If the underlying relationship is nonlinear, well, then maybe some of your inferences might not be as robust. Now, the next one is one we have seen before when we're talking about general conditions for inference, and this is the independence, independence condition, and there's a couple of ways to think about it. Either individual observations are independent of each other, so you could be sampling with replacement, or you could be thinking about your 10% rule that we have done when we thought about the independence condition for proportions and for means, where we would need to feel confident that the size of our sample is no more than 10% of the size of the population. 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.