowitsch, C. (2026b) “Linear Regression in R” C. Gräbner-Radkowitsch (ed.), Method and Inquiry, available at https://euf-methodology-hub.netlify.app/resources/linear-regression-r.

Ismay, C. and Kim, A. Y.-S. (2020) Statistical inference via data science: A ModernDive, into R and the tidyverse, Boca Raton: CRC Press, Taylor and Francis Group, available at https://moderndive.com/index.html.