Heteroscedasticity in linear regression models can be caused by direction dependence issues when dealing with nonnormal variables. This means that different models could equally explain the data, leading to violations of the homoscedasticity assumption. A new method called Direction Dependence Analysis (DDA) helps determine the direction of effects in linear models. By using visual diagnostics and homoscedasticity tests, researchers can make better decisions about the relationships between variables. A simulation showed that this approach works well, and an example demonstrated its usefulness in real-world cases where assumptions are violated.