# LinearModelTest_LinearModelBreuschPagan¶

LinearModelTest_LinearModelBreuschPagan(*args)

Test the homoskedasticity of the linear regression model residuals.

Available usages:

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample)

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample, linearModel)

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample, level)

LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample, linearModel, level)

Parameters: firstSample : 2-d sequence of float First tested sample, of dimension 1. secondSample : 2-d sequence of float Second tested sample, of dimension 1. linearModel : LinearModel A linear model. If not provided, it is built using the given samples. level : positive float Threshold p-value of the test (= first kind risk), it must be , equal to 0.05 by default. testResult : TestResult Structure containing the result of the test.

Notes

The LinearModelTest class is used through its static methods in order to evaluate the quality of the linear regression model between two samples (see LinearModel). The linear regression model between the scalar variable and the -dimensional one is as follows: where is the residual.

The Breusch-Pagan test checks the heteroskedasticity of the residuals. A linear regression model is fitted on the squared residuals. The statistic is computed using the Studendized version with the chi-squared distribution. If the binary quality measure is false, then the homoskedasticity hypothesis can be rejected with respect to the given level.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> distribution = ot.Normal()
>>> sample = distribution.getSample(30)
>>> func = ot.SymbolicFunction('x', '2 * x + 1')
>>> firstSample = sample
>>> secondSample = func(sample) + ot.Normal().getSample(30)
>>> test_result = ot.LinearModelTest.LinearModelBreuschPagan(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=BreuschPagan binaryQualityMeasure=true p-value threshold=0.05 p-value=0.700772 description=[]