Propagation of uncertainties with calculus

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SUMMARY

The discussion centers on the propagation of uncertainties in the function F = x/y, specifically addressing the calculation of dF using partial derivatives. The correct expression for dF is established as dF = (Δx/y) - (x/y²)Δy. It is clarified that when dealing with independent variables, uncertainties should be combined using the square root of the sum of squares method, represented as dF² = ((∂F/∂x)Δx)² + ((∂F/∂y)Δy)². The interpretation of negative partial derivatives is also discussed, emphasizing that they should not be converted to absolute values.

PREREQUISITES
  • Understanding of partial derivatives in calculus
  • Familiarity with uncertainty propagation techniques
  • Knowledge of independent and correlated variables in statistics
  • Basic proficiency in mathematical notation and expressions
NEXT STEPS
  • Study the method of uncertainty propagation using the square root of the sum of squares
  • Learn about the implications of correlated versus uncorrelated errors in measurements
  • Explore advanced calculus topics related to multivariable functions
  • Review statistical methods for analyzing measurement uncertainties
USEFUL FOR

Students and professionals in fields such as physics, engineering, and statistics who are involved in measurement analysis and uncertainty quantification will benefit from this discussion.

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Suppose F = x/y

dF= [itex]\frac{\partialF}{\partialx}[/itex][itex]\delta[/itex]x+[itex]\frac{\partialF}{\partialy}[/itex][itex]\delta[/itex]y

This gives

dF=[itex]\frac{\deltax}{Y}[/itex]-[itex]\frac{x}{y^2}[/itex][itex]\delta[/itex]y


That is, the partial derivative of y comes out negative. Should i leave it as a negative?

I see no reason to take the absolute value of the partial of y, but what happens when adding the two partials gives zero uncertainty? Would the uncertainty for that particular measurement just be zero?
 
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Your expression for dF is correct. However the interpretation as far as uncertainty is concerned is flawed. To get uncertainty for 2 independent variables you need square root of sum of squares. This is what you need unless there is some relationship between x and y.
 
Statistically uncorrelated errors add in quadrature:

[tex]dF^2=\left( \frac{\partial F}{\partial x}\delta x \right)^2 +\left( \frac{\partial F}{\partial y}\delta y \right)^2[/tex]
 

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