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>
