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Date: Fri, 28 Nov 2008 14:37:17 -0500THO
From: Hugh Haskell <hhaskell@mindspring.com>
Subject: Re: [Phys-l] Data Analysis Questions
To: Forum for Physics Educators <phys-l@carnot.physics.buffalo.edu>
At 11:17 -0500 11/28/08, Thomas O'Neill wrote:
I'd be interested in what sorts of results this exercise leads to. My
Actually, we do this as an introduction to uncertainty analysis and
Student's t test. We have our students pick a sample of twenty
nominal 100 ohm resistors from a larger set of 100. Measure the
resistance of each. They mix-up the first sample then re-measure
the resistance of each resistor. Then they measure the resistance
of a second random sample of 20 from the set of 100 after throwing
the first sample back in. Finally they measure the resistance of a
sample of 20 different resistors with the same nominal value. We
have reels of old (>30 years) resistors that on average are about 5%
higher than the new ones.
understanding is that resistors are sold in three (?) different
tolerance groups-1%, 5% 15% (?), but they are all manufactured by the
same process. The finished resistors are then measured and those that
meet the tolerance of the 1% group are put in one pile, those that
meet the tolerance of the 5% group are put in another pile and all
those that meet the 15% tolerance are put in a third pile. The rest
are rejected. If this is true then one would seldom find a resistor
in, say, the 15% category that is closer to the nominal value than
5%--hardly the expected Gaussian distribution. And in any event,
there should be few if any whose tolerance is greater than 15%.
If this scenario is true then it would be very difficult to get any
realistic statistics out of the results unless there was a mix of the
three tolerance categories sufficient to simulate the original
manufacturing distribution. I'd appreciate any input on this since my
evidence is entirely by hearsay.
Hugh
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Hugh Haskell
<mailto:haskell@ncssm.edu>
<mailto:hhaskell@mindspring.com>
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