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--- jakarta/commons/proper/math/trunk/xdocs/userguide/stat.xml 2005/06/05 02:47:28 180064
+++ jakarta/commons/proper/math/trunk/xdocs/userguide/stat.xml 2005/06/05 03:58:14 180065
@@ -394,7 +394,25 @@ System.out.println(regression.getSlopeSt
Chi-Square</a> test statistics as well as
<a href="http://www.cas.lancs.ac.uk/glossary_v1.1/hyptest.html#pvalue">
p-values</a> associated with <code>t-</code> and
- <code>Chi-Square</code> tests.
+ <code>Chi-Square</code> tests. The interfaces are
+ <a href="../apidocs/org/apache/commons/math/stat/inference/TTest.html">
+ TTest</a> and
+ <a href="../apidocs/org/apache/commons/math/stat/inference/ChiSquareTest.html">
+ ChiSquareTest</a>, with
+ provided implementations
+ <a href="../apidocs/org/apache/commons/math/stat/inference/TTestImpl.html">
+ TTestImpl</a> and
+ <a href="../apidocs/org/apache/commons/math/stat/inference/ChiSquareTestImpl.html">
+ ChiSquareTestImpl</a>.
+ Abstract and default factories are provided, with configuration
+ optional using commons-discovery to specify the concrete factory. The
+ <a href="../apidocs/org/apache/commons/math/stat/inference/TestUtils.html">
+ TestUtils</a> class provides static methods to get test instances or
+ to compute test statistics directly. The examples below all use the
+ static methods in <code>TestUtils</code> to execute tests. To get
+ test object instances, either use e.g.,
+ <code>TestUtils.getTTest()</code> or use the factory directly, e.g.,
+ <code>TestFactory.newInstance().createChiSquareTest()</code>.
</p>
<p>
<strong>Implementation Notes</strong>
@@ -442,8 +460,7 @@ System.out.println(regression.getSlopeSt
<source>
double[] observed = {1d, 2d, 3d};
double mu = 2.5d;
-TTestImpl testStatistic = new TTestImpl();
-System.out.println(testStatistic.t(mu, observed);
+System.out.println(TestUtils.t(mu, observed);
</source>
The code above will display the t-statisitic associated with a one-sample
t-test comparing the mean of the <code>observed</code> values against
@@ -460,7 +477,7 @@ sampleStats = SummaryStatistics.newInsta
for (int i = 0; i < observed.length; i++) {
sampleStats.addValue(observed[i]);
}
-System.out.println(testStatistic.t(mu, observed);
+System.out.println(TestUtils.t(mu, observed);
</source>
</dd>
<dd>To compute the p-value associated with the null hypothesis that the mean
@@ -469,8 +486,7 @@ System.out.println(testStatistic.t(mu, o
<source>
double[] observed = {1d, 2d, 3d};
double mu = 2.5d;
-TTestImpl testStatistic = new TTestImpl();
-System.out.println(testStatistic.tTest(mu, observed);
+System.out.println(TestUtils.tTest(mu, observed);
</source>
The snippet above will display the p-value associated with the null
hypothesis that the mean of the population from which the
@@ -478,7 +494,7 @@ System.out.println(testStatistic.tTest(m
</dd>
<dd>To perform the test using a fixed significance level, use:
<source>
-testStatistic.tTest(mu, observed, alpha);
+TestUtils.tTest(mu, observed, alpha);
</source>
where <code>0 < alpha < 0.5</code> is the significance level of
the test. The boolean value returned will be <code>true</code> iff the
@@ -495,24 +511,23 @@ testStatistic.tTest(mu, observed, alpha)
<p>
To compute the t-statistic:
<source>
-TTestImpl testStatistic = new TTestImpl();
-testStatistic.pairedT(sample1, sample2);
+TestUtils.pairedT(sample1, sample2);
</source>
</p>
<p>
To compute the p-value:
<source>
-testStatistic.pairedTTest(sample1, sample2);
+TestUtils.pairedTTest(sample1, sample2);
