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# cogent.maths.stats.distribution.chi_high

All Samples(24)  |  Call(18)  |  Derive(0)  |  Import(6)

```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
```
```        G /= q

p = chi_high(max(G,0), 1)

#find which tail we were in if the test was directional
```
```            (6*tot*df))
G = G/q
return G, chi_high(max(G,0), df)

```
```        G /= q

return G, chi_high(G, k - 1)

def G_fit_from_Dict2D(data):
```
```        df = (len(data) - 1) * (len([col for col in data.Cols]) - 1)

return test, chi_high(test, df)

```

```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
```
```        G /= q

p = chi_high(max(G,0), 1)

#find which tail we were in if the test was directional
```
```            (6*tot*df))
G = G/q
return G, chi_high(max(G,0), df)

```
```        G /= q

return G, chi_high(G, k - 1)

def G_fit_from_Dict2D(data):
```
```        df = (len(data) - 1) * (len([col for col in data.Cols]) - 1)

return test, chi_high(test, df)

```

```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)
```
```        G /= q

p = chi_high(max(G,0), 1)

#find which tail we were in if the test was directional
```
```            (6*tot*df))
G = G/q
return G, chi_high(max(G,0), df)

## Start functions for G goodness of fit
```
```        n = sum([arr.mean() for arr in data]) #total observations
G = williams_correction(n, a, G)
return G, chi_high(G, a-1) #a-1 degrees of freedom because of sum constraint
## End functions for G goodness of fit test

```
```    else:
try:
return chi_high(stat, 2 * len(probs))
except OverflowError, e:
return nan
```

```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)
```
```        G /= q

p = chi_high(max(G, 0), 1)

# find which tail we were in if the test was directional
```
```                 (6 * tot * df))
G = G / q
return G, chi_high(max(G, 0), df)

# Start functions for G goodness of fit
```
```    return (
# a-1 degrees of freedom because of sum constraint
G, chi_high(G, a - 1)
)
# End functions for G goodness of fit test
```
```    else:
try:
return chi_high(stat, 2 * len(probs))
except OverflowError as e:
return nan
```

```
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, \
```

```