From Excel file (uploaded '.xlsx'
or inserted '.csv') of the weights of loads of various (unequal)
sample sizes, with underlying Gaussian behavior, computes:
point estimates of the parameters, μ and σ;
and their confidence intervals for a given
confidence level.
By Monte Carlo, a histogram is produced leading to the
confidence intervals. If 'classes' is given 0, an automatic (Python)
number of classes is used. (This may lead to different 'classes'
for each graph.)
The default data (with 'seed' = 97531), lead to
μ (24.90, 24.91, 24.92) and
σ (0.19, 0.465, 0.74), in ~16 s time.
Warning: due to execution on the Internet,
run times are limited to ~1 min. |
