Answer:
Step-by-step explanation:
Plug into distance formula:
Points: (5, -2), (3, -4)
sqrt( ((x2 - x1)^2) + ((y2 - y1)^2))
sqrt( ((3 - 5)^2) + ((-4 + 2)^2))
sqrt( 4 + 4)
sqrt8 ≈ 2.8 OR
sqrt8 = 2√2
Answer:
Blue cars, B = 63 cars
Step-by-step explanation:
Let the blue cars be B.
Let the red cars be R.
Given the following data;
Ratio of B:R = 9:7 = 9 + 7 = 16
Red cars, R = 49
To find the number of blue cars;
First of all, we would determine the total number of cars using the expression;
R = 7/16 * x = 49
7x = 49 * 16
7x = 784
x = 112 cars
Now, we can find the number of blue cars;
B = 9/16 * 112
B = 1008/16
Blue cars, B = 63 cars
Answer:
12
Step-by-step explanation:
I think you’re right because I put the same thing on my quiz
Answer:
![E(X)= n \int_{0}^1 x^n dx = n [\frac{1}{n+1}- \frac{0}{n+1}]=\frac{n}{n+1}](https://tex.z-dn.net/?f=E%28X%29%3D%20n%20%5Cint_%7B0%7D%5E1%20x%5En%20dx%20%3D%20n%20%5B%5Cfrac%7B1%7D%7Bn%2B1%7D-%20%5Cfrac%7B0%7D%7Bn%2B1%7D%5D%3D%5Cfrac%7Bn%7D%7Bn%2B1%7D)
Step-by-step explanation:
A uniform distribution, "sometimes also known as a rectangular distribution, is a distribution that has constant probability".
We need to take in count that our random variable just take values between 0 and 1 since is uniform distribution (0,1). The maximum of the finite set of elements in (0,1) needs to be present in (0,1).
If we select a value 
we want this:
And we can express this like that:
for each possible i
We assume that the random variable 
are independent and 
from the definition of an uniform random variable between 0 and 1. So we can find the cumulative distribution like this:
And then cumulative distribution would be expressed like this:
For each value 
we can find the dendity function like this:
So then we have the pdf defined, and given by:
and 0 for other case
And now we can find the expected value for the random variable X like this:
