The Risk of Overfitting to Data

This fit does adequately capture incomes below forty thousand or incomes above about 85, leading us to believe that maybe a linear fit wasn't the best choice for this. The residuals are left over after you subtract the actual observed data minus the del's prediction. That's the amount of error, the what the model is not accounting for. So whenever there's a pattern in the residuals that tells you that your model failed to account for something, because if your model accounted for everything that's predictable, your residuals would look like white noise, just random data. What i want to talk about here is heteroscidasticity. Hetero scydasticity is the circumstance in which