Covariance between x and f(x)?

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SUMMARY

The discussion focuses on calculating the propagated error of the function f(x,t) when x is defined as x(t) = a √t, where 'a' is a constant. The main challenge is determining the covariance between t and x(t). The participant suggests that covariance may not be necessary and proposes defining g(t) = f(x(t), t) to simplify the uncertainty calculation using the derivative g'(t) and the chain rule. The conversation emphasizes the importance of understanding partial derivatives in this context.

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Homework Statement


As part of an assignment, I have to determine propagated error of some function:
[itex]f(x,t)[/itex]
I did it first with x & t being completely uncorrelated, but now I'm given x as a function of t, [itex]x(t)[/itex], and have to do the same.

Homework Equations


I know the linear approximation for finding the uncertainty [itex]\sigma_f[/itex], all I need is the covariance term.
The equation x(t) itself is simply [itex]x(t) = a \sqrt{t}[/itex], where a is just a constant.

The Attempt at a Solution


I seem to be having a hell of a time Googling to find a way to calculate the covariance between t and x(t).
 
Last edited:
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Hi hb1547! :smile:

I do not believe you need the covariance.
Let's define g(t)=f(x(t),t).
Then the uncertainty in g(t) is g'(t)σt.

Do you know how to calculate g'(t)?
It consists of a couple of partial derivatives and the chain rule...
 

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