scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
tiecorrect(rankvals)
Tie-corrector for ties in Mann Whitney U and Kruskal Wallis H tests. See Siegel, S. (1956) Nonparametric
Statistics for the Behavioral Sciences. New York: McGraw-Hill. Code adapted from |Stat rankind.c code.
Returns: T correction factor for U or H
ranksums(x, y)
Compute the Wilcoxon rank-sum statistic for two samples.
The Wilcoxon rank-sum test tests the null hypothesis that two sets of measurements are drawn from the same
distribution. The alternative hypothesis is that values in one sample are more likely to be larger than the values
in the other sample.
This test should be used to compare two samples from continuous distributions. It does not handle ties between
measurements in x and y. For tie-handling and an optional continuity correction see ‘stats.mannwhitneyu‘_
Parameters
x,y : array_like
The data from the two samples
Returns
z-statistic : float
References
[R32]
The test statistic under the large-sample approximation that the rank sum statistic is
normally distributed
p-value : float
The two-sided p-value of the test
wilcoxon(x, y=None)
Calculate the Wilcoxon signed-rank test
The Wilcoxon signed-rank test tests the null hypothesis that two related samples come from the same distribution.
It is a a non-parametric version of the paired T-test.
Notes
Parameters
x : array_like
The first set of measurements
y : array_like, optional, default None
The second set of measurements. If y is not given, then the x array is considered to
be the differences between the two sets of measurements.
Returns
z-statistic : float
The test statistic under the large-sample approximation that the signed-rank statistic
is normally distributed.
p-value : float
The two-sided p-value for the test
Because the normal approximation is used for the calculations, the samples used should be large. A typical rule
is to require that n > 20.
662 Chapter 3. Reference