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authorAnas Nashif <anas.nashif@intel.com>2012-10-30 12:57:26 -0700
committerAnas Nashif <anas.nashif@intel.com>2012-10-30 12:57:26 -0700
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+<html>
+<head>
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+<title>Parameters can be calculated</title>
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+<div class="section math_toolkit_dist_stat_tut_overview_parameters">
+<div class="titlepage"><div><div><h5 class="title">
+<a name="math_toolkit.dist.stat_tut.overview.parameters"></a><a class="link" href="parameters.html" title="Parameters can be calculated">Parameters
+ can be calculated</a>
+</h5></div></div></div>
+<p>
+ Sometimes it's the parameters that define the distribution that you need
+ to find. Suppose, for example, you have conducted a Students-t test for
+ equal means and the result is borderline. Maybe your two samples differ
+ from each other, or maybe they don't; based on the result of the test
+ you can't be sure. A legitimate question to ask then is "How many
+ more measurements would I have to take before I would get an X% probability
+ that the difference is real?" Parameter finders can answer questions
+ like this, and are necessarily different for each distribution. They
+ are implemented as static member functions of the distributions, for
+ example:
+ </p>
+<pre class="programlisting"><span class="identifier">students_t</span><span class="special">::</span><span class="identifier">find_degrees_of_freedom</span><span class="special">(</span>
+ <span class="number">1.3</span><span class="special">,</span> <span class="comment">// difference from true mean to detect</span>
+ <span class="number">0.05</span><span class="special">,</span> <span class="comment">// maximum risk of falsely rejecting the null-hypothesis.</span>
+ <span class="number">0.1</span><span class="special">,</span> <span class="comment">// maximum risk of falsely failing to reject the null-hypothesis.</span>
+ <span class="number">0.13</span><span class="special">);</span> <span class="comment">// sample standard deviation</span>
+</pre>
+<p>
+ Returns the number of degrees of freedom required to obtain a 95% probability
+ that the observed differences in means is not down to chance alone. In
+ the case that a borderline Students-t test result was previously obtained,
+ this can be used to estimate how large the sample size would have to
+ become before the observed difference was considered significant. It
+ assumes, of course, that the sample mean and standard deviation are invariant
+ with sample size.
+ </p>
+</div>
+<table xmlns:rev="http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" width="100%"><tr>
+<td align="left"></td>
+<td align="right"><div class="copyright-footer">Copyright &#169; 2006-2010 John Maddock, Paul A. Bristow, Hubert Holin, Xiaogang Zhang, Bruno
+ Lalande, Johan R&#229;de, Gautam Sewani and Thijs van den Berg<p>
+ Distributed under the Boost Software License, Version 1.0. (See accompanying
+ file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
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