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Mathematics 26 Online
OpenStudy (anonymous):

Sherry missed the lesson on normal distribution and needs to do her homework. Explain to Sherry how to use the mean and standard deviation of a normal distribution to determine the top 7% of the population. @ganeshie8

OpenStudy (anonymous):

pls help??!

OpenStudy (anonymous):

Let's see....

OpenStudy (anonymous):

We want \[\Phi\left(\frac{x-\mu}{\sigma}\right) = 1-0.07=0.93\]

OpenStudy (anonymous):

hmmm. whats the first symbol?

OpenStudy (anonymous):

We'd solve for \(x\). Where as \(\mu\) is the man, and \(\sigma\) is standard deviation.

OpenStudy (anonymous):

Hmm, well it finds cumulative distribution.

OpenStudy (anonymous):

\(x\) would tell us the score that is at the boundary between the 93% and the 7%.

OpenStudy (anonymous):

and then what will we do?

OpenStudy (anonymous):

Actually "determine the top 7% of the population" is sort of vague. I interpreted it to mean the score at which you would be at the border. The question is not very precise.

OpenStudy (anonymous):

eh i know but this is how it is word for word and its worth ten points

OpenStudy (anonymous):

First, you use \[ z=\frac{x-\mu}{\sigma} \] to find the \(z\) score. \(\mu\) is mean and \(\sigma\) is standard deviation. Next, you would look up \(z\) in your \(z\) table.\[ P = \Phi(z) \]In this case \(P\) is what you find in the table, and it is the probability that your score is under \(x\).

OpenStudy (anonymous):

With the current question, we have \(P\) is already provided. When looking at the top 7%, this is the opposite of being in the bottom 93%. So we let \(P = 0.93\). We do the \(z\) table look up, but instead of looking up \(z\), we find look to see what \(z\) value will give us \(0.93\).

OpenStudy (anonymous):

then we can finally solve for \(x\).

OpenStudy (anonymous):

so what do i plug in for x in the z score formula

OpenStudy (anonymous):

We are trying to find \(x\).

OpenStudy (anonymous):

im confused so thats the answer?

OpenStudy (anonymous):

Okay, first of all, do you have a \(z\) table with you?

OpenStudy (anonymous):

yess

OpenStudy (anonymous):

Tell me what it says when \(z=0\).

OpenStudy (anonymous):

.5000

OpenStudy (anonymous):

How, try to find a \(z\) that will result you in getting \(0.97\)

OpenStudy (anonymous):

I mean \(0.9300\)

OpenStudy (anonymous):

ok hold on

OpenStudy (anonymous):

I cant find that sorry

OpenStudy (anonymous):

https://figures.boundless.com/18108/full/normal01.jpe @wio

OpenStudy (anonymous):

You will have to use the closest thing.

OpenStudy (anonymous):

uhh ok 1.48

OpenStudy (anonymous):

So our \(z\) score is \(1.48\). Do you know what the mean and std dev are?

OpenStudy (anonymous):

\[ 1.48\approx \frac{x-\mu}{\sigma } \]

OpenStudy (anonymous):

then? no i dont, can you expllain were we got our z score from pls?

OpenStudy (anonymous):

Well, typically you learn the \(z\) score is just \[ z=\frac{x-\mu}{\sigma } \]This is just a normal fact. I could show you where it is from, but the truth is that it would probably confuse you more.

OpenStudy (anonymous):

no like in the lessoon we learned thattt the only way to get z score is throught the deviation and first score minus the second but howd youu get it without everything because i have to expllain iit too

OpenStudy (anonymous):

Well, I'm not sure then. I don't know what you are talking about.

OpenStudy (anonymous):

pls :/ if you cant explain, then can you just give me the final answer with compllete sentences

OpenStudy (anonymous):

It seems I can't really help out beyond what I have said.

OpenStudy (anonymous):

no can you like give me the summary of the answer I wont have you explain it to me plss?

OpenStudy (anonymous):

I don't know the answer.

OpenStudy (anonymous):

:( do you know anybody that can help me?

OpenStudy (anonymous):

@ranga

OpenStudy (anonymous):

@eliassaab

OpenStudy (anonymous):

@shamil98

OpenStudy (ranga):

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