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Data Science: Linear Regression

Harvard University via edX

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Overview

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.

Taught by

Rafael Irizarry

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Review for edX's Data Science: Linear Regression
2.0 Based on 1 reviews

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Dray P
2.0 5 months ago
Dray is taking this course right now.
This is supposed to be an introductory course in linear regressions using the programming language R. The problem is, there's no evidence that the professor knows how to program in R. None of his examples run. The course forums are full of people trying to figure out how to get the code to work. Some of the problems seem to stem from the professor using his own private R packages, which define commonly-used R commands differently than in 'standard' R (neither the syllabus nor the lectures give any hint as to what packages he's using). Even if you download the packages that students have guesse…
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