File:Regression lineaire ordonnees orthogonal.svg

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English: Illustration of least squares fitting. The data (red dots) are at co-ordinates (1,6), (2,5), (3,7) and (4,10). A linear approximation is obtained using least-squares estimation of the vertical offset (plain blue line) and of the perpendicular offset (total least squares, dashed blue line). Created using Scilab, modified with Inkscape.
Français : Illustration de la régression linéaire par la méthode des moindres carrés. Les données (points rouges) ont pour coordonnées (1 ; 6), (2 ; 5), (3 ; 7) et (4 ; 10). On effectue une régression linéaire en considérant les écarts en ordonnée (ligne bleue continue) et orthogonaux (moindres carrés totaux, trait pointillé bleu). Réalisé avec Scilab, modifié avec Inkscape.
Date (UTC)
Source This file was derived from: Linear least squares example2.svg
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This is a retouched picture, which means that it has been digitally altered from its original version. Modifications: redrawn with Scilab, with two different algorithms. The original can be viewed here: Linear least squares example2.svg. Modifications made by Cdang.


 
This W3C-unspecified vector image was created with Inkscape .

Scilab source

// Données

X = 1:4;
Y = [6, 5, 7, 10];

// régression horizontale
[a, b, sigma] = reglin(Y, X);

// régression verticale
[aa, bb, sigma] = reglin(X, Y);

// régression orthogonale
Ainit = [0, 1];

function [e] = resorth(A, X, Y)
    e = 1/(1 + A(2)^2)*(Y - A(1) - A(2)*X).^2
endfunction

[S, Aopt] = leastsq(list(resorth, X', Y'), Ainit);

// points projetés sur la droite

Xh = a*Y + b;
Yv = aa*X + bb;
normdo = sqrt(1 + Aopt(2)^2);
distcurv = (1/normdo)*(X + Aopt(2)*(Y - Aopt(1)));
Xorth = (1/normdo)*distcurv;
Yorth = Aopt(1) + (Aopt(2)/normdo)*distcurv;

// droite de régression verticale
xx1 = (y1 - bb)/aa;
xx2 = (y2 - bb)/aa;

// droite de régression orthogonale
xxx1 = (y1 - Aopt(1))/Aopt(2);
xxx2 = (y2 - Aopt(1))/Aopt(2);

// tracé
clf;
couleurs = [get(sdf(), 'color_map') ; 0.75, 0.75, 0.75];
xset('colormap', couleurs);

xsegs([X ; X], [Y ; Yv], 14) // segments verticaux
h2 = gce();
h2.thickness = 2;

xsegs([X ; Xorth], [Y ; Yorth], 14) // segments orthogonaux
h2 = gce();
h2.line_style = 2;

xpoly([xx1, xx2], [y1, y2]) // droite de régression verticale
h3 = gce();
h3.thickness = 2;
h3.foreground = 2;

xpoly([xxx1, xxx2], [y1, y2]) // droite de régression orthogonale
h5 = gce();
h5.line_style = 2;
h5.foreground = 2;

plot(X, Y, 'ok') // points
h5 = gce();
h5.children.mark_background = 5;
axe = gca();
axe.data_bounds = [0, 4 ; 5, 10.25];
axe.grid = [33, 33];
axe.tight_limits = 'on';
axe.isoview = 'on';
xtitle(' ', 'x', 'y')

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Original upload log[edit]

This image is a derivative work of the following images:

  • File:Linear_least_squares_example2.svg licensed with Cc-by-sa-3.0, GFDL
    • 2011-06-10T03:35:22Z Krishnavedala 279x274 (51647 Bytes) {{Information |Description ={{en|1=Illustration of [[:w:Linear_least_squares_(mathematicsleast squares fitting]]. The data (red dots) are at co-ordinates (1,6), (2,5), (3,7) and (4,10). A linear approximation is obtained u

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Date/TimeThumbnailDimensionsUserComment
current07:43, 5 April 2013Thumbnail for version as of 07:43, 5 April 2013317 × 384 (44 KB)Cdang (talk | contribs)line styles inverted + G
15:09, 3 April 2013Thumbnail for version as of 15:09, 3 April 2013317 × 384 (43 KB)Cdang (talk | contribs)== {{int:filedesc}} == {{Information |Description=={{en|1=Illustration of least squares fitting. The data (red dots) are at co-ordinates (1,6), (2,5), (3,7) and (4,10). A linear approximation is obtained using ...

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