99.99% of the cohort of 100 patients to whom you administer this drug, I will be able to conclude that at least 65 of them will be cured, on 99.99% percent of those data sets. And so there seems to be this preference. But you know you can check for yourself. -2. So I divide by n the total number of 1's. And I also understand that for some of you, it's a matter of scheduling. And it's actually beautiful. » Construct estimators using method of moments and maximum likelihood, and decide how to choose between them, Quantify uncertainty using confidence intervals and hypothesis testing, Choose between different models using goodness of fit test, Make prediction using linear, nonlinear and generalized linear models, Perform dimension reduction using principal component analysis (PCA). And the rest of it might fluctuate to the right, might fluctuate to the left. My best guess here is that I'm confident I have to add the extra error that I bet you making by predicting that here, the drug is not 80% effective but 78% effective. Or that you know Tanya did not text that we should all turn our head to the left now. So an estimator is a random variable. Now, if you look at the data they probably had, maybe it was scarce. What? We don't offer credit or certification for using OCW. Or maybe the study finds that this is beneficial for a majority of people. So who has taken, for example, a machine-learning class here? NEC Corporation Fund for Research in Computers and Communications. Would it be sometimes very close to 120, or sometimes for close to 10? If I put 20, there would definitely be one that's wrong. I love that you do not want to pronounce yourself with no data actually to make any decision. The goal of this exercise is not to compute those things, it's really to think about modeling assumptions. It's a beautiful variable. Julia has some statistics in there, but it really if you were to learn a statistical software, let's say you love doing this, this would be the one that would prove most useful for you in the future. I would like to receive email from MITx and learn about other offerings related to Fundamentals of Statistics. AUDIENCE: So both the midterms will be kept? Which number do you pick? You would need to see 72 that turn their head right to actually make this conclusion. So that's basically the things we're going to be looking at. Notes for 18.6501x, Fundamentals of Statistics on edX Resources. So there's some true randomness in some surveys. For a die we know that each face is going to come with probably 1/6. All right, this is not the easiest class. It's a random variable. All right, more practically, my goal here is to have you ready. PHILIPPE RIGOLLET: OK, so the course you're currently sitting in is 18.650. There's plenty of resources online if you want to expand on a particular topic or read it as said by somebody else. So sometimes the randomness we have here is real. The theory that we're going to develop is actually for a very clean use of data, which might be a little unpleasant. OK, so the pre-reqs here-- and who has looked at the first problem set already? That's a pretty large number, right? You're not going to say, you know what? Attendance is not going to be taken or anything like this. That's typically actually the same size of data that you would see for FMRI data. There's an explanation for it. And so people are trying to find things. But go back to your probability class and make sure that you know how to do all of them. Maybe I could have followed all those guys, and knew exactly what they were-- maybe I could have looked at pictures of them in the womb and guess how they were turning-- by the way that's one of the conclusions, they're guessing that we turn our head to the right because our head is turned to the right in the womb. And we know that, in this thing, there's basically a proportion of them, that's like p, and that's the proportion of them that's bending their head to the right. PHILIPPE RIGOLLET: The best of the two, not the best two. Who would be convinced by 80? Questions? All right, so what are the goals of this class? Same thing, you roll two dice. We actually can make some question. So here we said that 80 was larger than 50-- was allowing us to conclude at 64.5%. How does this work? How do we do it? I have only one data set. OK, so of course, statistics is not just for you to be able to read the press. Now the xi's no longer need to be 0's and 1's, so it's not going to boil down to being a number of ones divided by the total numbers. We don't want right or left. We'll have some problems to solve as well. Again, we're not going to just take this data set of those cases and look at them in detail. So I see the time now and it's 11:56, so we have another 30 minutes. It's another thing to put some error bars around. Statistics basically means averaging. 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. I'm not a majority. OK, so-- sorry about that. It does not scale super well to high dimensional data. If anything, it was actually probably the opposite. Enough that you can actually build a reasonable model for what they're actually doing. But it's a coin toss. I don't know if you ever saw this one by John Oliver about phacking. 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