scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
pdtrc
y=pdtrc(k,m) returns the sum of the terms from k+1 to infinity of the Poisson distribution: sum(exp(-m) * m**j
/ j!, j=k+1..inf) = gammainc( k+1, m). Arguments must both be positive and k an integer.
pdtri
m=pdtri(k,y) returns the Poisson variable m such that the sum from 0 to k of the Poisson density is equal to the
given probability y: calculated by gammaincinv( k+1, y). k must be a nonnegative integer and y between 0 and
1.
stdtr
p=stdtr(df,t) returns the integral from minus infinity to t of the Student t distribution with df > 0 degrees of
freedom: gamma((df+1)/2)/(sqrt(df*pi)*gamma(df/2)) * integral((1+x**2/df)**(-df/2-1/2), x=-inf..t)
stdtridf
t=stdtridf(p,t) returns the argument df such that stdtr(df,t) is equal to p.
stdtrit
t=stdtrit(df,p) returns the argument t such that stdtr(df,t) is equal to p.
chdtr
p=chdtr(v,x) Returns the area under the left hand tail (from 0 to x) of the Chi square probability density function
with v degrees of freedom: 1/(2**(v/2) * gamma(v/2)) * integral(t**(v/2-1) * exp(-t/2), t=0..x)
chdtrc
p=chdtrc(v,x) returns the area under the right hand tail (from x to infinity) of the Chi square probability density
function with v degrees of freedom: 1/(2**(v/2) * gamma(v/2)) * integral(t**(v/2-1) * exp(-t/2), t=x..inf)
chdtri
x=chdtri(v,p) returns the argument x such that chdtrc(v,x) is equal to p.
ndtr
y=ndtr(x) returns the area under the standard Gaussian probability density function, integrated from minus
infinity to x: 1/sqrt(2*pi) * integral(exp(-t**2 / 2),t=-inf..x)
ndtri
x=ndtri(y) returns the argument x for which the area udnder the Gaussian probability density function (integrated
from minus infinity to x) is equal to y.
smirnov
y=smirnov(n,e) returns the exact Kolmogorov-Smirnov complementary cumulative distribution function (Dn+
or Dn-) for a one-sided test of equality between an empirical and a theoretical distribution. It is equal to the
probability that the maximum difference between a theoretical distribution and an empirical one based on n
samples is greater than e.
smirnovi
e=smirnovi(n,y) returns e such that smirnov(n,e) = y.
kolmogorov
p=kolmogorov(y) returns the complementary cumulative distribution function of Kolmogorov’s limiting distribution
(Kn* for large n) of a two-sided test for equality between an empirical and a theoretical distribution. It is
equal to the (limit as n->infinity of the) probability that sqrt(n) * max absolute deviation > y.
kolmogi
y=kolmogi(p) returns y such that kolmogorov(y) = p
tklmbda
432 Chapter 3. Reference