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Scilab help >> CACSD > reglin

# 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)`

```// 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 polynom
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 — pseudoinverse
• leastsq — Solves non-linear least squares problems
• qr — QR decomposition
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