## What you'll learn

- How linear regression was originally developed by Galton
- What is confounding and how to detect it
- How to examine the relationships between variables by implementing linear regression in R

## Course description

Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part of our Professional Certificate Program in Data Science, covers 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 Moneyball. We will try to determine which measured outcomes best predict baseball runs by using 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. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.

## Associated Schools

### Harvard T.H. Chan School of Public Health

## You may also like

- Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.FreeAvailable now8 weeks
- Learn inference and modeling: two of the most widely used statistical tools in data analysis.FreeAvailable now8 weeks
- Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.FreeAvailable now8 weeks