# reglin

Linear regression

## Calling Sequence

    [a,b,sig]=reglin(x,y)

## Description

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)`

## Examples

    // simulation of data for a(3,5) and b(3,1)
    x=rand(5,100);
    aa=testmatrix('magi',5);aa=aa(1:3,:);
    bb=[9;10;11]
    y=aa*x +bb*ones(1,100)+ 0.1*rand(3,100);

    // identification
    [a,b,sig]=reglin(x,y);
    max(abs(aa-a))
    max(abs(bb-b))

    // an other example : fitting a polynomial
    f=1:100; x=[f.*f; f];
    y= [ 2,3]*x+ 10*ones(f) + 0.1*rand(f);
    [a,b]=reglin(x,y)

## See Also

- pinv
- leastsq
- qr