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Linear regression

Calling Sequence



solve the regression problem y=a*x+ b in the least square sense. sig is the standard deviation of the residual. x and y are two matrices of size x(p,n) and y(q,n), where n is the number of samples.

The estimator a is a matrix of size (q,p) and b is a vector of size (q,1)

// simulation of data for a(3,5) and b(3,1)
y=aa*x +bb*ones(1,100)+ 0.1*rand(3,100);
// identification 
// an other example : fitting a polynom 
f=1:100; x=[f.*f; f];
y= [ 2,3]*x+ 10*ones(f) + 0.1*rand(f);

See Also

  • pinv — pseudoinverse
  • leastsq — Solves non-linear least squares problems
  • qr — QR decomposition
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Last updated:
Thu May 12 11:44:20 CEST 2011