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

Packages that use MathException
Package
Description
Common classes used throughout the commons-math library.
Univariate real functions interpolation algorithms.
Implementations of common discrete and continuous distributions.
This package provided classes to solve estimation problems, it is deprecated since 2.0.
Fraction number type and fraction number formatting.
This package provides basic 3D geometry components.
Linear algebra support.
This package provides classes to solve Ordinary Differential Equations problems.
This package provides classes to handle discrete events occurring during Ordinary Differential Equations integration.
This package provides common interfaces for the optimization algorithms provided in sub-packages.
This package provides optimization algorithms for linear constrained problems.
Random number and random data generators.
Implementations of special functions such as Beta and Gamma.
Correlations/Covariance computations.
Classes providing hypothesis testing and confidence interval construction.
Statistical routines involving multivariate data.
Convenience routines and common data structures used throughout the commons-math library.
  • Uses of MathException in org.apache.commons.math

    Modifier and Type
    Class
    Description
    class 
    Error thrown when a method is called with an out of bounds argument.
    class 
    Error thrown when a numerical computation can not be performed because the numerical result failed to converge to a finite value.
    class 
    Deprecated.
    in 2.2 (to be removed in 3.0).
    class 
    Exception thrown when a sample contains several entries at the same abscissa.
    class 
    Exception thrown when an error occurs evaluating a function.
    class 
    Signals a configuration problem with any of the factory methods.
    class 
    Error thrown when a numerical computation exceeds its allowed number of functions evaluations.
    class 
    Error thrown when a numerical computation exceeds its allowed number of iterations.
  • Uses of MathException in org.apache.commons.math.analysis.interpolation

    Modifier and Type
    Method
    Description
    BicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)
    Computes an interpolating function for the data set.
    BivariateRealGridInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)
    Computes an interpolating function for the data set.
    LoessInterpolator.interpolate(double[] xval, double[] yval)
    Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with a SplineInterpolator on the resulting fit.
    MicrosphereInterpolator.interpolate(double[][] xval, double[] yval)
    Computes an interpolating function for the data set.
    MultivariateRealInterpolator.interpolate(double[][] xval, double[] yval)
    Computes an interpolating function for the data set.
    NevilleInterpolator.interpolate(double[] x, double[] y)
    Computes an interpolating function for the data set.
    SmoothingBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] zval)
    Deprecated.
    Computes an interpolating function for the data set.
    SmoothingPolynomialBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)
    Computes an interpolating function for the data set.
    TricubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval)
    Computes an interpolating function for the data set.
    TrivariateRealGridInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval)
    Computes an interpolating function for the data set.
    UnivariateRealInterpolator.interpolate(double[] xval, double[] yval)
    Computes an interpolating function for the data set.
    final double[]
    LoessInterpolator.smooth(double[] xval, double[] yval)
    Compute a loess fit on the data at the original abscissae.
    final double[]
    LoessInterpolator.smooth(double[] xval, double[] yval, double[] weights)
    Compute a weighted loess fit on the data at the original abscissae.
    Modifier
    Constructor
    Description
     
    LoessInterpolator(double bandwidth, int robustnessIters)
    Constructs a new LoessInterpolator with given bandwidth and number of robustness iterations.
     
    LoessInterpolator(double bandwidth, int robustnessIters, double accuracy)
    Constructs a new LoessInterpolator with given bandwidth, number of robustness iterations and accuracy.
  • Uses of MathException in org.apache.commons.math.distribution

