https://mscneuro.neuro.uni-bremen.de/index.php?action=history&feed=atom&title=Statistics 2026-10-11T19:16:43Z Revision history for this page on the wiki MediaWiki 1.43.5 https://mscneuro.neuro.uni-bremen.de/index.php?title=Statistics&diff=369&oldid=prev 2025-10-17T16:45:16Z

<p></p> <table style="background-color: #fff; color: #202122;" data-mw="interface"> <col class="diff-marker" /> <col class="diff-content" /> <col class="diff-marker" /> <col class="diff-content" /> <tr class="diff-title" lang="en"> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td> <td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 16:45, 17 October 2025</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1">Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">== The goal ==</del></div></td><td colspan="2" class="diff-side-added"></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are other (more extensive) statistics packages like​ * [https://docs.scipy.org/doc/scipy/reference/stats.html scipy.stats​] * [https://pingouin-stats.org/build/html/index.html pingouin] * [https://www.statsmodels.org/stable/index.html statsmodels]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>There are other (more extensive) statistics packages like​ * [https://docs.scipy.org/doc/scipy/reference/stats.html scipy.stats​] * [https://pingouin-stats.org/build/html/index.html pingouin] * [https://www.statsmodels.org/stable/index.html statsmodels]</div></td></tr> <tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr> </table>

Davrot https://mscneuro.neuro.uni-bremen.de/index.php?title=Statistics&diff=201&oldid=prev 2025-10-17T12:58:50Z

<p>Created page with &quot;== The goal == There are other (more extensive) statistics packages like​ * [https://docs.scipy.org/doc/scipy/reference/stats.html scipy.stats​] * [https://pingouin-stats.org/build/html/index.html pingouin] * [https://www.statsmodels.org/stable/index.html statsmodels] Questions to [mailto:davrot@uni-bremen.de David Rotermund] == [https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html#scipy.stats.fisher_exact Fisher Exact Test] == The [ht...&quot;</p> <p><b>New page</b></p><div>== The goal ==<br /> There are other (more extensive) statistics packages like​ * [https://docs.scipy.org/doc/scipy/reference/stats.html scipy.stats​] * [https://pingouin-stats.org/build/html/index.html pingouin] * [https://www.statsmodels.org/stable/index.html statsmodels]<br /> <br /> Questions to [mailto:davrot@uni-bremen.de David Rotermund]<br /> <br /> == [https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html#scipy.stats.fisher_exact Fisher Exact Test] ==<br /> The [https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html#scipy.stats.fisher_exact Fisher Exact Test] is not part of the numpy package. But we need it in machine learning.&lt;syntaxhighlight lang=&quot;python&quot;&gt;scipy.stats.fisher_exact(table, alternative=&#039;two-sided&#039;)&lt;/syntaxhighlight&gt;&lt;blockquote&gt;Perform a Fisher exact test on a 2x2 contingency table.&lt;/blockquote&gt;<br /> <br /> == [https://numpy.org/doc/stable/reference/routines.statistics.html#order-statistics Order statistics] ==<br /> {| class=&quot;wikitable&quot;<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.ptp.html#numpy.ptp ptp](a[, axis, out, keepdims])<br /> |Range of values (maximum - minimum) along an axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.percentile.html#numpy.percentile percentile](a, q[, axis, out, …])<br /> |Compute the q-th percentile of the data along the specified axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.nanpercentile.html#numpy.nanpercentile nanpercentile](a, q[, axis, out, …])<br /> |Compute the qth percentile of the data along the specified axis, while ignoring nan values.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.quantile.html#numpy.quantile quantile](a, q[, axis, out, overwrite_input, …])<br /> |Compute the q-th quantile of the data along the specified axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.nanquantile.html#numpy.nanquantile nanquantile](a, q[, axis, out, …])<br /> |Compute the qth quantile of the data along the specified axis, while ignoring nan values.<br /> |}<br /> <br /> == [https://numpy.org/doc/stable/reference/routines.statistics.html#averages-and-variances Averages and variances] ==<br /> {| class=&quot;wikitable&quot;<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.median.html#numpy.median median](a[, axis, out, overwrite_input, keepdims])<br /> |Compute the median along the specified axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.average.html#numpy.average average](a[, axis, weights, returned, keepdims])<br /> |Compute the weighted average along the specified axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean mean](a[, axis, dtype, out, keepdims, where])<br /> |Compute the arithmetic mean along the specified axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std std](a[, axis, dtype, out, ddof, keepdims, where])<br /> |Compute the standard deviation along the specified axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.var.html#numpy.var var](a[, axis, dtype, out, ddof, keepdims, where])<br /> |Compute the variance along the specified axis.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.nanmedian.html#numpy.nanmedian nanmedian](a[, axis, out, overwrite_input, …])<br /> |Compute the median along the specified axis, while ignoring NaNs.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.nanmean.html#numpy.nanmean nanmean](a[, axis, dtype, out, keepdims, where])<br /> |Compute the arithmetic mean along the specified axis, ignoring NaNs.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.nanstd.html#numpy.nanstd nanstd](a[, axis, dtype, out, ddof, …])<br /> |Compute the standard deviation along the specified axis, while ignoring NaNs.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.nanvar.html#numpy.nanvar nanvar](a[, axis, dtype, out, ddof, …])<br /> |Compute the variance along the specified axis, while ignoring NaNs.<br /> |}<br /> <br /> == [https://numpy.org/doc/stable/reference/routines.statistics.html#correlating Correlating] ==<br /> {| class=&quot;wikitable&quot;<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef corrcoef](x[, y, rowvar, bias, ddof, dtype])<br /> |Return Pearson product-moment correlation coefficients.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.correlate.html#numpy.correlate correlate](a, v[, mode])<br /> |Cross-correlation of two 1-dimensional sequences.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.cov.html#numpy.cov cov](m[, y, rowvar, bias, ddof, fweights, …])<br /> |Estimate a covariance matrix, given data and weights.<br /> |}<br /> <br /> == [https://numpy.org/doc/stable/reference/routines.statistics.html#histograms Histograms] ==<br /> {| class=&quot;wikitable&quot;<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.histogram.html#numpy.histogram histogram](a[, bins, range, density, weights])<br /> |Compute the histogram of a dataset.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.histogram2d.html#numpy.histogram2d histogram2d](x, y[, bins, range, density, …])<br /> |Compute the bi-dimensional histogram of two data samples.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.histogramdd.html#numpy.histogramdd histogramdd](sample[, bins, range, density, …])<br /> |Compute the multidimensional histogram of some data.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.bincount.html#numpy.bincount bincount](x, /[, weights, minlength])<br /> |Count number of occurrences of each value in array of non-negative ints.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.histogram_bin_edges.html#numpy.histogram_bin_edges histogram_bin_edges](a[, bins, range, weights])<br /> |Function to calculate only the edges of the bins used by the histogram function.<br /> |-<br /> |[https://numpy.org/doc/stable/reference/generated/numpy.digitize.html#numpy.digitize digitize](x, bins[, right])<br /> |Return the indices of the bins to which each value in input array belongs.<br /> |}</div>

Davrot