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