numpy.random.f(dfnum, dfden, size=None)
Draw samples from an F distribution.
Samples are drawn from an F distribution with specified parameters,
dfnum (degrees of freedom in numerator) and
dfden (degrees of freedom in denominator), where both parameters should be greater than zero.
The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates.
dfnum : float or array_like of floats
Degrees of freedom in numerator, should be > 0.
dfden : float or array_like of float
Degrees of freedom in denominator, should be > 0.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g.,
out : ndarray or scalar
Drawn samples from the parameterized Fisher distribution.
The F statistic is used to compare in-group variances to between-group variances. Calculating the distribution depends on the sampling, and so it is a function of the respective degrees of freedom in the problem. The variable
dfnum is the number of samples minus one, the between-groups degrees of freedom, while
dfden is the within-groups degrees of freedom, the sum of the number of samples in each group minus the number of groups.
|[R439440]||Glantz, Stanton A. “Primer of Biostatistics.”, McGraw-Hill, Fifth Edition, 2002.|
|[R440440]||Wikipedia, “F-distribution”, http://en.wikipedia.org/wiki/F-distribution|
An example from Glantz, pp 47-40:
Two groups, children of diabetics (25 people) and children from people without diabetes (25 controls). Fasting blood glucose was measured, case group had a mean value of 86.1, controls had a mean value of 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these data consistent with the null hypothesis that the parents diabetic status does not affect their children’s blood glucose levels? Calculating the F statistic from the data gives a value of 36.01.
Draw samples from the distribution:
>>> dfnum = 1. # between group degrees of freedom >>> dfden = 48. # within groups degrees of freedom >>> s = np.random.f(dfnum, dfden, 1000)
The lower bound for the top 1% of the samples is :
>>> sort(s)[-10] 7.61988120985
So there is about a 1% chance that the F statistic will exceed 7.62, the measured value is 36, so the null hypothesis is rejected at the 1% level.
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