example - consider a random variable X that follows a continuous uniform distribution 8 < X < 20 …
I don’t understand why F(21) = 1.
In my mind i read it as X cannot be greater than or equal to 20. So why would it be 1 (100% ??) if F(21)??
example - consider a random variable X that follows a continuous uniform distribution 8 < X < 20 …
I don’t understand why F(21) = 1.
In my mind i read it as X cannot be greater than or equal to 20. So why would it be 1 (100% ??) if F(21)??
F(21) = P(X ≤ 21)
As X cannot be greater than 20, it is _ always less than or equal to_ 21, so F(21) = 1.
Some more values of F:
The graph of F(X) looks like this:
__________/¯¯¯¯¯¯¯¯¯¯
The left part of the graph is at zero (vertically); the right part is at 1. The kink on the bottom is at X = 8; the kink on the top is at X = 20.
“As X cannot be greater than 20, it is _ always less than or equal to_ 21” Ok great so that’s my thinking and I understand that part… But what I dont understand is what is the true meaning in words behind F(21) = 1?
I’m thinking in probability terms and if something is equal to 1 there is a 100% probability. Clearly that’s wrong
That’s exactly what it means, and clearly it’s right.
There’s a 100% probability that X is less than or equal to 21; it’s _ always _ less than or equal to 21.
Ok I think I understand now. But what would be the difference then between
I guess I’m really getting stuck on what it means by the difference of =0 vs = 1 ?
Because in my mind from the first probability function of 8 < X < 20 … neither of the above can happen. but what is the difference in notation of 0 vs. 1
Let’s take a look at a few possible values for X: 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, and 20.
Consider F(1) = P(X ≤ 1), and test it for all of these possible values of X:
In fact, X is _ never _ less than or equal to 1, so P(X ≤ 1) = 0, so F(1) = 0.
Consider F(21) = P(X ≤ 21), and test it for all of these possible values of X:
In fact, X is _ always _ less than or equal to 21, so P(X ≤ 21) = 1, so F(21) = 1.
GOT IT! Thank you very much!
My pleasure.