Linear regression is arguably one of the most widely used techniques in the data science world. But, a comprehensive understanding of this technique is not universal and it is at a level that is less than desired.
First, a little history, the term regression was first used by Sir Francis Galton, a 19th century polymath. Galton was a pioneer in application of statistical methods in many branches of science, he studied the relative sizes of parents and their offsprings in various species of plants and animals. During this study he observed that a larger than average parent tends to produce a larger than average child, but the child is likely to be less large than the parent in terms of its relative position in its own generation. Galton termed this phenomenon “a regression towards mediocrity”.
Linear regression is an approach to model a relationship between a dependent variable (y) and one or more independent variables (x). In this paper, I discuss
Comment
link not work
It seems like the "Read full article" link is no longer working.
Totally agree. Fitting a regression is easy. To determine how well the data fits the model is real challenge. Important point made.
© 2019 Data Science Central ® Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central