You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@commons.apache.org by lu...@apache.org on 2013/10/28 08:10:12 UTC
svn commit: r1536265 -
/commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml
Author: luc
Date: Mon Oct 28 07:10:12 2013
New Revision: 1536265
URL: http://svn.apache.org/r1536265
Log:
Fixed userguide typos.
Thanks to Matt Adereth for the patch.
JIRA: MATH-1048
Modified:
commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml
Modified: commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml?rev=1536265&r1=1536264&r2=1536265&view=diff
==============================================================================
--- commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml (original)
+++ commons/proper/math/trunk/src/site/xdoc/userguide/stat.xml Mon Oct 28 07:10:12 2013
@@ -302,7 +302,7 @@ double totalSampleSum = aggregatedStats.
Strings, integers, longs and chars are all supported as value types,
as well as instances of any class that implements <code>Comparable.</code>
The ordering of values used in computing cumulative frequencies is by
- default the <i>natural ordering,</i> but this can be overriden by supplying a
+ default the <i>natural ordering,</i> but this can be overridden by supplying a
<code>Comparator</code> to the constructor. Adding values that are not
comparable to those that have already been added results in an
<code>IllegalArgumentException.</code>
@@ -385,7 +385,7 @@ System.out.println(f.getCumPct("z")); /
<li> When there are fewer than two observations in the model, or when
there is no variation in the x values (i.e. all x values are the same)
all statistics return <code>NaN</code>. At least two observations with
- different x coordinates are requred to estimate a bivariate regression
+ different x coordinates are required to estimate a bivariate regression
model.</li>
<li> getters for the statistics always compute values based on the current
set of observations -- i.e., you can get statistics, then add more data
@@ -529,7 +529,7 @@ System.out.println(regression.getInterce
OLSMultipleLinearRegression</a> provides Ordinary Least Squares Regression, and
<a href="../apidocs/org/apache/commons/math3/stat/regression/GLSMultipleLinearRegression.html">
GLSMultipleLinearRegression</a> implements Generalized Least Squares. See the javadoc for these
- classes for details on the algorithms and forumlas used.
+ classes for details on the algorithms and formulas used.
</p>
<p>
Data for OLS models can be loaded in a single double[] array, consisting of concatenated rows of data, each containing
@@ -864,7 +864,7 @@ new PearsonsCorrelation().correlation(ra
assumptions of the parametric t-test procedure, as discussed
<a href="http://www.basic.nwu.edu/statguidefiles/ttest_unpaired_ass_viol.html">
here</a></li>
- <li>p-values returned by t-, chi-square and Anova tests are exact, based
+ <li>p-values returned by t-, chi-square and ANOVA tests are exact, based
on numerical approximations to the t-, chi-square and F distributions in the
<code>distributions</code> package. </li>
<li>The G test implementation provides two p-values:
@@ -893,7 +893,7 @@ double[] observed = {1d, 2d, 3d};
double mu = 2.5d;
System.out.println(TestUtils.t(mu, observed));
</source>
- The code above will display the t-statisitic associated with a one-sample
+ The code above will display the t-statistic associated with a one-sample
t-test comparing the mean of the <code>observed</code> values against
<code>mu.</code>
</dd>
@@ -1026,7 +1026,7 @@ TestUtils.chiSquareTest(expected, observ
</source>
</dd>
<dd> To test the null hypothesis that <code>observed</code> conforms to
- <code>expected</code> with <code>alpha</code> siginficance level
+ <code>expected</code> with <code>alpha</code> significance level
(equiv. <code>100 * (1-alpha)%</code> confidence) where <code>
0 < alpha < 1 </code> use:
<source>
@@ -1058,7 +1058,7 @@ TestUtils.chiSquareTest(counts);
</source>
</dd>
<dd>To perform a chi-square test of independence with <code>alpha</code>
- siginficance level (equiv. <code>100 * (1-alpha)%</code> confidence)
+ significance level (equiv. <code>100 * (1-alpha)%</code> confidence)
where <code>0 < alpha < 1 </code> use:
<source>
TestUtils.chiSquareTest(counts, alpha);
@@ -1070,12 +1070,12 @@ TestUtils.chiSquareTest(counts, alpha);
<dt><strong>G tests</strong></dt>
<br></br>
<dd>G tests are an alternative to chi-square tests that are recommended
- when observed counts are small and / or incidence probabillities for
+ when observed counts are small and / or incidence probabilities for
some cells are small. See Ted Dunning's paper,
<a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5962">
Accurate Methods for the Statistics of Surprise and Coincidence</a> for
background and an empirical analysis showing now chi-square
- statistics can be misldeading in the presence of low incidence probabilities.
+ statistics can be misleading in the presence of low incidence probabilities.
This paper also derives the formulas used in computing G statistics and the
root log likelihood ratio provided by the <code>GTest</code> class.</dd>
<dd>
@@ -1116,7 +1116,7 @@ System.out.println(TestUtils.gDataSetsCo
System.out.println(TestUtils.gTestDataSetsComparison(obs1, obs2)); // p-value
</source>
</dd>
- <dd>For 2 x 2 designs, the <code>rootLogLikelihoodRaio</code> method
+ <dd>For 2 x 2 designs, the <code>rootLogLikelihoodRatio</code> method
computes the
<a href="http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html">
signed root log likelihood ratio.</a> For example, suppose that for two events
@@ -1129,7 +1129,7 @@ new GTest().rootLogLikelihoodRatio(5, 19
and B are independent.
</dd>
<br></br>
- <dt><strong>One-Way Anova tests</strong></dt>
+ <dt><strong>One-Way ANOVA tests</strong></dt>
<br></br>
<source>
double[] classA =
@@ -1151,7 +1151,7 @@ classes.add(classC);
double fStatistic = TestUtils.oneWayAnovaFValue(classes); // F-value
double pValue = TestUtils.oneWayAnovaPValue(classes); // P-value
</source>
- To test perform a One-Way Anova test with signficance level set at 0.01
+ To test perform a One-Way ANOVA test with significance level set at 0.01
(so the test will, assuming assumptions are met, reject the null
hypothesis incorrectly only about one in 100 times), use
<source>