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From l..@apache.org
Subject svn commit: r514875 - /jakarta/commons/proper/math/trunk/xdocs/userguide/distribution.xml
Date Mon, 05 Mar 2007 21:35:32 GMT
Author: luc
Date: Mon Mar  5 13:35:31 2007
New Revision: 514875

URL: http://svn.apache.org/viewvc?view=rev&rev=514875
Log:
added an entry for Pascal distribution
fixed typos
fixed a code example

Modified:
    jakarta/commons/proper/math/trunk/xdocs/userguide/distribution.xml

Modified: jakarta/commons/proper/math/trunk/xdocs/userguide/distribution.xml
URL: http://svn.apache.org/viewvc/jakarta/commons/proper/math/trunk/xdocs/userguide/distribution.xml?view=diff&rev=514875&r1=514874&r2=514875
==============================================================================
--- jakarta/commons/proper/math/trunk/xdocs/userguide/distribution.xml (original)
+++ jakarta/commons/proper/math/trunk/xdocs/userguide/distribution.xml Mon Mar  5 13:35:31
2007
@@ -60,11 +60,12 @@
             <tr><td>Exponential</td><td>createExponentialDistribution</td><td><div>Mean</div></td></tr>
             <tr><td>F</td><td>createFDistribution</td><td><div>Numerator
degrees of freedom</div><div>Denominator degrees of freedom</div></td></tr>
             <tr><td>Gamma</td><td>createGammaDistribution</td><td><div>Alpha</div><div>Beta</div></td></tr>
-            <tr><td>Hypergeometric</td><td>createHypogeometricDistribution</td><td><div>Population
size</div><div>Number of successes in population</div><div>Sample
size</div></td></tr>
+            <tr><td>Hypergeometric</td><td>createHypergeometricDistribution</td><td><div>Population
size</div><div>Number of successes in population</div><div>Sample
size</div></td></tr>
             <tr><td>Normal (Gaussian)</td><td>createNormalDistribution</td><td><div>Mean</div><div>Standard
Deviation</div></td></tr>
             <tr><td>Poisson</td><td>createPoissonDistribution</td><td><div>Mean</div></td></tr>
             <tr><td>t</td><td>createTDistribution</td><td><div>Degrees
of freedom</div></td></tr>
             <tr><td>Weibull</td><td>createWeibullDistribution</td><td><div>Shape</div><div>Scale</div><div>Location</div></td></tr>
+            <tr><td>Pascal</td><td>createPascalDistribution</td><td><div>numberOfSuccesses</div><div>probabilityOfSuccess</div></td></tr>
           </table>
         </p>
         <p>
@@ -74,13 +75,13 @@
           <code>P(X &lt;= x)</code> (i.e. the lower tail probability of <code>X</code>).
         </p>
         <source>DistributionFactory factory = DistributionFactory.newInstance();
-TDistribution t = factory.createBinomialDistribution(29);
+TDistribution t = factory.createTDistribution(29);
 double lowerTail = t.cumulativeProbability(-2.656);     // P(T &lt;= -2.656)
 double upperTail = 1.0 - t.cumulativeProbability(2.75); // P(T &gt;= 2.75)</source>
         <p>
           The inverse PDF and CDF values are just as easily computed using the
-          <code>inverseCumulativeProbability</code>methods.  For a distribution
<code>X</code>,
-          and a probability, <code>p</code>,  <code>inverseCumulativeProbability</code>
+          <code>inverseCumulativeProbability</code> methods.  For a distribution
<code>X</code>,
+          and a probability, <code>p</code>, <code>inverseCumulativeProbability</code>
           computes the domain value <code>x</code>, such that:
           <ul>
             <li><code>P(X &lt;= x) = p</code>, for continuous distributions</li>



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