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Current time:0:00Total duration:7:31

AP.STATS:

UNC‑3 (EU)

, UNC‑3.A (LO)

, UNC‑3.A.2 (EK)

- What we're going to do in this video is talk about a special
class of random variables known as binomial variables. And as we will see as we build
up our understanding of them, not only are they interesting
in their own right, but there's a lot of very powerful probability and statistics that we can do based on our understanding of binomial variables. So to make things concrete
as quickly as possible, I'll start with a very tangible example of a binomial variable and then we'll think a
little bit more abstractly abut what makes it binomial. So let's say that I have a coin. This is my coin here. It doesn't even have to be a fair coin. Let me just draw this really fast. So that's my coin. And let's say on a
given flip of that coin, the probability that I get
heads is zero point six and the probability that I'd get tails, well it'd be one minus zero point six or zero point four. And what I'm going to do
is I'm going to define a random variable X as being equal to the number of heads after after ten flips of my coin. Now, what makes this a binomial variable? Well, one of the first conditions that's often given for a binomial variable is that it's made up of a finite number of independent trials. So, it's made up made up of independent independent trials. Now, what do I mean by independent trials? Well, a trial is each flip of my coin. So a flip is equal to a trial in the language of this
statement that I just made. And what do I mean by each flip or each trial being independent? Well the probability of
whether I get heads or tails on each flip are independent of whether
I just got heads or tails on some previous flip. So, in this case, we are made up of independent trials. Now, another condition is each trial can be clearly classified as either a success or failure. Or another way of thinking about it: Each trial clearly has one
of two discreet outcomes. So each trial, and the example I'm giving, the flip is a trial, can be classified classified as either success or failure. So, in the context of
this random variable X, we could define heads as a success because that's what we
are happening to count up. And so you're either going
to have success or failure. You're either going to have heads or tails on each of these trials. Now another condition for
being a binomial variable is that you have a fixed number of trials. Fixed number of trials. So in this case, we're saying that we have ten trials, ten flips of our coin. And then the last condition
is the probability of success, in this context success is a heads, on each trial, each trial, is constant. And we've already talked about it. On each trial on each flip, the probability of heads is going to stay at zero point six. If for some reason that were to change from trial to trial, maybe if you were to swap the coin and each coin had a different probability then this would no longer
be a binomial variable. And so you might say, "Okay, that's reasonable, I get why this is a binomial variable. Can you give me an example of something that is not
a binomial variable?" Well let's say that I were
to define the variable Y and it's equal to the number of kings after taking two cards from a standard deck of cards. Standard deck. Without replacement. Without replacement. So you might immediately say, "Well, this feels like
it could be binomial." We have...each trial can be classified as either a success or failure. Each trial is when I take a card out. If I get a king that looks
like that would be a success. If I don't get a king
that would be a failure. So it seems to meet that right over there. It has a fixed number of trials. I'm taking two cards out of the deck so it seems to meet that. But what about these conditions that it's made up of independent trials or that the probability
of success on each trial is constant? Well, if I get a king the probability of king
on the first trial, probability I say king on first trial would be equal to, well, out of a deck of 52 cards, you're going to have four kings in it. So the probability of a
king on the first trial would be four out of 52. But what about the
probability of getting a king on the second on the second trial? What would this be equal to? Well, it depends on what
happened on the first trial. If the first trial you had a king, well then you would have, so let's see, this would be the situation given first trial, first king, well now there would be three kings left in a deck of 51 cards. But if you did not get a
king on the first trial, now you have four kings
in a deck of 51 cards because, remember, we're
doing it without replacement. You're just taking that
first card, whatever you did, and you're taking it aside. So what's interesting here is this is not made up of independent trials. It does not meet this condition. The probability on your second trial is dependent on what
happens on your first trial. And another way to think about it is because we aren't replacing
each card that we're picking, the probability of success on each trial also is not constant. And so that's why this right over here is not a binomial variable. Now, if Y, if we got rid of without replacement and if we said we did replace
every card after we picked it then things would be different. Then we actually would be
looking at a binomial variable. So instead of without replacement if I just said with replacement, well then your probability
of a king on each trial is going to be four out of 52. You have a finite number of trials. You're probability of success
is going to stay constant and they would be independent. And obviously each trial
could easily be classified as either a success or a failure.

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