T.R | Title | User | Personal Name | Date | Lines |
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1012.1 | Well, it ain't going to be easy... | AKQJ10::YARBROUGH | I prefer Pi | Fri Jan 13 1989 17:50 | 12 |
| In general, a set of N data points requires a polynomial of degree N-1 to
fit it exactly. If you can tolerate some error in the fit, a lower-order
polynomial might do. If you believe the data points approximate some
function, e.g. a Sine function, then a Least Squares fit can be found, i.e.
one can find a sinusoidal function that minimizes the sum of the squares of
all the "measurement errors".
Without knowing anything about the function values, I could not begin to
guess what class of functions might be useful in fitting your data. It
might be helpful to know what kind of problem you are trying to solve.
Lynn Yarbrough
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1012.2 | More Questions on Curve Fitting | REGENT::PETERS | Chris Peters | Wed May 03 1989 14:54 | 20 |
1012.3 | A low-tech first approach | COOKIE::PBERGH | Peter Bergh, DTN 435-2658 | Thu May 04 1989 11:32 | 13 |
| I suggest that yor first step should be to get yourself some lin-log
paper (one axis is linear, the other is logarithmic, a grid is
printed on the paper). Plot log(y) against x for the first curve and y
against x for the other. If the fit is reasonably close to a straight
line for these plots, draw the best lines you can and measure the
parameters from your drawing. Least squares does not make a whole lot
of sense with only a handful of points; the errors in the estimates
will make least squares no better than a hand-drawn line.
If it turns out that the fit is no good, try tinkering with some other
functions until you get a good fit. Try not to use more than two
parameters that describe each curve (again, because of the small number
of points).
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1012.4 | questions about curve fitting | PULSAR::WALLY | Wally Neilsen-Steinhardt | Thu May 04 1989 17:45 | 19 |
| .1 is still correct. see also 950.24 and some nearby replies.
Curve fitting is one of those problems for which there is no best
answer. It all depends on where you are going from there.
What are you trying to do?
optimize a system?
analyze a system?
approximate some system behavior?
interpolate or extrapolate?
average out the error?
The best curve fitting procedure for your problem depends on the
answers to these questions. It is also worth knowing how accurate
your point values are, and how expensive the data is.
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