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From pste...@apache.org
Subject svn commit: r617953 [2/3] - in /commons/proper/math/trunk/src/java/org/apache/commons/math: distribution/ fraction/ linear/ stat/ stat/descriptive/ stat/descriptive/moment/ stat/descriptive/rank/ stat/descriptive/summary/ stat/inference/ stat/regressio...
Date Sun, 03 Feb 2008 05:54:06 GMT
Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/ThirdMoment.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/ThirdMoment.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/ThirdMoment.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/ThirdMoment.java Sat Feb  2 21:54:00 2008
@@ -22,24 +22,24 @@
  * Computes a statistic related to the Third Central Moment.  Specifically,
  * what is computed is the sum of cubed deviations from the sample mean.
  * <p>
- * The following recursive updating formula is used:
+ * The following recursive updating formula is used:</p>
  * <p>
  * Let <ul>
  * <li> dev = (current obs - previous mean) </li>
  * <li> m2 = previous value of {@link SecondMoment} </li>
  * <li> n = number of observations (including current obs) </li>
  * </ul>
- * Then
+ * Then</p>
  * <p>
- * new value = old value - 3 * (dev/n) * m2 + (n-1) * (n -2) * (dev^3/n^2)
+ * new value = old value - 3 * (dev/n) * m2 + (n-1) * (n -2) * (dev^3/n^2)</p>
  * <p>
  * Returns <code>Double.NaN</code> if no data values have been added and
- * returns <code>0</code> if there is just one value in the data set.
+ * returns <code>0</code> if there is just one value in the data set.</p>
  * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/Variance.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/Variance.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/Variance.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/moment/Variance.java Sat Feb  2 21:54:00 2008
@@ -24,10 +24,10 @@
  * Computes the variance of the available values.  By default, the unbiased
  * "sample variance" definitional formula is used: 
  * <p>
- * variance = sum((x_i - mean)^2) / (n - 1)
+ * variance = sum((x_i - mean)^2) / (n - 1) </p>
  * <p>
  * where mean is the {@link Mean} and <code>n</code> is the number
- * of sample observations.  
+ * of sample observations.</p>
  * <p>
  * The definitional formula does not have good numerical properties, so
  * this implementation does not compute the statistic using the definitional
@@ -46,13 +46,14 @@
  * <code>incrementAll</code> and then executing <code>getResult</code> will
  * sometimes give a different, less accurate, result than executing 
  * <code>evaluate</code> with the full array of values. The former approach
- * should only be used when the full array of values is not available.
+ * should only be used when the full array of values is not available.</p>
  * <p>
  * The "population variance"  ( sum((x_i - mean)^2) / n ) can also
  * be computed using this statistic.  The <code>isBiasCorrected</code>
  * property determines whether the "population" or "sample" value is
  * returned by the <code>evaluate</code> and <code>getResult</code> methods.
  * To compute population variances, set this property to <code>false.</code>
+ * </p>
  * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
@@ -182,13 +183,13 @@
      * Returns the variance of the entries in the input array, or 
      * <code>Double.NaN</code> if the array is empty.
      * <p>
-     * See {@link Variance} for details on the computing algorithm.
+     * See {@link Variance} for details on the computing algorithm.</p>
      * <p>
-     * Returns 0 for a single-value (i.e. length = 1) sample.
+     * Returns 0 for a single-value (i.e. length = 1) sample.</p>
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * <p>
-     * Does not change the internal state of the statistic.
+     * Does not change the internal state of the statistic.</p>
      * 
      * @param values the input array
      * @return the variance of the values or Double.NaN if length = 0
@@ -206,13 +207,13 @@
      * the input array, or <code>Double.NaN</code> if the designated subarray
      * is empty.
      * <p>
-     * See {@link Variance} for details on the computing algorithm.
+     * See {@link Variance} for details on the computing algorithm.</p>
      * <p>
-     * Returns 0 for a single-value (i.e. length = 1) sample.
+     * Returns 0 for a single-value (i.e. length = 1) sample.</p>
      * <p>
-     * Does not change the internal state of the statistic.
+     * Does not change the internal state of the statistic.</p>
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * 
      * @param values the input array
      * @param begin index of the first array element to include
@@ -243,18 +244,18 @@
      * the input array, using the precomputed mean value.  Returns 
      * <code>Double.NaN</code> if the designated subarray is empty.
      * <p>
-     * See {@link Variance} for details on the computing algorithm.
+     * See {@link Variance} for details on the computing algorithm.</p>
      * <p>
      * The formula used assumes that the supplied mean value is the arithmetic
      * mean of the sample data, not a known population parameter.  This method
      * is supplied only to save computation when the mean has already been
-     * computed.
+     * computed.</p>
      * <p>
-     * Returns 0 for a single-value (i.e. length = 1) sample.
+     * Returns 0 for a single-value (i.e. length = 1) sample.</p>
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * <p>
-     * Does not change the internal state of the statistic.
+     * Does not change the internal state of the statistic.</p>
      * 
      * @param values the input array
      * @param mean the precomputed mean value
@@ -297,20 +298,20 @@
      * precomputed mean value.  Returns <code>Double.NaN</code> if the array
      * is empty.
      * <p>
-     * See {@link Variance} for details on the computing algorithm.
+     * See {@link Variance} for details on the computing algorithm.</p>
      * <p>
      * If <code>isBiasCorrected</code> is <code>true</code> the formula used
      * assumes that the supplied mean value is the arithmetic mean of the
      * sample data, not a known population parameter.  If the mean is a known
      * population parameter, or if the "population" version of the variance is
      * desired, set <code>isBiasCorrected</code> to <code>false</code> before
-     * invoking this method.
+     * invoking this method.</p>
      * <p>
-     * Returns 0 for a single-value (i.e. length = 1) sample.
+     * Returns 0 for a single-value (i.e. length = 1) sample.</p>
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * <p>
-     * Does not change the internal state of the statistic.
