Answer:
150
Step-by-step explanation:
100 : 160 :: e : 240 proportion
160e = (100)(240)
160e = 24000
elves = 150
Answer:
g >2 or g <2/3
Step-by-step explanation:
|3g-4| > 2
Absolute value equations have two solutions, one positive and one negative. When we take the negative, we have to flip the inequality
3g-4>2 or 3g-4<-2
Add 4 to each side
3g-4+4>2+4 3g-4+4 <-2+4
3g>6 3g<2
Divide each side by 3
3g/3 >6/3 3g/3 <2/3
g >2 or g <2/3
Answer:
y = -2x + 6
Step-by-step explanation:
2x + y = 6
Subtract 2x both sides
2x + y - 2x = 6 - 2x
y = -2x + 6
Answer:
x= 1/2y + 3/2z
Step-by-step explanation:
1/3 (2x-y) =2
multiply 1/3 into the ( )
2/3x -1/3y =2
bring y to the right side by subtrcting
2/3x =1/3y + 2
multiply by the reciprocal of 2/3, which is 3/2
x=3/6y+3/2z
SIMPLIFY!
x=1/2y+3/2z
Complete Question
The complete question is shown on the first uploaded image
Answer:
The sales level that is the cut-off between quarters that are considers as "failure" and those that are not is evaluated as

Step-by-step explanation:
From the question we are told that the
Mean is 
Standard deviation is 
Let Y be the random variable that denotes the sales made quarterly among health care information system
The normal distribution for this data is mathematically represented as
~ N 
From the question we are told that a quarter is consider a failure by the company if the sales level that quarter in the bottom 10% of the of all quarterly sales
So
The the Probability of obtaining quarterly sales level that is equal to 10% of all quarterly sale (It is not below 10%) is mathematically represented as
![P[Y < y ] = 0.10](https://tex.z-dn.net/?f=P%5BY%20%3C%20y%20%5D%20%3D%200.10)
This can be represented as a normal distribution in this manner
![P [ \frac{Y- \mu}{\sigma } < \frac{y -\mu}{\sigma} ] = 0.10](https://tex.z-dn.net/?f=P%20%5B%20%5Cfrac%7BY-%20%5Cmu%7D%7B%5Csigma%20%7D%20%20%3C%20%5Cfrac%7By%20-%5Cmu%7D%7B%5Csigma%7D%20%5D%20%3D%200.10)
Where
~
{This mean that this equal to the normal distribution between 0, 1 which is the generally range of every probability }
Therefor we have
![P[Z < \frac{y- 8}{1.3} ]](https://tex.z-dn.net/?f=P%5BZ%20%3C%20%5Cfrac%7By-%208%7D%7B1.3%7D%20%5D)
The cumulative distribution function for a normal distribution of y is mathematically represented as
![\phi [\frac{y- 8}{1.3} ] = 0.10](https://tex.z-dn.net/?f=%5Cphi%20%5B%5Cfrac%7By-%208%7D%7B1.3%7D%20%5D%20%3D%200.10)
This is because a cumulative distribution function of a random value Y or a distribution of Y evaluated at y is the probability that Y will take will take a value that is less or equal to y

Calculating the inverse of the cumulative distribution function value of 0.10 which is negative the the critical value(Critical values determine what probability a particular variable will have when a sampling distribution is normal or close to normal.) of 0.01
![\phi ^{-1}(0.10) =- [1 - \frac{0.10}{2}]](https://tex.z-dn.net/?f=%5Cphi%20%5E%7B-1%7D%280.10%29%20%3D-%20%5B1%20-%20%5Cfrac%7B0.10%7D%7B2%7D%5D)



