Propogation of uncertainty in a data set (finger-math at most)

AI Thread Summary
The discussion focuses on calculating uncertainty in a lab experiment measuring the distance traveled by a falling weight. The absolute uncertainties for time and distance are given as ±0.010s and ±0.001m, respectively. The participant is confused about how to apply these uncertainties to calculate the relative uncertainties for velocity, acceleration, and the y-intercept of the velocity versus time graph. They question whether each data point requires a unique relative uncertainty or if a single percentage can be applied across all points. The conversation emphasizes the need for clarity in handling uncertainties in a dataset, suggesting that individual calculations may be necessary for accuracy.
ZamielTheGrey
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Homework Statement


I have conducted a simple lab, that involves getting the distance traveled by a vertically falling weight at certain time intervals. Velocity is calculated for every time interval (distance traveled per time interval). Then I graph velocity versus time to get acceleration which comes out decently close to g.

Now the part I am confused about is calculating uncertainty for:
a) Velocity
b) Acceleration
c) y-intercept of velocity vs. time graph (acceleration line)

Absolute uncertainty for time is +-0.010s, and +-0.001m (1mm) for distance.

Homework Equations


Relative uncertainty % = Absolute Uncertainty / Measured value
* and / of uncertainties with each other adds their relative uncertainties

The Attempt at a Solution


Since this is of a data set, you have many measured values, and it makes no sense to have a single relative uncertainty in the above equation. It would be different for each data point, correct?

Am I missing something from this concept? Or do I literally have to calculate out a different uncertainty for each velocity? Another possibility could be finding relative uncertainty for the first data point and using that % throughout the problem for all of them but that can't be right...
 
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