scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
This performs a test of the distribution G(x) of an observed random variable against a given distribution F(x).
Under the null hypothesis the two distributions are identical, G(x)=F(x). The alternative hypothesis can be either
‘two_sided’ (default), ‘less’ or ‘greater’. The KS test is only valid for continuous distributions.
Notes
Parameters
rvs : string or array or callable
string: name of a distribution in scipy.stats
array: 1-D observations of random variables
callable: function to generate random variables, requires keyword argument size
cdf : string or callable
string: name of a distribution in scipy.stats, if rvs is a string then cdf can evaluate to
False or be the same as rvs callable: function to evaluate cdf
args : tuple, sequence
N : int
distribution parameters, used if rvs or cdf are strings
sample size if rvs is string or callable
alternative : ‘two_sided’ (default), ‘less’ or ‘greater’
defines the alternative hypothesis (see explanation)
mode : ‘approx’ (default) or ‘asymp’
Returns
D : float
defines the distribution used for calculating p-value
‘approx’ : use approximation to exact distribution of test statistic
‘asymp’ : use asymptotic distribution of test statistic
KS test statistic, either D, D+ or D-
p-value : float
one-tailed or two-tailed p-value
In the one-sided test, the alternative is that the empirical cumulative distribution function of the random variable
is “less” or “greater” than the cumulative distribution function F(x) of the hypothesis, G(x)=F(x).
Examples
>>> from scipy import stats
>>> import numpy as np
>>> from scipy.stats import kstest
>>> x = np.linspace(-15,15,9)
>>> kstest(x,’norm’)
(0.44435602715924361, 0.038850142705171065)
658 Chapter 3. Reference