    Modifier and Type
    Method
    Description
    double
    AbstractDistribution.cumulativeProbability(double x0, double x1)
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
    double
    AbstractIntegerDistribution.cumulativeProbability(double x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
    double
    AbstractIntegerDistribution.cumulativeProbability(double x0, double x1)
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
    abstract double
    AbstractIntegerDistribution.cumulativeProbability(int x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
    double
    AbstractIntegerDistribution.cumulativeProbability(int x0, int x1)
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
    double
    BetaDistributionImpl.cumulativeProbability(double x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
    double
    BetaDistributionImpl.cumulativeProbability(double x0, double x1)
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
    double
    BinomialDistributionImpl.cumulativeProbability(int x)
    For this distribution, X, this method returns P(X ≤ x).
    double
    ChiSquaredDistributionImpl.cumulativeProbability(double x)
    For this distribution, X, this method returns P(X < x).
    double
    Distribution.cumulativeProbability(double x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
    double
    Distribution.cumulativeProbability(double x0, double x1)
    For a random variable X whose values are distributed according to this distribution, this method returns P(x0 ≤ X ≤ x1).
    double
    ExponentialDistributionImpl.cumulativeProbability(double x)
    For this distribution, X, this method returns P(X < x).
    double
    FDistributionImpl.cumulativeProbability(double x)
    For this distribution, X, this method returns P(X < x).
    double
    GammaDistributionImpl.cumulativeProbability(double x)
    For this distribution, X, this method returns P(X < x).
    double
    IntegerDistribution.cumulativeProbability(int x)
    For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
    double
    IntegerDistribution.cumulativeProbability(int x0, int x1)
    For this distribution, X, this method returns P(x0 ≤ X ≤ x1).
    double
    NormalDistributionImpl.cumulativeProbability(double x)
    For this distribution, X, this method returns P(X < x).
    double
    PascalDistributionImpl.cumulativeProbability(int x)
    For this distribution, X, this method returns P(X ≤ x).
    double
    PoissonDistributionImpl.cumulativeProbability(int x)
    The probability distribution function P(X invalid input: '<'= x) for a Poisson distribution.
    double
    TDistributionImpl.cumulativeProbability(double x)
    For this distribution, X, this method returns P(X < x).
    double
    BetaDistribution.density(Double x)
    Return the probability density for a particular point.
    double
    HasDensity.density(P x)
    Deprecated.
    Compute the probability density function.
    double
    AbstractContinuousDistribution.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    int
    AbstractIntegerDistribution.inverseCumulativeProbability(double p)
    For a random variable X whose values are distributed according to this distribution, this method returns the largest x, such that P(X ≤ x) ≤ p.
    double
    BetaDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    int
    BinomialDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the largest x, such that P(X ≤ x) ≤ p.
    double
    ChiSquaredDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    double
    ContinuousDistribution.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns x such that P(X < x) = p.
    double
    ExponentialDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    double
    FDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    double
    GammaDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    int
    IntegerDistribution.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the largest x such that P(X ≤ x) invalid input: '<'= p.
    double
    NormalDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    int
    PascalDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the largest x, such that P(X ≤ x) ≤ p.
    double
    TDistributionImpl.inverseCumulativeProbability(double p)
    For this distribution, X, this method returns the critical point x, such that P(X < x) = p.
    double
    PoissonDistribution.normalApproximateProbability(int x)
    Calculates the Poisson distribution function using a normal approximation.
    double
    PoissonDistributionImpl.normalApproximateProbability(int x)
    Calculates the Poisson distribution function using a normal approximation.
    double
    AbstractContinuousDistribution.sample()
    Generates a random value sampled from this distribution.
    double[]
    AbstractContinuousDistribution.sample(int sampleSize)
    Generates a random sample from the distribution.
    int
    AbstractIntegerDistribution.sample()
    Generates a random value sampled from this distribution.
    int[]
    AbstractIntegerDistribution.sample(int sampleSize)
    Generates a random sample from the distribution.
    double
    ExponentialDistributionImpl.sample()
    Generates a random value sampled from this distribution.
    double
    NormalDistributionImpl.sample()
    Generates a random value sampled from this distribution.
    int
    PoissonDistributionImpl.sample()
    Generates a random value sampled from this distribution.
  • Uses of MathException in org.apache.commons.math.estimation

    Modifier and Type
    Class
    Description
    class 
    Deprecated.
    as of 2.0, everything in package org.apache.commons.math.estimation has been deprecated and replaced by package org.apache.commons.math.optimization.general
  • Uses of MathException in org.apache.commons.math.fraction

    Modifier and Type
    Class
    Description
    class 
    Error thrown when a double value cannot be converted to a fraction in the allowed number of iterations.
  • Uses of MathException in org.apache.commons.math.geometry

    Modifier and Type
    Class
    Description
    class 
    This class represents exceptions thrown while extractiong Cardan or Euler angles from a rotation.
    class 
    This class represents exceptions thrown while building rotations from matrices.
  • Uses of MathException in org.apache.commons.math.linear

