Simulations (cont’d)
What is the usefulness of simulations in regression analysis and how to implement them?
After this session, you should be able to implement a basic simulation for regression analysis in R and use it to test some of the hypotheses you made in you own analysis.
Summary
In this session, we first discuss why we should implement simulations. The basic idea behind simulations is that the Data Generating Process is known: that allows to evaluate the performance of our analysis, to study what happens if an hypothesis we made does not hald, etc. To do so, we start with a simple DGP and then complexify it. We first wonder whether our analysis performs well in a rather “pristine” setting. We then implement a simulation in R.
Session Outline
- Summary from last week
- Simulations: what, why and how?
- R coding together: finish our simulation
- Exercise
Materials
Exercise
During the lecture, we will continue the simple simulation exercise we started last week. In this exercise, we emulate an RCT studying the impact of additional courses on students’ grades.
You can find the document for this exercise here.