Error propagation with dependent variables

In summary, the student is struggling with calculating and propagating errors for quantities that are not independent in equations related to Microdosimetry theory. They have attempted using a simplification method but realized it only applies to independent variables. They are now seeking guidance on how to proceed with their calculation of yF based on f(y).
  • #1
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:

[itex]d(y) = \frac{yf(y)}{y_{F}} = \frac{yf(y)}{\int{yf(y)dy}}[/itex]

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

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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  • #2
How do you calculate yF based on f(y)?
 
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Likes lachy
  • #3
Edit: sorry, fixing up latex.

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

[itex]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})[/itex]

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

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

[itex]
\begin{align*}
\Delta y_{F} &= \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} \\
&= \Delta y \sqrt{(y_{1}\Delta f(y_{1}))^2 + (y_{2}\Delta f(y_{2}))^2 + ... + (y_{n}\Delta f(y_{n})])^2}
\end{align*}
[/itex]

Is this valid?
 
Last edited:

1. How do dependent variables affect error propagation?

Dependent variables can have a significant impact on error propagation because any error in the dependent variable will be carried through to all subsequent calculations and measurements. This can result in a larger overall error in the final result.

2. What is the difference between dependent and independent variables in error propagation?

Dependent variables are those that are affected by changes in other variables, while independent variables are not affected by changes in other variables. In error propagation, this means that dependent variables are more likely to introduce error into the final result, while independent variables are less likely to do so.

3. How can I minimize error propagation with dependent variables?

To minimize error propagation with dependent variables, it is important to carefully measure and control all variables involved in the calculation. This includes minimizing any sources of error in the measurement process and ensuring that all variables are accurately and precisely measured.

4. Can dependent variables ever have a positive effect on error propagation?

Yes, there are situations where dependent variables can actually reduce error propagation. This can happen when the dependent variable is measured with a higher precision than the independent variable, resulting in a smaller error being introduced into the final result. However, this is not always the case and dependent variables should still be carefully considered in error propagation calculations.

5. What are some common methods for handling error propagation with dependent variables?

There are several methods for handling error propagation with dependent variables, including using error propagation formulas and Monte Carlo simulations. It is important to carefully choose the most appropriate method based on the specific variables and calculations involved in order to accurately account for error in the final result.

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