scipy.stats.obrientransform¶

scipy.stats.
obrientransform
(*args)[source]¶ Compute the O’Brien transform on input data (any number of arrays).
Used to test for homogeneity of variance prior to running oneway stats. Each array in
*args
is one level of a factor. Iff_oneway
is run on the transformed data and found significant, the variances are unequal. From Maxwell and Delaney [1], p.112.Parameters:  args : tuple of array_like
Any number of arrays.
Returns:  obrientransform : ndarray
Transformed data for use in an ANOVA. The first dimension of the result corresponds to the sequence of transformed arrays. If the arrays given are all 1D of the same length, the return value is a 2D array; otherwise it is a 1D array of type object, with each element being an ndarray.
References
[1] (1, 2) S. E. Maxwell and H. D. Delaney, “Designing Experiments and Analyzing Data: A Model Comparison Perspective”, Wadsworth, 1990. Examples
We’ll test the following data sets for differences in their variance.
>>> x = [10, 11, 13, 9, 7, 12, 12, 9, 10] >>> y = [13, 21, 5, 10, 8, 14, 10, 12, 7, 15]
Apply the O’Brien transform to the data.
>>> from scipy.stats import obrientransform >>> tx, ty = obrientransform(x, y)
Use
scipy.stats.f_oneway
to apply a oneway ANOVA test to the transformed data.>>> from scipy.stats import f_oneway >>> F, p = f_oneway(tx, ty) >>> p 0.1314139477040335
If we require that
p < 0.05
for significance, we cannot conclude that the variances are different.