Math can be tricky, but understanding residuals can make it easier. Residuals are a way to measure the difference between a predicted value and the actual value in a regression analysis. Residuals are calculated by subtracting the predicted value from the observed value. They can be used to tell if a model is accurate or not. A negative residual indicates that the observed value is larger than the predicted value, and a positive residual indicates that the predicted value is larger than the observed value. When the residuals are small, it means that the model is accurate. Understanding how residuals work can give you a better understanding of how your data is being used. With the right knowledge, you can make the most of your data and make better decisions.