Introduction and Needed Packages

Now that we’re equipped with data visualization skills, data wrangling skills, and an understanding of how to import data and the concept of a tidy data format, let’s now proceed with data modeling. The fundamental premise of data modeling is to make explicit the relationship between:

• An outcome variable $𝑦$, also called a dependent variable or response variable

• An explanatory/predictor variable $𝑥$, also called an independent variable or covariate.

Another way to state this is using mathematical terminology. We’ll model the outcome variable $y$as a function of the explanatory/predictor variable $𝑥$. When we say “function” here, we aren’t referring to functions in R like the ggplot() function, but rather as a mathematical function. However, why do we have two different labels, explanatory and predictor, for the variable $𝑥$? That’s because even though the two terms ...