Uses of Class
org.apache.commons.math.MathException

Packages that use MathException

org.apache.commons.math.stat.correlation	Correlations/Covariance computations.
org.apache.commons.math.stat.inference	Classes providing hypothesis testing and confidence interval construction.
org.apache.commons.math.stat.regression	Statistical routines involving multivariate data.
org.apache.commons.math.util	Convenience routines and common data structures used throughout the commons-math library.

Uses of MathException in org.apache.commons.math.stat.correlation

Methods in org.apache.commons.math.stat.correlation that throw MathException

RealMatrix

PearsonsCorrelation.getCorrelationPValues() Returns a matrix of p-values associated with the (two-sided) null hypothesis that the corresponding correlation coefficient is zero.

Uses of MathException in org.apache.commons.math.stat.inference

Methods in org.apache.commons.math.stat.inference that throw MathException

double	OneWayAnovaImpl.anovaFValue(java.util.Collection<double[]> categoryData) Computes the ANOVA F-value for a collection of double[] arrays.
double	OneWayAnova.anovaFValue(java.util.Collection<double[]> categoryData) Computes the ANOVA F-value for a collection of double[] arrays.
double	OneWayAnovaImpl.anovaPValue(java.util.Collection<double[]> categoryData) Computes the ANOVA P-value for a collection of double[] arrays.
double	OneWayAnova.anovaPValue(java.util.Collection<double[]> categoryData) Computes the ANOVA P-value for a collection of double[] arrays.
boolean	OneWayAnovaImpl.anovaTest(java.util.Collection<double[]> categoryData, double alpha) Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
boolean	OneWayAnova.anovaTest(java.util.Collection<double[]> categoryData, double alpha) Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
static double	TestUtils.chiSquareTest(double[] expected, long[] observed)
double	ChiSquareTestImpl.chiSquareTest(double[] expected, long[] observed) Returns the observed significance level, or p-value, associated with a Chi-square goodness of fit test comparing the observed frequency counts to those in the expected array.
double	ChiSquareTest.chiSquareTest(double[] expected, long[] observed) Returns the observed significance level, or p-value, associated with a Chi-square goodness of fit test comparing the observed frequency counts to those in the expected array.
static boolean	TestUtils.chiSquareTest(double[] expected, long[] observed, double alpha)
boolean	ChiSquareTestImpl.chiSquareTest(double[] expected, long[] observed, double alpha) Performs a Chi-square goodness of fit test evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level alpha.
boolean	ChiSquareTest.chiSquareTest(double[] expected, long[] observed, double alpha) Performs a Chi-square goodness of fit test evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level alpha.
static double	TestUtils.chiSquareTest(long[][] counts)
double	ChiSquareTestImpl.chiSquareTest(long[][] counts)
double	ChiSquareTest.chiSquareTest(long[][] counts) Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
static boolean	TestUtils.chiSquareTest(long[][] counts, double alpha)
boolean	ChiSquareTestImpl.chiSquareTest(long[][] counts, double alpha)
boolean	ChiSquareTest.chiSquareTest(long[][] counts, double alpha) Performs a chi-square test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2-way table are independent of the rows, with significance level alpha.
double	UnknownDistributionChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) Returns the observed significance level, or p-value, associated with a Chi-Square two sample test comparing bin frequency counts in observed1 and observed2.
static double	TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
double	ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
boolean	UnknownDistributionChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) Performs a Chi-Square two sample test comparing two binned data sets.
static boolean	TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
boolean	ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
double	TTestImpl.homoscedasticTTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances.
double	TTest.homoscedasticTTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays, under the assumption that the two samples are drawn from subpopulations with equal variances.
static double	TestUtils.homoscedasticTTest(double[] sample1, double[] sample2)
boolean	TTestImpl.homoscedasticTTest(double[] sample1, double[] sample2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha, assuming that the subpopulation variances are equal.
boolean	TTest.homoscedasticTTest(double[] sample1, double[] sample2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha, assuming that the subpopulation variances are equal.
static boolean	TestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)
protected double	TTestImpl.homoscedasticTTest(double m1, double m2, double v1, double v2, double n1, double n2) Computes p-value for 2-sided, 2-sample t-test, under the assumption of equal subpopulation variances.
double	TTestImpl.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances.
double	TTest.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances, under the hypothesis of equal subpopulation variances.
static double	TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
double	MannWhitneyUTestImpl.mannWhitneyUTest(double[] x, double[] y) Ties give rise to biased variance at the moment.
double	MannWhitneyUTest.mannWhitneyUTest(double[] x, double[] y) Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U statistic comparing mean for two independent samples.
static double	TestUtils.oneWayAnovaFValue(java.util.Collection<double[]> categoryData)
