And we really don't have to worry about it too much when we're thinking about just bivariate data. We're talking about caffeine and hour studying in this case. If we started to have more variables that tried to explain the hour studying, then we would care about adjusted R squared, but we're not gonna do that just yet. Last but not least, this S variable, this is the standard deviation of the residuals, which we study in other videos. And why is that useful? Well, that's a measure of how well the regression line fits the data. It's a measure of, we could say, the typical error.