# LinearModelTest_LinearModelRSquared¶

LinearModelTest_LinearModelRSquared(*args)

Test the quality of the linear regression model based on the indicator.

Available usages:

LinearModelTest.LinearModelRSquared(firstSample, secondSample)

LinearModelTest.LinearModelRSquared(firstSample, secondSample, level)

LinearModelTest.LinearModelRSquared(firstSample, secondSample, linearModel)

LinearModelTest.LinearModelRSquared(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 Threshold p-value of the test (= 1 - first type risk), it must be , equal to 0.95 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, supposed to follow the standard Normal distribution.

The LinearModelRSquared test checks the quality of the linear regression model. It evaluates the indicator (regression variance analysis) and compares it to a 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.LinearModelRSquared(firstSample, secondSample)
>>> print(test_result)
class=TestResult name=Unnamed type=RSquared binaryQualityMeasure=false p-value threshold=0.95 p-value=0.822343 description=[]