There is 9 serving of 7 fluid ounces with a remainder of 1 fluid ounce
if you meant 8 fluid ounce servings then it would be 8 servings with no remainder
Answer:
(15,-14)
Step-by-step explanation:
Given that,
The midpoint of FG is (6-4) and the corrdinates of F are (-3,6).
Let (x,y) be the coordinates of point G. Using mid point formula,

So, the coordinates of G are (15,-14).
You have 32 tiles, you use 15 tiles, how many tiles are left?
You just went shopping with $32, you spent $15, how much money do you have left?
Your friend has 32 cards, he loses 15, how many cards does he have left?
We're told that



where the last fact is due to the law of total probability:



so that
and
are complementary.
By definition of conditional probability, we have



We make use of the addition rule and complementary probabilities to rewrite this as


![\implies P(B)-[1-P(A\cup B)^C]=[1-P(B)]-P(A\cup B^C)](https://tex.z-dn.net/?f=%5Cimplies%20P%28B%29-%5B1-P%28A%5Ccup%20B%29%5EC%5D%3D%5B1-P%28B%29%5D-P%28A%5Ccup%20B%5EC%29)
![\implies2P(B)=2-[P(A\cup B)^C+P(A\cup B^C)]](https://tex.z-dn.net/?f=%5Cimplies2P%28B%29%3D2-%5BP%28A%5Ccup%20B%29%5EC%2BP%28A%5Ccup%20B%5EC%29%5D)
![\implies2P(B)=[1-P(A\cup B)^C]+[1-P(A\cup B^C)]](https://tex.z-dn.net/?f=%5Cimplies2P%28B%29%3D%5B1-P%28A%5Ccup%20B%29%5EC%5D%2B%5B1-P%28A%5Ccup%20B%5EC%29%5D)


By the law of total probability,


and substituting this into
gives
![2P(B)=P(A\cup B)+[P(B)-P(A\cap B)]](https://tex.z-dn.net/?f=2P%28B%29%3DP%28A%5Ccup%20B%29%2B%5BP%28B%29-P%28A%5Ccap%20B%29%5D)


Outliers are data that are in a very far distance from other values in a set of data
Once an outlier is detected in a set of data, we can do the following to them:
- Discard the outlier
- Change the value of the outlier with another value within close range
- Consider the distribution given
We may have a set of data where some of the <em>values are far in distance from the majority of the data</em>. The set of such data are known as an outlier.
For example, give the set of data;
45 can be considered as an outlier since the <em>distance of data</em><em> to all other data is</em><em> large</em><em>.</em>
Once an outlier is detected in a set of data, we can do the following to them:
- Discard the outlier
- Change the value of the outlier with another value within close range
- Consider the distribution given
Learn more here: brainly.com/question/23258173