# Efficient way of doing linear regression

**URL:** https://discourse.julialang.org/t/efficient-way-of-doing-linear-regression/31232
**Category:** Performance
**Tags:** regression
**Created:** [November 18, 2019, 5:21pm UTC](https://discourse.julialang.org/t/efficient-way-of-doing-linear-regression/31232 "2019-11-18T17:21:15Z")
**Posts on this page:** 1
**Showing post:** 43

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### Author: ![chakravala](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chakravala/32/6832_2.png) [@chakravala](https://discourse.julialang.org/u/chakravala)
#### Post date: [August 14, 2020, 8:28pm UTC](https://discourse.julialang.org/t/efficient-way-of-doing-linear-regression/31232/43 "2020-08-14T20:28:13Z")

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If anyone is interested, i have implemented a faster and also numerically stable method for underdetermined linear systems in this post:

> [@Grassmann.jl A\\b 3x faster than Julia's StaticArrays.jl](https://discourse.julialang.org/t/grassmann-jl-a-b-3x-faster-than-julias-staticarrays-jl/41451/35):
>
> This was because of a special explicit case for dimensions 1 and 2 and 3, which are now accounted for in Grassmann.jl also. Also, support has been added for Moore-Penrose inverses for underdetermined and overdetermined linear systems. For underdetermined cases, the exterior product algorithm works ~20x faster than the SMatrix algorithm, and it is numerically stable. For overdetermined equations, the method used is based on the traditional normal equations, and this is prone to more numerical i…

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