I don't know if this is the proper place for asking this kind of questions and I apologise in advance if it isn't, but anyways: is there a way to propagate linear uncertainties (i.e. through first-degree partial derivatives) for functions in an open form, that is, that cannot be reduced? I am particularly interested in finding the uncertainty of $y$, given the equation:
$y = A\cdot sinh(y)\cdot\sqrt{BC}$
where $A$, $B$, $C$ all have a given uncertainty. Does anybody have any insight? Thank you.
Sure. Consider
$f(y) = y/\sinh y = g(A,B,C) = A \sqrt{BC}$
Then
$ \begin{align} \sigma_f = \sigma_y \left| \frac{df}{dy} \right|_{y = \overline{y}} = \sigma_y \mathop{\rm csch} \overline{y} \: (1 - \overline{y} \mathop{\rm \coth \overline{y}}) = \sigma_g \end{align} $
where $\overline{y}$ is the expected value of $y$. You can work out $\sigma_g$ as a function of $\sigma_{A,B,C}$ by the usual error propagation.
Of course, this technique won't work if $\sigma_y$ is so large that $\frac{df}{dy}$ becomes nonlinear within $\overline{y} \pm \sigma_y$.