+     * Does not change the internal state of the statistic.</p>
      * 
      * @param values the input array
      * @param mean the precomputed mean value

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Max.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Max.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Max.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Max.java Sat Feb  2 21:54:00 2008
@@ -26,12 +26,12 @@
  * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
  * <li>If any of the values equals <code>Double.POSITIVE_INFINITY</code>, 
  * the result is <code>Double.POSITIVE_INFINITY.</code></li>
- * </ul>
+ * </ul></p>
 * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */
@@ -65,8 +65,8 @@
     }
 
     /**
-         * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#clear()
-         */
+     * @see org.apache.commons.math.stat.descriptive.StorelessUnivariateStatistic#clear()
+     */
     public void clear() {
         value = Double.NaN;
         n = 0;
@@ -92,14 +92,14 @@
      * is empty.
      * <p>
      * Throws <code>IllegalArgumentException</code> if the array is null or
-     * the array index parameters are not valid.
+     * the array index parameters are not valid.</p>
      * <p>
      * <ul>
      * <li>The result is <code>NaN</code> iff all values are <code>NaN</code> 
      * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
      * <li>If any of the values equals <code>Double.POSITIVE_INFINITY</code>, 
      * the result is <code>Double.POSITIVE_INFINITY.</code></li>
-     * </ul>
+     * </ul></p>
      * 
      * @param values the input array
      * @param begin index of the first array element to include

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Median.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Median.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Median.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Median.java Sat Feb  2 21:54:00 2008
@@ -26,7 +26,7 @@
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Min.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Min.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Min.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Min.java Sat Feb  2 21:54:00 2008
@@ -28,12 +28,12 @@
  * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
  * <li>If any of the values equals <code>Double.NEGATIVE_INFINITY</code>, 
  * the result is <code>Double.NEGATIVE_INFINITY.</code></li>
- * </ul> 
+ * </ul></p>
  * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */
@@ -94,14 +94,14 @@
      * is empty.
      * <p>
      * Throws <code>IllegalArgumentException</code> if the array is null or
-     * the array index parameters are not valid.
+     * the array index parameters are not valid.</p>
      * <p>
      * <ul>
      * <li>The result is <code>NaN</code> iff all values are <code>NaN</code> 
      * (i.e. <code>NaN</code> values have no impact on the value of the statistic).</li>
      * <li>If any of the values equals <code>Double.NEGATIVE_INFINITY</code>, 
      * the result is <code>Double.NEGATIVE_INFINITY.</code></li>
-     * </ul> 
+     * </ul> </p>
      * 
      * @param values the input array
      * @param begin index of the first array element to include

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Percentile.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Percentile.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Percentile.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/rank/Percentile.java Sat Feb  2 21:54:00 2008
@@ -41,7 +41,7 @@
  * <code>floor(pos)</code> in the array and let <code>upper</code> be the
  * next element in the array.  Return <code>lower + d * (upper - lower)</code>
  * </li>
- * </ol>
+ * </ol></p>
  * <p>
  * To compute percentiles, the data must be (totally) ordered.  Input arrays
  * are copied and then sorted using  {@link java.util.Arrays#sort(double[])}.
@@ -50,16 +50,16 @@
  * <code>Double.NaN</code> larger than any other value (including 
  * <code>Double.POSITIVE_INFINITY</code>).  Therefore, for example, the median
  * (50th percentile) of  
- * <code>{0, 1, 2, 3, 4, Double.NaN}</code> evaluates to <code>2.5.</code>  
+ * <code>{0, 1, 2, 3, 4, Double.NaN}</code> evaluates to <code>2.5.</code></p>
  * <p>
  * Since percentile estimation usually involves interpolation between array 
  * elements, arrays containing  <code>NaN</code> or infinite values will often
- * result in <code>NaN<code> or infinite values returned.
+ * result in <code>NaN<code> or infinite values returned.</p>
  * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */
@@ -95,7 +95,7 @@
      * in the <code>values</code> array.
      * <p>
      * Calls to this method do not modify the internal <code>quantile</code>
-     * state of this statistic.
+     * state of this statistic.</p>
      * <p>
      * <ul>
      * <li>Returns <code>Double.NaN</code> if <code>values</code> has length 
@@ -105,10 +105,10 @@
      * <li>Throws <code>IllegalArgumentException</code> if <code>values</code>
      * is null or p is not a valid quantile value (p must be greater than 0
      * and less than or equal to 100) </li>
-     * </ul>
+     * </ul></p>
      * <p>
      * See {@link Percentile} for a description of the percentile estimation
-     * algorithm used.
+     * algorithm used.</p>
      * 
      * @param values input array of values
      * @param p the percentile value to compute
@@ -133,10 +133,10 @@
      * <li>Throws <code>IllegalArgumentException</code> if <code>values</code>
      * is null,  or <code>start</code> or <code>length</code> 
      * is invalid</li>
-     * </ul>
+     * </ul></p>
      * <p>
      * See {@link Percentile} for a description of the percentile estimation
-     * algorithm used.
+     * algorithm used.</p>
      * 
      * @param values the input array
      * @param start index of the first array element to include
@@ -156,7 +156,7 @@
      * values.
      * <p>
      * Calls to this method do not modify the internal <code>quantile</code>
-     * state of this statistic.
+     * state of this statistic.</p>
      * <p>
      * <ul>
      * <li>Returns <code>Double.NaN</code> if <code>length = 0</code></li>
@@ -166,10 +166,10 @@
      *  is null , <code>begin</code> or <code>length</code> is invalid, or 
      * <code>p</code> is not a valid quantile value (p must be greater than 0
      * and less than or equal to 100)</li>
-     * </ul>
+     * </ul></p>
      * <p>
-      * See {@link Percentile} for a description of the percentile estimation
-      * algorithm used.
