- #1

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q = [exp(x/2)][t^(1/2)]

where both x and t have known uncertainties.

I could have done the thing if there was no exp(x/2) term. But that term is causing me a lot of stress.

Can someone please help me.

I am really confused.

James

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- Thread starter JamesJames
- Start date

- #1

- 205

- 0

q = [exp(x/2)][t^(1/2)]

where both x and t have known uncertainties.

I could have done the thing if there was no exp(x/2) term. But that term is causing me a lot of stress.

Can someone please help me.

I am really confused.

James

- #2

cepheid

Staff Emeritus

Science Advisor

Gold Member

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[tex] \delta q^2 = \left(\frac{\partial q}{\partial t}\right)^2 \delta t^2 + \left(\frac{\partial q}{\partial x}\right)^2 \delta x^2 [/tex]

where delta q represents the uncertainty in q, for example. This formula makes sense to me sort of, because the uncertainty in q depends on the individual uncertainties in x and t, as well as the rate at which q changes with each one. For instance, if dq/dt (<--meant to be partial) is large, the even a small delta t will affect q significantly. However, a friend of mine with a math BSc was telling me that these formulas aren't strictly correct, and that you're not actually supposed to be doing calculus per se. Error analysis seems to me to be a very complicated, convoluted subject. Hopefully somebody here will be able to comment on whether using this formula is indeed the best method.

- #3

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At this time the formula that cephid quoted is correct. You have KNOWN errors, the problem arises when you do not know the errors. I have been reading alot about error calculation lately because my employer needs to know the no (&()&) error for some of the apparatuses we use and bought commercially. The calculation of errors is essentially a mathematical problem, a very complex mathematical problem. If you go to the NIST website (www.nist.gov[/url]) or the NPL ([url]www.npl.co.uk[/URL]) and look for uncertainty analysis you'll find a massive amount of material out there. A good place to start is with John Mandel, Statistical Analysis of Experimental Data, I have learned enough to be somewhat dangerous.

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