Now, the next one is the normal condition, which we have talked about when we were doing inference for proportions and for means, although it means something a little bit more sophisticated when we're dealing with a regression. The normal condition, and once again, many times people will just say, assume it's been met, but let me actually draw a regression line, but do it with a little perspective, and I'm gonna add a third dimension. Let's say that's the x-axis, and let's say this is the y-axis, and the true population regression line looks like this. And so the normal condition tells us that for any given x in the true population, the distribution of y's that you would expect is normal, is normal, so let me see if I can draw a normal distribution for the y's given that x. So that would be that normal distribution there, and then let's say for this x right over here, you would expect a normal distribution as well, so just like, just like this. So for given x, the distribution of y's should be normal. Once again, many times you'll just be told to assume that that has been met because it might, at least in an introductory statistics class, be a little bit hard to figure this out on your own.