+     * See {@link Percentile} for a description of the percentile estimation
+     * algorithm used.</p>
      * 
      * @param values array of input values
      * @param p  the percentile to compute

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Product.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Product.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Product.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Product.java Sat Feb  2 21:54:00 2008
@@ -24,12 +24,12 @@
  * Returns the product of the available values.
  * <p>
  * If there are no values in the dataset, or any of the values are 
- * <code>NaN</code>, then <code>NaN</code> is returned.  
-* <p>
+ * <code>NaN</code>, then <code>NaN</code> is returned.</p>
+ * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */
@@ -93,7 +93,7 @@
      * the input array, or <code>Double.NaN</code> if the designated subarray
      * is empty.
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * 
      * @param values the input array
      * @param begin index of the first array element to include

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Sum.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Sum.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Sum.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/Sum.java Sat Feb  2 21:54:00 2008
@@ -24,12 +24,12 @@
   * Returns the sum of the available values.
  * <p>
  * If there are no values in the dataset, or any of the values are 
- * <code>NaN</code>, then <code>NaN</code> is returned.  
+ * <code>NaN</code>, then <code>NaN</code> is returned.</p>
  * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */
@@ -93,7 +93,7 @@
      * the input array, or <code>Double.NaN</code> if the designated subarray
      * is empty.
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * 
      * @param values the input array
      * @param begin index of the first array element to include

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfLogs.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfLogs.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfLogs.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfLogs.java Sat Feb  2 21:54:00 2008
@@ -32,12 +32,12 @@
  * <li>If both <code>Double.POSITIVE_INFINITY</code> and 
  * <code>Double.NEGATIVE_INFINITY</code> are among the values, the result is
  * <code>NaN.</code></li>
- * </ul>
+ * </ul></p>
  * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */
@@ -101,9 +101,9 @@
      * the input array, or <code>Double.NaN</code> if the designated subarray
      * is empty.
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * <p>
-     * See {@link SumOfLogs}.
+     * See {@link SumOfLogs}.</p>
      * 
      * @param values the input array
      * @param begin index of the first array element to include

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfSquares.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfSquares.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfSquares.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/descriptive/summary/SumOfSquares.java Sat Feb  2 21:54:00 2008
@@ -24,12 +24,12 @@
  * Returns the sum of the squares of the available values.
  * <p>
  * If there are no values in the dataset, or any of the values are 
- * <code>NaN</code>, then <code>NaN</code> is returned.  
+ * <code>NaN</code>, then <code>NaN</code> is returned.</p>
  * <p>
  * <strong>Note that this implementation is not synchronized.</strong> If 
  * multiple threads access an instance of this class concurrently, and at least
  * one of the threads invokes the <code>increment()</code> or 
- * <code>clear()</code> method, it must be synchronized externally.
+ * <code>clear()</code> method, it must be synchronized externally.</p>
  * 
  * @version $Revision$ $Date$
  */
@@ -93,7 +93,7 @@
      * the input array, or <code>Double.NaN</code> if the designated subarray
      * is empty.
      * <p>
-     * Throws <code>IllegalArgumentException</code> if the array is null.
+     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
      * 
      * @param values the input array
      * @param begin index of the first array element to include

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/ChiSquareTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/ChiSquareTest.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/ChiSquareTest.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/ChiSquareTest.java Sat Feb  2 21:54:00 2008
@@ -33,7 +33,7 @@
      * frequency counts. 
      * <p>
      * This statistic can be used to perform a Chi-Square test evaluating the null hypothesis that
-     *  the observed counts follow the expected distribution.
+     *  the observed counts follow the expected distribution.</p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>Expected counts must all be positive.  
@@ -42,9 +42,9 @@
      * </li>
      * <li>The observed and expected arrays must have the same length and
      * their common length must be at least 2.  
-     * </li></ul><p>
+     * </li></ul></p><p>
      * If any of the preconditions are not met, an 
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param observed array of observed frequency counts
      * @param expected array of expected frequency counts
@@ -64,7 +64,7 @@
      * <p>
      * The number returned is the smallest significance level at which one can reject 
      * the null hypothesis that the observed counts conform to the frequency distribution 
-     * described by the expected counts. 
+     * described by the expected counts.</p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>Expected counts must all be positive.  
@@ -73,9 +73,9 @@
      * </li>
      * <li>The observed and expected arrays must have the same length and
      * their common length must be at least 2.  
-     * </li></ul><p>
+     * </li></ul></p><p>
      * If any of the preconditions are not met, an 
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param observed array of observed frequency counts
      * @param expected array of expected frequency counts
@@ -95,8 +95,8 @@
      * <p>
      * <strong>Example:</strong><br>
      * To test the hypothesis that <code>observed</code> follows 
-     * <code>expected</code> at the 99% level, use <p>
-     * <code>chiSquareTest(expected, observed, 0.01) </code>
+     * <code>expected</code> at the 99% level, use </p><p>
+     * <code>chiSquareTest(expected, observed, 0.01) </code></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>Expected counts must all be positive.  
@@ -106,9 +106,9 @@
      * <li>The observed and expected arrays must have the same length and
      * their common length must be at least 2.  
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul><p>
+     * </li></ul></p><p>
      * If any of the preconditions are not met, an 
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param observed array of observed frequency counts
      * @param expected array of expected frequency counts
@@ -127,19 +127,21 @@
      *  chi-square test of independence</a> based on the input <code>counts</code>
      *  array, viewed as a two-way table.  
      * <p>
-     * The rows of the 2-way table are <code>count[0], ... , count[count.length - 1] </code>
+     * The rows of the 2-way table are 
+     * <code>count[0], ... , count[count.length - 1] </code></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>All counts must be >= 0.  
      * </li>
-     * <li>The count array must be rectangular (i.e. all count[i] subarrays must have the same length). 
+     * <li>The count array must be rectangular (i.e. all count[i] subarrays
+     *  must have the same length). 
      * </li>
-     * <li>The 2-way table represented by <code>counts</code> must have at least 2 columns and
-     *        at least 2 rows.
