9.
<2 and <6 are right angles by the definition of perpendicular lines.
Since corresponding angles are congruent, m || n.
11.
In a triangle, the measures of the three angles add to 180 degrees.
If one angle is a right angle, then that angle measures 90 degrees. The other two angle measures plus 90 degrees add to 180 degrees, so the other two angle measures add to 90 degrees. In a right triangle there is a right angle and two other angles that are complementary.
Now look at your problem.
One angle is a right angle.
The other two angles are complementary, so their measures add to 90 deg.
8x + 2 + 9x + 3 = 90
17x + 5 = 90
17x = 85
x = 5
The correct answer for the problem above is -23 .
Explanation
1. collect the like terms
2x + x - 11 + 3 - 7x = 15
2x , x , & -7x are like terms
-11 & 3 are like terms
2x + x -7x = -4x
-11 + 3 = -8
2. Move constant to the right-hand side and change its sign .
-4x = 15 + 8
-4x = 23
3. Make the signs on both sides of the equation
-4x = 23 turning into 4x = -23
answer =
4x = -23
Answer:
123 full pages
Step-by-step explanation:
Given
Required
Determine the number of full page
To do this, we simply divide the total stamps by stamps in each page;
Then we record only the quotient
The quotient is the digits before the decimal
<em>Hence, she has 123 full pages</em>
12.5663706144 or yes, 12.57
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:
