---
title: "Linear Regression with Evenly-Spaced Abscissae"
url: https://daily.dev/posts/linear-regression-with-evenly-spaced-abscissae-jcixvo2nn
source_url: https://www.embeddedrelated.com/showarticle/1163.php
type: article
source: "EmbeddedRelated"
published: 2026-05-31T07:47:59.023Z
updated: 2026-05-31T09:05:29.367Z
tags: ["python", "numpy", "linear-regression"]
reading_time: 12
upvotes: 0
comments: 0
language: en
---

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# Linear Regression with Evenly-Spaced Abscissae

**[EmbeddedRelated](https://daily.dev/sources/embeddedrelated)** · 12 min read · 0 upvotes · 0 comments

## Summary

When x-coordinates (abscissae) are evenly spaced, the least-squares linear regression matrix simplifies dramatically. The intercept equals the plain mean of y values, and the slope is a weighted sum with arithmetic weights using the formula q = 12/(m³ - m). This eliminates the need for matrix solvers entirely. The post derives this from first principles using the normal equations, demonstrates it with NumPy examples, and provides a compact Python routine `linear_fit_equal_spacing` that computes slope and offset with a simple loop — useful for embedded or resource-constrained environments where matrix libraries may be unavailable.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.embeddedrelated.com/showarticle/1163.php>

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Tags: [#python](https://daily.dev/tags/python), [#numpy](https://daily.dev/tags/numpy), [#linear-regression](https://daily.dev/tags/linear-regression)

[View this post on daily.dev](https://daily.dev/posts/linear-regression-with-evenly-spaced-abscissae-jcixvo2nn)
