Simulation is an excellent technique to use when a situation is too complicated to use standard analytical procedures.

Simulation is a trial$-$and$-$error approach to problem solving.

The degree of risk is associated with the probability or magnitude of loss.

To use Excel to generate a normally distributed random variable, you must know the mean and standard deviation of the distribution and have a random number between $0$ and $1.$

Trials of a simulation show what would happen when values of the probabilistic input change.

In a Monte Carlo simulation, each simulation trial is dependent upon the result of a previous trial.

Verification is the process of ensuring that the simulation model provides an accurate representation of the real system.

In comparing different policies using simulation, one should use the same set of random numbers whenever possible. T

Validation determines that the computer procedure is operating as it is intended to operate.

A discrete$-$event simulation reviews the status of the system periodically, whether or not an event occurs.

A static simulation model is used in situations where the state of the system affects how the system changes or evolves