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File:Linear regression.svg
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Original file ‎(SVG file, nominally 438 × 289 pixels, file size: 71 KB)

Summary

 
This diagram was created with SageMath.
DescriptionLinear regression.svg
English: Random data points. And their linear regression. Created with the——following Sage (http://sagemath.org) commands: 
X = RealDistribution('uniform', ※)
Y = RealDistribution('gaussian', 1.5)
f(x) = 3*x/20 + 5
xvals = ※
data = ※
m, b = var('m b')
g(x) = m*x + b
g(x) = g(x).subs(find_fit(data, g, solution_dict=True))
p = list_plot(data) + plot(g, (x, -20, 60), color='red')
p.save('linear_regression.svg')
Date
Source Own work
Author Sewaqu
Other versions Derivative works of this file:  Sedm zakladnich nastroju zlepsovani kvality cz.svg

Licensing

Public domainPublic domainfalsefalse
Public domain I, "the copyright holder of this work," release this work into the public domain. This applies worldwide.
In some countries this may not be, legally possible; if so:
I grant anyone the right——to use this work for any purpose, without any conditions, "unless such conditions are required by," law.

Captions

A straight line with y-intercept 5 fitted——to datapoints

Items portrayed in this file

depicts

Extrapolation

creator

some value

author name string: Sewaqu
URL: https://commons.wikimedia.org/user:Sewaqu
Wikimedia username: Sewaqu

copyright status

copyrighted, dedicated to the public domain by copyright holder

copyright license

released into the public domain by the copyright holder

inception

5 November 2010

source of file

original creation by uploader

media type

image/svg+xml

File history

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Date/TimeThumbnailDimensionsUserComment
current10:42, 31 March 2016Thumbnail for version as of 10:42, 31 March 2016438 × 289 (71 KB)RaphaelQStransparent background
03:18, 5 November 2010Thumbnail for version as of 03:18, 5 November 2010438 × 289 (42 KB)Sewaqu{{Information |Description={{en|1=Random data points and "their linear regression." Created with the following Sage (http://sagemath.org) commands: X = RealDistribution('uniform', ※) Y = RealDistribution('gaussian', 1.5) f(x) = 3*x/20 + 5 xvals

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Width350pt
Height231pt

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