# Why are regression problems called “regression” problems?

I was just wondering why regression problems are called "regression" problems. What is the story behind the name?

One definition for regression: "Relapse to a less perfect or developed state."

## 3 Answers

The term "regression" was used by Francis Galton in his 1886 paper "Regression towards mediocrity in hereditary stature". To my knowledge he only used the term in the context of regression toward the mean. The term was then adopted by others to get more or less the meaning it has today as a general statistical method.

• Galton derived a linear approximation to estimate a son's height from the father's height in that paper. His equation was fitted so an average height father would have an average height son, but a taller than average father would have a son that is taller than average by 2/3 the amount his father is. Same with shorter than average. This could be argued to be a simple linear regression (today's meaning). And of course today regression has an even broader meaning: it's any model that makes continuous predictions. It is interesting how much his original usage of that word has changed. – rm999 May 21 '11 at 19:07
• Answer by NRH is correct. The following link gives lot more details on Francis Galton's paper "Regression towards mediocrity in hereditary stature" blog.minitab.com/blog/statistics-and-quality-data-analysis/… – Gaurav Singhal May 9 '16 at 8:23

As opposed to progressing, we are falling back to the mean, i.e. regressing. Hence the term regression ! I think its something that got picked up and stuck.

"Regression" comes from "regress" which in turn comes from latin "regressus" - to go back (to something).

In that sense, regression is the technique that allows "to go back" from messy, hard to interpret data, to a clearer and more meaningful model. As a physicist, I like the idea, as physicists see natural phenomena as the multiple possible outcomes of a relatively simple natural law.

In other words, the word regression seems to suggest that data is just the visible, tangible effect of a "statistical model". In other words, the model comes first, and your desire is use the data "to go back" to what originated them.