+     * <li>The 2-way table represented by <code>counts</code> must have at
+     *  least 2 columns and at least 2 rows.
      * </li>
-     * </li></ul><p>
+     * </li></ul></p><p>
      * If any of the preconditions are not met, an 
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param counts array representation of 2-way table
      * @return chiSquare statistic
@@ -156,7 +158,8 @@
      * chi-square test of independence</a> based on the input <code>counts</code>
      * array, viewed as a two-way table.  
      * <p>
-     * The rows of the 2-way table are <code>count[0], ... , count[count.length - 1] </code>
+     * The rows of the 2-way table are 
+     * <code>count[0], ... , count[count.length - 1] </code></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>All counts must be >= 0.  
@@ -166,9 +169,9 @@
      * <li>The 2-way table represented by <code>counts</code> must have at least 2 columns and
      *        at least 2 rows.
      * </li>
-     * </li></ul><p>
+     * </li></ul></p><p>
      * If any of the preconditions are not met, an 
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param counts array representation of 2-way table
      * @return p-value
@@ -185,12 +188,14 @@
      * with significance level <code>alpha</code>.  Returns true iff the null hypothesis can be rejected
      * with 100 * (1 - alpha) percent confidence.
      * <p>
-     * The rows of the 2-way table are <code>count[0], ... , count[count.length - 1] </code>
+     * The rows of the 2-way table are 
+     * <code>count[0], ... , count[count.length - 1] </code></p>
      * <p>
      * <strong>Example:</strong><br>
-     * To test the null hypothesis that the counts in <code>count[0], ... , count[count.length - 1] </code>
-     *  all correspond to the same underlying probability distribution at the 99% level, use <p>
-     * <code>chiSquareTest(counts, 0.01) </code>
+     * To test the null hypothesis that the counts in
+     * <code>count[0], ... , count[count.length - 1] </code>
+     *  all correspond to the same underlying probability distribution at the 99% level, use </p><p>
+     * <code>chiSquareTest(counts, 0.01) </code></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>All counts must be >= 0.  
@@ -200,9 +205,9 @@
      * <li>The 2-way table represented by <code>counts</code> must have at least 2 columns and
      *        at least 2 rows.
      * </li>
-     * </li></ul><p>
+     * </li></ul></p><p>
      * If any of the preconditions are not met, an 
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param counts array representation of 2-way table
      * @param alpha significance level of the test

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTest.java Sat Feb  2 21:54:00 2008
@@ -29,17 +29,17 @@
  * <li>Homoscedastic (equal variance assumption) or heteroscedastic
  * (for two sample tests)</li>
  * <li>Fixed significance level (boolean-valued) or returning p-values.
- * </li></ul>
+ * </li></ul></p>
  * <p>
  * Test statistics are available for all tests.  Methods including "Test" in
  * in their names perform tests, all other methods return t-statistics.  Among
  * the "Test" methods, <code>double-</code>valued methods return p-values;
  * <code>boolean-</code>valued methods perform fixed significance level tests.
  * Significance levels are always specified as numbers between 0 and 0.5
- * (e.g. tests at the 95% level  use <code>alpha=0.05</code>).
+ * (e.g. tests at the 95% level  use <code>alpha=0.05</code>).</p>
  * <p>
  * Input to tests can be either <code>double[]</code> arrays or 
- * {@link StatisticalSummary} instances.
+ * {@link StatisticalSummary} instances.</p>
  * 
  *
  * @version $Revision$ $Date$ 
@@ -56,7 +56,7 @@
      * <strong>Preconditions</strong>: <ul>
      * <li>The input arrays must have the same length and their common length
      * must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -76,23 +76,23 @@
      * at which one can reject the null hypothesis that the mean of the paired
      * differences is 0 in favor of the two-sided alternative that the mean paired 
      * difference is not equal to 0. For a one-sided test, divide the returned 
-     * value by 2.
+     * value by 2.</p>
      * <p>
      * This test is equivalent to a one-sample t-test computed using
      * {@link #tTest(double, double[])} with <code>mu = 0</code> and the sample
      * array consisting of the signed differences between corresponding elements of 
-     * <code>sample1</code> and <code>sample2.</code>
+     * <code>sample1</code> and <code>sample2.</code></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The input array lengths must be the same and their common length must
      * be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -111,20 +111,20 @@
      * <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>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The input array lengths must be the same and their common length 
      * must be at least 2.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -144,10 +144,10 @@
      * t statistic </a> given observed values and a comparison constant.
      * <p>
      * This statistic can be used to perform a one sample t-test for the mean.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu comparison constant
      * @param observed array of values
@@ -162,10 +162,10 @@
      * <code>sampleStats</code> to <code>mu</code>.
      * <p>
      * This statistic can be used to perform a one sample t-test for the mean.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li><code>observed.getN() > = 2</code>.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu comparison constant
      * @param sampleStats DescriptiveStatistics holding sample summary statitstics
@@ -180,27 +180,27 @@
      * equal variances hypothesis, use {@link #t(double[], double[])}.
      * <p>
      * This statistic can be used to perform a (homoscedastic) two-sample
-     * t-test to compare sample means.   
+     * t-test to compare sample means.</p>
      * <p>
-     * The t-statisitc is
+     * The t-statisitc is</p>
      * <p>
      * &nbsp;&nbsp;<code>  t = (m1 - m2) / (sqrt(1/n1 +1/n2) sqrt(var))</code>
-     * <p>
+     * </p><p>
      * where <strong><code>n1</code></strong> is the size of first sample; 
      * <strong><code> n2</code></strong> is the size of second sample; 
      * <strong><code> m1</code></strong> is the mean of first sample;  
      * <strong><code> m2</code></strong> is the mean of second sample</li>
      * </ul>
      * and <strong><code>var</code></strong> is the pooled variance estimate:
-     * <p>
+     * </p><p>
      * <code>var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))</code>
-     * <p> 
+     * </p><p> 
      * with <strong><code>var1<code></strong> the variance of the first sample and
      * <strong><code>var2</code></strong> the variance of the second sample.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -215,22 +215,22 @@
      * variances, use {@link #homoscedasticT(double[], double[])}.
