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From: "John D'Errico" <woodchips@rochester.rr.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: the mathematic relationship between two series of data
Date: Sun, 13 Jul 2008 14:58:01 +0000 (UTC)
Organization: John D'Errico (1-3LEW5R)
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"Hong Zhang" <oceanzhhd@gmail.com> wrote in message 
<g5d0sv$2un$1@fred.mathworks.com>...
> Hi,John,
> 
> "it sounds as if you do not have any mechanistic or 
> physical model for yourdata", That's to the point. 
> Actually, in my former dataset, A is the interval of a 
> series of measurment value and B is corresponding 
> cumulative probability. As there is no reference about the 
> distribution rule of such measurment, at first i think it 
> would be power-law which may be accord with the real life 
> condition.

If it should be some sort of a power law,
think about the form. You might consider
reading through my nonlinear shapes
submission:

http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?
objectId=10864&objectType=FILE


> I had no clear idea until now. Why it appears to be a 
> piecewise quadratic? why it has a small break? In fact, A 
> is the result of another matalb programming which is point 
> to an adjacency matrix.
> 
> Your suggestions do give me some hints and clues. I need to 
> think carefully about the mechanism of the independent 
> variable and its real life meaning.

Exactly. It is this introspection that is very
important when you do modeling. It helps
you to learn about your process, and perhaps
discover things that you know about the
system that you might not have seen
otherwise.

 
> BTW, Is estimatenoise to used to evaluate or improve the 
> curvefitting precision? It is obiviously important to be 
> considered.

Estimatenoise might help you if you are
using a smoothing spline to approximate
the relationship, since they can use that
information.


> But for me, this curvefitting process is 
> something like data mining. i concerns what relationship 
> the dataset emerge and why it appears like that.

Curvefitting can be a voyage of discovery,
helping you to learn about the process you
will fit. Or it can be as simple as a brute
force interpolation, or polynomial curve fit.
You may receive returns that are directly
related to the effort you expend in the
modeling process.

John