Index (Hipparchus::Statistics 3.1 API)

A B C D E F G H I K L M N O P Q R S T U V W
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

AbstractMultipleLinearRegression - Class in org.hipparchus.stat.regression: Abstract base class for implementations of MultipleLinearRegression.
AbstractMultipleLinearRegression() - Constructor for class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Empty constructor.
AbstractStorelessUnivariateStatistic - Class in org.hipparchus.stat.descriptive: Abstract base class for implementations of the StorelessUnivariateStatistic interface.
AbstractStorelessUnivariateStatistic() - Constructor for class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic: Empty constructor.
AbstractUnivariateStatistic - Class in org.hipparchus.stat.descriptive: Abstract base class for implementations of the UnivariateStatistic interface.
AbstractUnivariateStatistic() - Constructor for class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Default constructor.
AbstractUnivariateStatistic(AbstractUnivariateStatistic) - Constructor for class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Copy constructor, creates an identical copy of the original.
accept(double) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics
accept(double) - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic
accept(double) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics
addData(double[][]) - Method in class org.hipparchus.stat.regression.SimpleRegression: Adds the observations represented by the elements in data.
addData(double, double) - Method in class org.hipparchus.stat.regression.SimpleRegression: Adds the observation (x,y) to the regression data set.
addObservation(double[], double) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Adds an observation to the regression model.
addObservation(double[], double) - Method in class org.hipparchus.stat.regression.SimpleRegression: Adds one observation to the regression model.
addObservation(double[], double) - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression: Adds one observation to the regression model.
addObservations(double[][], double[]) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Adds multiple observations to the model.
addObservations(double[][], double[]) - Method in class org.hipparchus.stat.regression.SimpleRegression: Adds a series of observations to the regression model.
addObservations(double[][], double[]) - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression: Adds a series of observations to the regression model.
addValue(double) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Adds the value to the dataset.
addValue(double) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Add a value to the data
addValue(double[]) - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Add an n-tuple to the data
addValue(int) - Method in class org.hipparchus.stat.LongFrequency: Adds 1 to the frequency count for v.
addValue(T) - Method in class org.hipparchus.stat.Frequency: Adds 1 to the frequency count for v.
AggregatableStatistic<T> - Interface in org.hipparchus.stat.descriptive: An interface for statistics that can aggregate results.
aggregate(Iterable<? extends StatisticalSummary>) - Static method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Computes aggregated statistical summaries.
aggregate(Iterable<T>) - Method in interface org.hipparchus.stat.descriptive.AggregatableStatistic: Aggregates the results from the provided instances into this instance.
aggregate(FirstMoment) - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment: Aggregates the results of the provided instance into this instance.
aggregate(GeometricMean) - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean: Aggregates the provided instance into this instance.
aggregate(Mean) - Method in class org.hipparchus.stat.descriptive.moment.Mean: Aggregates the provided instance into this instance.
aggregate(SecondMoment) - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment: Aggregates the provided instance into this instance.
aggregate(Variance) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Aggregates the provided instance into this instance.
aggregate(Max) - Method in class org.hipparchus.stat.descriptive.rank.Max: Aggregates the provided instance into this instance.
aggregate(Min) - Method in class org.hipparchus.stat.descriptive.rank.Min: Aggregates the provided instance into this instance.
aggregate(RandomPercentile) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Aggregates the provided instance into this instance.
aggregate(StatisticalSummary...) - Static method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Computes aggregated statistical summaries.
aggregate(StreamingStatistics) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Aggregates the provided instance into this instance.
aggregate(Product) - Method in class org.hipparchus.stat.descriptive.summary.Product: Aggregates the provided instance into this instance.
aggregate(Sum) - Method in class org.hipparchus.stat.descriptive.summary.Sum: Aggregates the provided instance into this instance.
aggregate(SumOfLogs) - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs: Aggregates the provided instance into this instance.
aggregate(SumOfSquares) - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares: Aggregates the provided instance into this instance.
aggregate(T) - Method in interface org.hipparchus.stat.descriptive.AggregatableStatistic: Aggregates the provided instance into this instance.
aggregate(T...) - Method in interface org.hipparchus.stat.descriptive.AggregatableStatistic: Aggregates the results from the provided instances into this instance.
AlternativeHypothesis - Enum in org.hipparchus.stat.inference: Represents an alternative hypothesis for a hypothesis test.
anovaFValue(Collection<double[]>) - Method in class org.hipparchus.stat.inference.OneWayAnova: Computes the ANOVA F-value for a collection of double[] arrays.
anovaPValue(Collection<double[]>) - Method in class org.hipparchus.stat.inference.OneWayAnova: Computes the ANOVA P-value for a collection of double[] arrays.
anovaPValue(Collection<StreamingStatistics>, boolean) - Method in class org.hipparchus.stat.inference.OneWayAnova: Computes the ANOVA P-value for a collection of StreamingStatistics.
anovaTest(Collection<double[]>, double) - Method in class org.hipparchus.stat.inference.OneWayAnova: Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
append(StorelessCovariance) - Method in class org.hipparchus.stat.correlation.StorelessCovariance: Appends sc to this, effectively aggregating the computations in sc with this.
append(SimpleRegression) - Method in class org.hipparchus.stat.regression.SimpleRegression: Appends data from another regression calculation to this one.
apply(UnivariateStatistic) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Apply the given statistic to the data associated with this set of statistics.
approximateP(double, int, int) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Uses the Kolmogorov-Smirnov distribution to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic.
approximateP(double, int, int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Uses the Kolmogorov-Smirnov distribution to approximate \(P(D_{n,m} > d)\) where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic.
AVERAGE - Enum constant in enum org.hipparchus.stat.ranking.TiesStrategy: Ties get the average of applicable ranks

B

BinomialProportion - Class in org.hipparchus.stat.interval: Utility methods to generate confidence intervals for a binomial proportion.
binomialTest(int, int, double, AlternativeHypothesis) - Method in class org.hipparchus.stat.inference.BinomialTest: Returns the observed significance level, or p-value, associated with a Binomial test.
binomialTest(int, int, double, AlternativeHypothesis, double) - Method in class org.hipparchus.stat.inference.BinomialTest: Returns whether the null hypothesis can be rejected with the given confidence level.
BinomialTest - Class in org.hipparchus.stat.inference: Implements binomial test statistics.
BinomialTest() - Constructor for class org.hipparchus.stat.inference.BinomialTest: Empty constructor.
bootstrap(double[], double[], int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes bootstrap(x, y, iterations, true).
bootstrap(double[], double[], int, boolean) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Estimates the p-value of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution.
build() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder: Builds a StreamingStatistics instance with currently defined properties.
builder() - Static method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns a StreamingStatistics.StreamingStatisticsBuilder to source configured StreamingStatistics instances.

C

calculateAdjustedRSquared() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Returns the adjusted R-squared statistic, defined by the formula \(R_\mathrm{adj}^2 = 1 - \frac{\mathrm{SSR} (n - 1)}{\mathrm{SSTO} (n - p)}\) where SSR is the sum of squared residuals, SSTO is the total sum of squares, n is the number of observations and p is the number of parameters estimated (including the intercept).
calculateBeta() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Calculates the beta of multiple linear regression in matrix notation.
calculateBeta() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression: Calculates beta by GLS.
calculateBeta() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Calculates the regression coefficients using OLS.
calculateBetaVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Calculates the beta variance of multiple linear regression in matrix notation.
calculateBetaVariance() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression: Calculates the variance on the beta.
calculateBetaVariance() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Calculates the variance-covariance matrix of the regression parameters.
calculateErrorVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Calculates the variance of the error term.
calculateErrorVariance() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression: Calculates the estimated variance of the error term using the formula
calculateHat() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Compute the "hat" matrix.
calculateResiduals() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Calculates the residuals of multiple linear regression in matrix notation.
calculateResidualSumOfSquares() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Returns the sum of squared residuals.
calculateRSquared() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Returns the R-Squared statistic, defined by the formula \(R^2 = 1 - \frac{\mathrm{SSR}}{\mathrm{SSTO}}\) where SSR is the sum of squared residuals and SSTO is the total sum of squares
calculateTotalSumOfSquares() - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Returns the sum of squared deviations of Y from its mean.
calculateYVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Calculates the variance of the y values.
cdf(double, int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Calculates P(D_n < d) using the method described in [1] with quick decisions for extreme values given in [2] (see above).
cdf(double, int, boolean) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Calculates P(D_n < d) using method described in [1] with quick decisions for extreme values given in [2] (see above).
cdfExact(double, int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Calculates P(D_n < d).
chiSquare(double[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest: Computes the Chi-Square statistic comparing observed and expected frequency counts.
chiSquare(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the Chi-Square statistic comparing observed and expected frequency counts.
chiSquare(long[][]) - Method in class org.hipparchus.stat.inference.ChiSquareTest: Computes the Chi-Square statistic associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
chiSquare(long[][]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the Chi-Square statistic associated with a chi-square test of independence based on the input counts array, viewed as a two-way table.
chiSquareDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest: Computes a Chi-Square two sample test statistic comparing bin frequency counts in observed1 and observed2.
chiSquareDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a Chi-Square two sample test statistic comparing bin frequency counts in observed1 and observed2.
chiSquareTest(double[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest: 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.
chiSquareTest(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
chiSquareTest(double[], long[], double) - Method in class org.hipparchus.stat.inference.ChiSquareTest: 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.
chiSquareTest(double[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
chiSquareTest(long[][]) - Method in class org.hipparchus.stat.inference.ChiSquareTest: 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.
chiSquareTest(long[][]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
chiSquareTest(long[][], double) - Method in class org.hipparchus.stat.inference.ChiSquareTest: 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.
chiSquareTest(long[][], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
ChiSquareTest - Class in org.hipparchus.stat.inference: Implements Chi-Square test statistics.
ChiSquareTest() - Constructor for class org.hipparchus.stat.inference.ChiSquareTest: Empty constructor.
chiSquareTestDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.ChiSquareTest: Returns the observed significance level, or p-value, associated with a Chi-Square two sample test comparing bin frequency counts in observed1 and observed2.
chiSquareTestDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Returns the observed significance level, or p-value, associated with a Chi-Square two sample test comparing bin frequency counts in observed1 and observed2.
chiSquareTestDataSetsComparison(long[], long[], double) - Method in class org.hipparchus.stat.inference.ChiSquareTest: Performs a Chi-Square two sample test comparing two binned data sets.
chiSquareTestDataSetsComparison(long[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Performs a Chi-Square two sample test comparing two binned data sets.
clear() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Resets all statistics and storage.
clear() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.moment.Mean: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.moment.Skewness: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.moment.Variance: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Resets all statistics and storage.
clear() - Method in class org.hipparchus.stat.descriptive.rank.Max: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.rank.Min: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
clear() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic: Clears the internal state of the statistic.
clear() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Resets all statistics and storage.
clear() - Method in class org.hipparchus.stat.descriptive.summary.Product: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.summary.Sum: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance: Clears the internal state of the Statistic
clear() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic: Clears the internal state of the statistic.
clear() - Method in class org.hipparchus.stat.Frequency: Clears the frequency table
clear() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: As the name suggests, clear wipes the internals and reorders everything in the canonical order.
clear() - Method in class org.hipparchus.stat.regression.SimpleRegression: Clears all data from the model.
clear() - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression: Clears internal buffers and resets the regression model.
computeCorrelationMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.KendallsCorrelation: Computes the Kendall's Tau rank correlation matrix for the columns of the input rectangular array.
computeCorrelationMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation: Computes the correlation matrix for the columns of the input rectangular array.
computeCorrelationMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation: Computes the Spearman's rank correlation matrix for the columns of the input rectangular array.
computeCorrelationMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.KendallsCorrelation: Computes the Kendall's Tau rank correlation matrix for the columns of the input matrix.
computeCorrelationMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation: Computes the correlation matrix for the columns of the input matrix, using PearsonsCorrelation.correlation(double[], double[]).
computeCorrelationMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation: Computes the Spearman's rank correlation matrix for the columns of the input matrix.
computeCovarianceMatrix(double[][]) - Method in class org.hipparchus.stat.correlation.Covariance: Create a covariance matrix from a rectangular array whose columns represent covariates.
computeCovarianceMatrix(double[][], boolean) - Method in class org.hipparchus.stat.correlation.Covariance: Compute a covariance matrix from a rectangular array whose columns represent covariates.
computeCovarianceMatrix(RealMatrix) - Method in class org.hipparchus.stat.correlation.Covariance: Create a covariance matrix from a matrix whose columns represent covariates.
computeCovarianceMatrix(RealMatrix, boolean) - Method in class org.hipparchus.stat.correlation.Covariance: Compute a covariance matrix from a matrix whose columns represent covariates.
ConfidenceInterval - Class in org.hipparchus.stat.interval: Represents an interval estimate of a population parameter.
ConfidenceInterval(double, double, double) - Constructor for class org.hipparchus.stat.interval.ConfidenceInterval: Create a confidence interval with the given bounds and confidence level.
copy() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns a copy of this DescriptiveStatistics instance with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.Mean: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.Skewness: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.rank.Max: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.rank.Median: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.rank.Min: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
copy() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns a copy of this StreamingStatistics instance with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.summary.Product: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.summary.Sum: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs: Returns a copy of the statistic with the same internal state.
copy() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares: Returns a copy of the statistic with the same internal state.
copy() - Method in interface org.hipparchus.stat.descriptive.UnivariateStatistic: Returns a copy of the statistic with the same internal state.
copySelf() - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers: A deep copy function to clone the current instance.
correlation(double[], double[]) - Method in class org.hipparchus.stat.correlation.KendallsCorrelation: Computes the Kendall's Tau rank correlation coefficient between the two arrays.
correlation(double[], double[]) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation: Computes the Pearson's product-moment correlation coefficient between two arrays.
correlation(double[], double[]) - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation: Computes the Spearman's rank correlation coefficient between the two arrays.
covariance(double[], double[]) - Method in class org.hipparchus.stat.correlation.Covariance: Computes the covariance between the two arrays, using the bias-corrected formula.
covariance(double[], double[], boolean) - Method in class org.hipparchus.stat.correlation.Covariance: Computes the covariance between the two arrays.
Covariance - Class in org.hipparchus.stat.correlation: Computes covariances for pairs of arrays or columns of a matrix.
Covariance() - Constructor for class org.hipparchus.stat.correlation.Covariance: Create a Covariance with no data.
Covariance(double[][]) - Constructor for class org.hipparchus.stat.correlation.Covariance: Create a Covariance matrix from a rectangular array whose columns represent covariates.
Covariance(double[][], boolean) - Constructor for class org.hipparchus.stat.correlation.Covariance: Create a Covariance matrix from a rectangular array whose columns represent covariates.
Covariance(RealMatrix) - Constructor for class org.hipparchus.stat.correlation.Covariance: Create a covariance matrix from a matrix whose columns represent covariates.
Covariance(RealMatrix, boolean) - Constructor for class org.hipparchus.stat.correlation.Covariance: Create a covariance matrix from a matrix whose columns represent covariates.
COVARIANCE_MATRIX - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: COVARIANCE_MATRIX.
covarianceToCorrelation(RealMatrix) - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation: Derives a correlation matrix from a covariance matrix.
cumulativeProbability(double) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution

