From dbb836a93d50796ed8fc9cf106256d73a5f420ba Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Wed, 13 Dec 2023 20:56:17 +0100
Subject: [PATCH] Create README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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+# [Statistics](https://numpy.org/doc/stable/reference/routines.statistics.html#statistics)
+{:.no_toc}
+
+<nav markdown="1" class="toc-class">
+* TOC
+{:toc}
+</nav>
+
+## The goal
+
+There are other (more extensive) statistics packages like​
+* [scipy.stats​](https://docs.scipy.org/doc/scipy/reference/stats.html)
+* [pingouin](https://pingouin-stats.org/build/html/index.html)
+* [statsmodels](https://www.statsmodels.org/stable/index.html)
+  
+
+Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
+
+## [Fisher Exact Test](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html#scipy.stats.fisher_exact)
+
+The [Fisher Exact Test](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html#scipy.stats.fisher_exact) is not part of the numpy package. But we need it in machine learning. 
+
+```python
+scipy.stats.fisher_exact(table, alternative='two-sided')
+```
+
+> Perform a Fisher exact test on a 2x2 contingency table.
+
+
+## [Order statistics](https://numpy.org/doc/stable/reference/routines.statistics.html#order-statistics)
+
+|||
+|---|---|
+|[ptp](https://numpy.org/doc/stable/reference/generated/numpy.ptp.html#numpy.ptp)(a[, axis, out, keepdims]) |Range of values (maximum - minimum) along an axis.|
+|[percentile](https://numpy.org/doc/stable/reference/generated/numpy.percentile.html#numpy.percentile)(a, q[, axis, out, ...]) |Compute the q-th percentile of the data along the specified axis.|
+|[nanpercentile](https://numpy.org/doc/stable/reference/generated/numpy.nanpercentile.html#numpy.nanpercentile)(a, q[, axis, out, ...]) |Compute the qth percentile of the data along the specified axis, while ignoring nan values.|
+|[quantile](https://numpy.org/doc/stable/reference/generated/numpy.quantile.html#numpy.quantile)(a, q[, axis, out, overwrite_input, ...]) |Compute the q-th quantile of the data along the specified axis.|
+|[nanquantile](https://numpy.org/doc/stable/reference/generated/numpy.nanquantile.html#numpy.nanquantile)(a, q[, axis, out, ...]) |Compute the qth quantile of the data along the specified axis, while ignoring nan values.|
+
+## [Averages and variances](https://numpy.org/doc/stable/reference/routines.statistics.html#averages-and-variances)
+
+|||
+|---|---|
+|[median](https://numpy.org/doc/stable/reference/generated/numpy.median.html#numpy.median)(a[, axis, out, overwrite_input, keepdims])|Compute the median along the specified axis.|
+|[average](https://numpy.org/doc/stable/reference/generated/numpy.average.html#numpy.average)(a[, axis, weights, returned, keepdims])|Compute the weighted average along the specified axis.|
+|[mean](https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean)(a[, axis, dtype, out, keepdims, where])|Compute the arithmetic mean along the specified axis.|
+|[std](https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std)(a[, axis, dtype, out, ddof, keepdims, where])|Compute the standard deviation along the specified axis.|
+|[var](https://numpy.org/doc/stable/reference/generated/numpy.var.html#numpy.var)(a[, axis, dtype, out, ddof, keepdims, where])|Compute the variance along the specified axis.|
+|[nanmedian](https://numpy.org/doc/stable/reference/generated/numpy.nanmedian.html#numpy.nanmedian)(a[, axis, out, overwrite_input, ...])|Compute the median along the specified axis, while ignoring NaNs.|
+|[nanmean](https://numpy.org/doc/stable/reference/generated/numpy.nanmean.html#numpy.nanmean)(a[, axis, dtype, out, keepdims, where])|Compute the arithmetic mean along the specified axis, ignoring NaNs.|
+|[nanstd](https://numpy.org/doc/stable/reference/generated/numpy.nanstd.html#numpy.nanstd)(a[, axis, dtype, out, ddof, ...])|Compute the standard deviation along the specified axis, while ignoring NaNs.|
+|[nanvar](https://numpy.org/doc/stable/reference/generated/numpy.nanvar.html#numpy.nanvar)(a[, axis, dtype, out, ddof, ...]) |Compute the variance along the specified axis, while ignoring NaNs.|