Step-by-step explanation: Given that the graph shows the side view of a water slide with dimensions in feet.
We are to find the rate of change between the points (0, ?) and (?, 40).
From the graph, we note that
the y co-ordinate for the x co-ordinate 0 is 80 and the x co-ordinate for the y co-ordinate 40 is 5.
So, the given two points are (0, 80) and (5, 40).
The rate of change for a function f(x) between the points (a, b) and (c, d) is given by
Therefore, the rate of change for the given function between the points (0, 80) and (5, 40) is
Thus, the required rate of change is -8.
B. The mean; in a normal distribution, the mean is always greater than the median
For this case we have that by definition, the equation of a line in the slope-intersection form is given by:

Where:
m: It's the slope
b: It is the cut-off point with the y axis
On the other hand we have that if two lines are perpendicular, then the product of their slopes is -1. So:

The given line is:

So we have:

We find 

So, a line perpendicular to the one given is of the form:

We substitute the given point to find "b":

Finally we have:

In point-slope form we have:

ANswer:

Not exactly sure if I perfectly remember distributive property but you could right that like 18j + 24 + 15j
Answer:
See the proof below.
Step-by-step explanation:
Assuming this complete question: "For each given p, let Z have a binomial distribution with parameters p and N. Suppose that N is itself binomially distributed with parameters q and M. Formulate Z as a random sum and show that Z has a binomial distribution with parameters pq and M."
Solution to the problem
For this case we can assume that we have N independent variables
with the following distribution:
bernoulli on this case with probability of success p, and all the N variables are independent distributed. We can define the random variable Z like this:
From the info given we know that
We need to proof that
by the definition of binomial random variable then we need to show that:


The deduction is based on the definition of independent random variables, we can do this:

And for the variance of Z we can do this:
![Var(Z)_ = E(N) Var(X) + Var (N) [E(X)]^2](https://tex.z-dn.net/?f=%20Var%28Z%29_%20%3D%20E%28N%29%20Var%28X%29%20%2B%20Var%20%28N%29%20%5BE%28X%29%5D%5E2%20)
![Var(Z) =Mpq [p(1-p)] + Mq(1-q) p^2](https://tex.z-dn.net/?f=%20Var%28Z%29%20%3DMpq%20%5Bp%281-p%29%5D%20%2B%20Mq%281-q%29%20p%5E2)
And if we take common factor
we got:
![Var(Z) =Mpq [(1-p) + (1-q)p]= Mpq[1-p +p-pq]= Mpq[1-pq]](https://tex.z-dn.net/?f=%20Var%28Z%29%20%3DMpq%20%5B%281-p%29%20%2B%20%281-q%29p%5D%3D%20Mpq%5B1-p%20%2Bp-pq%5D%3D%20Mpq%5B1-pq%5D)
And as we can see then we can conclude that 