www.piclist.com/techref/index.htm?key=fitting+curvaceous

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

between

> 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

median

> 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

root.

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, spamolin@spam@.....embedinc.com, http://www.embedinc.com

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