Is the use of a computer really essential if one wishes to achieve realism in the outcomes of a simulation? Explain.

Can you explain continuous event simulation's four primary benefits?

What are the two types of issues that may be modelled with the use of simulation models?

Is simulation applicable in all research contexts? Can you still use simulation techniques if you do not know about the statistical distribution of the population under study ?

How can one use simulation models most effectively when seeking to represent situations that defy easy categorization?

Does a simulation have to be run on a computer for it to be useful? Explain.

To what extent would you be able to elaborate on the following four primary uses for continuous event simulation?

As we've previously seen, equations describing situations often contain uncertain parameters, that is, parameters that aren't necessarily a single value but instead are associated with a probability distribution function. When more than one of the variables is unknown, the outcome is difficult to visualize. A common way to overcome this difficulty is to simulate the scenario many times and count the number of times different ranges of outcomes occur. One such popular simulation is called a Monte Carlo Simulation. In this problem-solving exercise you will develop a program that will perform a Monte Carlo simulation on a simple profit function. Consider the following total profit function: PT=nPv Where Pr is the total profit, n is the number of vehicles sold and P, is the profit per vehicle. PART A Compute 5 iterations of a Monte Carlo simulation given the following information: n follows a uniform distribution with minimum of 1 and maximum 10 P, follows a normal distribution with a mean…

The value of continuous event simulation may be broken down into four categories.

Does it make sense to run a simulation on a computer for it to be useful? Explain.