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? But that gets tricky because some are positive and some are negative. And so a big negative residual, negative residual could counterbalance a big positive residual and it would look, they would add up to zero and then it would look like there's no residual. So you could just add up the absolute values.