28-30 May 2024, 10am - 4pm, Torrington Place (1-19), B07 - Teal Room
This Github repository will contain all resources related to the Introduction to Regression Analysis in R short course organized by the UCL Social Data Institute in May 2024.
Throughout this module we will be heavily relying on a free and open-source programming environment called R. On top of that, we will be using an interface called RStudio to make the experience a bit less painful.
Follow the guide at this link to install both: https://rstudio-education.github.io/hopr/starting.html. Make sure to install the latest version of R before installing RStudio. Please make sure to have done this before coming to the first class.
If you installed R and RStudio at some point in the past but are no longer using it, I recommend uninstalling both before installing the latest versions anew. It will be easier if everyone works with the same (latest) version.
This short course is not going to follow a traditional lecture setting. Please make sure to bring your laptop to each session, as we will be doing a lot of programming together. In addition to explaining the material, I will try to give you as much individual help as possible throughout.
In my experience, one of the most important factors in learning stats and programming for beginners is individual motivation. I therefore encourage you to think about how you might wish to apply statistical programming / regression analysis in your own projects.
The broad outline for the course is the following:
- Tuesday, 28 May: Introduction to R
- Wednesday, 29 May: Quantitative research designs
- Thursday, 30 May: Linear and multiple regression
In practice, I will adjust the material on the fly depending on how we are doing.
R for Data Science (2nd ed) https://r4ds.had.co.nz/ / probably the most popular introduction to R programming (online and free)
Data Analysis for Social Science: A Friendly and Practical Introduction https://press.princeton.edu/books/paperback/9780691199436/data-analysis-for-social-science / an excellent book to learn more holistically about data analysis and causal inference. You should be able to find it through the UCL library