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)