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From pste...@apache.org
Subject svn commit: r161613 - in jakarta/commons/proper/math/trunk: src/java/org/apache/commons/math/stat/inference/TTest.java src/java/org/apache/commons/math/stat/inference/TTestImpl.java xdocs/userguide/stat.xml
Date Sat, 16 Apr 2005 21:49:46 GMT
Author: psteitz
Date: Sat Apr 16 14:49:45 2005
New Revision: 161613

URL: http://svn.apache.org/viewcvs?view=rev&rev=161613
Log:
Fixed javadoc errors. One-sided t-test significance adjustment was
reversed in javadoc for boolean-valued test methods.
BZ #34448
Reported by: Gilles Gaillard

Modified:
    jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java
    jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java
    jakarta/commons/proper/math/trunk/xdocs/userguide/stat.xml

Modified: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java
URL: http://svn.apache.org/viewcvs/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java?view=diff&r1=161612&r2=161613
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java
(original)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java
Sat Apr 16 14:49:45 2005
@@ -110,7 +110,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis can be rejected with

      * confidence <code>1 - alpha</code>.  To perform a 1-sided test, use 
-     * <code>alpha / 2</code>
+     * <code>alpha * 2</code>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
@@ -347,7 +347,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis can be 
      * rejected with confidence <code>1 - alpha</code>.  To 
-     * perform a 1-sided test, use <code>alpha / 2</code>
+     * perform a 1-sided test, use <code>alpha * 2</code>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code>
at
@@ -356,7 +356,7 @@
      * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
      * at the 99% level, first verify that the measured sample mean is less 
      * than <code>mu</code> and then use 
-     * <br><code>tTest(mu, sample, 0.005) </code>
+     * <br><code>tTest(mu, sample, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -415,7 +415,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis can be rejected with
      * confidence <code>1 - alpha</code>.  To  perform a 1-sided test, use
-     * <code>alpha / 2.</code>
+     * <code>alpha * 2.</code>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code>
at
@@ -424,7 +424,7 @@
      * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
      * at the 99% level, first verify that the measured sample mean is less 
      * than <code>mu</code> and then use 
-     * <br><code>tTest(mu, sampleStats, 0.005) </code>
+     * <br><code>tTest(mu, sampleStats, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -535,7 +535,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis that the means are
      * equal can be rejected with confidence <code>1 - alpha</code>.  To 
-     * perform a 1-sided test, use <code>alpha / 2</code>
+     * perform a 1-sided test, use <code>alpha * 2</code>
      * <p>
      * See {@link #t(double[], double[])} for the formula used to compute the
      * t-statistic.  Degrees of freedom are approximated using the
@@ -549,9 +549,9 @@
      * <br><code>tTest(sample1, sample2, 0.05). </code>
      * </li>
      * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>,
-     * first verify that the measured  mean of <code>sample 1</code> is less
-     * than the mean of <code>sample 2</code> and then use 
-     * <br><code>tTest(sample1, sample2, 0.005) </code>
+     * at the 99% level, first verify that the measured  mean of <code>sample 1</code>
+     * is less than the mean of <code>sample 2</code> and then use 
+     * <br><code>tTest(sample1, sample2, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -591,7 +591,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis that the means are
      * equal can be rejected with confidence <code>1 - alpha</code>.  To 
-     * perform a 1-sided test, use <code>alpha / 2.</code>  To perform the test
+     * perform a 1-sided test, use <code>alpha * 2.</code>  To perform the test
      * without the assumption of equal subpopulation variances, use 
      * {@link #tTest(double[], double[], double)}.
      * <p>
@@ -607,7 +607,7 @@
      * at the 99% level, first verify that the measured mean of 
      * <code>sample 1</code> is less than the mean of <code>sample 2</code>
      * and then use
-     * <br><code>tTest(sample1, sample2, 0.005) </code>
+     * <br><code>tTest(sample1, sample2, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -723,7 +723,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis that the means are
      * equal can be rejected with confidence <code>1 - alpha</code>.  To 
-     * perform a 1-sided test, use <code>alpha / 2</code>
+     * perform a 1-sided test, use <code>alpha * 2</code>
      * <p>
      * See {@link #t(double[], double[])} for the formula used to compute the
      * t-statistic.  Degrees of freedom are approximated using the
@@ -739,7 +739,7 @@
      * at the 99% level,  first verify that the measured mean of  
      * <code>sample 1</code> is less than  the mean of <code>sample 2</code>
      * and then use 
-     * <br><code>tTest(sampleStats1, sampleStats2, 0.005) </code>
+     * <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>

Modified: jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java
URL: http://svn.apache.org/viewcvs/jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java?view=diff&r1=161612&r2=161613
==============================================================================
--- jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java
(original)
+++ jakarta/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java
Sat Apr 16 14:49:45 2005
@@ -123,7 +123,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis can be rejected with

