And so a negative residual is when your actual is below your predicted. So this right over here, this is r one. It is a negative residual. If you had, if you tried to find, let's say this residual right over here for this point, this r two, this would be a positive residual because the actual is larger than what would have actually been predicted. And so a residual is good for seeing, well, how good does your line, does your regression, does your model fit a given data point or how does a given data point compare to that? But what you probably wanna do is think about some combination of all the residuals and try to minimize it. Now you might say, well, why don't I just add up all the residuals and try to minimize that?