Calculating Uncertainty in z with Method 1 & Method 2

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SUMMARY

The discussion focuses on calculating uncertainty in the function z = x sin y using two methods. Method 1 employs a linear approximation for uncertainty propagation, yielding dz = 0.0039, while Method 2 utilizes the statistical approach, resulting in a larger uncertainty of δz = 0.062. Method 2 accounts for the squared contributions of uncertainties, providing a more comprehensive representation of error. The participants conclude that Method 2 is superior due to its statistical basis, which accommodates both positive and negative errors.

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  • Knowledge of statistical methods for error analysis.
  • Basic proficiency in trigonometric functions and their derivatives.
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opticaltempest
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Assume we have the function [tex]z = x\sin y[/tex]
Our best guest for our measurement is x=1.0 and y=2.0. The uncertainty in x is 0.05. The uncertainty in y is 0.10.

We want to calculate the final uncertainty as the initial uncertainties propagate through the function.

***** Method 1 *****
In Calculus III we find the propagation of uncertainties in multivariable functions using the following method:

[tex] dz = \frac{{\partial z}}{{\partial x}}dx + \frac{{\partial z}}{{\partial y}}dy[/tex]

So the uncertainty would be

[tex] \begin{array}{l}<br /> dz = \sin \left( y \right)dx + x\cos \left( y \right)dy \\ <br /> dz = \sin \left( {2.0} \right)\left( {0.05} \right) + \left( {1.0} \right)\cos \left( {2.0} \right)\left( {0.10} \right) \\ <br /> dz = 0.0039 \\ <br /> \end{array}[/tex]***** Method 2 *****

According to
https://www.amazon.com/dp/093570275X/?tag=pfamazon01-20

It says we should use this formula to calculate the propagated uncertainty:

[tex] \delta z = \sqrt {\left( {\frac{{\partial z}}{{\partial x}}dx} \right)^2 + \left( {\frac{{\partial z}}{{\partial y}}dy} \right)^2 } [/tex]

Using this method the uncertainty is

[tex] \begin{array}{l}<br /> \delta z = \sqrt {\left[ {\sin \left( {2.0} \right)\left( {0.05} \right)} \right]^2 + \left[ {\left( {1.0} \right)\cos \left( {2.0} \right)\left( {0.10} \right)} \right]^2 } \\ <br /> \delta z = 0.062 \\ <br /> \end{array}[/tex]

The uncertainty in method 2 is nearly 16 times larger than the uncertainty in method 1.
I am assuming method 2 represents the uncertainty better than method 1.

My question is: What is method 2 taking into account that method 1 isnt? Why does method 2 represent the uncertainty better than method 1?
 
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To reconcile the 2 approaches, I suggest you modify method 1 to use absolute value for both terms and then add. This would bring them closer.

Method 2 is the usual statistical approach, since errors can be negative or positive.
 
I believe the law of averages would rapidly assert itself in this scenario. The Chi squared probability is the most reliable method, IMO.
 

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