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# cogent.maths.stats.distribution.zprob

All Samples(24)  |  Call(14)  |  Derive(0)  |  Import(10)

```from numpy import floor, sqrt, array
from cogent.maths.stats.util import Freqs
from cogent.maths.stats.distribution import zprob

__author__ = "Gavin Huttley"
```
```                                                        (n - 1) * (n - 2))
if return_p:
return tau, zprob(stat / variance**0.5)
else:
return tau
```

```from numpy import floor, sqrt, array
from cogent.maths.stats.util import Freqs
from cogent.maths.stats.distribution import zprob

__author__ = "Gavin Huttley"
```
```                                                        (n - 1) * (n - 2))
if return_p:
return tau, zprob(stat / variance**0.5)
else:
return tau
```

```from __future__ import division
import warnings
from cogent.maths.stats.distribution import chi_high, z_low, z_high, zprob, \
t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
ndtri
```
```        return z_low(z)
else:
return zprob(z)

def t_tailed_prob(t, df, tails):
```
```    denominator = sqrt((prod / (total * (total-1)))*((total**3 - total - T)/12))
z = (numerator/denominator)
p = zprob(z)
return U, p

```

```from __future__ import division
import warnings
from cogent.maths.stats.distribution import chi_high, z_low, z_high, zprob, \
t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
ndtri
```
```        return z_low(z)
else:
return zprob(z)

def t_tailed_prob(t, df, tails):
```
```    denominator = sqrt((prod / (total * (total-1)))*((total**3 - total - T)/12))
z = (numerator/denominator)
p = zprob(z)
return U, p

```

```from __future__ import division
import warnings
from cogent.maths.stats.distribution import (chi_high, z_low, z_high, zprob,
t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
ndtri)
```
```def kendall_pval(tau, n):
'''Calculate the p-value for the passed tau and vector length n.'''
test_stat = tau/((2*(2*n+5))/float(9*n*(n-1)))**.5
return zprob(test_stat)

```
```        return z_low(z)
else:
return zprob(z)

def t_tailed_prob(t, df, tails):
```
```        return U, nan
else:
pval = zprob(numerator/float(denominator))
return U, pval

```
```        # isn't supported.
return nan
return zprob(z*((n-3)**.5))

def inverse_fisher_z_transform(z):
```

```from __future__ import division
import warnings
from cogent.maths.stats.distribution import (chi_high, z_low, z_high, zprob,
t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
ndtri)
```
```def kendall_pval(tau, n):
'''Calculate the p-value for the passed tau and vector length n.'''
test_stat = tau / ((2 * (2 * n + 5)) / float(9 * n * (n - 1))) ** .5
return zprob(test_stat)

```
```        return z_low(z)
else:
return zprob(z)

```
```        return U, nan
else:
pval = zprob(numerator / float(denominator))
return U, pval

```
```        # isn't supported.
return nan
return zprob(z * ((n - 3) ** .5))

```

```
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.distribution import z_low, z_high, zprob, chi_low, \
chi_high, t_low, t_high, tprob, poisson_high, poisson_low, poisson_exact, \
binomial_high, binomial_low, binomial_exact, f_low, f_high, fprob, \
```

```
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.distribution import z_low, z_high, zprob, chi_low, \
chi_high, t_low, t_high, tprob, poisson_high, poisson_low, poisson_exact, \
binomial_high, binomial_low, binomial_exact, f_low, f_high, fprob, \
```

```# cogent imports
from cogent.maths.stats import chisqprob
from cogent.maths.stats.distribution import zprob, tprob

__author__ = "Rob Knight"
```

```# cogent imports