      * <p>
      * This statistic can be used to perform a two-sample t-test to compare
-     * sample means.
+     * sample means.</p>
      * <p>
-     * The t-statisitc is
+     * The t-statisitc is</p>
      * <p>
      * &nbsp;&nbsp; <code>  t = (m1 - m2) / sqrt(var1/n1 + var2/n2)</code>
-     * <p>
+     * </p><p>
      *  where <strong><code>n1</code></strong> is the size of the first sample
      * <strong><code> n2</code></strong> is the size of the second sample; 
      * <strong><code> m1</code></strong> is the mean of the first sample;  
      * <strong><code> m2</code></strong> is the mean of the second sample;
      * <strong><code> var1</code></strong> is the variance of the first sample;
      * <strong><code> var2</code></strong> is the variance of the second sample;  
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -247,23 +247,23 @@
      * compute a t-statistic under the equal variances assumption.
      * <p>
      * This statistic can be used to perform a two-sample t-test to compare
-     * sample means.
+     * sample means.</p>
      * <p>
-      * The returned  t-statisitc is
+      * The returned  t-statisitc is</p>
      * <p>
      * &nbsp;&nbsp; <code>  t = (m1 - m2) / sqrt(var1/n1 + var2/n2)</code>
-     * <p>
+     * </p><p>
      * where <strong><code>n1</code></strong> is the size of the first sample; 
      * <strong><code> n2</code></strong> is the size of the second sample; 
      * <strong><code> m1</code></strong> is the mean of the first sample;  
      * <strong><code> m2</code></strong> is the mean of the second sample
      * <strong><code> var1</code></strong> is the variance of the first sample;  
      * <strong><code> var2</code></strong> is the variance of the second sample
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1 StatisticalSummary describing data from the first sample
      * @param sampleStats2 StatisticalSummary describing data from the second sample
@@ -282,27 +282,27 @@
      * {@link #t(StatisticalSummary, StatisticalSummary)}.
      * <p>
      * This statistic can be used to perform a (homoscedastic) two-sample
-     * t-test to compare sample means.
+     * t-test to compare sample means.</p>
      * <p>
-     * The t-statisitc returned is
+     * The t-statisitc returned is</p>
      * <p>
      * &nbsp;&nbsp;<code>  t = (m1 - m2) / (sqrt(1/n1 +1/n2) sqrt(var))</code>
-     * <p>
+     * </p><p>
      * where <strong><code>n1</code></strong> is the size of first sample; 
      * <strong><code> n2</code></strong> is the size of second sample; 
      * <strong><code> m1</code></strong> is the mean of first sample;  
      * <strong><code> m2</code></strong> is the mean of second sample
      * and <strong><code>var</code></strong> is the pooled variance estimate:
-     * <p>
+     * </p><p>
      * <code>var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))</code>
-     * <p> 
+     * </p><p> 
      * with <strong><code>var1<code></strong> the variance of the first sample and
      * <strong><code>var2</code></strong> the variance of the second sample.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1 StatisticalSummary describing data from the first sample
      * @param sampleStats2 StatisticalSummary describing data from the second sample
@@ -322,16 +322,16 @@
      * at which one can reject the null hypothesis that the mean equals 
      * <code>mu</code> in favor of the two-sided alternative that the mean
      * is different from <code>mu</code>. For a one-sided test, divide the 
-     * returned value by 2.
+     * returned value by 2.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sample array of sample data values
@@ -348,7 +348,7 @@
      * <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>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
@@ -358,16 +358,16 @@
      * 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, 0.02) </code>
-     * </li></ol>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the one-sample 
      * parametric t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/sg_glos.html#one-sample">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sample array of sample data values
@@ -388,17 +388,17 @@
      * at which one can reject the null hypothesis that the mean equals 
      * <code>mu</code> in favor of the two-sided alternative that the mean
      * is different from <code>mu</code>. For a one-sided test, divide the 
-     * returned value by 2.
+     * returned value by 2.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The sample must contain at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sampleStats StatisticalSummary describing sample data
@@ -416,7 +416,7 @@
      * <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>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
@@ -426,16 +426,16 @@
      * 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, 0.02) </code>
-     * </li></ol>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the one-sample 
      * parametric t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/sg_glos.html#one-sample">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The sample must include at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sampleStats StatisticalSummary describing sample data values
@@ -457,7 +457,7 @@
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * The test does not assume that the underlying popuation variances are
      * equal  and it uses approximated degrees of freedom computed from the 
@@ -467,17 +467,17 @@
      * as described 
      * <a href="http://www.itl.nist.gov/div898/handbook/prc/section3/prc31.htm">
      * here.</a>  To perform the test under the assumption of equal subpopulation
-     * variances, use {@link #homoscedasticTTest(double[], double[])}. 
+     * variances, use {@link #homoscedasticTTest(double[], double[])}.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -493,26 +493,26 @@
      * comparing the means of the input arrays, under the assumption that
      * the two samples are drawn from subpopulations with equal variances.
      * To perform the test without the equal variances assumption, use
-     * {@link #tTest(double[], double[])}.
+     * {@link #tTest(double[], double[])}.</p>
      * <p>
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * A pooled variance estimate is used to compute the t-statistic.  See
      * {@link #homoscedasticT(double[], double[])}. The sum of the sample sizes
-     * minus 2 is used as the degrees of freedom.
+     * minus 2 is used as the degrees of freedom.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -536,13 +536,12 @@
      * <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>
      * <p>
      * See {@link #t(double[], double[])} for the formula used to compute the
      * t-statistic.  Degrees of freedom are approximated using the
      * <a href="http://www.itl.nist.gov/div898/handbook/prc/section3/prc31.htm">
-     * Welch-Satterthwaite approximation.</a>
-    
+     * Welch-Satterthwaite approximation.</a></p>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>mean 1 = mean 2 </code> at
@@ -553,19 +552,19 @@
      * 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>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -594,11 +593,11 @@
      * 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
      * without the assumption of equal subpopulation variances, use 
-     * {@link #tTest(double[], double[], double)}.
