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Amiraneli [1.4K]
3 years ago
9

Given the original number n. Multiply the number by 8. Add 136. Divide this sum by 8. Subtract the original number, n, from the

quotient.
Mathematics
1 answer:
FromTheMoon [43]3 years ago
5 0

Answer:

8

Step-by-step explanation:

n×8=8n

8n+136=144n

144n÷8=18n

18n-n = 18

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Grading managers. Some companies "grade on a bell curve" to compare the performance of their managers and professional workers.
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Answer:

\mu = 250, \sigma = 175.781

Step-by-step explanation:

Previous concepts

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

The Z-score is "a numerical measurement used in statistics of a value's relationship to the mean (average) of a group of values, measured in terms of standard deviations from the mean".  

Solution to the problem

Let X the random variable that represent the grades of a population, and for this case we know the distribution for X is given by:

X \sim N(\mu,\sigma)  

For this case we have two conditions given:

P(X

P(X>475) = 0.1 or equivalently P(X

And the best way to solve this problem is using the normal standard distribution and the z score given by:

z=\frac{x-\mu}{\sigma}

So we can find a value from the normal standard distribution that accumulates 0.1 and 0.9 of the area in the left, for this case the two values are:

z= -1.28, z=-1.28

We can verify that P(Z<-1.28) =0.1[/tex] and P(Z<1.28) =0.9[/tex]

And then using the z score we have the following formulas:

-1.28 = \frac{25 -\mu}{\sigma}   (1)

1.28 = \frac{475 -\mu}{\sigma}   (2)

If we add equations (1) and (2) we got:

\frac{25 -\mu}{\sigma} + \frac{475 -\mu}{\sigma} =0

We can multiply both sides of the equation by \sigma and we got:

25+ 475 -2 \mu = 0

\mu = \frac{500}{2}= 250

And then we can find the standard deviation for example from equation (1) and we got:

\sigma = \frac{25-250}{-1.28}=175.781

So then the answer would be:

\mu = 250, \sigma = 175.781

 

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