Examples of using Our random variable in English and their translations into Portuguese
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Colloquial
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Official
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Medicine
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Financial
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Ecclesiastic
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Ecclesiastic
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Computer
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Official/political
So that's our random variable.
This is the mean of n observations of our random variable.
This is for our random variable, x.
All right, so what are the different values that we care about for our random variable?
So the expected value of our random variable is equal to the sum.
We just count the number of heads we got after 5 flips and that's our random variable, X.
So the expected value of our random variable is going to be each outcome.
And the first thing we did is we sat at that intersection andwe found a pretty good expected value of our random variable.
We defined our random variable, x, as the number of shots I make out of 6.
We're summing over all of the values that our random variable can take.
The probability that our random variable, the number of cars that passes in an hour, is equal to a particular number.
They're all particular finite numbers that we care that our random variable can take on.
We have our random variable, x, is equal to-- I don't know-- it's equal to the number of heads after 6 tosses of a fair coin.
This was the probability that our random variable-- that we have exactly 2 heads.
So we figured out before that the frequency-- now, it's a little inexact because I don't have-- actually,I have the exact numbers. 0 will show up in our random variable 0.01563% of the time.
And then we can calculate what the probability that our random variable is equal to k, is equal to this value.
In the last video we defined our random variable x as the number of heads we get after flipping a coin five times, and it's a fair coin.
If you actually then said, oh, this is a binomial distribution,so the probability that our random variable equals some given value, k.
So the expected value of X, the expected value of our random variable that's being described as binomial distribution-- it's equal to the sum.
And now let's do a bunch of rows so that we can calculate the probability that our random variable, x, could be 0 shots or 1 shot or whatever.
So the probability that X is equal to 1-- the probability that our random variable is equal to 1 is equal to 6 times 0.3 times 0.7 to the fifth.
And so, we're left with the expected value of our random variable, X, is equal to n times p.
The law of large numbers just says that if we take a sample of n observations of our random variable, and if we were to average all of those observations-- and let me define another variable.