In the previous video, we introduced something called a demand curve and the demand curve to recall. Let's just do a little review here. We'll review demand curves and demand curve for us was on, It's a representation of how consumers respond to different possible prices and let's call this D0. And we learned last time in the other video that we're going to treat this as a linear function. Even on the railroad, they might not be linear. All we really need is for the curve to be downward slope here. And I pointed out last time that many yous say, well, Larry, why isn't the curve out here? Or why isn't the curb back here, or why isn't the curse deeper? Why isn't the curve flatter? All those will really good questions, but the answer to that question is I've got the pen, I drew it. I put it there, okay? For me, the power is it's downward sloping which shows the inverse relationship between price and quantity. Great stuff, okay? Now one of the common questions people have is where do you find these demand curves in the real world? Well, you drew that there Larry because you've got the pen but in the real world, presumably firms need to figure these out and they do, firms have to figure this out. Okay, and they have people who will do that for them. And that brings us to thinking about this general issue in this course. I'm going to draw my axes system again, try and be a little bit more careful about it. And we'll put on this axis, price, and on this axis, well put quantity. And one of the things that happens in a course like this in essentially in all programs where you would be studying for microeconomics in your program is that the textbooks are essentially looking at what we would call expectation operators. In the real world, there's uncertainty. In the real world, there's uncertainty. Firms who make a product don't know for sure how many people are going to buy that product. If they introduce a new product, they have to come up with a price and when they invent a price out there, even if it's a product somebody's already making you're going to enter that market, say, I think I can also sell milk or I think I can also sell peanut butter. I think I can also sell sugar-coated little grains of oats and call it ready-to-eat breakfast cereal, whatever. I've quote a price and I got to think how many people are going to buy it. I got to figure out what the demand curve looks like. And in the real world, consumers, firms will think, if I quote a price, whoops, if I quote a price of P0, okay, that price of P0 gets sent out into the marketplace. Now I know that out in the marketplace, there's a distribution of consumers and that distribution is going to end up looking something like this. That curve sometimes called the bell curve. Or if you had a good course in statistics, a normal distribution. The normal distribution is a distribution of consumers or whatever the product, whatever it is, that looks like the bell curve. And what that says is that there's a low probability that at this price P0, consumers are only want to buy this amount. There's a higher probability they want to buy this amount. There's a higher probability they want to buy this amount. The highest possible probability is the mean of that set of that thing. That's the most likely amount. It's possible consumers will buy clear out here, but that's a low probability. It's possible consumers will buy clear out here. What a great deal for you if you quote that price and that many people want to buy it but that's a really low probability of it. The normal distribution, the bell curve captures the relative probability of any one of those points. You as a firm don't know for sure. You're setting that price up and you see what happens. So at this particular case, the best guess is that consumers are going to buy this amount, okay? Those of you who remember your stat class, that's normally would call that mu, the mean of the normal distribution. And that gives us a certain amount of output that we can expect. Now if we had a lower price, let's try P1. We would send that price signal out in the market. And we expect a different distribution of consumers. Whoops, that's a pretty bad try. Let me be a better draftsman than that. It's going to be another bell curve. But now it's going to be something that looks about like this. Okay, and here, the best estimate of how much we're going to sell, that is the highest probability outcome is the peak of that normal distribution and says, you know what, here is our expected amount. So this would be the expectation, we'll call that Q hat sub 0 or this would be the expectation, we'll call that Q hat sub 1, or we'll try one more and then I'll be done with this and you'll kind of get where we're going with this problem. We could have an even lower price. And the lower price, we have an idea that consumers distribution looks like this and our best guess of the amount we're going to sell. The highest probability event would be, we'll call that Q hat sub 2. These are just expectations. I've just shown you here three possible prices of many, many possible prices, but this is enough for you understand that the firm's expectation is if it quotes P0, it expects it's going to sell this amount, right? This amount here, which is this point on the demand curve. That's my expected spot on the demand curve. This is my expected spot on the demand curve. This is my expected spot. So let's fill these dots in. Here's what I'm expecting to sell. Here's what I'm expecting to sell. And here's what I'm expecting to sell. I'm going to connect those dots. It would be a lot more of them, right? And this would be the line, knows it might not even be linear. It might have some curvature to it. But this is my expected demand curve, right? In the real world, I know there's uncertainty out there. When I quote a price like P1, there is a probability that only this many people want to buy the product. There's also a probability that maybe way out here want to buy the products because these are low probability events. The best expectation, the most highest probability event is that they're going to buy exactly this amount. So that's where I get this demand curve. [COUGH] Again, it's probably not linear, but that's all right. So where do they get these data, where do firms get these data? Well, firms keep track of their data. [LAUGH] They sell products, they quote prices, and they sell the amount of output and they may build massive databases. In this world of Big Data, they hire statisticians who know all sorts of amazing tricks to use to try to tease out of that data what we've been talking about since we started this demand series, which is I know that demand is just some function of price. The amount people want to buy is some function of the price and as price goes up, people want to buy less. How much less? Well, these are my expectations. Probably just about every one of you, at least in the United States, everybody who shops for food in grocery stores has a little tag on their card, on their key chain, okay? It's a loyalty tag. It's a customer tag. It's a tag, here in town, there's a grocery store that many people shop at, it's a chain from St. Louis called Schnucks. And Schnucks has been many stores around town and I got on my keychain a little card that say Schnucks because they give me points towards cheaper gasoline, okay? Okay, it wasn't depending on how much money I spent at the store and why do they do this? Are they just trying to give me make me my friend? No, they want my data when I go in there, they swipe that card and then that card keeps track of every single thing. I bought and they keep track of how much does Larry by G last week when we had mayonnaise on sale very bought more. Okay, and I can C at that lower price for Manis how much Larry bought and it turns out we've been Been raising the price of Johnnie Walker Red Label Scotch last three weeks and Larry's never slowed up his purchase of that. All right, though. So so Larry sensitivity to different possible prices is all captured at that data and they've got everybody's data. They've got big make terabytes of data a day keeping track of all these consumers and then they take those data and they go to this company called Zillow, which is got everybody's house in America because everybody's house everybody's house in America has to be Kurt. It's a matter of record at the county clerk's office. Exactly who owns it and how much they paid that is required data free to the public Zillow has moved all that data electronically and they sell out the people like she cooks. She cooks takes that data in they know how much Larry's house is worth. That's a good proxy of what Larry's over the gum is versus Big Bob's versus James. Okay. So but they put all that in there. Then they got the Census Data the Census Data is down to a four Block Level and that census data is a four-block levels called a census tract and that Census Data tells you how what's the number average? Number of kids per household in that for Block region. What's the average age of the kids in that household what the number of average pets in that household. So they have all this indication of all these things while are you want one am I ought to buy pet food or baby diapers and all these sorts of things and they put it all in there along with my proven track record of all the times they swiped at the checkout what I buy and they get data and that data allows them to get really nice-looking demand curves of what people will actually spend for. May days versus Frozen Tombstone pizzas and all these sorts of things and they build these demand curves. It's a great thing.