      * confidence <code>1 - alpha</code>.  To perform a 1-sided test, use 
-     * <code>alpha / 2</code>
+     * <code>alpha * 2</code>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
@@ -420,7 +420,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis can be 
      * rejected with confidence <code>1 - alpha</code>.  To 
-     * perform a 1-sided test, use <code>alpha / 2</code>
+     * perform a 1-sided test, use <code>alpha * 2</code>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code>
at
@@ -429,7 +429,7 @@
      * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
      * at the 99% level, first verify that the measured sample mean is less 
      * than <code>mu</code> and then use 
-     * <br><code>tTest(mu, sample, 0.005) </code>
+     * <br><code>tTest(mu, sample, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -501,7 +501,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis can be rejected with
      * confidence <code>1 - alpha</code>.  To  perform a 1-sided test, use
-     * <code>alpha / 2.</code>
+     * <code>alpha * 2.</code>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code>
at
@@ -510,7 +510,7 @@
      * <li>To test the (one-sided) hypothesis <code> sample mean < mu </code>
      * at the 99% level, first verify that the measured sample mean is less 
      * than <code>mu</code> and then use 
-     * <br><code>tTest(mu, sampleStats, 0.005) </code>
+     * <br><code>tTest(mu, sampleStats, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -656,10 +656,10 @@
      * the 95% level,  use 
      * <br><code>tTest(sample1, sample2, 0.05). </code>
      * </li>
-     * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>,
-     * first verify that the measured  mean of <code>sample 1</code> is less
-     * than the mean of <code>sample 2</code> and then use 
-     * <br><code>tTest(sample1, sample2, 0.005) </code>
+     * <li>To test the (one-sided) hypothesis <code> mean 1 < mean 2 </code>
at
+     * the 99% level, first verify that the measured  mean of <code>sample 1</code>
+     * is less than the mean of <code>sample 2</code> and then use 
+     * <br><code>tTest(sample1, sample2, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -703,7 +703,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis that the means are
      * equal can be rejected with confidence <code>1 - alpha</code>.  To 
-     * perform a 1-sided test, use <code>alpha / 2.</code>  To perform the test
+     * perform a 1-sided test, use <code>alpha * 2.</code>  To perform the test
      * without the assumption of equal subpopulation variances, use 
      * {@link #tTest(double[], double[], double)}.
      * <p>
@@ -719,7 +719,7 @@
      * at the 99% level, first verify that the measured mean of 
      * <code>sample 1</code> is less than the mean of <code>sample 2</code>
      * and then use
-     * <br><code>tTest(sample1, sample2, 0.005) </code>
+     * <br><code>tTest(sample1, sample2, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>
@@ -855,7 +855,7 @@
      * <p>
      * Returns <code>true</code> iff the null hypothesis that the means are
      * equal can be rejected with confidence <code>1 - alpha</code>.  To 
-     * perform a 1-sided test, use <code>alpha / 2</code>
+     * perform a 1-sided test, use <code>alpha * 2</code>
      * <p>
      * See {@link #t(double[], double[])} for the formula used to compute the
      * t-statistic.  Degrees of freedom are approximated using the
@@ -871,7 +871,7 @@
      * at the 99% level,  first verify that the measured mean of  
      * <code>sample 1</code> is less than  the mean of <code>sample 2</code>
      * and then use 
-     * <br><code>tTest(sampleStats1, sampleStats2, 0.005) </code>
+     * <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code>
      * </li></ol>
      * <p>
      * <strong>Usage Note:</strong><br>

Modified: jakarta/commons/proper/math/trunk/xdocs/userguide/stat.xml
URL: http://svn.apache.org/viewcvs/jakarta/commons/proper/math/trunk/xdocs/userguide/stat.xml?view=diff&r1=161612&r2=161613
==============================================================================
--- jakarta/commons/proper/math/trunk/xdocs/userguide/stat.xml (original)
+++ jakarta/commons/proper/math/trunk/xdocs/userguide/stat.xml Sat Apr 16 14:49:45 2005
@@ -500,7 +500,7 @@
           </source>
            </p>
            <p>
-           To compute the (one-sided) p-value:
+           To compute the p-value:
            <source>
 testStatistic.pairedTTest(sample1, sample2);
            </source> 
@@ -515,7 +515,7 @@
            returned by <code>testStatistic.pairedTTest(sample1, sample2)</code>
            is less than <code>.05</code>
            </dd> 
-           <dd><strong>Example 2: </strong> unpaired, two-sample t-test
using
+           <dd><strong>Example 2: </strong> unpaired, two-sided, two-sample
t-test using
            <code>StatisticalSummary</code> instances, without assuming that
            subpopulation variances are equal.  
            <p>
@@ -543,7 +543,7 @@
           </source>
            </p>
            <p>
-           To compute the (one-sided) p-value:
+           To compute the p-value:
            <source>
 testStatistic.tTest(sample1, sample2);
            </source> 



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