Error term

The error term is the difference between what a model predicts and what actually happens in real life. It accounts for factors the model doesn’t include or measure, like random variation or other influences. Essentially, it captures the imperfections in the model’s predictions, acknowledging that no model can perfectly explain every detail of real-world outcomes. Understanding the error term helps us see how accurate our predictions are and where improvements might be needed.