Summing the error values in a regression model is misleading because negative errors cancel out positive errors.

Question

Quizplus · Accepted Answer

This is because the sum of errors is used to calculate the mean square error (MSE), which should reflect the overall magnitude of the errors. However, canceling errors may make the MSE appear smaller than it actually is, leading to an overoptimistic assessment of the model's performance. Instead of summing the errors, it is better to take their absolute values or square them before summing, so that they cannot cancel each other out.

Summing the Error Values in a Regression Model Is Misleading