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... I can (as with the previous data) obtain *excellent* numericalThere must be a misunderstanding somewhere. I am reposting the data
fits to your d vs. t data (i.e., rms errors in d less than 1 least
sig fig) with drag exponents (i.e., the n in F_drag = b v^n) anywhere
from 1/5 to 5.
With do = d at the first time, vo = v at the first time, r = b/m where**********************************************************************
m = mass of falling opbject and, F_drag = bv^n, I get the following
(all in SI > units)
r n do vo g rms deviation in d values
0.18 0.2 0.608 1.776 9.8 .0009
0.023 2.0 0.6078 1.772 9.8 .0007
0.0004 5.0 0.6079 1.763 9.8 .0008
The values of the best fit parameters will surely depend to some small
extent on the numerical method used. I used a simple "predictor-
corrector" type method. I could have obtained marginally better fits by
allowing g to be a free parameter as well, but that was clearly pushing
the data too hard.