Curve Fitting Analysis: Finding Uncertainty in Delta I

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Homework Help Overview

The discussion revolves around curve fitting analysis, specifically focusing on determining the uncertainty in the integral of a quadratic function derived from experimental data. The original poster is seeking guidance on how to calculate the uncertainty in the integral result, denoted as delta I, considering the uncertainties associated with the fitted parameters.

Discussion Character

  • Exploratory, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the need to account for uncertainties in the parameters when integrating the fitted function. There are suggestions about setting up multiple quadratic equations to establish upper and lower limits for the integral. Some participants also mention alternative methods, such as using the Newton-Cotes formula for integration, which may simplify the process.

Discussion Status

The conversation is ongoing, with participants exploring different methods to approach the problem. While some guidance has been provided regarding setting up equations and considering uncertainties, there is no explicit consensus on the best method to use.

Contextual Notes

Participants are working under the constraints of experimental measurements that inherently carry uncertainties. The original poster's confusion about the integration method indicates a need for clarification on how to properly account for these uncertainties in their calculations.

Winzer
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Homework Statement


I had to do a curve fit on some data and got an equation to the form:

Homework Equations


[tex]F(t) = a_0 + a_1 t + a_2 t^2[/tex]

The Attempt at a Solution


Each parameter has an associated uncertainty.
I need to integrate F(t) over a range to get I. How do I find the the uncertainty in delta I with the error for each parameter.
 
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Hi Winzer! :smile:

(try using the X2 and X2 tags just above the Reply box :wink:)
Winzer said:
Each parameter has an associated uncertainty.
I need to integrate F(t) over a range to get I. How do I find the the uncertainty in delta I with the error for each parameter.

I'm not sure what you're asking …

the integral is a0t + a1t2/2 + a2t3/3 + constant …

what is the problem with the uncertainty in that?
 
The op means that he has 3 values for t, experimentally measured so they have some uncertainty. So they could be A, with an error of +/- a, B with an error of +/- b, and C with an error of +/- c, where A,B and C are the measured values, and a,b and c are the uncertainties in measuring them.

To get the error range for I, you need to set up all 3 quadratic equations each fitted to
A+a, B+b, C+c, another to A,B,C and another to A-a, B-b, C-c.

Once you have those 3 equations, the integral for the first one is the upper limit for I, the integral for the second one is the estimate you will be using, and the integral for the 3rd one is the lower limit for I (assuming the parabola is concave up, otherwise the first integral is the lower limit and third one is upper).
 
Thanks Gib Z. For some reason I thought there was a quadrature way of doing it.
 
Winzer said:
Thanks Gib Z. For some reason I thought there was a quadrature way of doing it.

Ahh actually there is, and it might have been quicker ! =[ Using the Newton-Cotes formula for n=3 (Simpson's Rule) takes away the need to find parabolas fitting points and does the integration automatically! The formula is

[tex]\frac{b-a}{6} (f_0 + 4 f_1 + f_2)[/tex]

with error term of [itex]-\frac{(b-a)^5}{2880}\,f^{(4)}(\xi)[/itex] but these are parabolas anyway so the error term is zero. Hence we can easily see that if [itex]\Delta f_n[/itex] is the maximum uncertainty in measurement, the maximum uncertainty in the integral is :

[tex]\pm \frac{b-a}{6} ( \Delta f_0 + 4\Delta f_1 + \Delta f_2 )[/tex].

Somewhat an easier process to carry out!
 

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