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.
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Uses of MathException in org.apache.commons.math
Subclasses of MathException in org.apache.commons.mathModifier and TypeClassDescriptionclass
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
Methods in org.apache.commons.math.analysis.interpolation that throw MathExceptionModifier and TypeMethodDescriptionBicubicSplineInterpolator.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.final 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 aSplineInterpolator
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.Constructors in org.apache.commons.math.analysis.interpolation that throw MathExceptionModifierConstructorDescriptionLoessInterpolator
(double bandwidth, int robustnessIters) Constructs a newLoessInterpolator
with given bandwidth and number of robustness iterations.LoessInterpolator
(double bandwidth, int robustnessIters, double accuracy) Constructs a newLoessInterpolator
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 MathExceptionModifier and TypeMethodDescriptiondouble
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 <= x) for a Poisson distribution.double
TDistributionImpl.cumulativeProbability
(double x) For this distribution, X, this method returns P(X <x
).double
Return the probability density for a particular point.double
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) <= 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
Subclasses of MathException in org.apache.commons.math.estimationModifier and TypeClassDescriptionclass
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.fractionModifier and TypeClassDescriptionclass
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.geometryModifier and TypeClassDescriptionclass
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
Subclasses of MathException in org.apache.commons.math.linearModifier and TypeClassDescriptionclass
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
Subclasses of MathException in org.apache.commons.math.odeModifier and TypeClassDescriptionclass
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
Subclasses of MathException in org.apache.commons.math.ode.eventsModifier and TypeClassDescriptionclass
This exception is made available to users to report the error conditions that are triggered byEventHandler
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Uses of MathException in org.apache.commons.math.optimization
Subclasses of MathException in org.apache.commons.math.optimizationModifier and TypeClassDescriptionclass
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.linearModifier and TypeClassDescriptionclass
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
Methods in org.apache.commons.math.random that throw MathExceptionModifier and TypeMethodDescriptiondouble
RandomDataImpl.nextBeta
(double alpha, double beta) Generates a random value from theBeta Distribution
.int
RandomDataImpl.nextBinomial
(int numberOfTrials, double probabilityOfSuccess) Generates a random value from theBinomial Distribution
.double
RandomDataImpl.nextCauchy
(double median, double scale) Generates a random value from theCauchy Distribution
.double
RandomDataImpl.nextChiSquare
(double df) Generates a random value from theChiSquare Distribution
.double
RandomDataImpl.nextF
(double numeratorDf, double denominatorDf) Generates a random value from theF Distribution
.double
RandomDataImpl.nextGamma
(double shape, double scale) Generates a random value from theGamma Distribution
.int
RandomDataImpl.nextHypergeometric
(int populationSize, int numberOfSuccesses, int sampleSize) Generates a random value from theHypergeometric 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 thePascal Distribution
.double
RandomDataImpl.nextT
(double df) Generates a random value from theT Distribution
.double
RandomDataImpl.nextWeibull
(double shape, double scale) Generates a random value from theWeibull Distribution
.int
RandomDataImpl.nextZipf
(int numberOfElements, double exponent) Generates a random value from theZipf Distribution
. -
Uses of MathException in org.apache.commons.math.special
Methods in org.apache.commons.math.special that throw MathExceptionModifier and TypeMethodDescriptionstatic double
Erf.erf
(double x) Returns the error functionstatic double
Erf.erfc
(double x) Returns the complementary error functionstatic 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 MathExceptionModifier and TypeMethodDescriptionPearsonsCorrelation.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 MathExceptionModifier and TypeMethodDescriptiondouble
OneWayAnova.anovaFValue
(Collection<double[]> categoryData) Computes the ANOVA F-value for a collection ofdouble[]
arrays.double
OneWayAnovaImpl.anovaFValue
(Collection<double[]> categoryData) Computes the ANOVA F-value for a collection ofdouble[]
arrays.double
OneWayAnova.anovaPValue
(Collection<double[]> categoryData) Computes the ANOVA P-value for a collection ofdouble[]
arrays.double
OneWayAnovaImpl.anovaPValue
(Collection<double[]> categoryData) Computes the ANOVA P-value for a collection ofdouble[]
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 theobserved
frequency counts to those in theexpected
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 levelalpha
.double
ChiSquareTest.chiSquareTest
(long[][] counts) Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the inputcounts
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 levelalpha
.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 theobserved
frequency counts to those in theexpected
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 levelalpha
.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 inobserved1
andobserved2
.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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
, 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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
, 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 betweensample1
andsample2
is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance levelalpha
.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 betweensample1
andsample2
is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance levelalpha
.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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
.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 constantmu
.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 whichsample
is drawn equalsmu
.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 bysampleStats
with the constantmu
.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 bystats
is drawn equalsmu
.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 thatsampleStats1
andsampleStats2
describe datasets drawn from populations with the same mean, with significance levelalpha
.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 thatsample1
andsample2
are drawn from populations with the same mean, with significance levelalpha
.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 constantmu
.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 whichsample
is drawn equalsmu
.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 bysampleStats
with the constantmu
.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 bystats
is drawn equalsmu
.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 thatsampleStats1
andsampleStats2
describe datasets drawn from populations with the same mean, with significance levelalpha
. -
Uses of MathException in org.apache.commons.math.stat.regression
Methods in org.apache.commons.math.stat.regression that throw MathExceptionModifier and TypeMethodDescriptiondouble
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 MathExceptionModifier and TypeMethodDescriptiondouble
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
double
Implementing this interface provides a facility to transform from Object to Double.double
Attempts to transform the Object against the map of NumberTransformers.