It is actually much easier to apply a random forest (or really gradient boosted decision tree, which almost strictly dominates random forests) than a linear regression. Decision tree methods require far less data preprocessing than linear regression, because the model is able to infer feature relationships. Obviously if your features are linearly related to your target than linear regression is much more viable.
This is absolutely true, the one caveat is that you can explain the significance of features and the relationship to the response variables in simpler terms.
