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<title>GNU Scientific Library &ndash; Reference Manual: Covariance</title>

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<a name="Covariance"></a>
<div class="header">
<p>
Next: <a href="Correlation.html#Correlation" accesskey="n" rel="next">Correlation</a>, Previous: <a href="Autocorrelation.html#Autocorrelation" accesskey="p" rel="previous">Autocorrelation</a>, Up: <a href="Statistics.html#Statistics" accesskey="u" rel="up">Statistics</a> &nbsp; [<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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<hr>
<a name="Covariance-1"></a>
<h3 class="section">21.5 Covariance</h3>
<a name="index-covariance_002c-of-two-datasets"></a>

<dl>
<dt><a name="index-gsl_005fstats_005fcovariance"></a>Function: <em>double</em> <strong>gsl_stats_covariance</strong> <em>(const double <var>data1</var>[], const size_t <var>stride1</var>, const double <var>data2</var>[], const size_t <var>stride2</var>, const size_t <var>n</var>)</em></dt>
<dd><p>This function computes the covariance of the datasets <var>data1</var> and
<var>data2</var> which must both be of the same length <var>n</var>.
</p>
<div class="example">
<pre class="example">covar = (1/(n - 1)) \sum_{i = 1}^{n} (x_i - \Hat x) (y_i - \Hat y)
</pre></div>
</dd></dl>

<dl>
<dt><a name="index-gsl_005fstats_005fcovariance_005fm"></a>Function: <em>double</em> <strong>gsl_stats_covariance_m</strong> <em>(const double <var>data1</var>[], const size_t <var>stride1</var>, const double <var>data2</var>[], const size_t <var>stride2</var>, const size_t <var>n</var>, const double <var>mean1</var>, const double <var>mean2</var>)</em></dt>
<dd><p>This function computes the covariance of the datasets <var>data1</var> and
<var>data2</var> using the given values of the means, <var>mean1</var> and
<var>mean2</var>.  This is useful if you have already computed the means of
<var>data1</var> and <var>data2</var> and want to avoid recomputing them.
</p></dd></dl>




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