> For the complete documentation index, see [llms.txt](https://vikram-bajaj.gitbook.io/machine-learning-stanford-coursera/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://vikram-bajaj.gitbook.io/machine-learning-stanford-coursera/supervised-learning/linear-regression/linear-regression-in-one-variable.md).

# Linear Regression in One Variable

This is a supervised learning algorithm where we estimate the value of a dependent target variable using a linear combination of operations on an independent variable.

It is also called **Univariate Linear Regression**.

There is one input and one output.

Since it is a form of supervised learning, the end result is already known.

The general hypothesis function is of the form:

$$h\_θ(x) = θ\_0 + θ\_1x$$

![](https://1423730981-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M5-0RGuSGVoyMc2kCFR%2F-M5-0RgMLg-NeST4zMsc%2F-M5-0U9AThWWJLnEGZbl%2FLinearRegression.png?generation=1586990807569114\&alt=media)