Answer:
Step-by-step explanation:
14-(a/5)
Answer:
![E[X^2]= \frac{2!}{2^1 1!}= 1](https://tex.z-dn.net/?f=E%5BX%5E2%5D%3D%20%5Cfrac%7B2%21%7D%7B2%5E1%201%21%7D%3D%201)

Step-by-step explanation:
For this case we can use the moment generating function for the normal model given by:
![\phi(t) = E[e^{tX}]](https://tex.z-dn.net/?f=%20%5Cphi%28t%29%20%3D%20E%5Be%5E%7BtX%7D%5D)
And this function is very useful when the distribution analyzed have exponentials and we can write the generating moment function can be write like this:

And we have that the moment generating function can be write like this:

And we can write this as an infinite series like this:

And since this series converges absolutely for all the possible values of tX as converges the series e^2, we can use this to write this expression:
![E[e^{tX}]= E[1+ tX +\frac{1}{2} (tX)^2 +....+\frac{1}{n!}(tX)^n +....]](https://tex.z-dn.net/?f=E%5Be%5E%7BtX%7D%5D%3D%20E%5B1%2B%20tX%20%2B%5Cfrac%7B1%7D%7B2%7D%20%28tX%29%5E2%20%2B....%2B%5Cfrac%7B1%7D%7Bn%21%7D%28tX%29%5En%20%2B....%5D)
![E[e^{tX}]= 1+ E[X]t +\frac{1}{2}E[X^2]t^2 +....+\frac{1}{n1}E[X^n] t^n+...](https://tex.z-dn.net/?f=E%5Be%5E%7BtX%7D%5D%3D%201%2B%20E%5BX%5Dt%20%2B%5Cfrac%7B1%7D%7B2%7DE%5BX%5E2%5Dt%5E2%20%2B....%2B%5Cfrac%7B1%7D%7Bn1%7DE%5BX%5En%5D%20t%5En%2B...)
and we can use the property that the convergent power series can be equal only if they are equal term by term and then we have:
![\frac{1}{(2k)!} E[X^{2k}] t^{2k}=\frac{1}{k!} (\frac{t^2}{2})^k =\frac{1}{2^k k!} t^{2k}](https://tex.z-dn.net/?f=%5Cfrac%7B1%7D%7B%282k%29%21%7D%20E%5BX%5E%7B2k%7D%5D%20t%5E%7B2k%7D%3D%5Cfrac%7B1%7D%7Bk%21%7D%20%28%5Cfrac%7Bt%5E2%7D%7B2%7D%29%5Ek%20%3D%5Cfrac%7B1%7D%7B2%5Ek%20k%21%7D%20t%5E%7B2k%7D)
And then we have this:
![E[X^{2k}]=\frac{(2k)!}{2^k k!}, k=0,1,2,...](https://tex.z-dn.net/?f=E%5BX%5E%7B2k%7D%5D%3D%5Cfrac%7B%282k%29%21%7D%7B2%5Ek%20k%21%7D%2C%20k%3D0%2C1%2C2%2C...)
And then we can find the ![E[X^2]](https://tex.z-dn.net/?f=E%5BX%5E2%5D)
![E[X^2]= \frac{2!}{2^1 1!}= 1](https://tex.z-dn.net/?f=E%5BX%5E2%5D%3D%20%5Cfrac%7B2%21%7D%7B2%5E1%201%21%7D%3D%201)
And we can find the variance like this :
![Var(X^2) = E[X^4]-[E(X^2)]^2](https://tex.z-dn.net/?f=Var%28X%5E2%29%20%3D%20E%5BX%5E4%5D-%5BE%28X%5E2%29%5D%5E2)
And first we find:
![E[X^4]= \frac{4!}{2^2 2!}= 3](https://tex.z-dn.net/?f=E%5BX%5E4%5D%3D%20%5Cfrac%7B4%21%7D%7B2%5E2%202%21%7D%3D%203)
And then the variance is given by:

Answer:
m∠PNO = 60°
m∠O = 33°
Step-by-step explanation:
∡NPO is 87° because it's a vertical angle with the 87° angle
3) ∡PNO is supplementary to the 120° angle (they must add to 180°)
4) m∠O = 180 - (60 + 87) = 33
Answer:
don't know it sorry about tht
An x-intercept is the point where the function passes the x axis at y=0.
The y-intercept is the point where the function crosses the y axis at x=0
1. It is an x-intercept, so y = 0. The ordered pair would be (-6, 0)
2. It is a y-intercept, so x = 0. The ordered pair would be (0, -2.3)
3. It is a y-intercept, so x=0. The ordered pair would be (0, 3/4)
To find the x-intercept, set y = 0. To find the y-intercept, set x=0
4. y-intercept: y = 3(0) -9. y = -9 The y-intercept is at (0, -9)
x-intercept: 0 = 3x -9. 9 = 3x. x = 3. The x-intercept is at (3, 0)
5. x intercept: 0 = 5x +10. -10 = 5x. x = -2. The x-intercept is at (-2, 0)
y-intercept: y = 5(0) + 10. y = 10. The y-intercept is at (0, 10)
If you look at finding the y-intercepts in the two problems above, you may see there is a pattern forming. The y-intercept is the number that your adding or subtracting that is located after the x (ex. y = 4x - 2 - the y-intercept would be -2)
9. First, find the x and y-intercepts. The y intercept is -3 You’d graph that at (0, -3). 0 = -1/2x - 3. -1/2x = 3. x = -6 so the x-intercept is at (-6, 0). If you only need to graph 2 points, then you can graph just those two points and draw a line between them.
To graph y=-1/2x + 3, start at the y-intercept and use the slope (-1/2) to find other points. Because your slope is -1/2, you’d go down 1 unit and then to the right 2 units. That would be your next point. If you wanted your line to go further up, go up one unit and then to the left 2 units. That would be your next point.
I am not sure what you need to do on 11 and 12
I think you should try 7, 8 and 10 on your own and let me know if you have any questions on them or if you are stuck on anything.