</source>
</p>
<p>
To perform a fixed significance level test with alpha = .05:
<source>
-testStatistic.pairedTTest(sample1, sample2, .05);
+TestUtils.pairedTTest(sample1, sample2, .05);
</source>
</p>
The last example will return <code>true</code> iff the p-value
- returned by <code>testStatistic.pairedTTest(sample1, sample2)</code>
+ returned by <code>TestUtils.pairedTTest(sample1, sample2)</code>
is less than <code>.05</code>
</dd>
<dd><strong>Example 2: </strong> unpaired, two-sided, two-sample t-test using
@@ -538,20 +553,19 @@ testStatistic.pairedTTest(sample1, sampl
<p>
To compute the t-statistic:
<source>
-TTestImpl testStatistic = new TTestImpl();
-testStatistic.t(summary1, summary2);
+TestUtils.t(summary1, summary2);
</source>
</p>
<p>
To compute the p-value:
<source>
-testStatistic.tTest(sample1, sample2);
+TestUtils.tTest(sample1, sample2);
</source>
</p>
<p>
To perform a fixed significance level test with alpha = .05:
<source>
-testStatistic.tTest(sample1, sample2, .05);
+TestUtils.tTest(sample1, sample2, .05);
</source>
</p>
<p>
@@ -566,10 +580,9 @@ testStatistic.tTest(sample1, sample2, .0
<code>long[]</code> array of observed counts and a <code>double[]</code>
array of expected counts, use:
<source>
-ChiSquareTestImpl testStatistic = new ChiSquareTestImpl();
long[] observed = {10, 9, 11};
double[] expected = {10.1, 9.8, 10.3};
-System.out.println(testStatistic.chiSquare(expected, observed));
+System.out.println(TestUtils.chiSquare(expected, observed));
</source>
the value displayed will be
<code>sum((expected[i] - observed[i])^2 / expected[i])</code>
@@ -577,7 +590,7 @@ System.out.println(testStatistic.chiSqua
<dd> To get the p-value associated with the null hypothesis that
<code>observed</code> conforms to <code>expected</code> use:
<source>
-testStatistic.chiSquareTest(expected, observed);
+TestUtils.chiSquareTest(expected, observed);
</source>
</dd>
<dd> To test the null hypothesis that <code>observed</code> conforms to
@@ -585,7 +598,7 @@ testStatistic.chiSquareTest(expected, ob
(equiv. <code>100 * (1-alpha)%</code> confidence) where <code>
0 < alpha < 1 </code> use:
<source>
-testStatistic.chiSquareTest(expected, observed, alpha);
+TestUtils.chiSquareTest(expected, observed, alpha);
</source>
The boolean value returned will be <code>true</code> iff the null hypothesis
can be rejected with confidence <code>1 - alpha</code>.
@@ -595,7 +608,7 @@ testStatistic.chiSquareTest(expected, ob
chi-square test of independence</a> based on a two-dimensional (long[][])
<code>counts</code> array viewed as a two-way table, use:
<source>
-testStatistic.chiSquareTest(counts);
+TestUtils.chiSquareTest(counts);
</source>
The rows of the 2-way table are
<code>count[0], ... , count[count.length - 1]. </code><br></br>
@@ -609,14 +622,14 @@ testStatistic.chiSquareTest(counts);
the classifications represented by the counts in the columns of the input 2-way
table are independent of the rows, use:
<source>
-testStatistic.chiSquareTest(counts);
+ TestUtils.chiSquareTest(counts);
</source>
</dd>
<dd>To perform a chi-square test of independence with <code>alpha</code>
siginficance level (equiv. <code>100 * (1-alpha)%</code> confidence)
where <code>0 < alpha < 1 </code> use:
<source>
-testStatistic.chiSquareTest(counts, alpha);
+TestUtils.chiSquareTest(counts, alpha);
</source>
The boolean value returned will be <code>true</code> iff the null
hypothesis can be rejected with confidence <code>1 - alpha</code>.
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