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Studying for a test? Prepare with these 2 lessons on Inference for categorical data (chi-square tests).
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Video transcript
In this video, we'll just talk a little bit about what the chi-square distribution is, sometimes called the chi-squared distribution. And then in the next few videos, we'll actually use it to really test how well theoretical distributions explain observed ones, or how good a fit observed results are for theoretical distributions. So let's just think about it a little bit. So let's say I have some random variables. And each of them are independent, standard, normal, normally distributed random variables. So let me just remind you what that means. So let's say I have the random variable X. If X is normally distributed, we could write that X is a normal random variable with a mean of 0 and a variance of 1. Or you could say that the expected value of X, is equal to 0, or in that the variance of our random variable X is equal to 1. Or just to visualize it is that, when we take an instantiation of this very variable, we're sampling from a normal distribution, a standardized normal distribution that looks like this. Mean of 0 and then a variance of 1, which would also mean, of course, a standard deviation of 1. So that could be the standard deviation, or the variance, or the standard deviation, that would be equal to 1. So a chi-square distribution, if you just take one of these random variables-- and let me define it this way. Let me define a new random variable. Let me define a new random variable Q that is equal to-- you're essentially sampling from this the standard normal distribution and then squaring whatever number you got. So it is equal to this random variable X squared. The distribution for this random variable right here is going to be an example of the chi-square distribution. Actually what we're going to see in this video is that the chi-square, or the chi-squared distribution is actually a set of distributions depending on how many sums you have. Right now, we only have one random variable that we're squaring. So this is just one of the examples. And we'll talk more about them in a second. So this right here, this we could write that Q is a chi-squared distributed random variable. Or that we could use this notation right here. Q is-- we could write it like this. So this isn't an X anymore. This is the Greek letter chi, although it looks a lot like a curvy X. So it's a member of chi-squared. And since we're only taking one sum over here-- we're only taking the sum of one independent, normally distributed, standard or normally distributed variable, we say that this only has 1 degree of freedom. And we write that over here. So this right here is our degree of freedom. We have 1 degree of freedom right over there. So let's call this Q1. Let's say I have another random variable. Let's call this Q-- let me do it in a different color. Let me do Q2 in blue. Let's say I have another random variable, Q2, that is defined as-- let's say I have one independent, standard, normally distributed variable. I'll call that X1. And I square it. And then I have another independent, standard, normally distributed variable, X2. And I square it. So you could imagine both of these guys have distributions like this. And they're independent. So get to sample Q2, you essentially sample X1 from this distribution, square that value, sample X2 from the same distribution, essentially, square that value, and then add the two. And you're going to get Q2. This over here-- here we would write-- so this is Q1. Q2 here, Q2 we would write is a chi-squared, distributed random variable with 2 degrees of freedom. Right here. 2 degrees of freedom. And just to visualize kind of the set of chi-squared distributions, let's look at this over here. So this, I got this off of Wikipedia. This shows us some of the probability density functions for some of the chi-square distributions. This first one over here, for k of equal to 1, that's the degrees of freedom. So this is essentially our Q1. This is our probability density function for Q1. And notice it really spikes close to 0. And that makes sense. Because if you are sampling just once from this standard normal distribution, there's a very high likelihood that you're going to get something pretty close to 0. And then if you square something close to 0-- remember, these are decimals, they're going to be less than 1, pretty close to 0-- it's going to become even smaller. So you have a high probability of getting a very small value. You have high probabilities of getting values less than some threshold, this right here, less than, I guess, this is 1 right here. So the less than 1/2. And you have a very low probability of getting a large number. I mean, to get a 4, you would have to sample a 2 from this distribution. And we know that 2 is-- actually it's 2 variances or 2 standard deviations from the mean. So it's less likely. And actually that's to get a 4. So to get even larger numbers are going to be even less likely. So that's why you see this shape over here. Now when you have 2 degrees of freedom, it moderates a little bit. This is the shape, this blue line right here is the shape of Q2. And notice you're a little bit less likely to get values close to 0 and a little bit more likely to get numbers further out. But it still is kind of shifted or heavily weighted towards small numbers. And then if we had another random variable, another chi-squared distributed random variable-- so then we have, let's say, Q3. And let's define it as the sum of 3 of these independent variables, each of them that have a standard normal distribution. So X1, X2 squared plus X3 squared. Then all of a sudden, our Q3-- this is Q2 right here-- has a chi-squared distribution with 3 degrees of freedom. And so this guy right over here-- that will be this green line. Maybe I should have done this in green. This will be this green line over here. And then notice, now it's starting to become a little bit more likely that you'd get values in this range over here because you're taking the sum. Each of these are going to be pretty small values, but you're taking the sum. So it starts to shift it a little over to the right. And so the more degrees of freedom you have, the further this lump starts to move to the right and, to some degree, the more symmetric it gets. And what's interesting about this, I guess it's different than almost every other distribution we've looked at, although we've looked at others that have this property as well, is that you can't have a value below 0 because we're always just squaring these values. Each of these guys can have values below 0. They're normally distributed. They could have negative values. But since we're squaring and taking the sum of squares, this is always going to be positive. And the place that this is going to be useful-- and we're going to see in the next few videos-- is in measuring essentially error from an expected value. And if you took take this total error, you can figure out the probability of getting that total error if you assume some parameters. And we'll talk more about it in the next video. Now with that said, I just want to show you how to read a chi-squared distribution table. So if I were to ask you, if this is our distribution-- let me pick this blue one right here. So over here, we have 2 degrees of freedom because we're adding 2 of these guys right here. If I were to ask you, what is the probability of Q2 being greater than-- or, let me put it this way. What is the probability of Q2 being greater than 2.41? And I'm picking that value for a reason. So I want the probability of Q2 being greater than 2.41. What I want to do is I'll look at a chi-square table like this. Q2 is a version of chi-squared with 2 degrees of freedom. So I look at this row right here under 2 degrees of freedom. And I want the probability of getting a value above 2.41. And I picked 2.41 because it's actually at this table. And so most of these chi-squared-- the reason why we have these weird numbers like this instead of whole numbers or easy-to-read fractions is it is actually driven by the p value. It's driven by the probability of getting something larger than that value. So normally you would look at the other way. You'd say, OK, if I want to say, what chi-squared value for 2 degrees of freedom, there's a 30% chance of getting something larger than that? Then I would look up 2.41. But I'm doing it the other way just for the sake of this video. So if I want the probability of this random variable right here being greater than 2.41, or its p value, we read it right here. It is 30%. And just to visualize it on this chart, this chi-square distribution-- this was Q2, the blue one, over here-- 2.41 is going to sit-- let's see. This is 3. This is 2.5. So 2.41 is going to be someplace right around here. So essentially, what that table is telling us is, this entire area under this blue line right here, what is that? And that right there is going to be 30% of-- well, it's going to be 0.3. Or you could view it as 30% of the entire area under this curve, because obviously all the probabilities have to add up to 1. So that's our intro to the chi-square distribution. In the next video, we're actually going to use it to make some, or to test some, inferences.