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Thread: Fitting Curvaceous Curves
face picon face BY : Olin Lathrop email (remove spam text)

> The question is, I'd like a simple mathematical function that I can
> implement in a PIC that can tell me how good my "fit" of my estimated data
> to my real world data is.  This is sort of a statistics question:  how can
> you tell, mathematically, when two data sets follow the same curve, and if
> not, how close they are?
> I've tried some simple items.  First I tried sum of the differences
> the two curves at each X data point.  The result is that a really bad
> fitting curve can have a sum-of-differences of zero if it crosses
> symmetrically.  Also tried minimizing average of the differences, and
> of differences. (these allow big negative numbers)  and sum of absolute
> value of differences (Same problems as sum of differences)   None of these
> really gives a good indication.

My first knee jerk reaction is to do a sum of square differences.  For
linear metric that describes overall error, I would take the square root of
the summed result, but you still have a monotonic metric without the square

I also don't see why the sum of absolute value of differences should suffer
from the symmetric error problem.

Olin Lathrop, embedded systems consultant in Devens Massachusetts
(978) 772-3129, olinSTOPspamspamspamBeGoneembedinc.com, http://www.embedinc.com

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Subject (change) Fitting Curvaceous Curves

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