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

Packages that use MathException
org.apache.commons.math | Common classes used throughout the commons-math library.
org.apache.commons.math.analysis.interpolation | Univariate real functions interpolation algorithms.
org.apache.commons.math.distribution | Implementations of common discrete and continuous distributions.
org.apache.commons.math.estimation | This package provided classes to solve estimation problems, it is deprecated since 2.0.
org.apache.commons.math.fraction | Fraction number type and fraction number formatting.
org.apache.commons.math.geometry | This package provides basic 3D geometry components.
org.apache.commons.math.linear | Linear algebra support.
org.apache.commons.math.ode | This package provides classes to solve Ordinary Differential Equations problems.
org.apache.commons.math.ode.events | This package provides classes to handle discrete events occurring during Ordinary Differential Equations integration.
org.apache.commons.math.optimization | This package provides common interfaces for the optimization algorithms provided in sub-packages.
org.apache.commons.math.optimization.linear | This package provides optimization algorithms for linear constrained problems.
org.apache.commons.math.random | Random number and random data generators.
org.apache.commons.math.special | Implementations of special functions such as Beta and Gamma.
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

Subclasses of MathException in org.apache.commons.math
`class` | `ArgumentOutsideDomainException` Error thrown when a method is called with an out of bounds argument.
`class` | `ConvergenceException` Error thrown when a numerical computation can not be performed because the numerical result failed to converge to a finite value.
`class` | `DimensionMismatchException` Deprecated. in 2.2 (to be removed in 3.0). Please use its equivalent from package `org.apache.commons.math.exception`.
`class` | `DuplicateSampleAbscissaException` Exception thrown when a sample contains several entries at the same abscissa.
`class` | `FunctionEvaluationException` Exception thrown when an error occurs evaluating a function.
`class` | `MathConfigurationException` Signals a configuration problem with any of the factory methods.
`class` | `MaxEvaluationsExceededException` Error thrown when a numerical computation exceeds its allowed number of functions evaluations.
`class` | `MaxIterationsExceededException` Error thrown when a numerical computation exceeds its allowed number of iterations.

Uses of MathException in org.apache.commons.math.analysis.interpolation

Methods in org.apache.commons.math.analysis.interpolation that throw MathException
`MultivariateRealFunction` | `MicrosphereInterpolator.interpolate(double[][] xval, double[] yval)` Computes an interpolating function for the data set.
`MultivariateRealFunction` | `MultivariateRealInterpolator.interpolate(double[][] xval, double[] yval)` Computes an interpolating function for the data set.
`UnivariateRealFunction` | `UnivariateRealInterpolator.interpolate(double[] xval, double[] yval)` Computes an interpolating function for the data set.
`PolynomialSplineFunction` | `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.
`PolynomialFunctionLagrangeForm` | `NevilleInterpolator.interpolate(double[] x, double[] y)` Computes an interpolating function for the data set.
`BivariateRealFunction` | `SmoothingBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] zval)` Deprecated. Computes an interpolating function for the data set.
`BivariateRealFunction` | `BivariateRealGridInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)` Computes an interpolating function for the data set.
`BicubicSplineInterpolatingFunction` | `BicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)` Computes an interpolating function for the data set.
`BicubicSplineInterpolatingFunction` | `SmoothingPolynomialBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval)` Computes an interpolating function for the data set.
`TricubicSplineInterpolatingFunction` | `TricubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval)` Computes an interpolating function for the data set.
`TrivariateRealFunction` | `TrivariateRealGridInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval)` Computes an interpolating function for the data set.
`double[]` | `LoessInterpolator.smooth(double[] xval, double[] yval)` Compute a loess fit on the data at the original abscissae.
`double[]` | `LoessInterpolator.smooth(double[] xval, double[] yval, double[] weights)` Compute a weighted loess fit on the data at the original abscissae.

Constructors in org.apache.commons.math.analysis.interpolation that throw MathException
`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

