Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.

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.

Course By | Instructor |
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Harvard University | Rafael Irizarry |

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.

- 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

**Rafael Irizarry:** Professor of Biostatistics, T.H. Chan School of Public Health

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