I asked Minitab support how they did it. Here is their answer:
Date: Fri, 26 Sep 1997 15:07:50 -0400
To: [EMAIL PROTECTED]
From: Tech Support
Subject: number of bars in MINITAB histogram
Jonathan,
I finally found an answer for you. Here's the algorithm.
There are upper and lower bounds on the number of bars.
Lower bound = Round( (16.0*N)**(1.0/3.0) + 0.5 )
Upper bound = Lower bound + Round(0.5*N)
After you find the bounds, MINITAB will always try to get as close to the
lower bound as it can.
Then we have a "nice numbers" algorithm that finds interval midpoints,
given the constraints on the number of intervals.
But there is special code for date/time data and for highly granular data
(e.g., all 1's and 2's).
Find the largest integer p such that each data value can be written (within
fuzz) as an integer times 10**p.
Let BinWidth = 10**p.
Let BinCount = 1 + Round( ( range of data ) / BinWidth )
If BinCount is <= 10, then let the bin midpoints run from the data min to
the data max in increments of BinWidth.
Otherwise, use the "nice numbers" algorithm.
Hope this helps.
Andy Haines
Minitab, Inc.
At 11:01 PM 1/4/01 -0500, you wrote:
>To determine the number of classes for a histogram, Excel uses square root
>of the number of observations. Is it also true for the number of
>observations greater than 200, say, for 2000?. Does the MINITAB use the same
>for determining the number of classes for a histogram?
>Any help would be appreciated.
>
>
>
>
>
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