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I'm programming an n-dimensional polynomial fitting function. It uses the basic concept of least squares and a design matrix.

For example, a quadratic fit on 2d data:

This is a row in the design Matrix:

$X={({1, x, x^2})}$

And this is the matrix formula I'm using:

$(X^T*X)^{-1}(X^T*Y)=Ax^2+Bx+C$

This is nothing special or complicated.

I'm at the point however where I'd like to introduce confidence intervals. How do I compute the confidence intervals from this regression technique?

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Normal equations, huh? If you're going to stick with that approach, please please hook up a condition estimator as a sanity check; this is the approach most sensitive to roundoff error. QR decomposition is often a better choice. As an aside, the variances of the parameters are usually obtained from the diagonal entries of the variance-covariance matrix $\left(\mathbf{X}^T\mathbf{X}\right)^{-1}$ ; I would suppose any expression for the CIs would involve these as well. –  J. M. Sep 7 '10 at 9:11

1 Answer 1

A simple place to start: Look at the residues when you subtract out your putative function from your data set. A perfect fit (for a very small dataset, or a very perfectly quadratic dataset) will have residues of zero and a correlation coefficient that you can calculate. A bad but "middling" best-fit will have residues equally distributed in the positive and negative $y$-axis. A bad quadratic fit will skew positive on one side and negative on the other side, or positive in the middle and negative on the ends or vice versa. A better fit will have smaller residues.

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