Do you know that your data sets will always be either (a) linear (ie, y= mx +b) or (b) exponential (y = Aexp(Bx))? If that's the case, then you should be able to use linear least squares as suggested above. Since a and b are separate issues, you would have to try both, and deside on a case by case basis which is better.

Also, in the case of b, you can convert it to a linear problem by taking the log of y and plotting that against x. (At least that feels right at the moment... log(y) = log(A) + Bx... yeah, that's it).

If your data could be of other forms, like higher order polynomials, then you will have to try all options, and it would turn into a slow mess, since you would have to try all of them for any given set.

Good luck,
Scott

In reply to Re: Seeking abnormalities in data sets. by scain
in thread Seeking abnormalities in data sets. by ehdonhon

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