Answer:
Step-by-step explanation:
87+95+76+88+x/5=85
346+x=85x5
x=425-346
x=79
Answer:
C. (6, 5) and (3, -4)
Step-by-step explanation:
Given the equation 3x - y = 13, we need to figure out which points satisfy it. In order for an ordered pair to satisfy an equation, when we plug the x-coordinate in for x and the y-coordinate in for y, the equation should hold true.
Let's try with (6, 5):
3x - y = 13
3 * 6 - 5 =? 13
18 - 5 =? 13
13 = 13
Since this is true, we know that (6, 5) is indeed a solution.
Now let's try with (3, -4):
3x - y = 13
3 * (3) - (-4) =? 13
9 + 4 =?13
13 = 13
Again, since this is true, then (3, -4) must be a solution.
Thus, the answer is C.
<em>~ an aesthetics lover</em>
Answer:
The probability that Scott will wash is 2.5
Step-by-step explanation:
Given
Let the events be: P = Purple and G = Green
![P = 2](https://tex.z-dn.net/?f=P%20%3D%202)
![G = 3](https://tex.z-dn.net/?f=G%20%3D%203)
Required
The probability of Scott washing the dishes
If Scott washes the dishes, then it means he picks two spoons of the same color handle.
So, we have to calculate the probability of picking the same handle. i.e.
![P(Same) = P(G_1\ and\ G_2) + P(P_1\ and\ P_2)](https://tex.z-dn.net/?f=P%28Same%29%20%3D%20P%28G_1%5C%20and%5C%20G_2%29%20%2B%20P%28P_1%5C%20and%5C%20P_2%29)
This gives:
![P(G_1\ and\ G_2) = P(G_1) * P(G_2)](https://tex.z-dn.net/?f=P%28G_1%5C%20and%5C%20G_2%29%20%3D%20P%28G_1%29%20%2A%20P%28G_2%29)
![P(G_1\ and\ G_2) = \frac{n(G)}{Total} * \frac{n(G)-1}{Total - 1}](https://tex.z-dn.net/?f=P%28G_1%5C%20and%5C%20G_2%29%20%3D%20%5Cfrac%7Bn%28G%29%7D%7BTotal%7D%20%2A%20%5Cfrac%7Bn%28G%29-1%7D%7BTotal%20-%201%7D)
![P(G_1\ and\ G_2) = \frac{3}{5} * \frac{3-1}{5- 1}](https://tex.z-dn.net/?f=P%28G_1%5C%20and%5C%20G_2%29%20%3D%20%5Cfrac%7B3%7D%7B5%7D%20%2A%20%5Cfrac%7B3-1%7D%7B5-%201%7D)
![P(G_1\ and\ G_2) = \frac{3}{5} * \frac{2}{4}](https://tex.z-dn.net/?f=P%28G_1%5C%20and%5C%20G_2%29%20%3D%20%5Cfrac%7B3%7D%7B5%7D%20%2A%20%5Cfrac%7B2%7D%7B4%7D)
![P(G_1\ and\ G_2) = \frac{3}{10}](https://tex.z-dn.net/?f=P%28G_1%5C%20and%5C%20G_2%29%20%3D%20%5Cfrac%7B3%7D%7B10%7D)
![P(P_1\ and\ P_2) = P(P_1) * P(P_2)](https://tex.z-dn.net/?f=P%28P_1%5C%20and%5C%20P_2%29%20%3D%20P%28P_1%29%20%2A%20P%28P_2%29)
![P(P_1\ and\ P_2) = \frac{n(P)}{Total} * \frac{n(P)-1}{Total - 1}](https://tex.z-dn.net/?f=P%28P_1%5C%20and%5C%20P_2%29%20%3D%20%5Cfrac%7Bn%28P%29%7D%7BTotal%7D%20%2A%20%5Cfrac%7Bn%28P%29-1%7D%7BTotal%20-%201%7D)
![P(P_1\ and\ P_2) = \frac{2}{5} * \frac{2-1}{5- 1}](https://tex.z-dn.net/?f=P%28P_1%5C%20and%5C%20P_2%29%20%3D%20%5Cfrac%7B2%7D%7B5%7D%20%2A%20%5Cfrac%7B2-1%7D%7B5-%201%7D)
![P(P_1\ and\ P_2) = \frac{2}{5} * \frac{1}{4}](https://tex.z-dn.net/?f=P%28P_1%5C%20and%5C%20P_2%29%20%3D%20%5Cfrac%7B2%7D%7B5%7D%20%2A%20%5Cfrac%7B1%7D%7B4%7D)
![P(P_1\ and\ P_2) = \frac{1}{10}](https://tex.z-dn.net/?f=P%28P_1%5C%20and%5C%20P_2%29%20%3D%20%5Cfrac%7B1%7D%7B10%7D)
<em>Note that: 1 is subtracted because it is a probability without replacement</em>
So, we have:
![P(Same) = P(G_1\ and\ G_2) + P(P_1\ and\ P_2)](https://tex.z-dn.net/?f=P%28Same%29%20%3D%20P%28G_1%5C%20and%5C%20G_2%29%20%2B%20P%28P_1%5C%20and%5C%20P_2%29)
![P(Same) = \frac{3}{10} + \frac{1}{10}](https://tex.z-dn.net/?f=P%28Same%29%20%3D%20%5Cfrac%7B3%7D%7B10%7D%20%2B%20%5Cfrac%7B1%7D%7B10%7D)
![P(Same) = \frac{3+1}{10}](https://tex.z-dn.net/?f=P%28Same%29%20%3D%20%5Cfrac%7B3%2B1%7D%7B10%7D)
![P(Same) = \frac{4}{10}](https://tex.z-dn.net/?f=P%28Same%29%20%3D%20%5Cfrac%7B4%7D%7B10%7D)
![P(Same) = \frac{2}{5}](https://tex.z-dn.net/?f=P%28Same%29%20%3D%20%5Cfrac%7B2%7D%7B5%7D)