Answer:
Part A: x^2 + 5x - 8
Part B: 5x^3 + 3x^2 - 6x + 5
Step-by-step explanation:
Part A:
To find the total of side 1 and 2, add the two expressions which represent each side. Combine like terms to simplify.
3x^2 − 2x − 5 + (7x - 2x^2 − 3)
3x^2 - 2x^2 -2x + 7x -5 + -3
x^2 + 5x - 8
Part B:
To solve for the third side of a triangle using its perimeter, subtract the lengths of two of its sides from its perimeter. The perimeter is 5x^3 + 4x^2 − x − 3. Subtract 3x^2 − 2x − 5 and 7x - 2x^2 − 3. The remaining expression is the third side. Combine like terms to simplify.
5x^3 + 4x^2 − x − 3 - (3x^2 − 2x − 5) - (7x - 2x^2 − 3)
5x^3 + 4x^2 - x - 3 - 3x^2 + 2x + 5 - 7x + 2x^2 + 3
5x^3 + (4x^2 -3x^2 + 2x^2) + (-x + 2x - 7x) + (-3 + 5 + 3)
5x^3 + 3x^2 - 6x + 5
To find the inverse function simply replace "x" by "y", replace "y" by "x", and solve for y
f(x)=4x
y=4x
x=4y
y=x/4 or h(x)=(1/4)x
Answer:
He's not right
Step-by-step explanation:
L = 4/6 = 2/3
K = 2/3
Answer:
1. 18.84 in
2. 56.52 cm
3. 4.71 ft
4. 25.12 m
5. 37.68 ft
6. 12.56 yd
7. 43.96 in
8. 28.26 cm
9. 7.85 m
Step-by-step explanation:

Answer:
The expected value of X is
and the variance of X is 
The expected value of Y is
and the variance of Y is 
Step-by-step explanation:
(a) Let X be a discrete random variable with set of possible values D and probability mass function p(x). The expected value, denoted by E(X) or
, is

The probability mass function
of X is given by

Since the bus driver is equally likely to drive any of the 4 buses, the probability mass function
of Y is given by

The expected value of X is
![E(X)=\sum_{x\in [28,32,42,44]} x\cdot p_{X}(x)](https://tex.z-dn.net/?f=E%28X%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5Ccdot%20p_%7BX%7D%28x%29)

The expected value of Y is
![E(Y)=\sum_{x\in [28,32,42,44]} x\cdot p_{Y}(x)](https://tex.z-dn.net/?f=E%28Y%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5Ccdot%20p_%7BY%7D%28x%29)

(b) Let X have probability mass function p(x) and expected value E(X). Then the variance of X, denoted by V(X), is
![V(X)=\sum_{x\in D} (x-\mu)^2\cdot p(x)=E(X^2)-[E(X)]^2](https://tex.z-dn.net/?f=V%28X%29%3D%5Csum_%7Bx%5Cin%20D%7D%20%28x-%5Cmu%29%5E2%5Ccdot%20p%28x%29%3DE%28X%5E2%29-%5BE%28X%29%5D%5E2)
The variance of X is
![E(X^2)=\sum_{x\in [28,32,42,44]} x^2\cdot p_{X}(x)](https://tex.z-dn.net/?f=E%28X%5E2%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5E2%5Ccdot%20p_%7BX%7D%28x%29)


The variance of Y is
![E(Y^2)=\sum_{x\in [28,32,42,44]} x^2\cdot p_{Y}(x)](https://tex.z-dn.net/?f=E%28Y%5E2%29%3D%5Csum_%7Bx%5Cin%20%5B28%2C32%2C42%2C44%5D%7D%20x%5E2%5Ccdot%20p_%7BY%7D%28x%29)