static double	TestUtils.oneWayAnovaPValue(java.util.Collection<double[]> categoryData)
static boolean	TestUtils.oneWayAnovaTest(java.util.Collection<double[]> categoryData, double alpha)
double	TTestImpl.pairedT(double[] sample1, double[] sample2) Computes a paired, 2-sample t-statistic based on the data in the input arrays.
double	TTest.pairedT(double[] sample1, double[] sample2) Computes a paired, 2-sample t-statistic based on the data in the input arrays.
static double	TestUtils.pairedT(double[] sample1, double[] sample2)
double	TTestImpl.pairedTTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.
double	TTest.pairedTTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.
static double	TestUtils.pairedTTest(double[] sample1, double[] sample2)
boolean	TTestImpl.pairedTTest(double[] sample1, double[] sample2, double alpha) Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between sample1 and sample2 is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level alpha.
boolean	TTest.pairedTTest(double[] sample1, double[] sample2, double alpha) Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences between sample1 and sample2 is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance level alpha.
static boolean	TestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)
double	TTestImpl.tTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
double	TTest.tTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
static double	TestUtils.tTest(double[] sample1, double[] sample2)
boolean	TTestImpl.tTest(double[] sample1, double[] sample2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha.
boolean	TTest.tTest(double[] sample1, double[] sample2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sample1 and sample2 are drawn from populations with the same mean, with significance level alpha.
static boolean	TestUtils.tTest(double[] sample1, double[] sample2, double alpha)
double	TTestImpl.tTest(double mu, double[] sample) Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant mu.
double	TTest.tTest(double mu, double[] sample) Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the input array with the constant mu.
static double	TestUtils.tTest(double mu, double[] sample)
boolean	TTestImpl.tTest(double mu, double[] sample, double alpha) Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
boolean	TTest.tTest(double mu, double[] sample, double alpha) Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
static boolean	TestUtils.tTest(double mu, double[] sample, double alpha)
protected double	TTestImpl.tTest(double m, double mu, double v, double n) Computes p-value for 2-sided, 1-sample t-test.
protected double	TTestImpl.tTest(double m1, double m2, double v1, double v2, double n1, double n2) Computes p-value for 2-sided, 2-sample t-test.
double	TTestImpl.tTest(double mu, StatisticalSummary sampleStats) Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by sampleStats with the constant mu.
double	TTest.tTest(double mu, StatisticalSummary sampleStats) Returns the observed significance level, or p-value, associated with a one-sample, two-tailed t-test comparing the mean of the dataset described by sampleStats with the constant mu.
static double	TestUtils.tTest(double mu, StatisticalSummary sampleStats)
boolean	TTestImpl.tTest(double mu, StatisticalSummary sampleStats, double alpha) Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by stats is drawn equals mu.
boolean	TTest.tTest(double mu, StatisticalSummary sampleStats, double alpha) Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which the dataset described by stats is drawn equals mu.
static boolean	TestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)
double	TTestImpl.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.
double	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2) Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the datasets described by two StatisticalSummary instances.
static double	TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
boolean	TTestImpl.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sampleStats1 and sampleStats2 describe datasets drawn from populations with the same mean, with significance level alpha.
boolean	TTest.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha) Performs a two-sided t-test evaluating the null hypothesis that sampleStats1 and sampleStats2 describe datasets drawn from populations with the same mean, with significance level alpha.
static boolean	TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)
double	WilcoxonSignedRankTestImpl.wilcoxonSignedRankTest(double[] x, double[] y, boolean exactPValue) Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.
double	WilcoxonSignedRankTest.wilcoxonSignedRankTest(double[] x, double[] y, boolean exactPValue) Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.

Uses of MathException in org.apache.commons.math.stat.regression

Methods in org.apache.commons.math.stat.regression that throw MathException

double	SimpleRegression.getSignificance() Returns the significance level of the slope (equiv) correlation.
double	SimpleRegression.getSlopeConfidenceInterval() Returns the half-width of a 95% confidence interval for the slope estimate.
double	SimpleRegression.getSlopeConfidenceInterval(double alpha) Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.

Uses of MathException in org.apache.commons.math.util

Methods in org.apache.commons.math.util that throw MathException

double	TransformerMap.transform(java.lang.Object o) Attempts to transform the Object against the map of NumberTransformers.
double	NumberTransformer.transform(java.lang.Object o) Implementing this interface provides a facility to transform from Object to Double.
double	DefaultTransformer.transform(java.lang.Object o)