Answer:
Hey there!
The perimeter of the triangle is the distance around it.
We can find the distance of two points using the distance formula, and add up all the distances to find the total distance.
-4, -6 to 3, 3 is 2
3, 3 to 7, 2 is 5
7, 2 to -4, -6 is about 5.4
2+5+5.39=12.4, which is closest to 12.36.
Let me know if this helps :)
Answer:
from the foood you eat after its digested
Step-by-step explanation:
Answer:
- There is no power of 2 that is equal to 0.
- Its inverse does not have any x-intercepts.
Step-by-step explanation:
Given the function
![\:\:f\left(x\right)=\log \:_2\left(x\right)](https://tex.z-dn.net/?f=%5C%3A%5C%3Af%5Cleft%28x%5Cright%29%3D%5Clog%20%5C%3A_2%5Cleft%28x%5Cright%29)
This function can be written as:
![y=\log _2\left(x\right)](https://tex.z-dn.net/?f=y%3D%5Clog%20_2%5Cleft%28x%5Cright%29)
As the rule tells s that
![\:y=log_ax\Rightarrow \:a^y=x](https://tex.z-dn.net/?f=%5C%3Ay%3Dlog_ax%5CRightarrow%20%5C%3Aa%5Ey%3Dx)
so
![x=2^y\:\:](https://tex.z-dn.net/?f=x%3D2%5Ey%5C%3A%5C%3A)
As we know that the value of x=0 at y-intercept.
![0=2^y\:\:](https://tex.z-dn.net/?f=0%3D2%5Ey%5C%3A%5C%3A)
For any value of
, this statement is false as there is no power of 2 that is equal to 0.
Also
Lets interchange x and y in the equation
to find the inverse of the function.
![y=2^x\:\:](https://tex.z-dn.net/?f=y%3D2%5Ex%5C%3A%5C%3A)
So, it doesn't have any x-intercepts as for any value of x, the value of
can not be zero for this function.
Therefore, Its inverse does not have any x-intercepts.
Answer:
$15.3
Step-by-step explanation:
15% divided by 100
0.15
0.15*18=2.7
18-2.7=15.3
Let
![L_1](https://tex.z-dn.net/?f=L_1)
denote the event that a student is a freshman,
![L_2](https://tex.z-dn.net/?f=L_2)
a sophomore,
![L_3](https://tex.z-dn.net/?f=L_3)
a junior, and
![L_4](https://tex.z-dn.net/?f=L_4)
a senior.
Let
![E](https://tex.z-dn.net/?f=E)
denote the event that a student majors in engineering. By the law of total probability,
![\mathbb P(E)=\mathbb P(E\cap L_1)+\mathbb P(E\cap L_2)+\mathbb P(E\cap L_3)+\mathbb P(E\cap L_4)](https://tex.z-dn.net/?f=%5Cmathbb%20P%28E%29%3D%5Cmathbb%20P%28E%5Ccap%20L_1%29%2B%5Cmathbb%20P%28E%5Ccap%20L_2%29%2B%5Cmathbb%20P%28E%5Ccap%20L_3%29%2B%5Cmathbb%20P%28E%5Ccap%20L_4%29)
By the definition of conditional probability, we can expand each of these intersection probabilities to get
![\mathbb P(E)=\mathbb P(E\mid L_1)\mathbb P(L_1)+\mathbb P(E\mid L_2)\mathbb P(L_2)+\mathbb P(E\mid L_3)\mathbb P(L_3)+\mathbb P(E\mid L_4)\mathbb P(L_4)](https://tex.z-dn.net/?f=%5Cmathbb%20P%28E%29%3D%5Cmathbb%20P%28E%5Cmid%20L_1%29%5Cmathbb%20P%28L_1%29%2B%5Cmathbb%20P%28E%5Cmid%20L_2%29%5Cmathbb%20P%28L_2%29%2B%5Cmathbb%20P%28E%5Cmid%20L_3%29%5Cmathbb%20P%28L_3%29%2B%5Cmathbb%20P%28E%5Cmid%20L_4%29%5Cmathbb%20P%28L_4%29)