Calculating Uncertainty for ln x: A Short Guide

In summary, if you have a function f(x) and you want to find its central value and uncertainty, you can use the following formula: f(x_0) + \Delta x = f(x_0)
  • #1
fluppocinonys
19
1

Homework Statement


If x = (7.2[tex]\pm[/tex]0.6) m, determine the value of ln x with its associated uncertainty.

(Ans is 1.97[tex] \pm [/tex]0.08)


Homework Equations


[tex]A = k{B^m}{\rm{ }} \Rightarrow {\rm{ }}\frac{{\Delta A}}{A} = m\frac{{\Delta B}}{B}[/tex]
(perhaps?)

The Attempt at a Solution


i tried to ln7.2 and got 1.97, however ln0.6 = negative value. How to get 0.08?
Thanks
 
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  • #2
If you have any (differentiable) function f(x), and the measurement is given as [itex]x = x_0 + \Delta x[/itex], there is a general formula for giving the central value of f (which is just [itex]f(x_0)[/itex] as you would expect) and the uncertainty. Do you know what formula I'm hinting at?
 
  • #3
CompuChip said:
If you have any (differentiable) function f(x), and the measurement is given as [itex]x = x_0 + \Delta x[/itex], there is a general formula for giving the central value of f (which is just [itex]f(x_0)[/itex] as you would expect) and the uncertainty. Do you know what formula I'm hinting at?
No... :rolleyes:
I don't think I've learned that far
 
  • #4
Well, maybe it's time you learn it.
It's very useful and easy to remember:

[tex]\Delta f = f'(x_0) \cdot \Delta x[/tex] (*)
so the uncertainty in f is the derivative of f w.r.t. x (evaluated at the central value) times the uncertainty in x.
The justification is of course, that very close to [tex]x_0[/tex], we can approximate the function f by a straight line with slope [itex]f'(x_0)[/itex]. So if you vary x by an amount [itex]\Delta x[/itex], then you can approximate the change in f by the variation [tex]f'(x_0) \Delta x[/itex] of the line.

If you have multiple variables, like f(x, y, z, ...) then you can simply extend this to
[tex]\Delta f^2 = \left( \frac{\partial f(x_0, y_0, z_0, \cdots)}{\partial x} \Delta x \right)^2 + \left( \frac{\partial f(x_0, y_0, z_0, \cdots)}{\partial y} \Delta y \right)^2 + \left( \frac{\partial f(x_0, y_0, z_0, \cdots)}{\partial z} \Delta z \right)^2 + \cdots[/tex]
which looks like a combination of that identity and the Pythagorean theorem.

[If you haven't learned about partial derivatives, forget about that last paragraph, you should remember formula (*) though].

When you apply (*) to the special case [itex]f(x) = k x^m[/itex] you will get that
[tex]\frac{\Delta f}{f} = m \frac{\Delta x}{x}[/tex]
as you said. When you apply it to f(x) = ln(x) you will get the requested answer.
 
  • #5
All right, that was rather overwhelming...
Nonetheless, thanks for helping me! you're very helpful :D
 
  • #6
Hmm, looks impressive doesn't it.
Just play around with it and you'll see that it looks harder than it is (if you know how to differentiate and multiply, that is).
 

FAQ: Calculating Uncertainty for ln x: A Short Guide

What is measurement uncertainty?

Measurement uncertainty refers to the unavoidable imperfections and errors that exist in any type of measurement. It is the potential range of values that a measured quantity could have due to limitations in the measuring device, environmental factors, and human error.

How is measurement uncertainty expressed?

Measurement uncertainty is typically expressed as a margin of error or a confidence interval. This means that the true value of the measured quantity could fall within a certain range above or below the reported value.

Why is measurement uncertainty important?

Measurement uncertainty is important because it allows us to understand the limitations of our measurements and determine the level of confidence we can have in the reported values. It also helps to ensure the accuracy and reliability of scientific data.

How is measurement uncertainty calculated?

Measurement uncertainty is calculated by considering all potential sources of error and determining their individual contributions to the overall uncertainty. This is often done through statistical analysis and can vary depending on the type of measurement being performed.

How can measurement uncertainty be reduced?

Measurement uncertainty can be reduced by using more precise measuring devices, controlling environmental factors, and minimizing human error through proper training and procedures. It is also important to properly document and report the uncertainty associated with any measurements to ensure transparency and accuracy in scientific research.

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