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
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course 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 the book (also a 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 important to understand when it is appropriate to use. You will learn when to use it in this course.
HarvardX has partnered with DataCamp for all assignments. This allows students to program directly in a browser-based interface. You will not need to download any special software, but an up-to-date browser is recommended.
This course is part of the HarvardX Data Science Professional Certificate program.
Harvard T.H. Chan School of Public Health
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