D

DEFAULT_BIN_COUNT - Static variable in class org.hipparchus.stat.fitting.EmpiricalDistribution: Default bin count
DEFAULT_EPSILON - Static variable in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Default quantile estimation error setting
DEFAULT_NAN_STRATEGY - Static variable in class org.hipparchus.stat.ranking.NaturalRanking: default NaN strategy
DEFAULT_TIES_STRATEGY - Static variable in class org.hipparchus.stat.ranking.NaturalRanking: default ties strategy
density(double) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
DescriptiveStatistics - Class in org.hipparchus.stat.descriptive: Maintains a dataset of values of a single variable and computes descriptive statistics based on stored data.
DescriptiveStatistics() - Constructor for class org.hipparchus.stat.descriptive.DescriptiveStatistics: Construct a DescriptiveStatistics instance with an infinite window.
DescriptiveStatistics(double[]) - Constructor for class org.hipparchus.stat.descriptive.DescriptiveStatistics: Construct a DescriptiveStatistics instance with an infinite window and the initial data values in double[] initialDoubleArray.
DescriptiveStatistics(int) - Constructor for class org.hipparchus.stat.descriptive.DescriptiveStatistics: Construct a DescriptiveStatistics instance with the specified window.
DescriptiveStatistics(DescriptiveStatistics) - Constructor for class org.hipparchus.stat.descriptive.DescriptiveStatistics: Copy constructor.
dev - Variable in class org.hipparchus.stat.descriptive.moment.SecondMoment: Deviation of most recently added value from previous first moment.
df(double, double, double, double) - Method in class org.hipparchus.stat.inference.TTest: Computes approximate degrees of freedom for 2-sample t-test.
DOWNSIDE - Enum constant in enum org.hipparchus.stat.descriptive.moment.SemiVariance.Direction: The DOWNSIDE Direction is used to specify that the observations below the cutoff point will be used to calculate SemiVariance
DOWNSIDE_VARIANCE - Static variable in class org.hipparchus.stat.descriptive.moment.SemiVariance: The DOWNSIDE Direction is used to specify that the observations below the cutoff point will be used to calculate SemiVariance

E

EmpiricalDistribution - Class in org.hipparchus.stat.fitting: Represents an empirical probability distribution -- a probability distribution derived from observed data without making any assumptions about the functional form of the population distribution that the data come from.
EmpiricalDistribution() - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution: Creates a new EmpiricalDistribution with the default bin count.
EmpiricalDistribution(int) - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution: Creates a new EmpiricalDistribution with the specified bin count.
EmpiricalDistribution(int, RandomGenerator) - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution: Creates a new EmpiricalDistribution with the specified bin count using the provided RandomGenerator as the source of random data.
EmpiricalDistribution(RandomGenerator) - Constructor for class org.hipparchus.stat.fitting.EmpiricalDistribution: Creates a new EmpiricalDistribution with default bin count using the provided RandomGenerator as the source of random data.
entrySetIterator() - Method in class org.hipparchus.stat.Frequency: Return an Iterator over the set of keys and values that have been added.
equals(Object) - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic: Returns true iff object is the same type of StorelessUnivariateStatistic (the object's class equals this instance) returning the same values as this for getResult() and getN().
equals(Object) - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns true iff object is a MultivariateSummaryStatistics instance and all statistics have the same values as this.
equals(Object) - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Returns true iff o is a PSquarePercentile returning the same values as this for getResult() and getN() and also having equal markers
equals(Object) - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues: Returns true iff object is a StatisticalSummary instance and all statistics have the same values as this.
equals(Object) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns true iff object is a StreamingStatistics instance and all statistics have the same values as this.
equals(Object) - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance
equals(Object) - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic
equals(Object) - Method in class org.hipparchus.stat.Frequency
estimate(double[][], int) - Static method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization: Helper method to create a multivariate normal mixture model which can be used to initialize MultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution).
estimate(double[], int[], double, int, KthSelector) - Method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: Estimation based on K^th selection.
estimate(int) - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers: An Estimate of the percentile value of a given Marker
estimateErrorVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Estimates the variance of the error.
estimateRegressandVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Returns the variance of the regressand, ie Var(y).
estimateRegressandVariance() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression: Returns the variance of the regressand, ie Var(y).
estimateRegressionParameters() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Estimates the regression parameters b.
estimateRegressionParameters() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression: Estimates the regression parameters b.
estimateRegressionParametersStandardErrors() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Returns the standard errors of the regression parameters.
estimateRegressionParametersStandardErrors() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression: Returns the standard errors of the regression parameters.
estimateRegressionParametersVariance() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Estimates the variance of the regression parameters, ie Var(b).
estimateRegressionParametersVariance() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression: Estimates the variance of the regression parameters, ie Var(b).
estimateRegressionStandardError() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Estimates the standard error of the regression.
estimateResiduals() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Estimates the residuals, ie u = y - X*b.
estimateResiduals() - Method in interface org.hipparchus.stat.regression.MultipleLinearRegression: Estimates the residuals, ie u = y - X*b.
evaluate() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Returns the result of evaluating the statistic over the stored data.
evaluate(double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Returns the result of evaluating the statistic over the stored data.
evaluate(double[]) - Method in interface org.hipparchus.stat.descriptive.UnivariateStatistic: Returns the result of evaluating the statistic over the input array.
evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns the SemiVariance of the designated values against the cutoff, using instance properties variancDirection and biasCorrection.
evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Returns the Standard Deviation of the entries in the input array, using the precomputed mean value.
evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the variance of the entries in the input array, using the precomputed mean value.
evaluate(double[], double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Returns an estimate of the pth percentile of the values in the values array.
evaluate(double[], double[]) - Method in interface org.hipparchus.stat.descriptive.WeightedEvaluation: Returns the result of evaluating the statistic over the input array, using the supplied weights.
evaluate(double[], double[], double) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the weighted variance of the values in the input array, using the precomputed weighted mean value.
evaluate(double[], double[], double, int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the weighted variance of the entries in the specified portion of the input array, using the precomputed weighted mean value.
evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Mean: Returns the weighted arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the weighted variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Product: Returns the weighted product of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Sum: The weighted sum of the entries in the specified portion of the input array, or 0 if the designated subarray is empty.
evaluate(double[], double[], int, int) - Method in interface org.hipparchus.stat.descriptive.WeightedEvaluation: Returns the result of evaluating the statistic over the specified entries in the input array, using corresponding entries in the supplied weights array.
evaluate(double[], double, int, int) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Returns the Standard Deviation of the entries in the specified portion of the input array, using the precomputed mean value.
evaluate(double[], double, int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the variance of the entries in the specified portion of the input array, using the precomputed mean value.
evaluate(double[], double, SemiVariance.Direction) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns the SemiVariance of the designated values against the cutoff in the given direction, using the current value of the biasCorrection instance property.
evaluate(double[], double, SemiVariance.Direction, boolean, int, int) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns the SemiVariance of the designated values against the cutoff in the given direction with the provided bias correction.
evaluate(double[], double, KthSelector) - Method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: Evaluate method to compute the percentile for a given bounded array.
evaluate(double[], int[], double, KthSelector) - Method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: Evaluate method to compute the percentile for a given bounded array using earlier computed pivots heap.
This basically calls the index and then estimate functions to return the estimated percentile value.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Returns the result of evaluating the statistic over the specified entries in the input array.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean: Returns the geometric mean of the entries in the specified portion of the input array.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis: Returns the kurtosis of the entries in the specified portion of the input array.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Mean: Returns the arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns the SemiVariance of the designated values against the mean, using instance properties varianceDirection and biasCorrection.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Skewness: Returns the Skewness of the entries in the specified portion of the input array.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Returns the Standard Deviation of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Max: Returns the maximum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Median: Returns the result of evaluating the statistic over the specified entries in the input array.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Min: Returns the minimum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Returns an estimate of the quantileth percentile of the designated values in the values array.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns an estimate of the median, computed using the designated array segment as input data.
evaluate(double[], int, int) - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Returns the result of evaluating the statistic over the specified entries in the input array.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Product: Returns the product of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.Sum: The sum of the entries in the specified portion of the input array, or 0 if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs: Returns the sum of the natural logs of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares: Returns the sum of the squares of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
evaluate(double[], int, int) - Method in interface org.hipparchus.stat.descriptive.UnivariateStatistic: Returns the result of evaluating the statistic over the specified entries in the input array.
evaluate(double[], int, int, double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Returns an estimate of the pth percentile of the values in the values array, starting with the element in (0-based) position begin in the array and including length values.
evaluate(double[], SemiVariance.Direction) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: This method calculates SemiVariance for the entire array against the mean, using the current value of the biasCorrection instance property.
evaluate(double, double[]) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns an estimate of percentile over the given array.
evaluate(double, double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns an estimate of the given percentile, computed using the designated array segment as input data.
exactP(double, int, int, boolean) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes \(P(D_{n,m} > d)\) if strict is true; otherwise \(P(D_{n,m} \ge d)\), where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic.
exactP(double, int, int, boolean) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes \(P(D_{n,m} > d)\) if strict is true; otherwise \(P(D_{n,m} \ge d)\), where \(D_{n,m}\) is the 2-sample Kolmogorov-Smirnov statistic.
extrema(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder: Sets the computeExtrema setting of the factory.

