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# cogent.maths.stats.test.std

All Samples(8)  |  Call(4)  |  Derive(0)  |  Import(4)

```from cogent.util.array import without_diag
from cogent.maths.svd import three_item_combos, two_item_combos
from cogent.maths.stats.test import std

__author__ = "Rob Knight"
```
```            samples = list(tree_stats(n, make_tree, result_f))
means = average(samples)
stdevs = std(samples)
for i in zip(means, stdevs):
result.extend(i)
```

```#!/usr/bin/env python
"""Performs singular-value decomposition on a set of Q-matrices."""

from __future__ import division
from cogent.maths.stats.test import std # numpy.std is biased
```
```def var(x):
return std(x)**2

def ratio_two_best(eigenvalues):
"""Returns ratio of best to second best eigenvalue (from vector)."""
```

```from cogent.util.array import without_diag
from cogent.maths.svd import three_item_combos, two_item_combos
from cogent.maths.stats.test import std

__author__ = "Rob Knight"
```
```            samples = list(tree_stats(n, make_tree, result_f))
means = average(samples)
stdevs = std(samples)
for i in zip(means, stdevs):
result.extend(i)
```

```#!/usr/bin/env python
"""Performs singular-value decomposition on a set of Q-matrices."""

from __future__ import division
from cogent.maths.stats.test import std # numpy.std is biased
```
```def var(x):
return std(x)**2

def ratio_two_best(eigenvalues):
"""Returns ratio of best to second best eigenvalue (from vector)."""
```