Answer:
12/18 cups or 2/3 cups
Step-by-step explanation:
1/2 * 9 = 9/18
1/18 cup to make one cupcake
Answer:
One pipe drains 140 L/min and the other pipe drains 190 L/min
Step-by-step explanation:
Assume that the pipe which releases less is x L/min
∵ One pipe releases x L/min
∵ Other pipe releases 50 L/min more than it
∴ The other pipe releases x + 50 L/min
∵ Water is being drained out of a tank through these 2 pipes
∵ Water is being drained at rate 330 L/min through them
- Add their rates and equate the sum by 330
∴ x + x + 50 = 330
- Add the like terms in the left hand side
∴ 2x + 50 = 330
- Subtract 50 from both sides
∴ 2x = 280
- Divide both sides by 2
∴ x = 140
∵ x represents the rate of one pipe and x + 50 represents the
rate of the other pipe
∴ One pipe drains 140 L/m
∴ Other pipe drains 140 + 50 = 190 L/min
If you mean stretch, 1 is 41/4ft and 2 should be 47.63m
Answer:
a) 


b) 
for other case
c) ![E(Y_{(n)}) = \frac{n}{\theta^n} \frac{\theta^{n+1}}{n+1}= \theta [\frac{n}{n+1}]](https://tex.z-dn.net/?f=E%28Y_%7B%28n%29%7D%29%20%3D%20%5Cfrac%7Bn%7D%7B%5Ctheta%5En%7D%20%5Cfrac%7B%5Ctheta%5E%7Bn%2B1%7D%7D%7Bn%2B1%7D%3D%20%5Ctheta%20%5B%5Cfrac%7Bn%7D%7Bn%2B1%7D%5D)
![Var(Y_{(n)}) =\theta^2 [\frac{n}{(n+1)(n+2)}]](https://tex.z-dn.net/?f=%20Var%28Y_%7B%28n%29%7D%29%20%3D%5Ctheta%5E2%20%5B%5Cfrac%7Bn%7D%7B%28n%2B1%29%28n%2B2%29%7D%5D)
Step-by-step explanation:
We have a sample of
iid uniform on the interval
and we want to find the cumulative distribution function.
Part a
For this case we can define the CDF for
,
like this:



Part b
For this case we know that:

And since are independent we have:

And then we can find the density function calculating the derivate from the last expression and we got:

for other case
Part c
For this case we can find the mean with the following integral:



And after evaluate we got:
![E(Y_{(n)}) = \frac{n}{\theta^n} \frac{\theta^{n+1}}{n+1}= \theta [\frac{n}{n+1}]](https://tex.z-dn.net/?f=E%28Y_%7B%28n%29%7D%29%20%3D%20%5Cfrac%7Bn%7D%7B%5Ctheta%5En%7D%20%5Cfrac%7B%5Ctheta%5E%7Bn%2B1%7D%7D%7Bn%2B1%7D%3D%20%5Ctheta%20%5B%5Cfrac%7Bn%7D%7Bn%2B1%7D%5D)
For the variance first we need to find the second moment like this:



And after evaluate we got:
![E(Y^2_{(n)}) = \frac{n}{\theta^n} \frac{\theta^{n+2}}{n+2}= \theta^2 [\frac{n}{n+2}]](https://tex.z-dn.net/?f=E%28Y%5E2_%7B%28n%29%7D%29%20%3D%20%5Cfrac%7Bn%7D%7B%5Ctheta%5En%7D%20%5Cfrac%7B%5Ctheta%5E%7Bn%2B2%7D%7D%7Bn%2B2%7D%3D%20%5Ctheta%5E2%20%5B%5Cfrac%7Bn%7D%7Bn%2B2%7D%5D)
And the variance is given by:
![Var(Y_{(n)}) = E(Y^2_{(n)}) - [E(Y_{(n)})]^2](https://tex.z-dn.net/?f=%20Var%28Y_%7B%28n%29%7D%29%20%3D%20E%28Y%5E2_%7B%28n%29%7D%29%20-%20%5BE%28Y_%7B%28n%29%7D%29%5D%5E2)
And if we replace we got:
![Var(Y_{(n)}) =\theta^2 [\frac{n}{n+2}] -\theta^2 [\frac{n}{n+1}]^2](https://tex.z-dn.net/?f=%20Var%28Y_%7B%28n%29%7D%29%20%3D%5Ctheta%5E2%20%5B%5Cfrac%7Bn%7D%7Bn%2B2%7D%5D%20-%5Ctheta%5E2%20%5B%5Cfrac%7Bn%7D%7Bn%2B1%7D%5D%5E2%20)
![Var(Y_{(n)}) =\theta^2 [\frac{n}{n+2} -(\frac{n}{n+1})^2]](https://tex.z-dn.net/?f=%20Var%28Y_%7B%28n%29%7D%29%20%3D%5Ctheta%5E2%20%5B%5Cfrac%7Bn%7D%7Bn%2B2%7D%20-%28%5Cfrac%7Bn%7D%7Bn%2B1%7D%29%5E2%5D)
And after do some algebra we got:
![Var(Y_{(n)}) =\theta^2 [\frac{n}{(n+1)(n+2)}]](https://tex.z-dn.net/?f=%20Var%28Y_%7B%28n%29%7D%29%20%3D%5Ctheta%5E2%20%5B%5Cfrac%7Bn%7D%7B%28n%2B1%29%28n%2B2%29%7D%5D)
Answer:
C. 11, 1
Step-by-step explanation:
I could have sworn I already answered this. Oh well. I guess I can answer it again. Simply plug in -3 and 3 for <em>x</em><em>,</em><em> </em>then find<em> </em>the absolute value [ALWAYS POSITIVE].