Week 1

What we will cover

In this session, we will review the syllabus in more detail: what you should expect from this class, requirements, grading, among others. We will also start covering material related to regression analysis: A quick overview of OLS regressions in R, data inspection, comparing effect sizes, and outliers.

Code

Here is the R code we will review in class, with some additional data and questions

Notes

• How do I interpret log transformations of variables in a linear regression?

Answer: A lot of the time, we want to transform our dependent variable $y$ to $\log(y)$, so that it’s normally distributed (e.g. income), or sometimes we could also have a covariates included in our model in a log form. How do we interpret the coefficients in a linear regression model under these transformations? As we saw in class, you can actually interpret them as percentage changes! Take a look at this article to see how to exactly interpret these coefficients, depending on whether your dependent or independent variable (or both!) are in log form.