scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
>>> np.random.seed(987654321) # set random seed to get the same result
>>> kstest(’norm’,’’,N=100)
(0.058352892479417884, 0.88531190944151261)
is equivalent to this
>>> np.random.seed(987654321)
>>> kstest(stats.norm.rvs(size=100),’norm’)
(0.058352892479417884, 0.88531190944151261)
Test against one-sided alternative hypothesis:
>>> np.random.seed(987654321)
Shift distribution to larger values, so that cdf_dgp(x)< norm.cdf(x):
>>> x = stats.norm.rvs(loc=0.2, size=100)
>>> kstest(x,’norm’, alternative = ’less’)
(0.12464329735846891, 0.040989164077641749)
Reject equal distribution against alternative hypothesis: less
>>> kstest(x,’norm’, alternative = ’greater’)
(0.0072115233216311081, 0.98531158590396395)
Don’t reject equal distribution against alternative hypothesis: greater
>>> kstest(x,’norm’, mode=’asymp’)
(0.12464329735846891, 0.08944488871182088)
Testing t distributed random variables against normal distribution:
With 100 degrees of freedom the t distribution looks close to the normal distribution, and the kstest does not
reject the hypothesis that the sample came from the normal distribution
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(100,size=100),’norm’)
(0.072018929165471257, 0.67630062862479168)
With 3 degrees of freedom the t distribution looks sufficiently different from the normal distribution, that we
can reject the hypothesis that the sample came from the normal distribution at a alpha=10% level
>>> np.random.seed(987654321)
>>> stats.kstest(stats.t.rvs(3,size=100),’norm’)
(0.131016895759829, 0.058826222555312224)
chisquare(f_obs, f_exp=None, ddof=0)
Calculates a one-way chi square test.
The chi square test tests the null hypothesis that the categorical data has the given frequencies.
Parameters
f_obs : array
observed frequencies in each category
f_exp : array, optional
3.18. Statistical functions (scipy.stats) 659