Help me to make best fit with error

  • Thread starter shad0w2000
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In summary, the conversation discusses making a best fit to a mathematical function and the need for calculating the error on one of the parameters. The speaker shares their approach and asks for help in determining the error on 'a'. The solution is suggested to use a linear regression and use software such as Excel or Math'ca to calculate the standard errors for the regression parameters.
  • #1
shad0w2000
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Hi,

I have a set of datapoints (x_i,y_i) and I am going to make a best fit to y = c*Exp(a/x)

Making the fit isn't the problem (Mathematica, etc. can handle this), but what I need is the error on a.

What I have done so far is something like this:

P(c,a | {x_i,y_i} ) = k * P( {x_i,y_i} | c,a ) using Bayes.

And then

P({(x_1,y_1),(x_2,y_2),...}|c,a) = product of ( P(x_i,y_i | c,a) ) for all i's.

which is proportional with

product of ( P(y_i | x_i, c,a) )



But I can't get any further than this :)

Can anyone help me with this ?

Thanks in advance
 
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  • #2
How are you defining the error on 'a'? Totally missing what you mean!
 
  • #3
y = c*Exp(a/x)
Log y = Log c + a (1/x)
v = d + a z where v = Log y, d = Log c and z = 1/x.

Now you have a linear regression that you can compute with any type of software that will print out the standard errors for the regression parameters d and a. For example, Excel. Or use the Regress function in Math'ca (after you enter Needs["LinearRegression`"]).
 
Last edited:

1. What is the purpose of finding the best fit with error?

The purpose of finding the best fit with error is to determine the most accurate mathematical model that represents a set of data points, taking into account the inherent uncertainty or variability in the data. This allows for more reliable predictions and analysis of the data.

2. How do you calculate the best fit with error?

The best fit with error is calculated using a method called least squares regression, which involves minimizing the sum of the squared differences between the data points and the predicted values from the mathematical model. This can be done using mathematical equations or software programs.

3. What is the difference between a best fit and a best fit with error?

A best fit is a mathematical model that closely matches the observed data points, while a best fit with error takes into account the uncertainty or variability in the data. This means that a best fit with error will have a higher degree of accuracy and reliability compared to a simple best fit.

4. Can a best fit with error be used for any type of data?

Yes, a best fit with error can be used for any type of data, as long as there is a relationship between the variables being analyzed. This method is commonly used in fields such as statistics, physics, and economics to analyze and predict trends in data.

5. How do you interpret the error in a best fit with error?

The error in a best fit with error is typically represented as a margin of error or confidence interval. This indicates the range of possible values that the true data points may fall within, taking into account the variability in the data. A smaller error indicates a more accurate and precise fit, while a larger error indicates more uncertainty in the data.

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