How Do You Calculate Uncertainty in the Function f(x,z)=z/x?

In summary, the conversation discusses the calculation of "f" and its uncertainty, with a focus on using proper units and showing all work. The equation used is delta f(x,y)=df(x,y)/dx *(delta x) + df(x,y)/dy * delta y, but the attempts at solving it are incorrect. The correct equation is \left(\Delta f(x,y)\right)^2 = \left(\frac{\partial f}{\partial x}\right)^2\Delta x^2 + \left(\frac{\partial f}{\partial y}\right)^2\Delta y^2.
  • #1
glasshut137
23
0

Homework Statement



Calculate "f" and its uncertainty, watch the units, show all work.

Homework Equations



f(x,z)= z/x
x=100.5(+ or -) 3.8 cm
y=71(+ or -) 1 s

The Attempt at a Solution



ok so i know that to find uncertainty i have to use the equation delta f(x,y)=df(x,y)/dx *(delta x) + df(x,y)/dy * delta y. I got (delta f)= z*lnx*(delta x) + (delta z)/x. When i plug in the numbers the units don't match up, i get cm*s + s/cm.

im really confused on how to find the answer can someone please help. thanks.
 
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  • #2
glasshut137 said:
delta f(x,y)=df(x,y)/dx *(delta x) + df(x,y)/dy * delta y]
That is not correct. In general:

[tex]\left(\Delta f(x,y)\right)^2 = \left(\frac{\partial f}{\partial x}\right)^2\Delta x^2 + \left(\frac{\partial f}{\partial y}\right)^2\Delta y^2[/tex]

glasshut137 said:
I got (delta f)= z*lnx*(delta x) + (delta z)/x
This is also in correct.

[tex]\frac{d}{dx}\left(\frac{1}{x}\right) \neq \ln|x|[/tex]
 
Last edited:
  • #3
oh jeez you're right thanks
 
  • #4
glasshut137 said:
oh jeez you're right thanks
Take a note of my further comment, which was added after you replied.
 

What is uncertainty propagation?

Uncertainty propagation is the process of quantifying and analyzing how uncertainties in input parameters or variables can affect the results or outcomes of a scientific model or experiment.

Why is uncertainty propagation important in scientific research?

Uncertainty propagation is important because it allows scientists to evaluate the reliability and accuracy of their results and conclusions. It also helps identify which input parameters or variables have the greatest impact on the overall uncertainty of the results.

What methods are commonly used for uncertainty propagation?

There are several methods commonly used for uncertainty propagation, including Monte Carlo simulation, sensitivity analysis, and Bayesian inference. Each method has its own advantages and limitations, and the choice of method depends on the specific problem being studied.

How is uncertainty propagation different from error analysis?

Uncertainty propagation and error analysis are closely related, but they are not the same. Error analysis focuses on quantifying the errors or mistakes in measurements or observations, while uncertainty propagation considers the overall uncertainty in the results, which may also include errors from measurements or observations.

What are some challenges in uncertainty propagation?

One of the main challenges in uncertainty propagation is identifying and quantifying all sources of uncertainty in a model or experiment. This often requires a thorough understanding of the system and its underlying processes. Additionally, the computational and statistical methods used for uncertainty propagation may also introduce their own sources of uncertainty.

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