File:Regression lineaire abscisses ordonnees.svg
Summary
Category:Files by User:cdang
| Description |
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 horizontal offset (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 en abscisse (ligne bleue pointillée). Réalisé avec Scilab, modifié avec Inkscape. |
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| Date | |||
| Source |
Own work based on: Linear least squares example2.svg: |
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| Author | Cdang | ||
| SVG development | Category:Invalid SVG created with Batik:Trigonometry#0077Regression%20lineaire%20abscisses%20ordonnees.svg
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Source code

This media was created with Scilab (free and open source software for numerical computation)Category:Images with Scilab source code
Here is a listing of the source used to create this file.
Here is a listing of the source used to create this file.
// 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);
// points projetés sur la droite
X1 = a*Y + b;
Y1 = aa*X + bb;
// droite de régression horizontale
y1 = 4; x1 = a*y1 + b;
y2 = 10.25; x2 = a*y2 + b;
// droite de régression verticale
xx1 = (y1 - bb)/aa;
xx2 = (y2 - bb)/aa;
// tracé
clf;
couleurs = [get(sdf(), 'color_map') ; 0.75, 0.75, 0.75];
xset('colormap', couleurs);
xsegs([X ; X1], [Y ; Y], 14) // segments horizontaux
h1 = gce();
h1.line_style = 2;
xsegs([X ; X], [Y ; Y1], 14) // segments verticaux
h2 = gce();
h2.thickness = 2;
xpoly([xx1, xx2], [y1, y2]) // droite de régression verticale
h3 = gce();
h3.thickness = 2;
h3.foreground = 2;
xpoly([x1, x2], [y1, y2]) // droite de régression horizontale
h4 = gce();
h4.line_style = 2;
h4.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')
Licensing
I, the copyright holder of this work, hereby publish it under the following licenses:
This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
- You are free:
- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
| Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation License. |
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Original upload log
This image is a derivative work of the following images:
- 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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