 The point of regression is to filter out the noise and define the underlying real effects. And the reason this becomes very important in social sciences, particularly in economics, is because it's so difficult for us to conduct experiments. When you can conduct an experiment, you control for things. So I want to know, you know, if I feed a plant coffee versus water, will the plant grow better? So what I do is I control for everything I can control for. I have two plants. I put them in the same temperature, the same humidity, the same light level, all of this. I feed them the same quantity of liquid. The only thing I change is what the liquid is. This one gets coffee. This one gets water. And I observe, I measure how much they grow over time. In a controlled experiment, the point is to control everything except for the one thing you're testing for. That thing we're going to vary. And so when I see a difference in the plants, I conclude it must be due to the coffee versus the water because everything else was the same. In economics, you rarely can do that. I can't control for things. I have to just take the data that's shown to me. So when I take the data that's shown to me, if I put it into a multiple regression model, I get the same sort of thing.