F

FAILED - Enum constant in enum org.hipparchus.stat.ranking.NaNStrategy: NaNs result in an exception
fit(double[][]) - Method in class org.hipparchus.stat.projection.PCA: Fit our model to the data, ready for subsequence transforms.
fit(MixtureMultivariateNormalDistribution) - Method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization: Fit a mixture model to the data supplied to the constructor.
fit(MixtureMultivariateNormalDistribution, int, double) - Method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization: Fit a mixture model to the data supplied to the constructor.
fitAndTransform(double[][]) - Method in class org.hipparchus.stat.projection.PCA: Fit our model to the data and then transform it to the reduced dimensions.
FIXED - Enum constant in enum org.hipparchus.stat.ranking.NaNStrategy: NaNs are left in place
Frequency<T extends Comparable<T>> - Class in org.hipparchus.stat: Maintains a frequency distribution of Comparable values.
Frequency() - Constructor for class org.hipparchus.stat.Frequency: Default constructor.
Frequency(Comparator<? super T>) - Constructor for class org.hipparchus.stat.Frequency: Constructor allowing values Comparator to be specified.

G

g(double[], long[]) - Method in class org.hipparchus.stat.inference.GTest: Computes the G statistic for Goodness of Fit comparing observed and expected frequency counts.
g(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the G statistic for Goodness of Fit comparing observed and expected frequency counts.
gDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.GTest: Computes a G (Log-Likelihood Ratio) two sample test statistic for independence comparing frequency counts in observed1 and observed2.
gDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a G (Log-Likelihood Ratio) two sample test statistic for independence comparing frequency counts in observed1 and observed2.
geometricMean(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the geometric mean of the entries in the input array, or Double.NaN if the array is empty.
geometricMean(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the geometric mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
GeometricMean - Class in org.hipparchus.stat.descriptive.moment: Returns the geometric mean of the available values.
GeometricMean() - Constructor for class org.hipparchus.stat.descriptive.moment.GeometricMean: Create a GeometricMean instance.
GeometricMean(GeometricMean) - Constructor for class org.hipparchus.stat.descriptive.moment.GeometricMean: Copy constructor, creates a new GeometricMean identical to the original.
GeometricMean(SumOfLogs) - Constructor for class org.hipparchus.stat.descriptive.moment.GeometricMean: Create a GeometricMean instance using the given SumOfLogs instance.
getAdjustedRSquared() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the adjusted R-squared statistic, defined by the formula \( R_\mathrm{adj}^2 = 1 - \frac{\mathrm{SSR} (n - 1)}{\mathrm{SSTO} (n - p)} \) where SSR is the sum of squared residuals}, SSTO is the total sum of squares}, n is the number of observations and p is the number of parameters estimated (including the intercept).
getAggregateN(Collection<RandomPercentile>) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns the total number of values that have been consumed by the aggregates.
getAggregateQuantileRank(double, Collection<RandomPercentile>) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns the estimated quantile position of value in the combined dataset of the aggregates.
getAggregateRank(double, Collection<RandomPercentile>) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Computes the estimated rank of value in the combined dataset of the aggregates.
getAgrestiCoullInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion: Create an Agresti-Coull binomial confidence interval for the true probability of success of an unknown binomial distribution with the given observed number of trials, probability of success and confidence level.
getBinCount() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Returns the number of bins.
getBinStats() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Returns a List of StreamingStatistics instances containing statistics describing the values in each of the bins.
getCenter() - Method in class org.hipparchus.stat.projection.PCA: Get by column center (or mean) of the fitted data.
getClopperPearsonInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion: Create a Clopper-Pearson binomial confidence interval for the true probability of success of an unknown binomial distribution with the given observed number of trials, probability of success and confidence level.
getComponents() - Method in class org.hipparchus.stat.projection.PCA: Returns the principal components of our projection model.
getConfidenceLevel() - Method in class org.hipparchus.stat.interval.ConfidenceInterval: Get asserted probability that the interval contains the population parameter.
getCorrelationMatrix() - Method in class org.hipparchus.stat.correlation.KendallsCorrelation: Returns the correlation matrix.
getCorrelationMatrix() - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation: Returns the correlation matrix.
getCorrelationMatrix() - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation: Calculate the Spearman Rank Correlation Matrix.
getCorrelationPValues() - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation: Returns a matrix of p-values associated with the (two-sided) null hypothesis that the corresponding correlation coefficient is zero.
getCorrelationStandardErrors() - Method in class org.hipparchus.stat.correlation.PearsonsCorrelation: Returns a matrix of standard errors associated with the estimates in the correlation matrix.
getCorrelationStandardErrors().getEntry(i,j) is the standard error associated with getCorrelationMatrix.getEntry(i,j)
getCount(int) - Method in class org.hipparchus.stat.LongFrequency: Returns the number of values equal to v.
getCount(T) - Method in class org.hipparchus.stat.Frequency: Returns the number of values equal to v.
getCovariance() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns the covariance of the available values.
getCovariance() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns the covariance of the available values.
getCovariance(int, int) - Method in class org.hipparchus.stat.correlation.StorelessCovariance: Get the covariance for an individual element of the covariance matrix.
getCovarianceMatrix() - Method in class org.hipparchus.stat.correlation.Covariance: Returns the covariance matrix
getCovarianceMatrix() - Method in class org.hipparchus.stat.correlation.StorelessCovariance: Returns the covariance matrix
getCovarianceOfParameters(int, int) - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the covariance between regression parameters i and j.
getCumFreq(int) - Method in class org.hipparchus.stat.LongFrequency: Returns the cumulative frequency of values less than or equal to v.
getCumFreq(T) - Method in class org.hipparchus.stat.Frequency: Returns the cumulative frequency of values less than or equal to v.
getCumPct(int) - Method in class org.hipparchus.stat.LongFrequency: Returns the cumulative percentage of values less than or equal to v (as a proportion between 0 and 1).
getCumPct(T) - Method in class org.hipparchus.stat.Frequency: Returns the cumulative percentage of values less than or equal to v (as a proportion between 0 and 1).
getData() - Method in class org.hipparchus.stat.correlation.StorelessCovariance: Return the covariance matrix as two-dimensional array.
getData() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Get a copy of the stored data array.
getDataRef() - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Get a reference to the stored data array.
getDiagonalOfHatMatrix(double[]) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Gets the diagonal of the Hat matrix also known as the leverage matrix.
getDimension() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns the dimension of the data
getDimension() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns the dimension of the data
getDimension() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic: Returns the dimension of the statistic.
getDimension() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic: Returns the dimension of the statistic.
getElement(int) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the element at the specified index
getErrorSumSquares() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the sum of squared errors (SSE) associated with the regression model.
getEstimationType() - Method in class org.hipparchus.stat.descriptive.rank.Median: Get the estimation type used for computation.
getEstimationType() - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Get the estimation type used for computation.
getFittedModel() - Method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization: Gets the fitted model.
getGeneratorUpperBounds() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Returns a fresh copy of the array of upper bounds of the subintervals of [0,1] used in generating data from the empirical distribution.
getGeometricMean() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the geometric mean of the available values.
getGeometricMean() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the geometric mean of the i^th entries of the arrays that correspond to each multivariate sample
getGeometricMean() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the geometric mean of the i^th entries of the arrays that correspond to each multivariate sample
getGeometricMean() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the geometric mean of the values that have been added.
getIntercept() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the intercept of the estimated regression line, if SimpleRegression.hasIntercept() is true; otherwise 0.
getInterceptStdErr() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the standard error of the intercept estimate, usually denoted s(b0).
getKernel(StreamingStatistics) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: The within-bin smoothing kernel.
getKthSelector() - Method in class org.hipparchus.stat.descriptive.rank.Median: Get the kthSelector used for computation.
getKthSelector() - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Get the kthSelector used for computation.
getKurtosis() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the Kurtosis of the available values.
getLocalizedString(Locale) - Method in enum org.hipparchus.stat.LocalizedStatFormats
getLogLikelihood() - Method in class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization: Gets the log likelihood of the data under the fitted model.
getLowerBound() - Method in class org.hipparchus.stat.interval.ConfidenceInterval: Get lower endpoint of the interval.
getMax() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the maximum of the available values
getMax() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the maximum of the i^th entries of the arrays that correspond to each multivariate sample
getMax() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the maximum of the i^th entries of the arrays that correspond to each multivariate sample
getMax() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Returns the maximum of the available values
getMax() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
getMax() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the maximum of the available values
getMean() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the arithmetic mean of the available values
getMean() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the mean of the i^th entries of the arrays that correspond to each multivariate sample
getMean() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the mean of the i^th entries of the arrays that correspond to each multivariate sample
getMean() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Returns the arithmetic mean of the available values
getMean() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
getMean() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the arithmetic mean of the available values
getMeanSquareError() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.
getMeanSquareError() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.
getMedian() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns an estimate of the median of the values that have been entered.
getMin() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the minimum of the available values
getMin() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the minimum of the i^th entries of the arrays that correspond to each multivariate sample
getMin() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the minimum of the i^th entries of the arrays that correspond to each multivariate sample
getMin() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Returns the minimum of the available values
getMin() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
getMin() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the minimum of the available values
getMode() - Method in class org.hipparchus.stat.Frequency: Returns the mode value(s) in comparator order.
getN() - Method in class org.hipparchus.stat.correlation.Covariance: Returns the number of observations (length of covariate vectors)
getN() - Method in class org.hipparchus.stat.correlation.StorelessCovariance: This Covariance method is not supported by a StorelessCovariance, since the number of bivariate observations does not have to be the same for different pairs of covariates - i.e., N as defined in Covariance.getN() is undefined.
getN() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the number of available values
getN() - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.moment.Mean: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.moment.Skewness: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns the number of available values
getN() - Method in class org.hipparchus.stat.descriptive.rank.Max: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.rank.Min: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
getN() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns the number of available values
getN() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Returns the number of available values
getN() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
getN() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic: Returns the number of values that have been added.
getN() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the number of available values
getN() - Method in class org.hipparchus.stat.descriptive.summary.Product: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.summary.Sum: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance: Get the number of vectors in the sample.
getN() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic: Returns the number of values that have been added.
getN() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Gets the number of observations added to the regression model.
getN() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the number of observations added to the regression model.
getN() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the number of observations that have been added to the model.
getN() - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression: Returns the number of observations added to the regression model.
getNanStrategy() - Method in class org.hipparchus.stat.ranking.NaturalRanking: Return the NaNStrategy
getNaNStrategy() - Method in class org.hipparchus.stat.descriptive.rank.Median: Get the NaN Handling strategy used for computation.
getNaNStrategy() - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Get the NaN Handling strategy used for computation.
getNextValue() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Generates a random value from this distribution.
getNormalApproximationInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion: Create a binomial confidence interval using normal approximation for the true probability of success of an unknown binomial distribution with the given observed number of trials, probability of success and confidence level.
getNumberOfParameters() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the number of parameters estimated in the model.
getNumComponents() - Method in class org.hipparchus.stat.projection.PCA: GEt number of components.
getNumericalMean() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
getNumericalVariance() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
getOmegaInverse() - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression: Get the inverse of the covariance.
getOrderOfRegressors() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Gets the order of the regressors, useful if some type of reordering has been called.
getParameterEstimate(int) - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the parameter estimate for the regressor at the given index.
getParameterEstimates() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns a copy of the regression parameters estimates.
getPartialCorrelations(int) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: In the original algorithm only the partial correlations of the regressors is returned to the user.
getPct(int) - Method in class org.hipparchus.stat.LongFrequency: Returns the percentage of values that are equal to v (as a proportion between 0 and 1).
getPct(T) - Method in class org.hipparchus.stat.Frequency: Returns the percentage of values that are equal to v (as a proportion between 0 and 1).
getPercentile(double) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns an estimate for the pth percentile of the stored values.
getPercentile(double) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns an estimate of the given percentile of the values that have been entered.
getPercentileValue() - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers: Returns Percentile value computed thus far.
getPivotingStrategy() - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Get the PivotingStrategy used in KthSelector for computation.
getPopulationVariance() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the population variance of the available values.
getPopulationVariance() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the population variance of the values that have been added.
getQuadraticMean() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the quadratic mean of the available values.
getQuadraticMean() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the quadratic mean, a.k.a.
getQuantile() - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Returns the value of the quantile field (determines what percentile is computed when evaluate() is called with no quantile argument).
getQuantile() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Get quantile estimated by this statistic.
getQuantileRank(double) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns the estimated quantile position of value in the dataset.
getR() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns Pearson's product moment correlation coefficient, usually denoted r.
getRank(double) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Gets the estimated rank of value, i.e.
getRankCorrelation() - Method in class org.hipparchus.stat.correlation.SpearmansCorrelation: Returns a PearsonsCorrelation instance constructed from the ranked input data.
getRegressionSumSquares() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).
getRegressionSumSquares() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).
getResult() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.moment.Mean: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.moment.Skewness: Returns the value of the statistic based on the values that have been added.
getResult() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.rank.Max: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.rank.Min: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns an estimate of the median.
getResult() - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic: Returns the current value of the Statistic.
getResult() - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.summary.Product: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.summary.Sum: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares: Returns the current value of the Statistic.
getResult() - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance: Get the covariance matrix.
getResult() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic: Returns the current value of the Statistic.
getResult(double) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns an estimate of the given percentile.
getRSquare() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the coefficient of determination, usually denoted r-square.
getRSquared() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the coefficient of multiple determination, usually denoted r-square.
getSampleStats() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Returns a StatisticalSummary describing this distribution.
getSecondMoment() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns a statistic related to the Second Central Moment.
getSignificance() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the significance level of the slope (equiv) correlation.
getSkewness() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the skewness of the available values.
getSlope() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the slope of the estimated regression line.
getSlopeConfidenceInterval() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the half-width of a 95% confidence interval for the slope estimate.
getSlopeConfidenceInterval(double) - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.
getSlopeStdErr() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the standard error of the slope estimate, usually denoted s(b1).
getSortedValues() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the current set of values in an array of double primitives, sorted in ascending order.
getSourceString() - Method in enum org.hipparchus.stat.LocalizedStatFormats
getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the standard deviation of the available values.
getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the standard deviation of the i^th entries of the arrays that have been added using MultivariateSummaryStatistics.addValue(double[])
getStandardDeviation() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the standard deviation of the i^th entries of the arrays that correspond to each multivariate sample
getStandardDeviation() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Returns the standard deviation of the available values.
getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
getStandardDeviation() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the standard deviation of the values that have been added.
getStdErrorOfEstimate(int) - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the standard error of the parameter estimate at index, usually denoted s(b_index).
getStdErrorOfEstimates() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the standard error of the parameter estimates, usually denoted s(b_i).
getSum() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the sum of the values that have been added to Univariate.
getSum() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the sum of the i^th entries of the arrays that correspond to each multivariate sample
getSum() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the sum of the i^th entries of the arrays that correspond to each multivariate sample
getSum() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Returns the sum of the values that have been added to Univariate.
getSum() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
getSum() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the sum of the values that have been added to Univariate.
getSumFreq() - Method in class org.hipparchus.stat.Frequency: Returns the sum of all frequencies.
getSumLog() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the sum of logs of the i^th entries of the arrays that correspond to each multivariate sample
getSumLog() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the sum of logs of the i^th entries of the arrays that correspond to each multivariate sample
getSummary() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Return a StatisticalSummaryValues instance reporting current statistics.
getSumOfCrossProducts() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the sum of crossproducts, x_i*y_i.
getSumOfLogs() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the sum of the logs of the values that have been added.
getSumOfSquares() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the sum of the squares of the available values.
getSumOfSquares() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the sum of the squares of the values that have been added.
getSumSq() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns an array whose i^th entry is the sum of squares of the i^th entries of the arrays that correspond to each multivariate sample
getSumSq() - Method in interface org.hipparchus.stat.descriptive.StatisticalMultivariateSummary: Returns an array whose i^th entry is the sum of squares of the i^th entries of the arrays that correspond to each multivariate sample
getSumSquaredErrors() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the sum of squared errors (SSE) associated with the regression model.
getSupportLowerBound() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
getSupportUpperBound() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
getTiesStrategy() - Method in class org.hipparchus.stat.ranking.NaturalRanking: Return the TiesStrategy
getTotalSumSquares() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns the sum of squared deviations of the y values about their mean.
getTotalSumSquares() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the sum of squared deviations of the y values about their mean.
getUniqueCount() - Method in class org.hipparchus.stat.Frequency: Returns the number of values in the frequency table.
getUpperBound() - Method in class org.hipparchus.stat.interval.ConfidenceInterval: Get upper endpoint of the interval.
getUpperBounds() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Returns a fresh copy of the array of upper bounds for the bins.
getValues() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the current set of values in an array of double primitives.
getVariance() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the variance of the available values.
getVariance() - Method in interface org.hipparchus.stat.descriptive.StatisticalSummary: Returns the variance of the available values.
getVariance() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues
getVariance() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns the variance of the available values.
getVariance() - Method in class org.hipparchus.stat.projection.PCA: Get principal component variances.
getVarianceDirection() - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns the varianceDirection property.
getWilsonScoreInterval(int, double, double) - Static method in class org.hipparchus.stat.interval.BinomialProportion: Create an Wilson score binomial confidence interval for the true probability of success of an unknown binomial distribution with the given observed number of trials, probability of success and confidence level.
getWindowSize() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Returns the maximum number of values that can be stored in the dataset, or INFINITE_WINDOW (-1) if there is no limit.
getWorkArray(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Get the work array to operate.
getX() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Get the X sample data.
getXSumSquares() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the sum of squared deviations of the x values about their mean.
getY() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Get the Y sample data.
GLSMultipleLinearRegression - Class in org.hipparchus.stat.regression: The GLS implementation of multiple linear regression.
GLSMultipleLinearRegression() - Constructor for class org.hipparchus.stat.regression.GLSMultipleLinearRegression: Empty constructor.
GREATER_THAN - Enum constant in enum org.hipparchus.stat.inference.AlternativeHypothesis: Represents a right-sided test.
gTest(double[], long[]) - Method in class org.hipparchus.stat.inference.GTest: Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing the observed frequency counts to those in the expected array.
gTest(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing the observed frequency counts to those in the expected array.
gTest(double[], long[], double) - Method in class org.hipparchus.stat.inference.GTest: Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level alpha.
gTest(double[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level alpha.
GTest - Class in org.hipparchus.stat.inference: Implements G Test statistics.
GTest() - Constructor for class org.hipparchus.stat.inference.GTest: Empty constructor.
gTestDataSetsComparison(long[], long[]) - Method in class org.hipparchus.stat.inference.GTest: Returns the observed significance level, or p-value, associated with a G-Value (Log-Likelihood Ratio) for two sample test comparing bin frequency counts in observed1 and observed2.
gTestDataSetsComparison(long[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Returns the observed significance level, or p-value, associated with a G-Value (Log-Likelihood Ratio) for two sample test comparing bin frequency counts in observed1 and observed2.
gTestDataSetsComparison(long[], long[], double) - Method in class org.hipparchus.stat.inference.GTest: Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
gTestDataSetsComparison(long[], long[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.
gTestIntrinsic(double[], long[]) - Method in class org.hipparchus.stat.inference.GTest: Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.
gTestIntrinsic(double[], long[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.

