LinearModelTest_LinearModelResidualMean

LinearModelTest_LinearModelResidualMean(*args)

Test zero mean value of the residual of the linear regression model.

Available usages:

LinearModelTest.LinearModelResidualMean(firstSample, secondSample)

LinearModelTest.LinearModelResidualMean(firstSample, secondSample, level)

LinearModelTest.LinearModelResidualMean(firstSample, secondSample, linearModel)

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

Parameters:

fisrtSample : 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 < 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 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 Y and the n-dimensional one \vect{X} = (X_i)_{i \leq n} is as follows:

\tilde{Y} = a_0 + \sum_{i=1}^n a_i X_i + \epsilon

where \epsilon is the residual, supposed to follow the standard Normal distribution.

The LinearModelResidualMean Test checks, under the hypothesis of a gaussian sample, if the mean of the residual is equal to zero. It is based on the Student test (equality of mean for two gaussian samples).

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.LinearModelResidualMean(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=ResidualMean binaryQualityMeasure=true p-value threshold=0.05 p-value=1 description=[]