Since we want just the top 20% applicants and the data is normally distributed, we can use a z-score table to check the z-score that gives this percentage.
The z-score table usually shows the percentage for the values below a certain z-score, but since the whole distribution accounts to 100%, we can do the following.
We want a z* such that:
![P(z>z^*)=0.20](https://tex.z-dn.net/?f=P%28z%3Ez%5E%2A%29%3D0.20)
But, to use a value that is in a z-score table, we do the following:
![\begin{gathered} P(zz^*)=1 \\ P(zz^*)=1-0.20=0.80 \end{gathered}](https://tex.z-dn.net/?f=%5Cbegin%7Bgathered%7D%20P%28zz%5E%2A%29%3D1%20%5C%5C%20P%28zz%5E%2A%29%3D1-0.20%3D0.80%20%5Cend%7Bgathered%7D)
So, we want a z-score that give a percentage of 80% for the value below it.
Using the z-score table or a z-score calculator, we can see that:
![\begin{gathered} P(zNow that we have the z-score cutoff, we can convert it to the score cutoff by using:[tex]z=\frac{x-\mu}{\sigma}\Longrightarrow x=z\sigma+\mu](https://tex.z-dn.net/?f=%5Cbegin%7Bgathered%7D%20P%28zNow%20that%20we%20have%20the%20z-score%20cutoff%2C%20we%20can%20convert%20it%20to%20the%20score%20cutoff%20by%20using%3A%5Btex%5Dz%3D%5Cfrac%7Bx-%5Cmu%7D%7B%5Csigma%7D%5CLongrightarrow%20x%3Dz%5Csigma%2B%5Cmu)
Where z is the z-score we have, μ is the mean and σ is the standard deviation, so:
![\begin{gathered} x=0.8416\cdot9+64 \\ x=7.5744.64 \\ x=71.5744\cong72 \end{gathered}](https://tex.z-dn.net/?f=%5Cbegin%7Bgathered%7D%20x%3D0.8416%5Ccdot9%2B64%20%5C%5C%20x%3D7.5744.64%20%5C%5C%20x%3D71.5744%5Ccong72%20%5Cend%7Bgathered%7D)
so, the cutoff score is approximately 72.