When evaluating the effectiveness of a linear regression model, we use residuals to do this. But what is a residual? Well, they’re just the difference between the actual data and the value predicted by a linear regression model, or y-ŷ. A point closer to the best fit line has a smaller residual while a point farther from the best fit line has a larger residual. But what does this all mean? Well, if we have a positive residual, then the actual value is greater than the predicted value and we say that the model underestimates the true value by a certain amount. Likewise, if we have a negative residual, then the actual value is less than the predicted value and we say that the model overestimates the true value by a certain amount.