Uncertainty Derivations/Calculation

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In summary, the conversation discusses the task of showing f = xn given that F = aXn = f +- f +δf where a is a constant. It is also mentioned that X = x +- δx, with x representing the average and δx representing the uncertainty in x. The conversation also touches on the use of the power rule for error propagation and the process of deriving δf/f. There is also confusion about the disappearance of the constant a in the equation f = xn.
  • #1
tmobilerocks
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Homework Statement


If F = aXn = f +- f +δf where a is a constant, show f = xn and [itex]\frac{δf}{f}[/itex] = [itex]\frac{nδx}{x}[/itex].

X = x +- δx

x refers to the average and δx refers to uncertainty in x.


Homework Equations


The power rule for error propagation shows that the uncertainty is multiplied n times (where n is the power raised).


The Attempt at a Solution


I'm having trouble showing that f = xn. Through the use of algebraic manipulation, I was able to get a(x+δx)n = f + δf. I then made the assumption to ignore the constant a and by deduction say x = 5 +- 0.5, set f = xn because it is continuously multiplied by whatever the function x is to the nth power. The second part is easier- mainly I just took the differential δf = n*xn-1δx. This simplifies to [itex]\frac{δf}{f}[/itex] = n[itex]\frac{δx}{x}[/itex]
 
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  • #2
Hello t and welcome to PF. If I want to help, I have to be able to read what you've written. Could you proofread your stuff and explain what isn't completely universally known language ?

I recognize some aspects from error propagation theory, but I find your jargon hard to understand.
 
  • #3
Hello,

I am supposed to show that f = xn given that F = aXn = f ± δf where a is a constant. X is equal to x ± δx. In plain English, X is equal to the average x, plus or minus the uncertainty in x (δx).

I am having trouble showing that f = xn. I have already derived δf/f using the method shown above.

Thanks again!
 
  • #4
Yes, it still looks weird that a should disappear from f. Is that really how it is presented to you ?
I mean if F = 1000 X2 and X = 5.000 ± 0.001 there is no way that f can be x2
 
  • #5
which is the desired result.

Great job on your attempt at solving this problem! You are correct in saying that the power rule for error propagation states that the uncertainty is multiplied n times when raised to the power of n. This means that when we have a function F = aXn, the uncertainty δF can be calculated using the formula δF = n*aXn-1*δX.

To show that f = xn, we can use algebraic manipulation as you mentioned. By substituting F with f and X with x, we get: f = a*xn = f + δf. From here, we can cancel out the f on both sides and we are left with a(x+δx)n = δf. Since a is a constant, we can ignore it and focus on the (x+δx)n term. By using the binomial expansion, we get (x+δx)n = xn + nxn-1*δx + ... + δxn. Since we are only interested in the first two terms, we can ignore the rest and we are left with f = xn + nxn-1*δx, which simplifies to f = xn.

For the second part, you are correct in saying that δf = n*xn-1*δx. From here, we can divide both sides by f to get \frac{δf}{f} = n*xn-1*δx/f. We know that xn = f, so we can substitute this in to get \frac{δf}{f} = n*δx/x, which is the desired result.

Overall, great job on your attempt and explanation of the problem! Keep up the good work.
 

1. What is uncertainty in a scientific measurement?

Uncertainty in a scientific measurement refers to the inherent limitations and errors in the measurement process. It is the degree of doubt or range of possible values that may be attributed to a measured quantity.

2. Why is it important to calculate uncertainty in scientific measurements?

Calculating uncertainty allows us to understand the reliability and accuracy of our measurements. It also helps us to compare and evaluate different data sets, and to make informed decisions based on the level of uncertainty present.

3. What is the difference between systematic and random uncertainty?

Systematic uncertainty is caused by flaws or limitations in the measurement process itself, such as faulty equipment or human error. Random uncertainty is caused by unpredictable variations in the measurement, and can be reduced by taking multiple measurements and calculating the average.

4. How is uncertainty calculated in scientific experiments?

Uncertainty is typically calculated by taking into account the precision and accuracy of the measuring instrument, as well as any potential sources of error. It is often expressed as a percentage or a range of values.

5. Can uncertainty be eliminated in scientific measurements?

No, uncertainty is an inherent part of the measurement process. However, it can be reduced by using more precise instruments, minimizing sources of error, and taking multiple measurements. It is important to always report the level of uncertainty in any scientific measurement.

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