+     * {@link #tTest(double[], double[], double)}.</p>
      * <p>
      * A pooled variance estimate is used to compute the t-statistic. See
      * {@link #t(double[], double[])} for the formula. The sum of the sample
-     * sizes minus 2 is used as the degrees of freedom.
+     * sizes minus 2 is used as the degrees of freedom.</p>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>mean 1 = mean 2 </code> at
@@ -609,19 +608,19 @@
      * <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>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -645,24 +644,24 @@
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * The test does not assume that the underlying popuation variances are
      * equal  and it uses approximated degrees of freedom computed from the 
      * sample data to compute the p-value.   To perform the test assuming
      * equal variances, use 
-     * {@link #homoscedasticTTest(StatisticalSummary, StatisticalSummary)}.
+     * {@link #homoscedasticTTest(StatisticalSummary, StatisticalSummary)}.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1  StatisticalSummary describing data from the first sample
      * @param sampleStats2  StatisticalSummary describing data from the second sample
@@ -685,21 +684,21 @@
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * See {@link #homoscedasticT(double[], double[])} for the formula used to
      * compute the t-statistic. The sum of the  sample sizes minus 2 is used as
-     * the degrees of freedom.
+     * the degrees of freedom.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1  StatisticalSummary describing data from the first sample
      * @param sampleStats2  StatisticalSummary describing data from the second sample
@@ -724,12 +723,12 @@
      * <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>
      * <p>
      * See {@link #t(double[], double[])} for the formula used to compute the
      * t-statistic.  Degrees of freedom are approximated using the
      * <a href="http://www.itl.nist.gov/div898/handbook/prc/section3/prc31.htm">
-     * Welch-Satterthwaite approximation.</a>
+     * Welch-Satterthwaite approximation.</a></p>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>mean 1 = mean 2 </code> at
@@ -741,20 +740,20 @@
      * <code>sample 1</code> is less than  the mean of <code>sample 2</code>
      * and then use 
      * <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code>
-     * </li></ol>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1 StatisticalSummary describing sample data values
      * @param sampleStats2 StatisticalSummary describing sample data values

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/TTestImpl.java Sat Feb  2 21:54:00 2008
@@ -27,7 +27,7 @@
  * Implements t-test statistics defined in the {@link TTest} interface.
  * <p>
  * Uses commons-math {@link org.apache.commons.math.distribution.TDistribution}
- * implementation to estimate exact p-values.
+ * implementation to estimate exact p-values.</p>
  *
  * @version $Revision$ $Date$
  */
@@ -65,7 +65,7 @@
      * <strong>Preconditions</strong>: <ul>
      * <li>The input arrays must have the same length and their common length
      * must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -95,23 +95,23 @@
      * at which one can reject the null hypothesis that the mean of the paired
      * differences is 0 in favor of the two-sided alternative that the mean paired 
      * difference is not equal to 0. For a one-sided test, divide the returned 
-     * value by 2.
+     * value by 2.</p>
      * <p>
      * This test is equivalent to a one-sample t-test computed using
      * {@link #tTest(double, double[])} with <code>mu = 0</code> and the sample
      * array consisting of the signed differences between corresponding elements of 
-     * <code>sample1</code> and <code>sample2.</code>
+     * <code>sample1</code> and <code>sample2.</code></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The input array lengths must be the same and their common length must
      * be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -136,20 +136,20 @@
      * <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>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The input array lengths must be the same and their common length 
      * must be at least 2.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -172,10 +172,10 @@
      * t statistic </a> given observed values and a comparison constant.
      * <p>
      * This statistic can be used to perform a one sample t-test for the mean.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu comparison constant
      * @param observed array of values
@@ -197,10 +197,10 @@
      * <code>sampleStats</code> to <code>mu</code>.
      * <p>
      * This statistic can be used to perform a one sample t-test for the mean.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li><code>observed.getN() > = 2</code>.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu comparison constant
      * @param sampleStats DescriptiveStatistics holding sample summary statitstics
@@ -222,27 +222,27 @@
      * equal variances hypothesis, use {@link #t(double[], double[])}.
      * <p>
      * This statistic can be used to perform a (homoscedastic) two-sample
-     * t-test to compare sample means.   
+     * t-test to compare sample means.</p>
      * <p>
-     * The t-statisitc is
+     * The t-statisitc is</p>
      * <p>
      * &nbsp;&nbsp;<code>  t = (m1 - m2) / (sqrt(1/n1 +1/n2) sqrt(var))</code>
-     * <p>
+     * </p><p>
      * where <strong><code>n1</code></strong> is the size of first sample; 
      * <strong><code> n2</code></strong> is the size of second sample; 
      * <strong><code> m1</code></strong> is the mean of first sample;  
      * <strong><code> m2</code></strong> is the mean of second sample</li>
      * </ul>
      * and <strong><code>var</code></strong> is the pooled variance estimate:
-     * <p>
+     * </p><p>
      * <code>var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))</code>
-     * <p> 
+     * </p><p> 
      * with <strong><code>var1<code></strong> the variance of the first sample and
      * <strong><code>var2</code></strong> the variance of the second sample.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -266,22 +266,22 @@
      * variances, use {@link #homoscedasticT(double[], double[])}.
      * <p>
      * This statistic can be used to perform a two-sample t-test to compare
-     * sample means.
