Or you could describe to me some more macro properties of what the data would be like. But I will certainly describe to you what point you need to be at if you want to start applying statistical methodology. But just in the future, so you know you will have a break. So maybe I don't want it to be too volatile. But I can be summarized as my name, my email address, my height and my weight, and maybe for most of you, this is basically enough. So again these are not-- I'm not predicting on 100 patients exactly the number of them they're going to be cured. The 10 best will be kept, and this will count for a total of 30% of the final grade. And for this maybe you need to beam something to it, and measure how fast it's coming back. Starting at 72, you can start making this conclusion. So those are the kind of questions that you may ask with probability. And they were like, let's all turn our left side to the left. Courses And this grade will count for 30% of the grade. Statistics is the opposite. There's a bunch of things that we need to understand what those things actually mean. Meaning that, it's not like all these guys called each other and it's actually a flash mob. So here, I found a bunch of press titles that-- I think the key word I was looking for was "study finds"-- if I want to do this. Since we have about less than 10 minutes. God knows how people interact. Would it be sometimes very close to 120, or sometimes for close to 10? For this as well, no pressure. It's a beautiful variable. So those are actually a good thing. And so I know, but the question is-- is 124 a large enough number or not? Now we're going to have to quantify how much of this preference. AUDIENCE: I noticed that the midterm dates aren't dated in the syllabus. So an estimator is different from an estimate. For example, one that you might find interesting is that this study finds that students benefit from waiting to declare a major. All these things, we need to understand so we can understand how to build those dikes or how to make decisions based on those data. All right, so we'll talk about maximum likelihood estimator. I mean, I'm sure you might have done this in AP stat or something. Is it going to be something that's like, out of their people-- let's see, for example, for the floods. And so there's a lot of things to put into place. And maybe you can extend it to more sophisticated methods that we did not cover in this class. But I can go back to the probability, make some inference about what my probability will look like, and then say, OK, then I can make those predictions later on. And so here let's just keep it vague, but you need to keep in mind what population this is actually making a statement about. Or maybe the study finds that this is beneficial for a majority of people. All right, and I throw out the fact that my patient is either a man or woman. Probability 1/6 for each of them. So maybe you want to just restrict it. But what remains is just sort of randomness that can be averaged out. This is 99 point-- this 99% -- no, so this is 95% confidence. And then when I plug in my numbers. They're offered at the graduate level, I believe. It's like you know curing a common cold. Any variable that takes only two possible values can be reduced to a Bernoulli. And then your goal will be to estimate those parameters. That's a pretty large number, right? And here I don't even have to tell you anything about the numbers or anything. PHILIPPE RIGOLLET: OK, so the course you're currently sitting in is 18.650. You can say, if you're rolling a fair die, you're going to have 1/6 of the time in your data you're going to have one. Which number do you pick? It will depend on the true unknown value of p. But from those particular values that we got, so 120 and-- how many couples was there? Turns out that there is data, and there is in the very serious journal Nature, someone published a very serious paper which actually looks pretty serious. And by late, I mean 24 hours late. Maybe probably The Thinker is more famous. But we can start saying, let's blah, and put some variables, and ask questions about those variables. And so a neonatal right-side preference makes a surprising romantic reappearance in later life. And so that's why I need the pre-requisite, because this is what we're going to use to describe the randomness. Actually this is, if you think about it, this is terrible because this puts non-zero probability on negative scores. And those are all over the place, those things. You want to make sure. And you can check that the number, you get will be 18. And in particular, I keep those variables to be random because I'm going to think of a random couple kissing left to right as the outcome of a random process, just like flipping a coin be getting heads or tails. But I want you to be able to understand what the limitations are, and when you make conclusions based on data, that those conclusions might be erroneous, for example. Introduction to Probability and Statistics. There's plenty of resources online if you want to expand on a particular topic or read it as said by somebody else. It might be challenging at times, but I can promise you that you will maybe suffer. All right, so basically those numbers actually don't come from anywhere. You will be allowed to cheat sheet, because, well, you can always forget something.