H

hashCode() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic: Returns hash code based on getResult() and getN().
hashCode() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Returns hash code based on values of statistics
hashCode() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Returns hash code based on getResult() and getN().
hashCode() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues: Returns hash code based on values of statistics
hashCode() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Returns hash code based on values of statistics.
hashCode() - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance
hashCode() - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic
hashCode() - Method in class org.hipparchus.stat.Frequency
hasIntercept() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: A getter method which determines whether a constant is included.
hasIntercept() - Method in class org.hipparchus.stat.regression.RegressionResults: Returns true if the regression model has been computed including an intercept.
hasIntercept() - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns true if the model includes an intercept term.
hasIntercept() - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression: Returns true if a constant has been included false otherwise.
height(int) - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers: Returns the marker height (or percentile) of a given marker index.
homoscedasticT(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
homoscedasticT(double[], double[]) - Method in class org.hipparchus.stat.inference.TTest: Computes a 2-sample t statistic, under the hypothesis of equal subpopulation variances.
homoscedasticT(double, double, double, double, double, double) - Method in class org.hipparchus.stat.inference.TTest: Computes t test statistic for 2-sample t-test under the hypothesis of equal subpopulation variances.
homoscedasticT(StatisticalSummary, StatisticalSummary) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, under the assumption of equal subpopulation variances.
homoscedasticT(StatisticalSummary, StatisticalSummary) - Method in class org.hipparchus.stat.inference.TTest: Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, under the assumption of equal subpopulation variances.
homoscedasticTTest(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
homoscedasticTTest(double[], double[]) - Method in class org.hipparchus.stat.inference.TTest: 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.
homoscedasticTTest(double[], double[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
homoscedasticTTest(double[], double[], double) - Method in class org.hipparchus.stat.inference.TTest: 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.
homoscedasticTTest(double, double, double, double, double, double) - Method in class org.hipparchus.stat.inference.TTest: Computes p-value for 2-sided, 2-sample t-test, under the assumption of equal subpopulation variances.
homoscedasticTTest(StatisticalSummary, StatisticalSummary) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
homoscedasticTTest(StatisticalSummary, StatisticalSummary) - Method in class org.hipparchus.stat.inference.TTest: 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.

I

ILLEGAL_STATE_PCA - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: ILLEGAL_STATE_PCA.
incMoment - Variable in class org.hipparchus.stat.descriptive.moment.Kurtosis: Determines whether or not this statistic can be incremented or cleared.
incMoment - Variable in class org.hipparchus.stat.descriptive.moment.Mean: Determines whether or not this statistic can be incremented or cleared.
incMoment - Variable in class org.hipparchus.stat.descriptive.moment.Skewness: Determines whether or not this statistic can be incremented or cleared.
incMoment - Variable in class org.hipparchus.stat.descriptive.moment.Variance: Whether or not Variance.increment(double) should increment the internal second moment.
increment(double) - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.moment.GeometricMean: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.moment.Kurtosis: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.moment.Mean: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.moment.SecondMoment: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.moment.Skewness: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.rank.Max: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.rank.Min: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile
increment(double) - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.summary.Product: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.summary.Sum: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.summary.SumOfLogs: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double) - Method in class org.hipparchus.stat.descriptive.summary.SumOfSquares: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double[]) - Method in class org.hipparchus.stat.correlation.StorelessCovariance: Increment the covariance matrix with one row of data.
increment(double[]) - Method in interface org.hipparchus.stat.descriptive.StorelessMultivariateStatistic: Updates the internal state of the statistic to reflect the addition of the new value.
increment(double[]) - Method in class org.hipparchus.stat.descriptive.vector.VectorialCovariance: Add a new vector to the sample.
increment(double[]) - Method in class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic: Updates the internal state of the statistic to reflect the addition of the new value.
incrementAll(double[]) - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Updates the internal state of the statistic to reflect addition of all values in the values array.
incrementAll(double[], int, int) - Method in interface org.hipparchus.stat.descriptive.StorelessUnivariateStatistic: Updates the internal state of the statistic to reflect addition of the values in the designated portion of the values array.
incrementValue(int, long) - Method in class org.hipparchus.stat.LongFrequency: Increments the frequency count for v.
incrementValue(T, long) - Method in class org.hipparchus.stat.Frequency: Increments the frequency count for v.
index(double, int) - Method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: Finds the index of array that can be used as starting index to estimate percentile.
InferenceTestUtils - Class in org.hipparchus.stat.inference: A collection of static methods to create inference test instances or to perform inference tests.
INFINITE_WINDOW - Static variable in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Represents an infinite window size.
INSUFFICIENT_DATA_FOR_T_STATISTIC - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: INSUFFICIENT_DATA_FOR_T_STATISTIC.
INVALID_REGRESSION_OBSERVATION - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: INVALID_REGRESSION_OBSERVATION.
inverseCumulativeProbability(double) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution
isBiasCorrected() - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns true iff biasCorrected property is set to true.
isBiasCorrected() - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Check if bias is corrected.
isBiasCorrected() - Method in class org.hipparchus.stat.descriptive.moment.Variance: Check if bias is corrected.
isBiasCorrection() - Method in class org.hipparchus.stat.projection.PCA: Check whether scaling (correlation), if in use, adjusts for bias.
isLoaded() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Property indicating whether or not the distribution has been loaded.
isNoIntercept() - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Chekc if the model has no intercept term.
isScale() - Method in class org.hipparchus.stat.projection.PCA: Check whether scaling (correlation) or no scaling (covariance) is used.
isSupportConnected() - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution

K

KendallsCorrelation - Class in org.hipparchus.stat.correlation: Implementation of Kendall's Tau-b rank correlation.
KendallsCorrelation() - Constructor for class org.hipparchus.stat.correlation.KendallsCorrelation: Create a KendallsCorrelation instance without data.
KendallsCorrelation(double[][]) - Constructor for class org.hipparchus.stat.correlation.KendallsCorrelation: Create a KendallsCorrelation from a rectangular array whose columns represent values of variables to be correlated.
KendallsCorrelation(RealMatrix) - Constructor for class org.hipparchus.stat.correlation.KendallsCorrelation: Create a KendallsCorrelation from a RealMatrix whose columns represent variables to be correlated.
kolmogorovSmirnovStatistic(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the two-sample Kolmogorov-Smirnov test statistic, \(D_{n,m}=\sup_x |F_n(x)-F_m(x)|\) where \(n\) is the length of x, \(m\) is the length of y, \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in x and \(F_m\) is the empirical distribution of the y values.
kolmogorovSmirnovStatistic(double[], double[]) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes the two-sample Kolmogorov-Smirnov test statistic, \(D_{n,m}=\sup_x |F_n(x)-F_m(x)|\) where \(n\) is the length of x, \(m\) is the length of y, \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in x and \(F_m\) is the empirical distribution of the y values.
kolmogorovSmirnovStatistic(RealDistribution, double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the one-sample Kolmogorov-Smirnov test statistic, \(D_n=\sup_x |F_n(x)-F(x)|\) where \(F\) is the distribution (cdf) function associated with distribution, \(n\) is the length of data and \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in data.
kolmogorovSmirnovStatistic(RealDistribution, double[]) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes the one-sample Kolmogorov-Smirnov test statistic, \(D_n=\sup_x |F_n(x)-F(x)|\) where \(F\) is the distribution (cdf) function associated with distribution, \(n\) is the length of data and \(F_n\) is the empirical distribution that puts mass \(1/n\) at each of the values in data.
kolmogorovSmirnovTest(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the p-value, or observed significance level, of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution.
kolmogorovSmirnovTest(double[], double[]) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes the p-value, or observed significance level, of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution.
kolmogorovSmirnovTest(double[], double[], boolean) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the p-value, or observed significance level, of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution.
kolmogorovSmirnovTest(double[], double[], boolean) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes the p-value, or observed significance level, of a two-sample Kolmogorov-Smirnov test evaluating the null hypothesis that x and y are samples drawn from the same probability distribution.
kolmogorovSmirnovTest(RealDistribution, double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
kolmogorovSmirnovTest(RealDistribution, double[]) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
kolmogorovSmirnovTest(RealDistribution, double[], boolean) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
kolmogorovSmirnovTest(RealDistribution, double[], boolean) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes the p-value, or observed significance level, of a one-sample Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
kolmogorovSmirnovTest(RealDistribution, double[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Performs a Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
kolmogorovSmirnovTest(RealDistribution, double[], double) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Performs a Kolmogorov-Smirnov test evaluating the null hypothesis that data conforms to distribution.
KolmogorovSmirnovTest - Class in org.hipparchus.stat.inference: Implementation of the Kolmogorov-Smirnov (K-S) test for equality of continuous distributions.
KolmogorovSmirnovTest() - Constructor for class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Construct a KolmogorovSmirnovTest instance.
KolmogorovSmirnovTest(long) - Constructor for class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Construct a KolmogorovSmirnovTest instance providing a seed for the PRNG used by the KolmogorovSmirnovTest.bootstrap(double[], double[], int) method.
KS_SUM_CAUCHY_CRITERION - Static variable in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Convergence criterion for KolmogorovSmirnovTest.ksSum(double, double, int)
ksSum(double, double, int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes \( 1 + 2 \sum_{i=1}^\infty (-1)^i e^{-2 i^2 t^2} \) stopping when successive partial sums are within tolerance of one another, or when maxIterations partial sums have been computed.
Kurtosis - Class in org.hipparchus.stat.descriptive.moment: Computes the Kurtosis of the available values.
Kurtosis() - Constructor for class org.hipparchus.stat.descriptive.moment.Kurtosis: Construct a Kurtosis.
Kurtosis(FourthMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.Kurtosis: Construct a Kurtosis from an external moment.
Kurtosis(Kurtosis) - Constructor for class org.hipparchus.stat.descriptive.moment.Kurtosis: Copy constructor, creates a new Kurtosis identical to the original.

L

LARGE_SAMPLE_PRODUCT - Static variable in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: When product of sample sizes exceeds this value, 2-sample K-S test uses asymptotic distribution to compute the p-value.
LEGACY - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: This is the default type used in the Percentile.This method has the following formulae for index and estimates
\( \begin{align} &index = (N+1)p\ \\ &estimate = x_{\lceil h\,-\,1/2 \rceil} \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}\)
LESS_THAN - Enum constant in enum org.hipparchus.stat.inference.AlternativeHypothesis: Represents a left-sided test.
load(double[]) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Computes the empirical distribution from the provided array of numbers.
load(File) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Computes the empirical distribution from the input file.
load(URL) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Computes the empirical distribution using data read from a URL.
LocalizedStatFormats - Enum in org.hipparchus.stat: Enumeration for localized messages formats used in exceptions messages.
LongFrequency - Class in org.hipparchus.stat: Maintains a frequency distribution of Long values.
LongFrequency() - Constructor for class org.hipparchus.stat.LongFrequency: Default constructor.
LongFrequency(Comparator<? super Long>) - Constructor for class org.hipparchus.stat.LongFrequency: Constructor allowing values Comparator to be specified.

M

m1 - Variable in class org.hipparchus.stat.descriptive.moment.SecondMoment: First moment of values that have been added
m2 - Variable in class org.hipparchus.stat.descriptive.moment.SecondMoment: Second moment of values that have been added
mannWhitneyU(double[], double[]) - Method in class org.hipparchus.stat.inference.MannWhitneyUTest: Computes the Mann-Whitney U statistic comparing means for two independent samples possibly of different lengths.
mannWhitneyUTest(double[], double[]) - Method in class org.hipparchus.stat.inference.MannWhitneyUTest: Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.
mannWhitneyUTest(double[], double[], boolean) - Method in class org.hipparchus.stat.inference.MannWhitneyUTest: Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U Test comparing means for two independent samples.
MannWhitneyUTest - Class in org.hipparchus.stat.inference: An implementation of the Mann-Whitney U test.
MannWhitneyUTest() - Constructor for class org.hipparchus.stat.inference.MannWhitneyUTest: Create a test instance using where NaN's are left in place and ties get the average of applicable ranks.
MannWhitneyUTest(NaNStrategy, TiesStrategy) - Constructor for class org.hipparchus.stat.inference.MannWhitneyUTest: Create a test instance using the given strategies for NaN's and ties.
max(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the maximum of the entries in the input array, or Double.NaN if the array is empty.
max(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the maximum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
Max - Class in org.hipparchus.stat.descriptive.rank: Returns the maximum of the available values.
Max() - Constructor for class org.hipparchus.stat.descriptive.rank.Max: Create a Max instance.
Max(Max) - Constructor for class org.hipparchus.stat.descriptive.rank.Max: Copy constructor, creates a new Max identical to the original.
MAXIMAL - Enum constant in enum org.hipparchus.stat.ranking.NaNStrategy: NaNs are considered maximal in the ordering
MAXIMUM - Enum constant in enum org.hipparchus.stat.ranking.TiesStrategy: Ties get the maximum applicable rank
MAXIMUM_PARTIAL_SUM_COUNT - Static variable in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Bound on the number of partial sums in KolmogorovSmirnovTest.ksSum(double, double, int)
maxValuesRetained(double) - Static method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Returns the maximum number of double values that a RandomPercentile instance created with the given epsilon value will retain in memory.
mean(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the arithmetic mean of the entries in the input array, or Double.NaN if the array is empty.
mean(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the arithmetic mean of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
Mean - Class in org.hipparchus.stat.descriptive.moment: Computes the arithmetic mean of a set of values.
Mean() - Constructor for class org.hipparchus.stat.descriptive.moment.Mean: Constructs a Mean.
Mean(FirstMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.Mean: Constructs a Mean with an External Moment.
Mean(Mean) - Constructor for class org.hipparchus.stat.descriptive.moment.Mean: Copy constructor, creates a new Mean identical to the original.
meanDifference(double[], double[]) - Static method in class org.hipparchus.stat.StatUtils: Returns the mean of the (signed) differences between corresponding elements of the input arrays -- i.e., sum(sample1[i] - sample2[i]) / sample1.length.
Median - Class in org.hipparchus.stat.descriptive.rank: Returns the median of the available values.
Median() - Constructor for class org.hipparchus.stat.descriptive.rank.Median: Default constructor.
merge(Collection<? extends Frequency<? extends T>>) - Method in class org.hipparchus.stat.Frequency: Merge a Collection of Frequency objects into this instance.
merge(Frequency<? extends T>) - Method in class org.hipparchus.stat.Frequency: Merge another Frequency object's counts into this instance.
MillerUpdatingRegression - Class in org.hipparchus.stat.regression: This class is a concrete implementation of the UpdatingMultipleLinearRegression interface.
MillerUpdatingRegression(int, boolean) - Constructor for class org.hipparchus.stat.regression.MillerUpdatingRegression: Primary constructor for the MillerUpdatingRegression.
MillerUpdatingRegression(int, boolean, double) - Constructor for class org.hipparchus.stat.regression.MillerUpdatingRegression: This is the augmented constructor for the MillerUpdatingRegression class.
min(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the minimum of the entries in the input array, or Double.NaN if the array is empty.
min(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the minimum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
Min - Class in org.hipparchus.stat.descriptive.rank: Returns the minimum of the available values.
Min() - Constructor for class org.hipparchus.stat.descriptive.rank.Min: Create a Min instance.
Min(Min) - Constructor for class org.hipparchus.stat.descriptive.rank.Min: Copy constructor, creates a new Min identical to the original.
MINIMAL - Enum constant in enum org.hipparchus.stat.ranking.NaNStrategy: NaNs are considered minimal in the ordering
MINIMUM - Enum constant in enum org.hipparchus.stat.ranking.TiesStrategy: Ties get the minimum applicable rank
mode(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the sample mode(s).
mode(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the sample mode(s).
moment - Variable in class org.hipparchus.stat.descriptive.moment.Kurtosis: Fourth Moment on which this statistic is based
moment - Variable in class org.hipparchus.stat.descriptive.moment.Mean: First moment on which this statistic is based.
moment - Variable in class org.hipparchus.stat.descriptive.moment.Skewness: Third moment on which this statistic is based
moment - Variable in class org.hipparchus.stat.descriptive.moment.Variance: SecondMoment is used in incremental calculation of Variance
moments(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder: Sets the computeMoments setting of the factory
MultipleLinearRegression - Interface in org.hipparchus.stat.regression: The multiple linear regression can be represented in matrix-notation.
MultivariateNormalMixtureExpectationMaximization - Class in org.hipparchus.stat.fitting: Expectation-Maximization algorithm for fitting the parameters of multivariate normal mixture model distributions.
MultivariateNormalMixtureExpectationMaximization(double[][]) - Constructor for class org.hipparchus.stat.fitting.MultivariateNormalMixtureExpectationMaximization: Creates an object to fit a multivariate normal mixture model to data.
MultivariateSummaryStatistics - Class in org.hipparchus.stat.descriptive: Computes summary statistics for a stream of n-tuples added using the addValue method.
MultivariateSummaryStatistics(int) - Constructor for class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Construct a MultivariateSummaryStatistics instance for the given dimension.
MultivariateSummaryStatistics(int, boolean) - Constructor for class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Construct a MultivariateSummaryStatistics instance for the given dimension.