    Modifier and Type
    Class
    Description
    class 
    This class represents exceptions thrown when a matrix expected to be positive definite is not.
    class 
    This class represents exceptions thrown when a matrix expected to be symmetric is not
  • Uses of MathException in org.apache.commons.math.ode

    Modifier and Type
    Class
    Description
    class 
    This exception is made available to users to report the error conditions that are triggered while computing the differential equations.
    class 
    This exception is made available to users to report the error conditions that are triggered during integration
  • Uses of MathException in org.apache.commons.math.ode.events

    Modifier and Type
    Class
    Description
    class 
    This exception is made available to users to report the error conditions that are triggered by EventHandler
  • Uses of MathException in org.apache.commons.math.optimization

    Modifier and Type
    Class
    Description
    class 
    Deprecated.
    in 2.2 (to be removed in 3.0).
  • Uses of MathException in org.apache.commons.math.optimization.linear

    Modifier and Type
    Class
    Description
    class 
    This class represents exceptions thrown by optimizers when no solution fulfills the constraints.
    class 
    This class represents exceptions thrown by optimizers when a solution escapes to infinity.
  • Uses of MathException in org.apache.commons.math.random

    Modifier and Type
    Method
    Description
    double
    RandomDataImpl.nextBeta(double alpha, double beta)
    Generates a random value from the Beta Distribution.
    int
    RandomDataImpl.nextBinomial(int numberOfTrials, double probabilityOfSuccess)
    Generates a random value from the Binomial Distribution.
    double
    RandomDataImpl.nextCauchy(double median, double scale)
    Generates a random value from the Cauchy Distribution.
    double
    RandomDataImpl.nextChiSquare(double df)
    Generates a random value from the ChiSquare Distribution.
    double
    RandomDataImpl.nextF(double numeratorDf, double denominatorDf)
    Generates a random value from the F Distribution.
    double
    RandomDataImpl.nextGamma(double shape, double scale)
    Generates a random value from the Gamma Distribution.
    int
    RandomDataImpl.nextHypergeometric(int populationSize, int numberOfSuccesses, int sampleSize)
    Generates a random value from the Hypergeometric Distribution.
    double
    RandomDataImpl.nextInversionDeviate(ContinuousDistribution distribution)
    Generate a random deviate from the given distribution using the inversion method.
    int
    RandomDataImpl.nextInversionDeviate(IntegerDistribution distribution)
    Generate a random deviate from the given distribution using the inversion method.
    int
    RandomDataImpl.nextPascal(int r, double p)
    Generates a random value from the Pascal Distribution.
    double
    RandomDataImpl.nextT(double df)
    Generates a random value from the T Distribution.
    double
    RandomDataImpl.nextWeibull(double shape, double scale)
    Generates a random value from the Weibull Distribution.
    int
    RandomDataImpl.nextZipf(int numberOfElements, double exponent)
    Generates a random value from the Zipf Distribution.
  • Uses of MathException in org.apache.commons.math.special

    Modifier and Type
    Method
    Description
    static double
    Erf.erf(double x)
    Returns the error function
    static double
    Erf.erfc(double x)
    Returns the complementary error function
    static double
    Beta.regularizedBeta(double x, double a, double b)
    Returns the regularized beta function I(x, a, b).
    static double
    Beta.regularizedBeta(double x, double a, double b, double epsilon)
    Returns the regularized beta function I(x, a, b).
    static double
    Beta.regularizedBeta(double x, double a, double b, double epsilon, int maxIterations)
    Returns the regularized beta function I(x, a, b).
    static double
    Beta.regularizedBeta(double x, double a, double b, int maxIterations)
    Returns the regularized beta function I(x, a, b).
    static double
    Gamma.regularizedGammaP(double a, double x)
    Returns the regularized gamma function P(a, x).
    static double
    Gamma.regularizedGammaP(double a, double x, double epsilon, int maxIterations)
    Returns the regularized gamma function P(a, x).
    static double
    Gamma.regularizedGammaQ(double a, double x)
    Returns the regularized gamma function Q(a, x) = 1 - P(a, x).
    static double
    Gamma.regularizedGammaQ(double a, double x, double epsilon, int maxIterations)
    Returns the regularized gamma function Q(a, x) = 1 - P(a, x).
  • Uses of MathException in org.apache.commons.math.stat.correlation

