@pytest.mark.parametrize('param_alpha, param_breusch_pagan_pvalue, expected_result', [(1, (- 10), False), (0.05, 0.049, False), (0.1, 0.11, True), (0.05, 10, True)])
def test_is_homokedastic_must_return_false_when_breusch_pagan_pvalue_is_smaller_than_alpha_otherwise_true(self, param_alpha, param_breusch_pagan_pvalue, expected_result):
    analytical_data = [[0.1, 0.2, 0.15]]
    concentration_data = [[0.1, 0.2, 0.3]]
    linearity_validator = LinearityValidator(analytical_data, concentration_data, param_alpha)
    linearity_validator.breusch_pagan_pvalue = param_breusch_pagan_pvalue
    assert (linearity_validator.is_homoscedastic is expected_result)
@pytest.mark.parametrize('param_alpha, param_breusch_pagan_pvalue, expected_result', [(1, (- 10), False), (0.05, 0.049, False), (0.1, 0.11, True), (0.05, 10, True)])
def test_is_homokedastic_must_return_false_when_breusch_pagan_pvalue_is_smaller_than_alpha_otherwise_true(self, param_alpha, param_breusch_pagan_pvalue, expected_result):
    analytical_data = [[0.1, 0.2, 0.15]]
    concentration_data = [[0.1, 0.2, 0.3]]
    linearity_validator = LinearityValidator(analytical_data, concentration_data, param_alpha)
    linearity_validator.breusch_pagan_pvalue = param_breusch_pagan_pvalue
    assert (linearity_validator.is_homoscedastic is expected_result)