# Unexpected result from r.quantile function

I am watching some old videos of chalkdust on statistics. In one of the videos, the professor calculates quartiles as follows:

But the r.quantile function gives completely different result:

sage:
sage: r.quantile([89, 90, 86, 96, 84, 100, 85, 96, 88, 89])
0%   25%   50%   75%  100%
84.0  86.5  89.0  94.5 100.0
sage:


Why is that?

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According to the documentation of quantile it takes an optional argument named type which is an integer from 1 to 9. These correspond to different algorithms for calculating quantiles; the default is algorithm 7. You will have to find out which one corresponds to the lecture; my guess is type 5.

sage: r.quantile([89, 90, 86, 96, 84, 100, 85, 96, 88, 89], type=5)
0%  25%  50%  75% 100%
84   86   89   96  100

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Thanks type=5 did it.

( 2020-02-19 08:19:54 -0500 )edit

Have a serious look at the section Type of r.help("quantile"). Quantile estimation from a sample is non a trivialproblem, and has caused ink overflows by the barrel...

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