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 Harvard University's 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 you will examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. You will try to determine which measured outcomes best predict baseball runs by using linear regression.
You 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.
Topics of study
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
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