Can Nonlinear Equation Fitting Yield Reliable Parameters for Varying Data?

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Homework Help Overview

The discussion revolves around fitting a nonlinear equation, specifically $$y=\frac{kx}{5+cx}$$, to a set of data points. The original poster is seeking to determine the parameters k and c for the best fit, while questioning the validity of traditional fitting techniques due to claims that these parameters may vary with the independent variable x.

Discussion Character

  • Exploratory, Assumption checking, Problem interpretation

Approaches and Questions Raised

  • Participants explore the implications of fitting parameters that may change with x, questioning the assumptions behind standard fitting methods. Some suggest starting with data visualization, while others discuss the potential for alternative fitting models, such as cubic splines.

Discussion Status

The conversation is ongoing, with participants providing insights into the nature of parameter variability and its impact on fitting accuracy. There is a recognition that if parameters are indeed variable, traditional fitting may not yield reliable results. However, alternative modeling approaches are being considered.

Contextual Notes

The original poster notes that the values of x are discrete and that there are limits on the parameters k and c, which are related to the system being studied. Concerns about the accuracy of fits when parameters vary are central to the discussion.

patric44
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Homework Statement
why nonlinear least square method will not work well with the function
y=kx/(5+cx)
Relevant Equations
y=kx/(5+cx)
I was trying to fit a set of data to the nonlinear equation
$$
y=\frac{kx}{5+cx}
$$
and find the parameters k,c that will result in a best fit, but (I was told without explanation) that the parameters change as we increase x, so regular fitting techniques such as nonlinear least square will not work?
can any one explain this to me, if the parameters vary as a function of the independent variable what is the best way for the fitting, and is that even possible?
 
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The idea of fitting is based on the assumption that the parameters do NOT change with x.
Start with a plot of the data.
 
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If there are no limits on the behavior of the parameters ##k## and ##c##, it is not only possible to fit the data, it is too easy. In that case, you could always just set ##c=1## and ##k_i = y_i \frac {5+x_i} {x_i}##. If there are no repeated ##x_i## values associated with non-equal ##y_i## values, this would give you a perfect fit. If there are some repeated values in ##x_i##s, you can just use the mean of the associated ##y_i## values. In some cases, this might give you a reasonable model, but I would not count on it.
You do not say if there is any random behavior in the data, or what the nature of the random behavior might be.
 
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patric44 said:
I was told without explanation

Yeah...

What's the range of your ##x## ? If it's in the several thousands ##y## is a constant !

##\ ##
 
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there is a limit on the values of k,c I suppose, they are related to some constants about the system.
the values of x is discrete like 2,4,6,... and no values in between, the values of y ranges from 100 to say 3000 and so on, what I am confused about is how its possible for a parameter to vary as a function of the independent variable like i was told?!, I mean if they really change then no fit will be accurate? not only the non linear least squares
 
patric44 said:
what I am confused about is how its possible for a parameter to vary as a function of the independent variable like i was told?!, I mean if they really change then no fit will be accurate? not only the non linear least squares
If the parameters ##k## and ##c## change and you perform a non-linear least squares fit to determine the best CONSTANT ##k## and ##c##, then the fit will not be accurate. However, if you allow a different model, then you might get a good enough fit. Or you might try something like a cubic spline, which would fit the data in pieces with a continuous derivative. It all depends on what you want to use the model for.
 
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FactChecker said:
If the parameters ##k## and ##c## change and you perform a non-linear least squares fit to determine the best CONSTANT ##k## and ##c##, then the fit will not be accurate. However, if you allow a different model, then you might get a good enough fit. Or you might try something like a cubic spline, which would fit the data in pieces with a continuous derivative. It all depends on what you want to use the model for.
the idea is that I want to determine the values of k,c for the specific model given by ##y=kx/(5+cx)##, because I will use the formula of the model in further calculations. from what I unstrood from you guys now I believe that the person who told me that the parameters vary could be mistaken or misinterpreted something, I really don't know
 

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