# Linear Regression

DevFeed: [Linear Regression](<https://devfeed.tech/articles/linear-regression-40328.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2013/08/18/linear-regression/>)

Published: 2013-08-18T17:43:20Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [linear-regression](<https://devfeed.tech/topics/linear-regression.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [conditional-probability](<https://devfeed.tech/tags/conditional-probability.md>), [covariance](<https://devfeed.tech/tags/covariance.md>), [data](<https://devfeed.tech/tags/data.md>), [expectation](<https://devfeed.tech/tags/expectation.md>), [linear-regression](<https://devfeed.tech/tags/linear-regression.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [python](<https://devfeed.tech/tags/python.md>), [regression](<https://devfeed.tech/tags/regression.md>), [statistics](<https://devfeed.tech/tags/statistics.md>), [variance](<https://devfeed.tech/tags/variance.md>)

## AI overview

This tutorial introduces linear regression as a basic form of statistical learning. It explains independent and dependent variables, uses a two-variable linear model to predict one variable from another, and describes estimating the model parameters from sample pairs.

## Source excerpt

Machine learning is broadly split into two camps, statistical learning and non-statistical learning. The latter we've started to get a good picture of on this blog; we approached Perceptrons, decision trees, and neural networks from a non-statistical perspective. And generally "statistical" learning is just that, a perspective. Data is phrased in terms of independent and dependent variables, and statistical techniques are leveraged against the data. In this post we'll focus on the simplest example of this, linear regression, and in the sequel see it applied to various learning problems.