Answer:
a. 0.691
b. 0.382
c. 0.933
d. $88.490
e. $58.168
f. 5th percentile: $42.103
95th percentile: $107.897
Step-by-step explanation:
We have, for the purchase amounts by customers, a normal distribution with mean $75 and standard deviation of $20.
a. This can be calculated using the z-score:
![z=\dfrac{X-\mu}{\sigma}=\dfrac{85-75}{20}=\dfrac{10}{20}=0.5\\\\\\P(X](https://tex.z-dn.net/?f=z%3D%5Cdfrac%7BX-%5Cmu%7D%7B%5Csigma%7D%3D%5Cdfrac%7B85-75%7D%7B20%7D%3D%5Cdfrac%7B10%7D%7B20%7D%3D0.5%5C%5C%5C%5C%5C%5CP%28X%3C85%29%3DP%28z%3C0.5%29%3D0.691)
The probability that a randomly selected customer spends less than $85 at this store is 0.691.
b. We have to calculate the z-scores for both values:
![z_1=\dfrac{X_1-\mu}{\sigma}=\dfrac{65-75}{20}=\dfrac{-10}{20}=-0.5\\\\\\z_2=\dfrac{X_2-\mu}{\sigma}=\dfrac{85-75}{20}=\dfrac{10}{20}=0.5\\\\\\\\P(65](https://tex.z-dn.net/?f=z_1%3D%5Cdfrac%7BX_1-%5Cmu%7D%7B%5Csigma%7D%3D%5Cdfrac%7B65-75%7D%7B20%7D%3D%5Cdfrac%7B-10%7D%7B20%7D%3D-0.5%5C%5C%5C%5C%5C%5Cz_2%3D%5Cdfrac%7BX_2-%5Cmu%7D%7B%5Csigma%7D%3D%5Cdfrac%7B85-75%7D%7B20%7D%3D%5Cdfrac%7B10%7D%7B20%7D%3D0.5%5C%5C%5C%5C%5C%5C%5C%5CP%2865%3CX%3C85%29%3DP%28-0.5%3Cz%3C0.5%29%3DP%28z%3C0.5%29-P%28z%3C-0.5%29%5C%5C%5C%5CP%2865%3Cx%3C85%29%3D0.691-0.309%3D0.382)
The probability that a randomly selected customer spends between $65 and $85 at this store is 0.382.
c. We recalculate the z-score for X=45.
![z=\dfrac{X-\mu}{\sigma}=\dfrac{45-75}{20}=\dfrac{-30}{20}=-1.5\\\\\\P(X>45)=P(z>-1.5)=0.933](https://tex.z-dn.net/?f=z%3D%5Cdfrac%7BX-%5Cmu%7D%7B%5Csigma%7D%3D%5Cdfrac%7B45-75%7D%7B20%7D%3D%5Cdfrac%7B-30%7D%7B20%7D%3D-1.5%5C%5C%5C%5C%5C%5CP%28X%3E45%29%3DP%28z%3E-1.5%29%3D0.933)
The probability that a randomly selected customer spends more than $45 at this store is 0.933.
d. In this case, first we have to calculate the z-score that satisfies P(z<z*)=0.75, and then calculate the X* that corresponds to that z-score z*.
Looking in a standard normal distribution table, we have that:
![P(z](https://tex.z-dn.net/?f=P%28z%3C0.67449%29%3D0.75)
Then, we can calculate X as:
![X^*=\mu+z^*\cdot\sigma=75+0.67449\cdot 20=75+13.4898=88.490](https://tex.z-dn.net/?f=X%5E%2A%3D%5Cmu%2Bz%5E%2A%5Ccdot%5Csigma%3D75%2B0.67449%5Ccdot%2020%3D75%2B13.4898%3D88.490)
75% of the customers will not spend more than $88.49.
e. In this case, first we have to calculate the z-score that satisfies P(z>z*)=0.8, and then calculate the X* that corresponds to that z-score z*.
Looking in a standard normal distribution table, we have that:
![P(z>-0.84162)=0.80](https://tex.z-dn.net/?f=P%28z%3E-0.84162%29%3D0.80)
Then, we can calculate X as:
![X^*=\mu+z^*\cdot\sigma=75+(-0.84162)\cdot 20=75-16.8324=58.168](https://tex.z-dn.net/?f=X%5E%2A%3D%5Cmu%2Bz%5E%2A%5Ccdot%5Csigma%3D75%2B%28-0.84162%29%5Ccdot%2020%3D75-16.8324%3D58.168)
80% of the customers will spend more than $58.17.
f. We have to calculate the two points that are equidistant from the mean such that 90% of all customer purchases are between these values.
In terms of the z-score, we can express this as:
![P(|z|](https://tex.z-dn.net/?f=P%28%7Cz%7C%3Cz%2A%29%3D0.9)
The value for z* is ±1.64485.
We can now calculate the values for X as:
![X_1=\mu+z_1\cdot\sigma=75+(-1.64485)\cdot 20=75-32.897=42.103\\\\\\X_2=\mu+z_2\cdot\sigma=75+1.64485\cdot 20=75+32.897=107.897](https://tex.z-dn.net/?f=X_1%3D%5Cmu%2Bz_1%5Ccdot%5Csigma%3D75%2B%28-1.64485%29%5Ccdot%2020%3D75-32.897%3D42.103%5C%5C%5C%5C%5C%5CX_2%3D%5Cmu%2Bz_2%5Ccdot%5Csigma%3D75%2B1.64485%5Ccdot%2020%3D75%2B32.897%3D107.897)
5th percentile: $42.103
95th percentile: $107.897