+     * sample means.</p>
      * <p>
-     * The t-statisitc is
+     * The t-statisitc is</p>
      * <p>
      * &nbsp;&nbsp; <code>  t = (m1 - m2) / sqrt(var1/n1 + var2/n2)</code>
-     * <p>
+     * </p><p>
      *  where <strong><code>n1</code></strong> is the size of the first sample
      * <strong><code> n2</code></strong> is the size of the second sample; 
      * <strong><code> m1</code></strong> is the mean of the first sample;  
      * <strong><code> m2</code></strong> is the mean of the second sample;
      * <strong><code> var1</code></strong> is the variance of the first sample;
      * <strong><code> var2</code></strong> is the variance of the second sample;  
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -307,23 +307,23 @@
      * compute a t-statistic under the equal variances assumption.
      * <p>
      * This statistic can be used to perform a two-sample t-test to compare
-     * sample means.
+     * sample means.</p>
      * <p>
-      * The returned  t-statisitc is
+      * The returned  t-statisitc is</p>
      * <p>
      * &nbsp;&nbsp; <code>  t = (m1 - m2) / sqrt(var1/n1 + var2/n2)</code>
-     * <p>
+     * </p><p>
      * where <strong><code>n1</code></strong> is the size of the first sample; 
      * <strong><code> n2</code></strong> is the size of the second sample; 
      * <strong><code> m1</code></strong> is the mean of the first sample;  
      * <strong><code> m2</code></strong> is the mean of the second sample
      * <strong><code> var1</code></strong> is the variance of the first sample;  
      * <strong><code> var2</code></strong> is the variance of the second sample
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1 StatisticalSummary describing data from the first sample
      * @param sampleStats2 StatisticalSummary describing data from the second sample
@@ -351,27 +351,27 @@
      * {@link #t(StatisticalSummary, StatisticalSummary)}.
      * <p>
      * This statistic can be used to perform a (homoscedastic) two-sample
-     * t-test to compare sample means.
+     * t-test to compare sample means.</p>
      * <p>
-     * The t-statisitc returned is
+     * The t-statisitc returned is</p>
      * <p>
      * &nbsp;&nbsp;<code>  t = (m1 - m2) / (sqrt(1/n1 +1/n2) sqrt(var))</code>
-     * <p>
+     * </p><p>
      * where <strong><code>n1</code></strong> is the size of first sample; 
      * <strong><code> n2</code></strong> is the size of second sample; 
      * <strong><code> m1</code></strong> is the mean of first sample;  
      * <strong><code> m2</code></strong> is the mean of second sample
      * and <strong><code>var</code></strong> is the pooled variance estimate:
-     * <p>
+     * </p><p>
      * <code>var = sqrt(((n1 - 1)var1 + (n2 - 1)var2) / ((n1-1) + (n2-1)))</code>
      * <p> 
      * with <strong><code>var1<code></strong> the variance of the first sample and
      * <strong><code>var2</code></strong> the variance of the second sample.
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1 StatisticalSummary describing data from the first sample
      * @param sampleStats2 StatisticalSummary describing data from the second sample
@@ -400,16 +400,16 @@
      * at which one can reject the null hypothesis that the mean equals 
      * <code>mu</code> in favor of the two-sided alternative that the mean
      * is different from <code>mu</code>. For a one-sided test, divide the 
-     * returned value by 2.
+     * returned value by 2.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sample array of sample data values
@@ -434,7 +434,7 @@
      * 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>
-     * <p>
+     * </p><p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
      * the 95% level, use <br><code>tTest(mu, sample, 0.05) </code>
@@ -443,16 +443,16 @@
      * 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, 0.02) </code>
-     * </li></ol>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the one-sample 
      * parametric t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/sg_glos.html#one-sample">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array length must be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sample array of sample data values
@@ -479,17 +479,17 @@
      * at which one can reject the null hypothesis that the mean equals 
      * <code>mu</code> in favor of the two-sided alternative that the mean
      * is different from <code>mu</code>. For a one-sided test, divide the 
-     * returned value by 2.
+     * returned value by 2.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The sample must contain at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sampleStats StatisticalSummary describing sample data
@@ -514,7 +514,7 @@
      * <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>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>sample mean = mu </code> at
@@ -524,16 +524,16 @@
      * 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, 0.02) </code>
-     * </li></ol>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the one-sample 
      * parametric t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/sg_glos.html#one-sample">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The sample must include at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param mu constant value to compare sample mean against
      * @param sampleStats StatisticalSummary describing sample data values
@@ -559,7 +559,7 @@
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * The test does not assume that the underlying popuation variances are
      * equal  and it uses approximated degrees of freedom computed from the 
@@ -569,17 +569,17 @@
      * as described 
      * <a href="http://www.itl.nist.gov/div898/handbook/prc/section3/prc31.htm">
      * here.</a>  To perform the test under the assumption of equal subpopulation
-     * variances, use {@link #homoscedasticTTest(double[], double[])}. 
+     * variances, use {@link #homoscedasticTTest(double[], double[])}.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -609,21 +609,21 @@
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * A pooled variance estimate is used to compute the t-statistic.  See
      * {@link #homoscedasticT(double[], double[])}. The sum of the sample sizes
-     * minus 2 is used as the degrees of freedom.
+     * minus 2 is used as the degrees of freedom.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -656,12 +656,12 @@
      * <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>
      * <p>
      * See {@link #t(double[], double[])} for the formula used to compute the
      * t-statistic.  Degrees of freedom are approximated using the
      * <a href="http://www.itl.nist.gov/div898/handbook/prc/section3/prc31.htm">
-     * Welch-Satterthwaite approximation.</a>
+     * Welch-Satterthwaite approximation.</a></p>
       
      * <p>
      * <strong>Examples:</strong><br><ol>
@@ -673,19 +673,19 @@
      * 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>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -718,11 +718,11 @@
      * 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
      * without the assumption of equal subpopulation variances, use 
-     * {@link #tTest(double[], double[], double)}.
+     * {@link #tTest(double[], double[], double)}.</p>
      * <p>
      * A pooled variance estimate is used to compute the t-statistic. See
      * {@link #t(double[], double[])} for the formula. The sum of the sample
-     * sizes minus 2 is used as the degrees of freedom.
