This is the seventh course in the HarvardX Professional Certificate in Data Science, a series of courses that prepare you to do data analysis in R, from simple computations to machine learning. We assume that you have either taken the preceding courses in the series or that you are already familiar with the content covered in them.
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. In this course, we cover how to implement linear regression and adjust for confounding in practice using R.
In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in the book (and movie) Moneyball. We will try to determine which measured outcomes best predict baseball runs and to do this we'll use linear regression.
We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process, and it is essential to understand when it is appropriate to use. You will learn when to use it in this course.
The bookdown-version of this course is available on this Github Page