N

n - Variable in class org.hipparchus.stat.descriptive.moment.SecondMoment: Count of values that have been added
NaNStrategy - Enum in org.hipparchus.stat.ranking: Strategies for handling NaN values in rank transformations.
NaturalRanking - Class in org.hipparchus.stat.ranking: Ranking based on the natural ordering on doubles.
NaturalRanking() - Constructor for class org.hipparchus.stat.ranking.NaturalRanking: Create a NaturalRanking with default strategies for handling ties and NaNs.
NaturalRanking(RandomGenerator) - Constructor for class org.hipparchus.stat.ranking.NaturalRanking: Create a NaturalRanking with TiesStrategy.RANDOM and the given RandomGenerator as the source of random data.
NaturalRanking(NaNStrategy) - Constructor for class org.hipparchus.stat.ranking.NaturalRanking: Create a NaturalRanking with the given NaNStrategy.
NaturalRanking(NaNStrategy, RandomGenerator) - Constructor for class org.hipparchus.stat.ranking.NaturalRanking: Create a NaturalRanking with the given NaNStrategy, TiesStrategy.RANDOM and the given source of random data.
NaturalRanking(NaNStrategy, TiesStrategy) - Constructor for class org.hipparchus.stat.ranking.NaturalRanking: Create a NaturalRanking with the given NaNStrategy and TiesStrategy.
NaturalRanking(TiesStrategy) - Constructor for class org.hipparchus.stat.ranking.NaturalRanking: Create a NaturalRanking with the given TiesStrategy.
nDev - Variable in class org.hipparchus.stat.descriptive.moment.SecondMoment: Deviation of most recently added value from previous first moment, normalized by previous sample size.
newCovarianceData(double[][]) - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression: Add the covariance data.
newMarkers(List<Double>, double) - Static method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: A creation method to build Markers
newSampleData(double[], double[][]) - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Loads model x and y sample data, overriding any previous sample.
newSampleData(double[], double[][], double[][]) - Method in class org.hipparchus.stat.regression.GLSMultipleLinearRegression: Replace sample data, overriding any previous sample.
newSampleData(double[], int, int) - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Loads model x and y sample data from a flat input array, overriding any previous sample.
newSampleData(double[], int, int) - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Loads model x and y sample data from a flat input array, overriding any previous sample.
newXSampleData(double[][]) - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Loads new x sample data, overriding any previous data.
newXSampleData(double[][]) - Method in class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Loads new x sample data, overriding any previous data.
newYSampleData(double[]) - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Loads new y sample data, overriding any previous data.
NO_REGRESSORS - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: NO_REGRESSORS.
normalize(double...) - Static method in class org.hipparchus.stat.StatUtils: Normalize (standardize) the sample, so it is has a mean of 0 and a standard deviation of 1.
NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: NOT_ENOUGH_DATA_FOR_NUMBER_OF_PREDICTORS.
NOT_ENOUGH_DATA_REGRESSION - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: NOT_ENOUGH_DATA_REGRESSION.
NOT_SUPPORTED_NAN_STRATEGY - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: NOT_SUPPORTED_NAN_STRATEGY.

O

OLSMultipleLinearRegression - Class in org.hipparchus.stat.regression: Implements ordinary least squares (OLS) to estimate the parameters of a multiple linear regression model.
OLSMultipleLinearRegression() - Constructor for class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Create an empty OLSMultipleLinearRegression instance.
OLSMultipleLinearRegression(double) - Constructor for class org.hipparchus.stat.regression.OLSMultipleLinearRegression: Create an empty OLSMultipleLinearRegression instance, using the given singularity threshold for the QR decomposition.
OneWayAnova - Class in org.hipparchus.stat.inference: Implements one-way ANOVA (analysis of variance) statistics.
OneWayAnova() - Constructor for class org.hipparchus.stat.inference.OneWayAnova: Empty constructor.
oneWayAnovaFValue(Collection<double[]>) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the ANOVA F-value for a collection of double[] arrays.
oneWayAnovaPValue(Collection<double[]>) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes the ANOVA P-value for a collection of double[] arrays.
oneWayAnovaTest(Collection<double[]>, double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.
org.hipparchus.stat - package org.hipparchus.stat: Data storage, manipulation and summary routines.
org.hipparchus.stat.correlation - package org.hipparchus.stat.correlation: Correlations/Covariance computations.
org.hipparchus.stat.descriptive - package org.hipparchus.stat.descriptive: Generic univariate and multivariate summary statistic objects.
org.hipparchus.stat.descriptive.moment - package org.hipparchus.stat.descriptive.moment: Summary statistics based on moments.
org.hipparchus.stat.descriptive.rank - package org.hipparchus.stat.descriptive.rank: Summary statistics based on ranks.
org.hipparchus.stat.descriptive.summary - package org.hipparchus.stat.descriptive.summary: Other summary statistics.
org.hipparchus.stat.descriptive.vector - package org.hipparchus.stat.descriptive.vector: Multivariate statistics.
org.hipparchus.stat.fitting - package org.hipparchus.stat.fitting: Statistical methods for fitting distributions.
org.hipparchus.stat.inference - package org.hipparchus.stat.inference: Classes providing hypothesis testing.
org.hipparchus.stat.interval - package org.hipparchus.stat.interval: Utilities to calculate binomial proportion confidence intervals.
org.hipparchus.stat.projection - package org.hipparchus.stat.projection: Parent package for projections like decomposition (principal component analysis).
org.hipparchus.stat.ranking - package org.hipparchus.stat.ranking: Classes providing rank transformations.
org.hipparchus.stat.regression - package org.hipparchus.stat.regression: Statistical routines involving multivariate data.
OUT_OF_BOUND_SIGNIFICANCE_LEVEL - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: OUT_OF_BOUND_SIGNIFICANCE_LEVEL.
OUT_OF_BOUNDS_CONFIDENCE_LEVEL - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: OUT_OF_BOUNDS_CONFIDENCE_LEVEL.
OUT_OF_BOUNDS_QUANTILE_VALUE - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: OUT_OF_BOUNDS_QUANTILE_VALUE.

P

pairedT(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a paired, 2-sample t-statistic based on the data in the input arrays.
pairedT(double[], double[]) - Method in class org.hipparchus.stat.inference.TTest: Computes a paired, 2-sample t-statistic based on the data in the input arrays.
pairedTTest(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
pairedTTest(double[], double[]) - Method in class org.hipparchus.stat.inference.TTest: 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.
pairedTTest(double[], double[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
pairedTTest(double[], double[], double) - Method in class org.hipparchus.stat.inference.TTest: 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.
PCA - Class in org.hipparchus.stat.projection: Principal component analysis (PCA) is a statistical technique for reducing the dimensionality of a dataset.
PCA(int) - Constructor for class org.hipparchus.stat.projection.PCA: A default PCA will center but not scale.
PCA(int, boolean, boolean) - Constructor for class org.hipparchus.stat.projection.PCA: Create a PCA with the ability to adjust scaling parameters.
PearsonsCorrelation - Class in org.hipparchus.stat.correlation: Computes Pearson's product-moment correlation coefficients for pairs of arrays or columns of a matrix.
PearsonsCorrelation() - Constructor for class org.hipparchus.stat.correlation.PearsonsCorrelation: Create a PearsonsCorrelation instance without data.
PearsonsCorrelation(double[][]) - Constructor for class org.hipparchus.stat.correlation.PearsonsCorrelation: Create a PearsonsCorrelation from a rectangular array whose columns represent values of variables to be correlated.
PearsonsCorrelation(RealMatrix) - Constructor for class org.hipparchus.stat.correlation.PearsonsCorrelation: Create a PearsonsCorrelation from a RealMatrix whose columns represent variables to be correlated.
PearsonsCorrelation(RealMatrix, int) - Constructor for class org.hipparchus.stat.correlation.PearsonsCorrelation: Create a PearsonsCorrelation from a covariance matrix.
PearsonsCorrelation(Covariance) - Constructor for class org.hipparchus.stat.correlation.PearsonsCorrelation: Create a PearsonsCorrelation from a Covariance.
pelzGood(double, int) - Method in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Computes the Pelz-Good approximation for \(P(D_n < d)\) as described in [2] in the class javadoc.
percentile(double[], double) - Static method in class org.hipparchus.stat.StatUtils: Returns an estimate of the pth percentile of the values in the values array.
percentile(double[], int, int, double) - Static method in class org.hipparchus.stat.StatUtils: Returns an estimate of the pth percentile of the values in the values array, starting with the element in (0-based) position begin in the array and including length values.
Percentile - Class in org.hipparchus.stat.descriptive.rank: Provides percentile computation.
Percentile() - Constructor for class org.hipparchus.stat.descriptive.rank.Percentile: Constructs a Percentile with the following defaults.
Percentile(double) - Constructor for class org.hipparchus.stat.descriptive.rank.Percentile: Constructs a Percentile with the specific quantile value and the following default method type: Percentile.EstimationType.LEGACY default NaN strategy: NaNStrategy.REMOVED a Kth Selector : KthSelector
Percentile(double, Percentile.EstimationType, NaNStrategy, KthSelector) - Constructor for class org.hipparchus.stat.descriptive.rank.Percentile: Constructs a Percentile with the specific quantile value, Percentile.EstimationType, NaNStrategy and KthSelector.
Percentile(Percentile) - Constructor for class org.hipparchus.stat.descriptive.rank.Percentile: Copy constructor, creates a new Percentile identical to the original
Percentile.EstimationType - Enum in org.hipparchus.stat.descriptive.rank: An enum for various estimation strategies of a percentile referred in wikipedia on quantile with the names of enum matching those of types mentioned in wikipedia.
percentiles(double, RandomGenerator) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder: Sets the computePercentiles setting of the factory.
PG_SUM_RELATIVE_ERROR - Static variable in class org.hipparchus.stat.inference.KolmogorovSmirnovTest: Convergence criterion for the sums in #pelzGood(double, double, int)}
populationVariance(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the population variance of the entries in the input array, or Double.NaN if the array is empty.
populationVariance(double[], double) - Static method in class org.hipparchus.stat.StatUtils: Returns the population variance of the entries in the input array, using the precomputed mean value.
populationVariance(double[], double, int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the population variance of the entries in the specified portion of the input array, using the precomputed mean value.
populationVariance(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the population variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
predict(double) - Method in class org.hipparchus.stat.regression.SimpleRegression: Returns the "predicted" y value associated with the supplied x value, based on the data that has been added to the model when this method is activated.
processDataPoint(double) - Method in interface org.hipparchus.stat.descriptive.rank.PSquarePercentile.PSquareMarkers: Process a data point by moving the marker heights based on estimator.
product(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the product of the entries in the input array, or Double.NaN if the array is empty.
product(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the product of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
Product - Class in org.hipparchus.stat.descriptive.summary: Returns the product of the available values.
Product() - Constructor for class org.hipparchus.stat.descriptive.summary.Product: Create a Product instance.
Product(Product) - Constructor for class org.hipparchus.stat.descriptive.summary.Product: Copy constructor, creates a new Product identical to the original.
PSquarePercentile - Class in org.hipparchus.stat.descriptive.rank: A StorelessUnivariateStatistic estimating percentiles using the P² Algorithm as explained by Raj Jain and Imrich Chlamtac in P² Algorithm for Dynamic Calculation of Quantiles and Histogram Without Storing Observations.
PSquarePercentile(double) - Constructor for class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Constructs a PSquarePercentile with the specific percentile value.
PSquarePercentile(PSquarePercentile) - Constructor for class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Copy constructor, creates a new PSquarePercentile identical to the original.
PSquarePercentile.PSquareMarkers - Interface in org.hipparchus.stat.descriptive.rank: An interface that encapsulates abstractions of the P-square algorithm markers as is explained in the original works.

