scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
Examples
t-statistic
prob : float or array
two-tailed p-value
>>> from scipy import stats
>>> import numpy as np
>>> #fix seed to get the same result
>>> np.random.seed(7654567)
>>> rvs = stats.norm.rvs(loc=5,scale=10,size=(50,2))
test if mean of random sample is equal to true mean, and different mean. We reject the null hypothesis in the
second case and don’t reject it in the first case
>>> stats.ttest_1samp(rvs,5.0)
(array([-0.68014479, -0.04323899]), array([ 0.49961383, 0.96568674]))
>>> stats.ttest_1samp(rvs,0.0)
(array([ 2.77025808, 4.11038784]), array([ 0.00789095, 0.00014999]))
examples using axis and non-scalar dimension for population mean
>>> stats.ttest_1samp(rvs,[5.0,0.0])
(array([-0.68014479, 4.11038784]), array([ 4.99613833e-01, 1.49986458e-04]))
>>> stats.ttest_1samp(rvs.T,[5.0,0.0],axis=1)
(array([-0.68014479, 4.11038784]), array([ 4.99613833e-01, 1.49986458e-04]))
>>> stats.ttest_1samp(rvs,[[5.0],[0.0]])
(array([[-0.68014479, -0.04323899],
[ 2.77025808, 4.11038784]]), array([[ 4.99613833e-01, 9.65686743e-01],
[ 7.89094663e-03, 1.49986458e-04]]))
ttest_ind(a, b, axis=0)
Calculates the T-test for the means of TWO INDEPENDENT samples of scores.
This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected)
values.
Parameters
a, b : sequence of ndarrays
The arrays must have the same shape, except in the dimension corresponding to axis
(the first, by default).
axis : int, optional
Axis can equal None (ravel array first), or an integer (the axis over which to operate
on a and b).
Returns
t : float or array
t-statistic
prob : float or array
two-tailed p-value
692 Chapter 3. Reference