Calculating Error in Linearized Data: What is the Method?

In summary, the conversation discusses how to calculate error for a linearized set of data that has been curved to a square root. The formula for finding the uncertainty in a new quantity, y, is given, and the conversation demonstrates how to use it through an example. The final result for the example is 2.2360 ± 0.0044. The conversation also mentions how this formula can be applied to finding the error in more complex quantities.
  • #1
Dennisl
3
0

Homework Statement


I'm doing a lab and I'm having difficulty calculating error for a linearized set of data. I need to find the error for a set of data that has been curved to a square root. for example my x= 5.00 +- .02 becomes sqrt(5) but what is the error?


Homework Equations





The Attempt at a Solution



I am debating whether to convert the original uncertainty to percent uncertainty and just multiply that by the linearized data or to convert the original uncertainty to percent uncertainty, square root the percentage and then multiply that by the linearized data.
 
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  • #2
Let us denote the new quantity, [tex]y[/tex], which is a function of whatever variables you may have, [tex]x_1, x_2, x_3...[/tex], which have their respective uncertainties [tex]\Delta x_1, \Delta x_2, \Delta x_3...[/tex]

Then the uncertainty in [tex]y[/tex] is given by the formula:

[tex]\Delta y = \sqrt{(\frac{\partial y}{\partial x_1}\cdot \Delta x_1)^2+(\frac{\partial y}{\partial x_2}\cdot \Delta x_2)^2+(\frac{\partial y}{\partial x_3}\cdot \Delta x_3)^2+...}[/tex]
 
  • #3
so for my example point 5.00 +- .02 then y=[tex]\sqrt{\left(\frac{\sqrt{5}}{5}\times\frac{.02}{5.00}\right)^{2}[/tex] = 2.2361 [tex]\pm[/tex] .0018 (ignoring sig figs)?
 
  • #4
No no, [tex]\frac{\partial y}{\partial x}[/tex] means to take the partial derivative of y with respect to x (That means taking the derivative with respect to x, while treating every other parameter as constant)

So in your case, [tex]y=\sqrt{x}=x^{1/2}[/tex]
[tex]\frac{\partial y}{\partial x}=\frac{1}{2\sqrt{x}}[/tex]

Now that you know what the notation means, try and find the error.

My result is 2.2360±0.0044, if you want to compare.
 
  • #5
Oh I didn't realize those were d's. so it is [tex]\sqrt{\left(\frac{1}{2\sqrt{5}}\times.02\right)^{2}}[/tex]
I get 2.2360±0.0044 as well. I see how it works. Thank you for your help!
 
  • #6
Sure thing. :) Just try and keep the general formula in mind, since eventually you'll have to deal with errors in quantities like:

[tex]z=\sqrt{x+y}[/tex]
[tex]\Delta x=5.00\pm 0.02[/tex]
[tex]\Delta y=6.00\pm 0.03[/tex]

See if you can find [tex]\Delta z[/tex]
My answer is: ±0.0054
 

FAQ: Calculating Error in Linearized Data: What is the Method?

What is linearized uncertainty?

Linearized uncertainty is a method used to estimate the uncertainty of a measurement through linearization, which involves approximating a nonlinear function with a linear function in a small region around a specific point. This allows for easier and more accurate calculation of uncertainty.

How is linearized uncertainty different from other methods of uncertainty estimation?

Linearized uncertainty differs from other methods, such as Monte Carlo simulation or interval arithmetic, in that it uses linearization to simplify the calculations and provide a more precise estimate of uncertainty. It is particularly useful for complex systems with nonlinear relationships between variables.

What are the benefits of using linearized uncertainty?

The main benefit of using linearized uncertainty is that it allows for a more accurate estimation of uncertainty compared to other methods. It also simplifies the calculations and can save time and resources in the uncertainty analysis process.

Are there any limitations to using linearized uncertainty?

Linearized uncertainty is most effective when the nonlinear function being approximated is close to linear in the region of interest. If the function is highly nonlinear, the linearization may result in a significant amount of error and may not provide an accurate estimate of uncertainty.

How can I apply linearized uncertainty in my own research or experiments?

Linearized uncertainty can be applied in various scientific disciplines, such as physics, engineering, and chemistry. It is particularly useful for experiments involving complex systems with nonlinear relationships between variables. To use this method, you will need to have a good understanding of the underlying mathematical principles and be able to accurately linearize the function of interest.

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