+     * sizes minus 2 is used as the degrees of freedom.</p>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>mean 1 = mean 2 </code> at
@@ -733,19 +733,19 @@
      * <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>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The observed array lengths must both be at least 2.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sample1 array of sample data values
      * @param sample2 array of sample data values
@@ -773,24 +773,24 @@
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * The test does not assume that the underlying popuation variances are
      * equal  and it uses approximated degrees of freedom computed from the 
      * sample data to compute the p-value.   To perform the test assuming
      * equal variances, use 
-     * {@link #homoscedasticTTest(StatisticalSummary, StatisticalSummary)}.
+     * {@link #homoscedasticTTest(StatisticalSummary, StatisticalSummary)}.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1  StatisticalSummary describing data from the first sample
      * @param sampleStats2  StatisticalSummary describing data from the second sample
@@ -820,21 +820,21 @@
      * The number returned is the smallest significance level
      * at which one can reject the null hypothesis that the two means are
      * equal in favor of the two-sided alternative that they are different. 
-     * For a one-sided test, divide the returned value by 2.
+     * For a one-sided test, divide the returned value by 2.</p>
      * <p>
      * See {@link #homoscedasticT(double[], double[])} for the formula used to
      * compute the t-statistic. The sum of the  sample sizes minus 2 is used as
-     * the degrees of freedom.
+     * the degrees of freedom.</p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the p-value depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">here</a>
-     * <p>
+     * </p><p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1  StatisticalSummary describing data from the first sample
      * @param sampleStats2  StatisticalSummary describing data from the second sample
@@ -868,12 +868,12 @@
      * <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>
      * <p>
      * See {@link #t(double[], double[])} for the formula used to compute the
      * t-statistic.  Degrees of freedom are approximated using the
      * <a href="http://www.itl.nist.gov/div898/handbook/prc/section3/prc31.htm">
-     * Welch-Satterthwaite approximation.</a>
+     * Welch-Satterthwaite approximation.</a></p>
      * <p>
      * <strong>Examples:</strong><br><ol>
      * <li>To test the (2-sided) hypothesis <code>mean 1 = mean 2 </code> at
@@ -885,20 +885,20 @@
      * <code>sample 1</code> is less than  the mean of <code>sample 2</code>
      * and then use 
      * <br><code>tTest(sampleStats1, sampleStats2, 0.02) </code>
-     * </li></ol>
+     * </li></ol></p>
      * <p>
      * <strong>Usage Note:</strong><br>
      * The validity of the test depends on the assumptions of the parametric
      * t-test procedure, as discussed 
      * <a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
-     * here</a>
+     * here</a></p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>The datasets described by the two Univariates must each contain
      * at least 2 observations.
      * </li>
      * <li> <code> 0 < alpha < 0.5 </code>
-     * </li></ul>
+     * </li></ul></p>
      *
      * @param sampleStats1 StatisticalSummary describing sample data values
      * @param sampleStats2 StatisticalSummary describing sample data values
@@ -959,7 +959,7 @@
     /**
      * Computes t test statistic for 2-sample t-test.
      * <p>
-     * Does not assume that subpopulation variances are equal.
+     * Does not assume that subpopulation variances are equal.</p>
      * 
      * @param m1 first sample mean
      * @param m2 second sample mean
@@ -1013,7 +1013,7 @@
      * Computes p-value for 2-sided, 2-sample t-test.
      * <p>
      * Does not assume subpopulation variances are equal. Degrees of freedom
-     * are estimated from the data.
+     * are estimated from the data.</p>
      * 
      * @param m1 first sample mean
      * @param m2 second sample mean
@@ -1038,7 +1038,7 @@
      * Computes p-value for 2-sided, 2-sample t-test, under the assumption
      * of equal subpopulation variances.
      * <p>
-     * The sum of the sample sizes minus 2 is used as degrees of freedom.
+     * The sum of the sample sizes minus 2 is used as degrees of freedom.</p>
      * 
      * @param m1 first sample mean
      * @param m2 second sample mean

Modified: commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/UnknownDistributionChiSquareTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/UnknownDistributionChiSquareTest.java?rev=617953&r1=617952&r2=617953&view=diff
==============================================================================
--- commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/UnknownDistributionChiSquareTest.java (original)
+++ commons/proper/math/trunk/src/java/org/apache/commons/math/stat/inference/UnknownDistributionChiSquareTest.java Sat Feb  2 21:54:00 2008
@@ -40,7 +40,7 @@
      * <br/><code>K = &sqrt;[&sum(observed2 / &sum;(observed1)]</code>
      * </p>
      * <p>This statistic can be used to perform a Chi-Square test evaluating the null hypothesis that
-     * both observed counts follow the same distribution.
+     * both observed counts follow the same distribution.</p>
      * <p>
      * <strong>Preconditions</strong>: <ul>
      * <li>Observed counts must be non-negative.
@@ -51,9 +51,9 @@
      * </li>
      * <li>The arrays <code>observed1</code> and <code>observed2</code> must have the same length and
      * their common length must be at least 2.
-     * </li></ul><p>
+     * </li></ul></p><p>
      * If any of the preconditions are not met, an
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param observed1 array of observed frequency counts of the first data set
      * @param observed2 array of observed frequency counts of the second data set
@@ -91,7 +91,7 @@
      * their common length must be at least 2.
      * </li></ul><p>
      * If any of the preconditions are not met, an
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param observed1 array of observed frequency counts of the first data set
      * @param observed2 array of observed frequency counts of the second data set
@@ -127,7 +127,7 @@
      * <li> <code> 0 < alpha < 0.5 </code>
      * </li></ul><p>
      * If any of the preconditions are not met, an
-     * <code>IllegalArgumentException</code> is thrown.
+     * <code>IllegalArgumentException</code> is thrown.</p>
      *
      * @param observed1 array of observed frequency counts of the first data set
      * @param observed2 array of observed frequency counts of the second data set



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