# linear-regression

Linear regression is a statistical modeling method that fits data using a function linear in its unknown parameters.

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## Regression and Linear Combinations

DevFeed: [Regression and Linear Combinations](<https://devfeed.tech/articles/regression-and-linear-combinations-40446.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2021/03/29/regression-and-linear-combinations/>)

Published: 2021-03-29T09:00:00Z

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>), [math](<https://devfeed.tech/topics/math.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [gradient-descent](<https://devfeed.tech/tags/gradient-descent.md>), [kernelization](<https://devfeed.tech/tags/kernelization.md>), [linear-algebra](<https://devfeed.tech/tags/linear-algebra.md>), [linear-combination](<https://devfeed.tech/tags/linear-combination.md>), [linear-regression](<https://devfeed.tech/tags/linear-regression.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [programming](<https://devfeed.tech/tags/programming.md>), [regression](<https://devfeed.tech/tags/regression.md>)

### AI overview

The article explains why linear combinations matter to programmers, using linear regression as a practical example. It describes representing inputs and weights as vectors, incorporating an intercept into the input vector, and formulating regression as a least-squares optimization problem. The supplied excerpt then begins introducing basis functions for modeling nonlinearity.

### Source excerpt

Recently I've been helping out with a linear algebra course organized by Tai-Danae Bradley and Jack Hidary, and one of the questions that came up a few times was, "why should programmers care about the concept of a linear combination?" For those who don't know, given vectors $ v_1, \dots, v_n$, a linear combination of the vectors is a choice of some coefficients $ a_i$ with which to weight the vectors in a sum $ v = \sum_{i=1}^n a_i v_i$.

## 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.