Q

quantile() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Returns the quantile estimated by this statistic in the range [0.0-1.0]

R

R_1 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_1 has the following formulae for index and estimates
\( \begin{align} &index= Np + 1/2\, \\ &estimate= x_{\lceil h\,-\,1/2 \rceil} \\ &minLimit = 0 \\ \end{align}\)
R_2 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_2 has the following formulae for index and estimates
\( \begin{align} &index= Np + 1/2\, \\ &estimate=\frac{x_{\lceil h\,-\,1/2 \rceil} + x_{\lfloor h\,+\,1/2 \rfloor}}{2} \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}\)
R_3 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_3 has the following formulae for index and estimates
\( \begin{align} &index= Np \\ &estimate= x_{\lfloor h \rceil}\, \\ &minLimit = 0.5/N \\ \end{align}\)
R_4 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_4 has the following formulae for index and estimates
\( \begin{align} &index= Np\, \\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 1/N \\ &maxLimit = 1 \\ \end{align}\)
R_5 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_5 has the following formulae for index and estimates
\( \begin{align} &index= Np + 1/2\\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 0.5/N \\ &maxLimit = (N-0.5)/N \end{align}\)
R_6 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_6 has the following formulae for index and estimates
\( \begin{align} &index= (N + 1)p \\ &estimate= x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 1/(N+1) \\ &maxLimit = N/(N+1) \\ \end{align}\)
R_7 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_7 implements Microsoft Excel style computation has the following formulae for index and estimates.
\( \begin{align} &index = (N-1)p + 1 \\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = 0 \\ &maxLimit = 1 \\ \end{align}\)
R_8 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_8 has the following formulae for index and estimates
\( \begin{align} &index = (N + 1/3)p + 1/3 \\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = (2/3)/(N+1/3) \\ &maxLimit = (N-1/3)/(N+1/3) \\ \end{align}\)
R_9 - Enum constant in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: The method R_9 has the following formulae for index and estimates
\( \begin{align} &index = (N + 1/4)p + 3/8\\ &estimate = x_{\lfloor h \rfloor} + (h - \lfloor h \rfloor) (x_{\lfloor h \rfloor + 1} - x_{\lfloor h \rfloor}) \\ &minLimit = (5/8)/(N+1/4) \\ &maxLimit = (N-3/8)/(N+1/4) \\ \end{align}\)
RANDOM - Enum constant in enum org.hipparchus.stat.ranking.TiesStrategy: Ties get a random integral value from among applicable ranks
randomData - Variable in class org.hipparchus.stat.fitting.EmpiricalDistribution: RandomDataGenerator instance to use in repeated calls to getNext()
RandomPercentile - Class in org.hipparchus.stat.descriptive.rank: A StorelessUnivariateStatistic estimating percentiles using the RANDOM Algorithm.
RandomPercentile() - Constructor for class org.hipparchus.stat.descriptive.rank.RandomPercentile: Constructs a RandomPercentile with quantile estimation error set to the default (RandomPercentile.DEFAULT_EPSILON), using the default PRNG as source of random data.
RandomPercentile(double) - Constructor for class org.hipparchus.stat.descriptive.rank.RandomPercentile: Constructs a RandomPercentile with quantile estimation error epsilon using the default PRNG as source of random data.
RandomPercentile(double, RandomGenerator) - Constructor for class org.hipparchus.stat.descriptive.rank.RandomPercentile: Constructs a RandomPercentile with quantile estimation error epsilon using randomGenerator as its source of random data.
RandomPercentile(RandomGenerator) - Constructor for class org.hipparchus.stat.descriptive.rank.RandomPercentile: Constructs a RandomPercentile with default estimation error using randomGenerator as its source of random data.
RandomPercentile(RandomPercentile) - Constructor for class org.hipparchus.stat.descriptive.rank.RandomPercentile: Copy constructor, creates a new RandomPercentile identical to the original.
rank(double[]) - Method in class org.hipparchus.stat.ranking.NaturalRanking: Rank data using the natural ordering on Doubles, with NaN values handled according to nanStrategy and ties resolved using tiesStrategy.
rank(double[]) - Method in interface org.hipparchus.stat.ranking.RankingAlgorithm: Performs a rank transformation on the input data, returning an array of ranks.
RankingAlgorithm - Interface in org.hipparchus.stat.ranking: Interface representing a rank transformation.
reduce(double, Collection<RandomPercentile>) - Method in class org.hipparchus.stat.descriptive.rank.RandomPercentile: Computes the given percentile by combining the data from the collection of aggregates.
regress() - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Conducts a regression on the data in the model, using all regressors.
regress() - Method in class org.hipparchus.stat.regression.SimpleRegression: Performs a regression on data present in buffers and outputs a RegressionResults object.
regress() - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression: Performs a regression on data present in buffers and outputs a RegressionResults object
regress(int) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Conducts a regression on the data in the model, using a subset of regressors.
regress(int[]) - Method in class org.hipparchus.stat.regression.MillerUpdatingRegression: Conducts a regression on the data in the model, using regressors in array Calling this method will change the internal order of the regressors and care is required in interpreting the hatmatrix.
regress(int[]) - Method in class org.hipparchus.stat.regression.SimpleRegression: Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object
regress(int[]) - Method in interface org.hipparchus.stat.regression.UpdatingMultipleLinearRegression: Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object
RegressionResults - Class in org.hipparchus.stat.regression: Results of a Multiple Linear Regression model fit.
RegressionResults(double[], double[][], boolean, long, int, double, double, double, boolean, boolean) - Constructor for class org.hipparchus.stat.regression.RegressionResults: Constructor for Regression Results.
REMOVED - Enum constant in enum org.hipparchus.stat.ranking.NaNStrategy: NaNs are removed before computing ranks
removeData(double[][]) - Method in class org.hipparchus.stat.regression.SimpleRegression: Removes observations represented by the elements in data.
removeData(double, double) - Method in class org.hipparchus.stat.regression.SimpleRegression: Removes the observation (x,y) from the regression data set.
removeMostRecentValue() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Removes the most recent value from the dataset.
replaceMostRecentValue(double) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Replaces the most recently stored value with the given value.
reSeed(long) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Reseeds the random number generator used by EmpiricalDistribution.getNextValue().
reseedRandomGenerator(long) - Method in class org.hipparchus.stat.fitting.EmpiricalDistribution: Reseed the underlying PRNG.
rootLogLikelihoodRatio(long, long, long, long) - Method in class org.hipparchus.stat.inference.GTest: Calculates the root log-likelihood ratio for 2 state Datasets.
rootLogLikelihoodRatio(long, long, long, long) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Calculates the root log-likelihood ratio for 2 state Datasets.

S

SecondMoment - Class in org.hipparchus.stat.descriptive.moment: Computes a statistic related to the Second Central Moment.
SecondMoment() - Constructor for class org.hipparchus.stat.descriptive.moment.SecondMoment: Create a SecondMoment instance.
SecondMoment(SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.SecondMoment: Copy constructor, creates a new SecondMoment identical to the original.
SemiVariance - Class in org.hipparchus.stat.descriptive.moment: Computes the semivariance of a set of values with respect to a given cutoff value.
SemiVariance() - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance: Constructs a SemiVariance with default (true) biasCorrected property and default (Downside) varianceDirection property.
SemiVariance(boolean) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance: Constructs a SemiVariance with the specified biasCorrected property and default (Downside) varianceDirection property.
SemiVariance(boolean, SemiVariance.Direction) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance: Constructs a SemiVariance with the specified isBiasCorrected property and the specified Direction property.
SemiVariance(SemiVariance) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance: Copy constructor, creates a new SemiVariance identical to the original.
SemiVariance(SemiVariance.Direction) - Constructor for class org.hipparchus.stat.descriptive.moment.SemiVariance: Constructs a SemiVariance with the specified Direction property and default (true) biasCorrected property
SemiVariance.Direction - Enum in org.hipparchus.stat.descriptive.moment: The direction of the semivariance - either upside or downside.
SEQUENTIAL - Enum constant in enum org.hipparchus.stat.ranking.TiesStrategy: Ties assigned sequential ranks in order of occurrence
setData(double[]) - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Set the data array.
setData(double[]) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Set the data array.
setData(double[], int, int) - Method in class org.hipparchus.stat.descriptive.AbstractUnivariateStatistic: Set the data array.
setData(double[], int, int) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Set the data array.
setNoIntercept(boolean) - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Set intercept flag.
setQuantile(double) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Sets the value of the quantile field (determines what percentile is computed when evaluate() is called with no quantile argument).
setWindowSize(int) - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: WindowSize controls the number of values that contribute to the reported statistics.
SIGNIFICANCE_LEVEL - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: SIGNIFICANCE_LEVEL.
SimpleRegression - Class in org.hipparchus.stat.regression: Estimates an ordinary least squares regression model with one independent variable.
SimpleRegression() - Constructor for class org.hipparchus.stat.regression.SimpleRegression: Create an empty SimpleRegression instance
SimpleRegression(boolean) - Constructor for class org.hipparchus.stat.regression.SimpleRegression: Create a SimpleRegression instance, specifying whether or not to estimate an intercept.
Skewness - Class in org.hipparchus.stat.descriptive.moment: Computes the skewness of the available values.
Skewness() - Constructor for class org.hipparchus.stat.descriptive.moment.Skewness: Constructs a Skewness.
Skewness(Skewness) - Constructor for class org.hipparchus.stat.descriptive.moment.Skewness: Copy constructor, creates a new Skewness identical to the original.
Skewness(ThirdMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.Skewness: Constructs a Skewness with an external moment.
SpearmansCorrelation - Class in org.hipparchus.stat.correlation: Spearman's rank correlation.
SpearmansCorrelation() - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation: Create a SpearmansCorrelation without data.
SpearmansCorrelation(RealMatrix) - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation: Create a SpearmansCorrelation from the given data matrix.
SpearmansCorrelation(RealMatrix, RankingAlgorithm) - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation: Create a SpearmansCorrelation with the given input data matrix and ranking algorithm.
SpearmansCorrelation(RankingAlgorithm) - Constructor for class org.hipparchus.stat.correlation.SpearmansCorrelation: Create a SpearmansCorrelation with the given ranking algorithm.
StandardDeviation - Class in org.hipparchus.stat.descriptive.moment: Computes the sample standard deviation.
StandardDeviation() - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation: Constructs a StandardDeviation.
StandardDeviation(boolean) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation: Constructs a StandardDeviation with the specified value for the isBiasCorrected property.
StandardDeviation(boolean, SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation: Constructs a StandardDeviation with the specified value for the isBiasCorrected property and the supplied external moment.
StandardDeviation(SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation: Constructs a StandardDeviation from an external second moment.
StandardDeviation(StandardDeviation) - Constructor for class org.hipparchus.stat.descriptive.moment.StandardDeviation: Copy constructor, creates a new StandardDeviation identical to the original.
StatisticalMultivariateSummary - Interface in org.hipparchus.stat.descriptive: Reporting interface for basic multivariate statistics.
StatisticalSummary - Interface in org.hipparchus.stat.descriptive: Reporting interface for basic univariate statistics.
StatisticalSummaryValues - Class in org.hipparchus.stat.descriptive: Value object representing the results of a univariate statistical summary.
StatisticalSummaryValues(double, double, long, double, double, double) - Constructor for class org.hipparchus.stat.descriptive.StatisticalSummaryValues: Constructor.
StatUtils - Class in org.hipparchus.stat: StatUtils provides static methods for computing statistics based on data stored in double[] arrays.
StorelessCovariance - Class in org.hipparchus.stat.correlation: Covariance implementation that does not require input data to be stored in memory.
StorelessCovariance(int) - Constructor for class org.hipparchus.stat.correlation.StorelessCovariance: Create a bias corrected covariance matrix with a given dimension.
StorelessCovariance(int, boolean) - Constructor for class org.hipparchus.stat.correlation.StorelessCovariance: Create a covariance matrix with a given number of rows and columns and the indicated bias correction.
StorelessMultivariateStatistic - Interface in org.hipparchus.stat.descriptive: Base interface implemented by storeless multivariate statistics.
StorelessUnivariateStatistic - Interface in org.hipparchus.stat.descriptive: Extends the definition of UnivariateStatistic with StorelessUnivariateStatistic.increment(double) and StorelessUnivariateStatistic.incrementAll(double[]) methods for adding values and updating internal state.
StreamingStatistics - Class in org.hipparchus.stat.descriptive: Computes summary statistics for a stream of data values added using the addValue method.
StreamingStatistics() - Constructor for class org.hipparchus.stat.descriptive.StreamingStatistics: Construct a new StreamingStatistics instance, maintaining all statistics other than percentiles.
StreamingStatistics(double, RandomGenerator) - Constructor for class org.hipparchus.stat.descriptive.StreamingStatistics: Construct a new StreamingStatistics instance, maintaining all statistics other than percentiles and with/without percentiles per the arguments.
StreamingStatistics.StreamingStatisticsBuilder - Class in org.hipparchus.stat.descriptive: Builder for StreamingStatistics instances.
StreamingStatisticsBuilder() - Constructor for class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder: Simple constructor.
sum(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the sum of the values in the input array, or Double.NaN if the array is empty.
sum(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the sum of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
Sum - Class in org.hipparchus.stat.descriptive.summary: Returns the sum of the available values.
Sum() - Constructor for class org.hipparchus.stat.descriptive.summary.Sum: Create a Sum instance.
Sum(Sum) - Constructor for class org.hipparchus.stat.descriptive.summary.Sum: Copy constructor, creates a new Sum identical to the original.
sumDifference(double[], double[]) - Static method in class org.hipparchus.stat.StatUtils: Returns the sum of the (signed) differences between corresponding elements of the input arrays -- i.e., sum(sample1[i] - sample2[i]).
sumLog(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the sum of the natural logs of the entries in the input array, or Double.NaN if the array is empty.
sumLog(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the sum of the natural logs of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
sumOfLogs(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder: Sets the computeSumOfLogs setting of the factory
SumOfLogs - Class in org.hipparchus.stat.descriptive.summary: Returns the sum of the natural logs for this collection of values.
SumOfLogs() - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfLogs: Create a SumOfLogs instance.
SumOfLogs(SumOfLogs) - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfLogs: Copy constructor, creates a new SumOfLogs identical to the original.
sumOfSquares(boolean) - Method in class org.hipparchus.stat.descriptive.StreamingStatistics.StreamingStatisticsBuilder: Sets the computeSumOfSquares setting of the factory.
SumOfSquares - Class in org.hipparchus.stat.descriptive.summary: Returns the sum of the squares of the available values.
SumOfSquares() - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfSquares: Create a SumOfSquares instance.
SumOfSquares(SumOfSquares) - Constructor for class org.hipparchus.stat.descriptive.summary.SumOfSquares: Copy constructor, creates a new SumOfSquares identical to the original.
sumSq(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the sum of the squares of the entries in the input array, or Double.NaN if the array is empty.
sumSq(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the sum of the squares of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.

