Error propagation with dependent variables

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SUMMARY

This discussion focuses on error propagation in the context of Microdosimetry theory, specifically for dependent variables in the dose-weighted lineal energy distribution function, d(y). The user has calculated the constant y_F with uncertainty but seeks guidance on determining the uncertainty in d(y) when the variables are not independent. The solution involves treating each measured channel f(y) as independent for the purpose of applying the simplification method, leading to a valid approach for calculating the uncertainty in y_F using partial derivatives.

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lachy
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Homework Statement


Based on Microdosimetry theory, trying to figure out error propagation for a lot of quantities that are produced from radiation spectra. I am having trouble finding information on how to calculate and propagate errors when the quantities in my equations are not independent.

Homework Equations



I have a function called the dose-weighted lineal energy distribution:

d(y) = \frac{yf(y)}{y_{F}} = \frac{yf(y)}{\int{yf(y)dy}}

I have calculated the constant y_F\pm\Delta y_F using the measured quantity f(y)\pm\sqrt{f(y)} but how do I find the uncertainty in the d(y) distribution when these quantities are not independent? Note: \Delta y \approx 0 so this only concerns f(y) and y_F.

The Attempt at a Solution


I had attempted doing this with the simplification method that I did in one of my 3rd year stats classes however I realized that this only applies for independent variables; don't know where to go know.

Thanks :)
 
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How do you calculate yF based on f(y)?
 
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Edit: sorry, fixing up latex.

Thanks for responding. Given that each channel has been measured independently of the others, the counts in each channel f(y)\pm \sqrt{f(y)} can be used in the "simplification method". To expand on the definition:

y_F = \int_{0}^{\infty}yf(y)dy = \Delta y \sum_{i = 1}^{n}y_{i}f(y_{i}) = \Delta y(y_{1}f(y_{1}) + y_{2}f(y_{2}) + ... + y_{n}f(y_{n})

where \Delta y is the lineal energy channel width, no the error in y - forgive my lack of consistency. Anyway, \Delta \Delta y \approx 0 so we don't consider it in the error calculation except as a scaling constant.

Treating each f(y_{i})\pm \Delta f(y_{i}) as indepdent variables we get:

<br /> \begin{align*}<br /> \Delta y_{F} &amp;= \sqrt{(\frac{\partial}{\partial f(y_{1})}[y_{F}]\Delta f(y_{1}))^2 + (\frac{\partial}{\partial f(y_{2})}[y_{F}]\Delta f(y_{2}))^2 + ... + (\frac{\partial}{\partial f(y_{n})}[y_{F}]\Delta f(y_{n}))^2} \\<br /> &amp;= \Delta y \sqrt{(y_{1}\Delta f(y_{1}))^2 + (y_{2}\Delta f(y_{2}))^2 + ... + (y_{n}\Delta f(y_{n})])^2}<br /> \end{align*}<br />

Is this valid?
 
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