Interesting, although it seems this method seems to favor outliers. With my sample dataset below it creates more bins
with few or no items on the high end of the spectrum whereas
nearly everything else continues to be lumped into the first category.
[0 -296 ] 86
[297 -580 ] 8
[581 -864 ] 4
[865 -1148 ] 1
[1149 -1432 ] 2
[1433 -1716 ] 0
[1717 -2000 ] 1
vs. kvale's
[12 -24 ] 33
[42 -76 ] 27
[80 -128 ] 14
[150 -250 ] 9
[280 -460 ] 9
[550 -950 ] 7
[1226 -2000 ] 3
12, 14, 16, 18, 18, 20, 20, 20, 20, 20, 20, 20, 22, 24, 24, 24, 24, 30, 30, 30, 32, 35, 35, 35, 35, 35, 35, 35, 36, 40, 42, 46, 48, 48, 50, 50, 50, 50, 54, 54, 55, 56, 56, 58, 58, 60, 60, 60, 60, 63, 64, 67, 67, 68, 70, 70, 76, 80, 86, 86, 86, 90, 90, 99, 100, 100, 100, 100, 104, 105, 128, 150, 150, 154, 169, 190, 200, 200, 200, 250, 280, 291, 291, 300, 325, 325, 330, 450, 460, 550, 566, 600, 700, 750, 770, 950, 1226, 1250, 2000, 15, 22, 24
--
In Bob We Trust, All Others Bring Data.
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