T

t(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
t(double[], double[]) - Method in class org.hipparchus.stat.inference.TTest: Computes a 2-sample t statistic, without the hypothesis of equal subpopulation variances.
t(double, double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a t statistic given observed values and a comparison constant.
t(double, double[]) - Method in class org.hipparchus.stat.inference.TTest: Computes a t statistic given observed values and a comparison constant.
t(double, double, double, double) - Method in class org.hipparchus.stat.inference.TTest: Computes t test statistic for 1-sample t-test.
t(double, double, double, double, double, double) - Method in class org.hipparchus.stat.inference.TTest: Computes t test statistic for 2-sample t-test.
t(double, StatisticalSummary) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a t statistic to use in comparing the mean of the dataset described by sampleStats to mu.
t(double, StatisticalSummary) - Method in class org.hipparchus.stat.inference.TTest: Computes a t statistic to use in comparing the mean of the dataset described by sampleStats to mu.
t(StatisticalSummary, StatisticalSummary) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, without the assumption of equal subpopulation variances.
t(StatisticalSummary, StatisticalSummary) - Method in class org.hipparchus.stat.inference.TTest: Computes a 2-sample t statistic, comparing the means of the datasets described by two StatisticalSummary instances, without the assumption of equal subpopulation variances.
TIES_ARE_NOT_ALLOWED - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: TIES_ARE_NOT_ALLOWED.
TiesStrategy - Enum in org.hipparchus.stat.ranking: Strategies for handling tied values in rank transformations.
TOO_MANY_REGRESSORS - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: TOO_MANY_REGRESSORS.
toString() - Method in class org.hipparchus.stat.descriptive.AbstractStorelessUnivariateStatistic
toString() - Method in class org.hipparchus.stat.descriptive.DescriptiveStatistics: Generates a text report displaying univariate statistics from values that have been added.
toString() - Method in class org.hipparchus.stat.descriptive.MultivariateSummaryStatistics: Generates a text report displaying summary statistics from values that have been added.
toString() - Method in class org.hipparchus.stat.descriptive.rank.PSquarePercentile: Returns a string containing the last observation, the current estimate of the quantile and all markers.
toString() - Method in class org.hipparchus.stat.descriptive.StatisticalSummaryValues: Generates a text report displaying values of statistics.
toString() - Method in class org.hipparchus.stat.descriptive.StreamingStatistics: Generates a text report displaying summary statistics from values that have been added.
toString() - Method in class org.hipparchus.stat.Frequency: Return a string representation of this frequency distribution.
toString() - Method in class org.hipparchus.stat.interval.ConfidenceInterval: Get String representation of the confidence interval.
transform(double[][]) - Method in class org.hipparchus.stat.projection.PCA: Transform the supplied data using our projection model.
tTest(double[], double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
tTest(double[], double[]) - Method in class org.hipparchus.stat.inference.TTest: Returns the observed significance level, or p-value, associated with a two-sample, two-tailed t-test comparing the means of the input arrays.
tTest(double[], double[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
tTest(double[], double[], double) - Method in class org.hipparchus.stat.inference.TTest: 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.
tTest(double, double[]) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
tTest(double, double[]) - Method in class org.hipparchus.stat.inference.TTest: 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.
tTest(double, double[], double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
tTest(double, double[], double) - Method in class org.hipparchus.stat.inference.TTest: Performs a two-sided t-test evaluating the null hypothesis that the mean of the population from which sample is drawn equals mu.
tTest(double, double, double, double) - Method in class org.hipparchus.stat.inference.TTest: Computes p-value for 2-sided, 1-sample t-test.
tTest(double, double, double, double, double, double) - Method in class org.hipparchus.stat.inference.TTest: Computes p-value for 2-sided, 2-sample t-test.
tTest(double, StatisticalSummary) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
tTest(double, StatisticalSummary) - Method in class org.hipparchus.stat.inference.TTest: 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.
tTest(double, StatisticalSummary, double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
tTest(double, StatisticalSummary, double) - Method in class org.hipparchus.stat.inference.TTest: 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.
tTest(StatisticalSummary, StatisticalSummary) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
tTest(StatisticalSummary, StatisticalSummary) - Method in class org.hipparchus.stat.inference.TTest: 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.
tTest(StatisticalSummary, StatisticalSummary, double) - Static method in class org.hipparchus.stat.inference.InferenceTestUtils: 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.
tTest(StatisticalSummary, StatisticalSummary, double) - Method in class org.hipparchus.stat.inference.TTest: 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.
TTest - Class in org.hipparchus.stat.inference: An implementation for Student's t-tests.
TTest() - Constructor for class org.hipparchus.stat.inference.TTest: Empty constructor.
TWO_OR_MORE_CATEGORIES_REQUIRED - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: TWO_OR_MORE_CATEGORIES_REQUIRED.
TWO_OR_MORE_VALUES_IN_CATEGORY_REQUIRED - Enum constant in enum org.hipparchus.stat.LocalizedStatFormats: TWO_OR_MORE_VALUES_IN_CATEGORY_REQUIRED.
TWO_SIDED - Enum constant in enum org.hipparchus.stat.inference.AlternativeHypothesis: Represents a two-sided test.

U

UnivariateStatistic - Interface in org.hipparchus.stat.descriptive: Base interface implemented by all statistics.
UpdatingMultipleLinearRegression - Interface in org.hipparchus.stat.regression: An interface for regression models allowing for dynamic updating of the data.
UPSIDE - Enum constant in enum org.hipparchus.stat.descriptive.moment.SemiVariance.Direction: The UPSIDE Direction is used to specify that the observations above the cutoff point will be used to calculate SemiVariance
UPSIDE_VARIANCE - Static variable in class org.hipparchus.stat.descriptive.moment.SemiVariance: The UPSIDE Direction is used to specify that the observations above the cutoff point will be used to calculate SemiVariance.

V

validateCovarianceData(double[][], double[][]) - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Validates that the x data and covariance matrix have the same number of rows and that the covariance matrix is square.
validateSampleData(double[][], double[]) - Method in class org.hipparchus.stat.regression.AbstractMultipleLinearRegression: Validates sample data.
valueOf(String) - Static method in enum org.hipparchus.stat.descriptive.moment.SemiVariance.Direction: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.stat.inference.AlternativeHypothesis: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.stat.LocalizedStatFormats: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.stat.ranking.NaNStrategy: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.hipparchus.stat.ranking.TiesStrategy: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.hipparchus.stat.descriptive.moment.SemiVariance.Direction: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.stat.descriptive.rank.Percentile.EstimationType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.stat.inference.AlternativeHypothesis: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.stat.LocalizedStatFormats: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.stat.ranking.NaNStrategy: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.hipparchus.stat.ranking.TiesStrategy: Returns an array containing the constants of this enum type, in the order they are declared.
valuesIterator() - Method in class org.hipparchus.stat.Frequency: Returns an Iterator over the set of values that have been added.
variance(double...) - Static method in class org.hipparchus.stat.StatUtils: Returns the variance of the entries in the input array, or Double.NaN if the array is empty.
variance(double[], double) - Static method in class org.hipparchus.stat.StatUtils: Returns the variance of the entries in the input array, using the precomputed mean value.
variance(double[], double, int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the variance of the entries in the specified portion of the input array, using the precomputed mean value.
variance(double[], int, int) - Static method in class org.hipparchus.stat.StatUtils: Returns the variance of the entries in the specified portion of the input array, or Double.NaN if the designated subarray is empty.
Variance - Class in org.hipparchus.stat.descriptive.moment: Computes the variance of the available values.
Variance() - Constructor for class org.hipparchus.stat.descriptive.moment.Variance: Constructs a Variance with default (true) isBiasCorrected property.
Variance(boolean) - Constructor for class org.hipparchus.stat.descriptive.moment.Variance: Constructs a Variance with the specified isBiasCorrected property.
Variance(boolean, SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.Variance: Constructs a Variance with the specified isBiasCorrected property and the supplied external second moment.
Variance(SecondMoment) - Constructor for class org.hipparchus.stat.descriptive.moment.Variance: Constructs a Variance based on an external second moment.
Variance(Variance) - Constructor for class org.hipparchus.stat.descriptive.moment.Variance: Copy constructor, creates a new Variance identical to the original.
varianceDifference(double[], double[], double) - Static method in class org.hipparchus.stat.StatUtils: Returns the variance of the (signed) differences between corresponding elements of the input arrays -- i.e., var(sample1[i] - sample2[i]).
VectorialCovariance - Class in org.hipparchus.stat.descriptive.vector: Returns the covariance matrix of the available vectors.
VectorialCovariance(int, boolean) - Constructor for class org.hipparchus.stat.descriptive.vector.VectorialCovariance: Constructs a VectorialCovariance.
VectorialStorelessStatistic - Class in org.hipparchus.stat.descriptive.vector: Uses an independent StorelessUnivariateStatistic instance for each component of a vector.
VectorialStorelessStatistic(int, StorelessUnivariateStatistic) - Constructor for class org.hipparchus.stat.descriptive.vector.VectorialStorelessStatistic: Create a new VectorialStorelessStatistic with the given dimension and statistic implementation.

W

WeightedEvaluation - Interface in org.hipparchus.stat.descriptive: Weighted evaluation for statistics.
wilcoxonSignedRank(double[], double[]) - Method in class org.hipparchus.stat.inference.WilcoxonSignedRankTest: Computes the Wilcoxon signed ranked statistic comparing means for two related samples or repeated measurements on a single sample.
wilcoxonSignedRankTest(double[], double[], boolean) - Method in class org.hipparchus.stat.inference.WilcoxonSignedRankTest: Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.
WilcoxonSignedRankTest - Class in org.hipparchus.stat.inference: An implementation of the Wilcoxon signed-rank test.
WilcoxonSignedRankTest() - Constructor for class org.hipparchus.stat.inference.WilcoxonSignedRankTest: Create a test instance where NaN's are left in place and ties get the average of applicable ranks.
WilcoxonSignedRankTest(NaNStrategy, TiesStrategy) - Constructor for class org.hipparchus.stat.inference.WilcoxonSignedRankTest: Create a test instance using the given strategies for NaN's and ties.
withBiasCorrected(boolean) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns a copy of this instance with the given biasCorrected setting.
withBiasCorrection(boolean) - Method in class org.hipparchus.stat.descriptive.moment.StandardDeviation: Returns a new copy of this standard deviation with the given bias correction setting.
withBiasCorrection(boolean) - Method in class org.hipparchus.stat.descriptive.moment.Variance: Returns a new copy of this variance with the given bias correction setting.
withEstimationType(Percentile.EstimationType) - Method in class org.hipparchus.stat.descriptive.rank.Median: Build a new instance similar to the current one except for the estimation type.
withEstimationType(Percentile.EstimationType) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Build a new instance similar to the current one except for the estimation type.
withKthSelector(KthSelector) - Method in class org.hipparchus.stat.descriptive.rank.Median: Build a new instance similar to the current one except for the kthSelector instance specifically set.
withKthSelector(KthSelector) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Build a new instance similar to the current one except for the kthSelector instance specifically set.
withNaNStrategy(NaNStrategy) - Method in class org.hipparchus.stat.descriptive.rank.Median: Build a new instance similar to the current one except for the NaN handling strategy.
withNaNStrategy(NaNStrategy) - Method in class org.hipparchus.stat.descriptive.rank.Percentile: Build a new instance similar to the current one except for the NaN handling strategy.
withVarianceDirection(SemiVariance.Direction) - Method in class org.hipparchus.stat.descriptive.moment.SemiVariance: Returns a copy of this instance with the given direction setting.

A B C D E F G H I K L M N O P Q R S T U V W
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form