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<title>GNU Scientific Library – Reference Manual: Median and Percentiles</title>
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<a name="Median-and-Percentiles"></a>
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Next: <a href="Example-statistical-programs.html#Example-statistical-programs" accesskey="n" rel="next">Example statistical programs</a>, Previous: <a href="Maximum-and-Minimum-values.html#Maximum-and-Minimum-values" accesskey="p" rel="previous">Maximum and Minimum values</a>, Up: <a href="Statistics.html#Statistics" accesskey="u" rel="up">Statistics</a> [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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<a name="Median-and-Percentiles-1"></a>
<h3 class="section">21.9 Median and Percentiles</h3>
<p>The median and percentile functions described in this section operate on
sorted data. For convenience we use <em>quantiles</em>, measured on a scale
of 0 to 1, instead of percentiles (which use a scale of 0 to 100).
</p>
<dl>
<dt><a name="index-gsl_005fstats_005fmedian_005ffrom_005fsorted_005fdata"></a>Function: <em>double</em> <strong>gsl_stats_median_from_sorted_data</strong> <em>(const double <var>sorted_data</var>[], size_t <var>stride</var>, size_t <var>n</var>)</em></dt>
<dd><p>This function returns the median value of <var>sorted_data</var>, a dataset
of length <var>n</var> with stride <var>stride</var>. The elements of the array
must be in ascending numerical order. There are no checks to see
whether the data are sorted, so the function <code>gsl_sort</code> should
always be used first.
</p>
<p>When the dataset has an odd number of elements the median is the value
of element <em>(n-1)/2</em>. When the dataset has an even number of
elements the median is the mean of the two nearest middle values,
elements <em>(n-1)/2</em> and <em>n/2</em>. Since the algorithm for
computing the median involves interpolation this function always returns
a floating-point number, even for integer data types.
</p></dd></dl>
<dl>
<dt><a name="index-gsl_005fstats_005fquantile_005ffrom_005fsorted_005fdata"></a>Function: <em>double</em> <strong>gsl_stats_quantile_from_sorted_data</strong> <em>(const double <var>sorted_data</var>[], size_t <var>stride</var>, size_t <var>n</var>, double <var>f</var>)</em></dt>
<dd><p>This function returns a quantile value of <var>sorted_data</var>, a
double-precision array of length <var>n</var> with stride <var>stride</var>. The
elements of the array must be in ascending numerical order. The
quantile is determined by the <var>f</var>, a fraction between 0 and 1. For
example, to compute the value of the 75th percentile <var>f</var> should have
the value 0.75.
</p>
<p>There are no checks to see whether the data are sorted, so the function
<code>gsl_sort</code> should always be used first.
</p>
<p>The quantile is found by interpolation, using the formula
where <em>i</em> is <code>floor</code>(<em>(n - 1)f</em>) and <em>\delta</em> is
<em>(n-1)f - i</em>.
</p>
<p>Thus the minimum value of the array (<code>data[0*stride]</code>) is given by
<var>f</var> equal to zero, the maximum value (<code>data[(n-1)*stride]</code>) is
given by <var>f</var> equal to one and the median value is given by <var>f</var>
equal to 0.5. Since the algorithm for computing quantiles involves
interpolation this function always returns a floating-point number, even
for integer data types.
</p></dd></dl>
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Next: <a href="Example-statistical-programs.html#Example-statistical-programs" accesskey="n" rel="next">Example statistical programs</a>, Previous: <a href="Maximum-and-Minimum-values.html#Maximum-and-Minimum-values" accesskey="p" rel="previous">Maximum and Minimum values</a>, Up: <a href="Statistics.html#Statistics" accesskey="u" rel="up">Statistics</a> [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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