Methods in org.apache.commons.math.distribution that throw MathException
`double` | `GammaDistributionImpl.cumulativeProbability(double 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` | `TDistributionImpl.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` | `Distribution.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 x)` For a random variable X whose values are distributed according to this distribution, this method returns P(X ≤ x).
`double` | `ExponentialDistributionImpl.cumulativeProbability(double x)` For this distribution, X, this method returns P(X < x).
`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` | `NormalDistributionImpl.cumulativeProbability(double x)` For this distribution, X, 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` | `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).
`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` | `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).
`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` | `BinomialDistributionImpl.cumulativeProbability(int x)` For this distribution, X, this method returns P(X ≤ x).
`double` | `PoissonDistributionImpl.cumulativeProbability(int x)` The probability distribution function P(X <= x) for a Poisson distribution.
`double` | `PascalDistributionImpl.cumulativeProbability(int 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` | `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` | `IntegerDistribution.cumulativeProbability(int x0, int x1)` For this distribution, X, this method returns P(x0 ≤ X ≤ x1).
`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` | `GammaDistributionImpl.inverseCumulativeProbability(double p)` For this distribution, X, this method returns the critical point 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` | `TDistributionImpl.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` | `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`.
`int` | `BinomialDistributionImpl.inverseCumulativeProbability(double p)` For this distribution, X, this method returns the largest 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` | `ExponentialDistributionImpl.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) <= p.
`double` | `ContinuousDistribution.inverseCumulativeProbability(double p)` For this distribution, X, this method returns 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`.
`double` | `NormalDistributionImpl.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.
`int` | `AbstractIntegerDistribution.sample()` Generates a random value sampled from this distribution.
`int` | `PoissonDistributionImpl.sample()` Generates a random value sampled from this distribution.
`double` | `ExponentialDistributionImpl.sample()` Generates a random value sampled from this distribution.
`double` | `NormalDistributionImpl.sample()` Generates a random value sampled from this distribution.
`double[]` | `AbstractContinuousDistribution.sample(int sampleSize)` Generates a random sample from the distribution.
`int[]` | `AbstractIntegerDistribution.sample(int sampleSize)` Generates a random sample from the distribution.

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

Subclasses of MathException in org.apache.commons.math.estimation
`class` | `EstimationException` 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

Subclasses of MathException in org.apache.commons.math.fraction
`class` | `FractionConversionException` 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

Subclasses of MathException in org.apache.commons.math.geometry
`class` | `CardanEulerSingularityException` This class represents exceptions thrown while extractiong Cardan or Euler angles from a rotation.
`class` | `NotARotationMatrixException` This class represents exceptions thrown while building rotations from matrices.

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

Subclasses of MathException in org.apache.commons.math.linear
`class` | `NotPositiveDefiniteMatrixException` This class represents exceptions thrown when a matrix expected to be positive definite is not.
`class` | `NotSymmetricMatrixException` This class represents exceptions thrown when a matrix expected to be symmetric is not

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

Subclasses of MathException in org.apache.commons.math.ode
`class` | `DerivativeException` This exception is made available to users to report the error conditions that are triggered while computing the differential equations.
`class` | `IntegratorException` 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

Subclasses of MathException in org.apache.commons.math.ode.events
`class` | `EventException` 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

Subclasses of MathException in org.apache.commons.math.optimization
`class` | `OptimizationException` Deprecated. in 2.2 (to be removed in 3.0).

Uses of MathException in org.apache.commons.math.optimization.linear

Subclasses of MathException in org.apache.commons.math.optimization.linear
`class` | `NoFeasibleSolutionException` This class represents exceptions thrown by optimizers when no solution fulfills the constraints.
`class` | `UnboundedSolutionException` This class represents exceptions thrown by optimizers when a solution escapes to infinity.

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

Methods in org.apache.commons.math.random that throw MathException
`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

Methods in org.apache.commons.math.special that throw MathException
`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

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` | `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.
`static double` | `TestUtils.chiSquareTest(double[] expected, long[] observed)`
`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.
`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.
`static boolean` | `TestUtils.chiSquareTest(double[] expected, long[] observed, double 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`.
`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`.
`static double` | `TestUtils.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.
`double` | `ChiSquareTestImpl.chiSquareTest(long[][] counts)`
`static boolean` | `TestUtils.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`.
`boolean` | `ChiSquareTestImpl.chiSquareTest(long[][] counts, double alpha)`
`static double` | `TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)`
`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`.
`double` | `ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)`
`static boolean` | `TestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)`
`boolean` | `UnknownDistributionChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)` Performs a Chi-Square two sample test comparing two binned data sets.
`boolean` | `ChiSquareTestImpl.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)`
`static double` | `TestUtils.homoscedasticTTest(double[] sample1, double[] sample2)`
`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 boolean` | `TestUtils.homoscedasticTTest(double[] sample1, double[] sample2, double alpha)`
`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.
`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.
`static double` | `TestUtils.homoscedasticTTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`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.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` | `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.pairedTTest(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 boolean` | `TestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha)`
`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 double` | `TestUtils.tTest(double[] sample1, double[] sample2)`
`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 boolean` | `TestUtils.tTest(double[] sample1, double[] sample2, double alpha)`
`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 double` | `TestUtils.tTest(double mu, double[] sample)`
`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 boolean` | `TestUtils.tTest(double mu, double[] sample, double alpha)`
`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`.
`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.
`static double` | `TestUtils.tTest(double mu, StatisticalSummary sampleStats)`
`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 boolean` | `TestUtils.tTest(double mu, StatisticalSummary sampleStats, double alpha)`
`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 double` | `TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2)`
`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 boolean` | `TestUtils.tTest(StatisticalSummary sampleStats1, StatisticalSummary sampleStats2, double alpha)`
`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`.

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` | `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` | `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.
`double` | `DefaultTransformer.transform(Object o)`

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