scipy tutorial - Baustatik-Info-Server

Examples
>>> from scipy import stats
>>> import numpy as np
>>> from scipy.stats import ks_2samp
>>> #fix random seed to get the same result
>>> np.random.seed(12345678);
>>> n1 = 200 # size of first sample
>>> n2 = 300 # size of second sample
different distribution we can reject the null hypothesis since the pvalue is below 1%
>>> rvs1 = stats.norm.rvs(size=n1,loc=0.,scale=1);
>>> rvs2 = stats.norm.rvs(size=n2,loc=0.5,scale=1.5)
>>> ks_2samp(rvs1,rvs2)
(0.20833333333333337, 4.6674975515806989e-005)
SciPy Reference Guide, Release 0.8.dev
slightly different distribution we cannot reject the null hypothesis at a 10% or lower alpha since the pvalue at
0.144 is higher than 10%
>>> rvs3 = stats.norm.rvs(size=n2,loc=0.01,scale=1.0)
>>> ks_2samp(rvs1,rvs3)
(0.10333333333333333, 0.14498781825751686)
identical distribution we cannot reject the null hypothesis since the pvalue is high, 41%
>>> rvs4 = stats.norm.rvs(size=n2,loc=0.0,scale=1.0)
>>> ks_2samp(rvs1,rvs4)
(0.07999999999999996, 0.41126949729859719)
mannwhitneyu(x, y, use_continuity=True)
Computes the Mann-Whitney rank test on samples x and y.
Notes
Parameters
x, y : array_like
Array of samples, should be one-dimensional.
use_continuity : bool, optional
Returns
u : float
Whether a continuity correction (1/2.) should be taken into account. Default is True.
The Mann-Whitney statistics.
prob : float
One-sided p-value assuming a asymptotic normal distribution.
Use only when the number of observation in each sample is > 20 and you have 2 independent samples of ranks.
Mann-Whitney U is significant if the u-obtained is LESS THAN or equal to the critical value of U.
This test corrects for ties and by default uses a continuity correction. The reported p-value is for a one-sided
hypothesis, to get the two-sided p-value multiply the returned p-value by 2.
3.18. Statistical functions (scipy.stats) 661