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