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From pste...@apache.org
Subject svn commit: r1408281 - /commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml
Date Mon, 12 Nov 2012 13:37:21 GMT
Author: psteitz
Date: Mon Nov 12 13:37:20 2012
New Revision: 1408281

URL: http://svn.apache.org/viewvc?rev=1408281&view=rev
Log:
Consistently use G for G Test.

Modified:
    commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml

Modified: commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml?rev=1408281&r1=1408280&r2=1408281&view=diff
==============================================================================
--- commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml (original)
+++ commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml Mon Nov 12 13:37:20 2012
@@ -878,7 +878,7 @@ new PearsonsCorrelation().correlation(ra
            methods supporting fixed significance level tests assume that the hypotheses
            are two-sided.  One sided tests can be performed by dividing returned p-values
            (resp. critical values) by 2.</li>
-           <li>Degrees of freedom for g- and chi-square tests are integral values,
based on the
+           <li>Degrees of freedom for G- and chi-square tests are integral values,
based on the
            number of observed or expected counts (number of observed counts - 1).</li>
           </ul>
           </p>
@@ -1067,19 +1067,19 @@ TestUtils.chiSquareTest(counts, alpha);
           hypothesis can be rejected with confidence <code>1 - alpha</code>.
           </dd>
           <br></br>
-          <dt><strong>g tests</strong></dt>
+          <dt><strong>G tests</strong></dt>
           <br></br>
-          <dd>g tests are an alternative to chi-square tests that are recommended
+          <dd>G tests are an alternative to chi-square tests that are recommended
           when observed counts are small and / or incidence probabillities for 
           some cells are small. See Ted Dunning's paper,
           <a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5962">
           Accurate Methods for the Statistics of Surprise and Coincidence</a> for
           background and an empirical analysis showing now chi-square
           statistics can be misldeading in the presence of low incidence probabilities.
-          This paper also derives the formulas used in computing g statistics and the
+          This paper also derives the formulas used in computing G statistics and the
           root log likelihood ratio provided by the <code>GTest</code> class.</dd>
           <dd>
-          <dd>To compute a g-test statistic measuring the agreement between a
+          <dd>To compute a G-test statistic measuring the agreement between a
           <code>long[]</code> array of observed counts and a <code>double[]</code>
           array of expected counts, use:
           <source>
@@ -1112,7 +1112,7 @@ TestUtils.gTest(expected, observed, alph
           <source>
 long[] obs1 = new long[]{268, 199, 42};
 long[] obs2 = new long[]{807, 759, 184};
-System.out.println(TestUtils.gDataSetsComparison(obs1, obs2)); // g statistic
+System.out.println(TestUtils.gDataSetsComparison(obs1, obs2)); // G statistic
 System.out.println(TestUtils.gTestDataSetsComparison(obs1, obs2)); // p-value
           </source>
           </dd>



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