Answer: a. 0.1500 b. 0.0367
Step-by-step explanation:
Let X is a random variable with a distribution that may be known or unknown:
= the mean of X
= the standard deviation of X
If we draw random samples of size n, then the random samples contains sample means
, tends to be normally distributed
Here,
= 75
=5
n=20
a.
![P(76<\overline{X}<77)=P((76-75)/((5)/(√(20)))<\frac{\overline{X}-\mu}{(\sigma)/(√(n))}<(77-75)/((5)/(√(20))))\\\\=P(0.89<Z<1.79) \ \ [z=\frac{\overline{X}-\mu}{(\sigma)/(√(n))}]\\\\=P(z<1.79)-P(z<0.89)\\\\=0.963273-0.813267=0.150006\approx0.1500](https://img.qammunity.org/2021/formulas/business/college/3ygd998oqikorul38n22jedtekamawrhom.png)
b.
![P(\overline{X}>77)=P(\frac{\overline{X}-\mu}{(\sigma)/(√(n))}>(77-75)/((5)/(√(20))))\\\\=P(Z>1.79) \ \ [z=\frac{\overline{X}-\mu}{(\sigma)/(√(n))}]\\\\=1-P(z<1.79)\\\\=1-0.963273=0.036727\approx0.0367](https://img.qammunity.org/2021/formulas/business/college/jy6zob47fcb6kz4xdhb34lqbbepe56im0t.png)