Data Science: Linear Regression

Data Science: Linear Regression
5 months
Certification paid
Course by EdX
Data Science: Linear Regression
What will you 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
About the course

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 ofourProfessional 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.

Data Science: Linear Regression
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.
Rafael Irizarry
Rafael Irizarry
Professor of Biostatistics Harvard University
All the courses on this platform are free of charge. The authors are top universities and corporations that seek to maintain high quality standards. If you do not meet a deadline for assignments, you lose points. Like on other platforms, the videos in which the theory is explained are followed by practical assignments. Courses are available in English, Chinese, Spanish, French and Hindi.
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