@pytest.mark.parametrize('param_insignificant_intercept, param_alpha, expected_result', [(0.051, 0.05, True), (10, 0.1, True), (0.049, 0.05, False), (0.001, 0.1, False)])
def test_insignificant_intercept_must_return_true_when_intercept_pvalue_is_greater_than_alpha(self, linearity_validator_obj, param_alpha, param_insignificant_intercept, expected_result):
    'Given homokedastic data\n        When check_hypothesis is called\n        Then intercept_not_significant must assert true'
    linearity_validator_obj.alpha = param_alpha
    linearity_validator_obj.fitted_result.pvalues = (param_insignificant_intercept, 'mock value')
    assert (linearity_validator_obj.insignificant_intercept is expected_result)
Given homokedastic data
When check_hypothesis is called
Then intercept_not_significant must assert true
@pytest.mark.parametrize('param_insignificant_intercept, param_alpha, expected_result', [(0.051, 0.05, True), (10, 0.1, True), (0.049, 0.05, False), (0.001, 0.1, False)])
def test_insignificant_intercept_must_return_true_when_intercept_pvalue_is_greater_than_alpha(self, linearity_validator_obj, param_alpha, param_insignificant_intercept, expected_result):
    'Given homokedastic data\n        When check_hypothesis is called\n        Then intercept_not_significant must assert true'
    linearity_validator_obj.alpha = param_alpha
    linearity_validator_obj.fitted_result.pvalues = (param_insignificant_intercept, 'mock value')
    assert (linearity_validator_obj.insignificant_intercept is expected_result)