    Modifier and Type
    Method
    Description
    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

    Modifier and Type
    Method
    Description
    double
    OneWayAnova.anovaFValue(Collection<double[]> categoryData)
    Computes the ANOVA F-value for a collection of double[] arrays.
    double
    OneWayAnovaImpl.anovaFValue(Collection<double[]> categoryData)
    Computes the ANOVA F-value for a collection of double[] arrays.
    double
    OneWayAnova.anovaPValue(Collection<double[]> categoryData)
    Computes the ANOVA P-value for a collection of double[] arrays.
    double
    OneWayAnovaImpl.anovaPValue(Collection<double[]> categoryData)
    Computes the ANOVA P-value for a collection of double[] arrays.
    boolean
    OneWayAnova.anovaTest(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
    OneWayAnovaImpl.anovaTest(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.
    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.
    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.
    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.
    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
    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.
    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.
    double
    ChiSquareTestImpl.chiSquareTest(long[][] counts)
     
    boolean
    ChiSquareTestImpl.chiSquareTest(long[][] counts, double alpha)
     
    static double
    TestUtils.chiSquareTest(double[] expected, long[] observed)
     
    static boolean
    TestUtils.chiSquareTest(double[] expected, long[] observed, double alpha)
     
    static double
    TestUtils.chiSquareTest(long[][] counts)
     
    static boolean
    TestUtils.chiSquareTest(long[][] counts, double alpha)
     
    double
    ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
     
    boolean
    ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
     
    static double
    TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
     
    static boolean
    TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double 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.
    boolean
    UnknownDistributionChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
    Performs a Chi-Square two sample test comparing two binned data sets.
    static double
    TestUtils.homoscedasticTTest(double[] sample1, double[] sample2)
     
    static boolean
    TestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)
     
    static double
    TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
     
    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.
    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.
    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.
    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.
    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.
    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.
    static double
    TestUtils.oneWayAnovaFValue(Collection<double[]> categoryData)
     
    static double
    TestUtils.oneWayAnovaPValue(Collection<double[]> categoryData)
     
    static boolean
    TestUtils.oneWayAnovaTest(Collection<double[]> categoryData, double alpha)
     
    static double
    TestUtils.pairedT(double[] sample1, double[] sample2)
     
    double
    TTest.pairedT(double[] sample1, double[] sample2)
    Computes a paired, 2-sample t-statistic based on the data in the input arrays.
    double
    TTestImpl.pairedT(double[] sample1, double[] sample2)
    Computes a paired, 2-sample t-statistic based on the data in the input arrays.
    static double
    TestUtils.pairedTTest(double[] sample1, double[] sample2)
     
    static boolean
    TestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)
     
    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.
    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.
    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.
    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.
    static double
    TestUtils.tTest(double[] sample1, double[] sample2)
     
    static boolean
    TestUtils.tTest(double[] sample1, double[] sample2, double alpha)
     
    static double
    TestUtils.tTest(double mu, double[] sample)
     
    static boolean
    TestUtils.tTest(double mu, double[] sample, double alpha)
     
    static double
    TestUtils.tTest(double mu, StatisticalSummary sampleStats)
     
    static boolean
    TestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)
     
    static double
    TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)
     
    static boolean
    TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)
     
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
    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.
  • Uses of MathException in org.apache.commons.math.stat.regression

    Modifier and Type
    Method
    Description
    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

    Modifier and Type
    Method
    Description
    double
    ContinuedFraction.evaluate(double x)
    Evaluates the continued fraction at the value x.
    double
    ContinuedFraction.evaluate(double x, double epsilon)
    Evaluates the continued fraction at the value x.
    double
    ContinuedFraction.evaluate(double x, double epsilon, int maxIterations)
    Evaluates the continued fraction at the value x.
    double
    ContinuedFraction.evaluate(double x, int maxIterations)
    Evaluates the continued fraction at the value x.
    double
    DefaultTransformer.transform(Object o)
     
    double
    NumberTransformer.transform(Object o)
    Implementing this interface provides a facility to transform from Object to Double.
    double
    TransformerMap.transform(Object o)
    Attempts to transform the Object against the map of NumberTransformers.