Resistance Propagation of Error

AI Thread Summary
The discussion centers on a lab involving resistance measurements in series, parallel, and combination circuits, with specific coded and measured resistances provided. The user expresses confusion about comparing calculated equivalent resistance with measured values and how to handle uncertainty in their results. They have identified a significant discrepancy in the parallel setup but are unsure about the significance of discrepancies in other setups. Suggestions include using the master rule for uncertainty and standard deviations to quantify error. Clarification on these methods is sought to better understand the analysis of results.
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We recently did a Resistance in Circuits lab.

My coded resistances were
33, 100 and 220.
The measured resistance was
33.3, 101.6 and 219

So we did 3 setups. (Series, Parallel and Combination)

Series = 351.8 measured ohms
Parallel = 22.5 measured ohms
Combinations = 101.5 measured ohms

The links below are scanned images of my work.
http://img.photobucket.com/albums/v600/bozym618/seriesandparallel.jpg

http://img.photobucket.com/albums/v600/bozym618/combination.jpg



I am confused on what i am comparing.
I find the calculated Req with the uncertainty... so I am comparing that to the measured, but i am confused on what to do with the uncertainty.
I need to find the discrepancy and say if it is signi. or insigni.

For Parallel i found, 22.5 -22.3 = 0.2 and the error was 0.141
So its Sig. But the rest confused me.

I hope someone can help me understand.
thanks in advance.
 
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anyone have any ideas?
 
What equations did you use? There would be a couple ways to measure resistance, and a couple ways to find the error. If you used R = \frac{V}{i} or any number of other equations for resistance then you would be able to find the uncertainty with the master rule for uncertainty, which is \pd q = \sqrt{(\frac{\partial q}{\partial x}\delta x)^2 +... + (\frac{\partial q}{\partial z}\delta z)^2 for whatever variables you have.

You could also use standard deviations for uncertainty, which would be my suggestion.
 
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