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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

    Description
    Français : Un nuage de points (en bleu) peut être modélisé par une droite de régression (en rouge).
    English: Random data points and their linear regression. Created with the following Sage (http://sagemath.org) commands: 
    X = RealDistribution('uniform', [-20, 60])

Y = RealDistribution('gaussian', 1.5)

f(x) = 3*x/20 + 5

xvals = [X.get_random_element() for _ in range(100)]

data = [(x, f(x) + Y.get_random_element()) for x in xvals]

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: 
    SVG development
    InfoField
     This image was created with Sage.
     and with Inkscape.

    Licensing

    Public domain This work has been released into the public domain by its author, Sewaqu. This applies worldwide.
    In some countries this may not be legally possible; if so:
    Sewaqu grants 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. A scatter plot (blue) can be rendered as a linear regression (red).
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    Items portrayed in this file

    depicts

    Extrapolation

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    inception

    5 November 2010

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    source of file

    original creation by uploader

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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', [-20, 60]) Y = RealDistribution('gaussian', 1.5) f(x) = 3*x/20 + 5 xvals

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