# [Numpy-discussion] Bug in the F distribution?

Alan Jackson alan@ajackson....
Fri Jul 3 21:21:42 CDT 2009

```I've tried the same scheme using R and it seems to give the right

> quantile( rf(10000000,10,10), .99)
99%
4.84548
> quantile( rf(10000000,11,10), .99)
99%
4.770002
> quantile( rf(10000000,11,11), .99)
99%
4.465655
> quantile( rf(10000000,10,11), .99)
99%
4.539423

>> I either found a bug in the F distribution, or I'm really messed up.
>>
>> From a table I find
>>
>> dfnum  dfden  F(P<.01)
>> 10      10     4.85
>> 11      10     4.78
>> 11      11     4.46
>> 10      11     4.54
>>
>> So let's calculate the same quantities using numpy...
>>
>> import scipy.stats as stats
>> import numpy as np
>> In [89]: stats.scoreatpercentile(np.random.f(10,10,1000000), 99)
>> Out[89]: 4.8575912131878365
>> In [90]: stats.scoreatpercentile(np.random.f(11,10,1000000), 99)
>> Out[90]: 5.2721528315236501
>> In [91]: stats.scoreatpercentile(np.random.f(11,11,1000000), 99)
>> Out[91]: 4.4695161332631841
>> In [92]: stats.scoreatpercentile(np.random.f(10,11,1000000), 99)
>> Out[92]: 4.1229323443042674
>>
>>
>> So at 10,10 and 11,11 it works (maybe), but all the other values are clearly
>> off. I tried re-running the example I put into the documentation last summer,
>> which worked, and I don't get the right answer any longer.

--
-----------------------------------------------------------------------
| Alan K. Jackson            | To see a World in a Grain of Sand      |
| alan@ajackson.org          | And a Heaven in a Wild Flower,         |
| www.ajackson.org           | Hold Infinity in the palm of your hand |
| Houston, Texas             | And Eternity in an hour. - Blake       |
-----------------------------------------------------------------------
```