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Previous: <a href="Miscellaneous-Functions.html#Miscellaneous-Functions" accesskey="p" rel="prev">Miscellaneous Functions</a>, Up: <a href="Functions.html#Functions" accesskey="u" rel="up">Functions</a> [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Function-Index.html#Function-Index" title="Index" rel="index">Index</a>]</p>
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<a name="Statistical-Distribution-Functions-1"></a>
<h4 class="subsection">7.7.10 Statistical Distribution Functions</h4>
<p><small>PSPP</small> can calculate several functions of standard statistical
distributions. These functions are named systematically based on the
function and the distribution. The table below describes the
statistical distribution functions in general:
</p>
<dl compact="compact">
<dt>PDF.<var>dist</var> (<var>x</var>[, <var>param</var>…])</dt>
<dd><p>Probability density function for <var>dist</var>. The domain of <var>x</var>
depends on <var>dist</var>. For continuous distributions, the result is
the density of the probability function at <var>x</var>, and the range is
nonnegative real numbers. For discrete distributions, the result is
the probability of <var>x</var>.
</p>
</dd>
<dt>CDF.<var>dist</var> (<var>x</var>[, <var>param</var>…])</dt>
<dd><p>Cumulative distribution function for <var>dist</var>, that is, the
probability that a random variate drawn from the distribution is less
than <var>x</var>. The domain of <var>x</var> depends <var>dist</var>. The result is
a probability.
</p>
</dd>
<dt>SIG.<var>dist</var> (<var>x</var>[, <var>param</var>…)</dt>
<dd><p>Tail probability function for <var>dist</var>, that is, the probability
that a random variate drawn from the distribution is greater than
<var>x</var>. The domain of <var>x</var> depends <var>dist</var>. The result is a
probability. Only a few distributions include an <code>/NAME/</code> function.
</p>
</dd>
<dt>IDF.<var>dist</var> (<var>p</var>[, <var>param</var>…])</dt>
<dd><p>Inverse distribution function for <var>dist</var>, the value of <var>x</var> for
which the CDF would yield <var>p</var>. The value of <var>p</var> is a
probability. The range depends on <var>dist</var> and is identical to the
domain for the corresponding CDF.
</p>
</dd>
<dt>RV.<var>dist</var> ([<var>param</var>…])</dt>
<dd><p>Random variate function for <var>dist</var>. The range depends on the
distribution.
</p>
</dd>
<dt>NPDF.<var>dist</var> (<var>x</var>[, <var>param</var>…])</dt>
<dd><p>Noncentral probability density function. The result is the density of
the given noncentral distribution at <var>x</var>. The domain of <var>x</var>
depends on <var>dist</var>. The range is nonnegative real numbers. Only a
few distributions include an <code>/NAME/</code> function.
</p>
</dd>
<dt>NCDF.<var>dist</var> (<var>x</var>[, <var>param</var>…])</dt>
<dd><p>Noncentral cumulative distribution function for <var>dist</var>, that is,
the probability that a random variate drawn from the given noncentral
distribution is less than <var>x</var>. The domain of <var>x</var> depends
<var>dist</var>. The result is a probability. Only a few distributions
include an NCDF function.
</p></dd>
</dl>
<p>The individual distributions are described individually below.
</p>
<table class="menu" border="0" cellspacing="0">
<tr><td align="left" valign="top">• <a href="Continuous-Distributions.html#Continuous-Distributions" accesskey="1">Continuous Distributions</a>:</td><td> </td><td align="left" valign="top">
</td></tr>
<tr><td align="left" valign="top">• <a href="Discrete-Distributions.html#Discrete-Distributions" accesskey="2">Discrete Distributions</a>:</td><td> </td><td align="left" valign="top">
</td></tr>
</table>
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