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#Com.numericalmethod.suanshu.stats.test.distribution Classes and Interfaces - 23 results found.
| Name | Description | Type | Package | Framework |
| AndersonDarling | This algorithm calculates the Anderson-Darling k-sample test statistics and p-values. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
| AndersonDarlingPValue | This algorithm calculates the p-value when the Anderson-Darling statistic and the number of samples are given. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
| AS159 | Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
| AS159 .RandomMatrix | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu | |
| ChiSquareIndependenceTest | Pearson's chi-square test of independence assesses whether paired observations on two variables, expressed in a contingency table, are independent of each other. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
| ChiSquareIndependenceTest .Type | the available distributions used for the testThe default is the asymptotic distribution of Fisher's exact test. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
| CramerVonMises2Samples | This algorithm calculates the two sample Cramer-Von Mises test statistic and p-value. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
| DAgostino | D'Agostino's K2 test is a goodness-of-fit measure of departure from normality. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
| FisherExactDistribution | Fisher's exact test distribution is, as its name states, exact, and can therefore be used regardless of the sample characteristics. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
| JarqueBera | The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
| JarqueBeraDistribution | Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
| KolmogorovDistribution | The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| KolmogorovOneSidedDistribution | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu | |
| KolmogorovSmirnov | The Kolmogorov-Smirnov test (KS test) compares a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test). | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| KolmogorovSmirnov .Side | the available types of the Kolmogorov-Smirnov statistic check whether the cdf of a sample lies above the null hypothesis | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| KolmogorovSmirnov .Type | the available types of the Kolmogorov-Smirnov teststhe one-sample Kolmogorov-Smirnov test | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| KolmogorovSmirnov1Sample | The one-sample Kolmogorov-Smirnov test (one-sample KS test) compares a sample with a reference probability distribution. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| KolmogorovSmirnov2Samples | The two-sample Kolmogorov-Smirnov test (two-sample KS test) tests for the equality of the distributions of two samples. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| KolmogorovTwoSamplesDistribution | Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| KolmogorovTwoSamplesDistribution .Side | the available types of Kolmogorov-Smirnov two-sample testtwo-sample; two-sided | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
| Lilliefors | Lilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
| ShapiroWilk | The Shapiro-Wilk test tests the null hypothesis that a sample comes from a normally distributed population. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
| ShapiroWilkDistribution | Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics, which tests the null hypothesis that a sample comes from a normally distributed population. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |