@pytest.mark.parametrize('param_anova_f_pvalue, 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_valid_anova_f_pvalue_must_return_true_when_r_squared_is_greater_than_0990(self, param_alpha, linearity_validator_obj, param_anova_f_pvalue, expected_result):
    'Given data with an aceptable regression model\n        When valid_anova_f_pvalue is called\n        Then anova_f_pvalue < alpha must assert true'
    linearity_validator_obj.alpha = param_alpha
    linearity_validator_obj.fitted_result.f_pvalue = param_anova_f_pvalue
    assert (linearity_validator_obj.valid_anova_f_pvalue is expected_result)
Given data with an aceptable regression model
When valid_anova_f_pvalue is called
Then anova_f_pvalue < alpha must assert true
@pytest.mark.parametrize('param_anova_f_pvalue, 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_valid_anova_f_pvalue_must_return_true_when_r_squared_is_greater_than_0990(self, param_alpha, linearity_validator_obj, param_anova_f_pvalue, expected_result):
    'Given data with an aceptable regression model\n        When valid_anova_f_pvalue is called\n        Then anova_f_pvalue < alpha must assert true'
    linearity_validator_obj.alpha = param_alpha
    linearity_validator_obj.fitted_result.f_pvalue = param_anova_f_pvalue
    assert (linearity_validator_obj.valid_anova_f_pvalue is expected_result)