a$.$ A sample of $100$ arrivals of a customer at retail sales depot is according to the following distribution:

$\backslash$table$[[$Time between arrival $($min$),$Frequency$],[0\mathrm{.}5,2],[1\mathrm{.}0,6],[1\mathrm{.}5,10],[2\mathrm{.}0,25],[2\mathrm{.}5,20],[3\mathrm{.}0,14],[3\mathrm{.}5,10],[4\mathrm{.}0,7],[4\mathrm{.}5,4],[5\mathrm{.}0,2]]$

A study of time required to service customers by adding up the bills, receiving payments and placing packages, yields the following distribution:

$\backslash$table$[[$Service time $($min$),$Frequency$],[0\mathrm{.}5,12],[1\mathrm{.}0,21],[1\mathrm{.}5,36],[2\mathrm{.}0,19],[2\mathrm{.}5,7],[3\mathrm{.}0,5]]$

Estimate the average percentage of customer waiting time and average percentage of Idle time of the server by monte carlo simulation for the next $10$ arrivals!

Use the random numbers for arrivals:

$\backslash$table$[[93,22,53,64,39,07,10,63,76,35]]$

Use the random numbers for service:

$\backslash$table$[[78,76,58,54,74,92,38,70,96,92]]$

b$.$ Find real word medical dataset $($published in $2020)$ and create the simulation data of it use Monte Carlo method!