Package | Description |
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org.apache.commons.math3.distribution |
Implementations of common discrete and continuous distributions.
|
org.apache.commons.math3.stat.inference |
Classes providing hypothesis testing and confidence interval
construction.
|
Constructor and Description |
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HypergeometricDistribution(int populationSize,
int numberOfSuccesses,
int sampleSize)
Construct a new hypergeometric distribution with the specified population
size, number of successes in the population, and sample size.
|
Modifier and Type | Method and Description |
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static double |
TestUtils.chiSquare(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquare(double[] expected,
long[] observed)
|
static double |
TestUtils.chiSquare(long[][] counts) |
double |
ChiSquareTest.chiSquare(long[][] counts)
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. |
static double |
TestUtils.chiSquareDataSetsComparison(long[] observed1,
long[] observed2) |
double |
ChiSquareTest.chiSquareDataSetsComparison(long[] observed1,
long[] observed2)
Computes a
Chi-Square two sample test statistic comparing bin frequency counts
in
observed1 and observed2 . |
static double |
TestUtils.chiSquareTest(double[] expected,
long[] observed) |
double |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed)
Returns the observed significance level, or
p-value, associated with a
Chi-square goodness of fit test comparing the
observed
frequency counts to those in the expected array. |
static boolean |
TestUtils.chiSquareTest(double[] expected,
long[] observed,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(double[] expected,
long[] observed,
double alpha)
Performs a
Chi-square goodness of fit test evaluating the null hypothesis that the
observed counts conform to the frequency distribution described by the expected
counts, with significance level
alpha . |
static double |
TestUtils.chiSquareTest(long[][] counts) |
double |
ChiSquareTest.chiSquareTest(long[][] counts)
Returns the observed significance level, or
p-value, associated with a
chi-square test of independence based on the input
counts
array, viewed as a two-way table. |
static boolean |
TestUtils.chiSquareTest(long[][] counts,
double alpha) |
boolean |
ChiSquareTest.chiSquareTest(long[][] counts,
double alpha)
Performs a
chi-square test of independence evaluating the null hypothesis that the
classifications represented by the counts in the columns of the input 2-way table
are independent of the rows, with significance level
alpha . |
static double |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2) |
double |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2)
Returns the observed significance level, or
p-value, associated with a Chi-Square two sample test comparing
bin frequency counts in
observed1 and
observed2 . |
static boolean |
TestUtils.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha) |
boolean |
ChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a Chi-Square two sample test comparing two binned data
sets.
|
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