HypothesisTest_Pearson

HypothesisTest_Pearson(firstSample, secondSample, level=0.95)

Test whether two discrete samples are independent.

Available usages:

HypothesisTest.Pearson(firstSample, secondSample)

HypothesisTest.Pearson(firstSample, secondSample, level)

Parameters:

fisrtSample : 2-d sequence of float

First tested sample, of dimension n \geq 1.

secondSample : 2-d sequence of float

Second tested sample, of dimension 1.

level : positive float < 1

Threshold p-value of the test (= 1 - first type risk), it must be < 1, equal to 0.95 by default.

Returns:

testResult : TestResult

Structure containing the result of the test.

Notes

The Pearson Test is used to check whether two samples which are assumed to form a gaussian vector are independent (based on the evaluation of the linear correlation coefficient).

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distCol = [ot.Normal(), ot.Normal()]
>>> firstSample = ot.Normal().getSample(30)
>>> secondSample = ot.Normal().getSample(30)
>>> test_result = ot.HypothesisTest.Pearson(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=TwoSamplePearson binaryQualityMeasure=true p-value threshold=0.05 p-value=0.984737 description=[]