@pytest.mark.parametrize('param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result', [(True, True, True, True), (True, False, False, False), (True, True, False, False), (False, True, True, False), (False, True, False, False), (False, False, False, False)])
def test_valid_regression_model(self, mocker, param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result):
    mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.significant_slope', new_callable=PropertyMock, return_value=param_significant_slope)
    mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.insignificant_intercept', new_callable=PropertyMock, return_value=param_insignificant_intercept)
    mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.valid_r_squared', new_callable=PropertyMock, return_value=param_valid_r_squared)
    analytical_data = [[0.1, 0.2, 0.15]]
    concentration_data = [[0.2, 0.2, 0.3]]
    linearity_validator = LinearityValidator(analytical_data, concentration_data)
    assert (linearity_validator.valid_regression_model is expected_result)
@pytest.mark.parametrize('param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result', [(True, True, True, True), (True, False, False, False), (True, True, False, False), (False, True, True, False), (False, True, False, False), (False, False, False, False)])
def test_valid_regression_model(self, mocker, param_significant_slope, param_insignificant_intercept, param_valid_r_squared, expected_result):
    mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.significant_slope', new_callable=PropertyMock, return_value=param_significant_slope)
    mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.insignificant_intercept', new_callable=PropertyMock, return_value=param_insignificant_intercept)
    mocker.patch('unit.test_validators.test_linearity_validator.LinearityValidator.valid_r_squared', new_callable=PropertyMock, return_value=param_valid_r_squared)
    analytical_data = [[0.1, 0.2, 0.15]]
    concentration_data = [[0.2, 0.2, 0.3]]
    linearity_validator = LinearityValidator(analytical_data, concentration_data)
    assert (linearity_validator.valid_regression_model is expected_result)