scipy.stats.mood

scipy.stats.mood(x, y, axis=0)

Perform Mood’s test for equal scale parameters.

Mood’s two-sample test for scale parameters is a non-parametric test for the null hypothesis that two samples are drawn from the same distribution with the same scale parameter.

Parameters:

x, y : array_like

Arrays of sample data.

axis: int, optional

The axis along which the samples are tested. x and y can be of different length along axis. If axis is None, x and y are flattened and the test is done on all values in the flattened arrays.

Returns:

z : scalar or ndarray

The z-score for the hypothesis test. For 1-D inputs a scalar is returned;

p-value : scalar ndarray

The p-value for the hypothesis test.

See also

fligner

A non-parametric test for the equality of k variances

ansari

A non-parametric test for the equality of 2 variances

bartlett

A parametric test for equality of k variances in normal samples

levene

A parametric test for equality of k variances

Notes

The data are assumed to be drawn from probability distributions f(x) and f(x/s) / s respectively, for some probability density function f. The null hypothesis is that s == 1.

For multi-dimensional arrays, if the inputs are of shapes (n0, n1, n2, n3) and (n0, m1, n2, n3), then if axis=1, the resulting z and p values will have shape (n0, n2, n3). Note that n1 and m1 don’t have to be equal, but the other dimensions do.

Examples

>>> from scipy import stats
>>> x2 = np.random.randn(2, 45, 6, 7)
>>> x1 = np.random.randn(2, 30, 6, 7)
>>> z, p = stats.mood(x1, x2, axis=1)
>>> p.shape
(2, 6, 7)

Find the number of points where the difference in scale is not significant:

>>> (p > 0.1).sum()
74

Perform the test with different scales:

>>> x1 = np.random.randn(2, 30)
>>> x2 = np.random.randn(2, 35) * 10.0
>>> stats.mood(x1, x2, axis=1)
(array([-5.84332354, -5.6840814 ]), array([5.11694980e-09, 1.31517628e-08]))