diff --git a/src/site/xdoc/userguide/stat.xml b/src/site/xdoc/userguide/stat.xml index 2a46a7c44..24655812c 100644 --- a/src/site/xdoc/userguide/stat.xml +++ b/src/site/xdoc/userguide/stat.xml @@ -302,7 +302,7 @@ double totalSampleSum = aggregatedStats.getSum(); Strings, integers, longs and chars are all supported as value types, as well as instances of any class that implements Comparable. The ordering of values used in computing cumulative frequencies is by - default the natural ordering, but this can be overriden by supplying a + default the natural ordering, but this can be overridden by supplying a Comparator to the constructor. Adding values that are not comparable to those that have already been added results in an IllegalArgumentException. @@ -385,7 +385,7 @@ System.out.println(f.getCumPct("z")); // displays 1
  • 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 NaN. 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.
  • 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.getInterceptStdErr() ); OLSMultipleLinearRegression provides Ordinary Least Squares Regression, and GLSMultipleLinearRegression 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.

    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(ranking.rank(x), ranking.rank(y)) assumptions of the parametric t-test procedure, as discussed here

    • -
  • p-values returned by t-, chi-square and Anova tests are exact, based +
  • 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 distributions package.
  • 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)); - 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 observed values against mu. @@ -1026,7 +1026,7 @@ TestUtils.chiSquareTest(expected, observed);
    To test the null hypothesis that observed conforms to - expected with alpha siginficance level + expected with alpha significance level (equiv. 100 * (1-alpha)% confidence) where 0 < alpha < 1 use: @@ -1058,7 +1058,7 @@ TestUtils.chiSquareTest(counts);
    To perform a chi-square test of independence with alpha - siginficance level (equiv. 100 * (1-alpha)% confidence) + significance level (equiv. 100 * (1-alpha)% confidence) where 0 < alpha < 1 use: TestUtils.chiSquareTest(counts, alpha); @@ -1070,12 +1070,12 @@ TestUtils.chiSquareTest(counts, alpha);
    G tests


    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, Accurate Methods for the Statistics of Surprise and Coincidence 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 GTest class.
    @@ -1116,7 +1116,7 @@ System.out.println(TestUtils.gDataSetsComparison(obs1, obs2)); // G statistic System.out.println(TestUtils.gTestDataSetsComparison(obs1, obs2)); // p-value
    -
    For 2 x 2 designs, the rootLogLikelihoodRaio method +
    For 2 x 2 designs, the rootLogLikelihoodRatio method computes the signed root log likelihood ratio. For example, suppose that for two events @@ -1129,7 +1129,7 @@ new GTest().rootLogLikelihoodRatio(5, 1995, 0, 100000); and B are independent.


    -
    One-Way Anova tests
    +
    One-Way ANOVA tests


    double[] classA = @@ -1151,7 +1151,7 @@ classes.add(classC); double fStatistic = TestUtils.oneWayAnovaFValue(classes); // F-value double pValue = TestUtils.oneWayAnovaPValue(classes); // P-value - 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