Ops Ch 14 T/F

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T/F: A table of random numbers must be normally distributed and efficiently generated.

F

T/F: Excel can only be used to simulate systems that can be represented by continuous random variables.

F

T/F: In Excel the VLOOKUP function is used to determine values for continuous random variables.

F

T/F: Simulation applies mathematical models to determine optimal solutions to business problems.

F

T/F: Simulation results will always equal analytical results if 30 trials of the simulation have been conducted.

F

T/F: Simulations should always be started with an empty system.

F

T/F: Starting conditions have no impact on the validity of a simulation model.

F

T/F: When using Excel to simulate a system, it is necessary to compute only the probability (relative frequency) distribution.

F

T/F: A long period of real time can be represented by a short period of simulated time.

T

T/F: Applications of simulation models reflecting continuous distributions are more common than those of models employing discrete distributions.

T

T/F: Cumulative probabilities are analogous to the discrete ranges of random numbers used in the Monte Carlo process.

T

T/F: Developing the cumulative probability distribution helps to determine random number ranges.

T

T/F: In a continuous probability function, the area under the curve represents the probability of occurrence of the random variable.

T

T/F: In computer mathematical simulation, a system is replicated with a mathematical model that is analyzed with the computer.

T

T/F: In the Monte Carlo process, values for a random variable are generated by sampling from a probability distribution.

T

T/F: Integrating a function from zero to any value of a random variable x is an important step in determining the cumulative probability of that value of x.

T

T/F: It's often difficult to validate that the results of a simulation truly replicate reality.

T

T/F: Manual simulation is limited because of the amount of real time required to simulate even one trial.

T

T/F: Monte Carlo is a technique for selecting numbers randomly from a probability distribution.

T

T/F: Random numbers are equally likely to occur.

T

T/F: Random numbers are typically generated on the computer using a numerical technique.

T

T/F: Random numbers generated by a mathematical process instead of a physical process are pseudorandom numbers.

T

T/F: The Monte Carlo process is analogous to gambling devices.

T

T/F: Validation of a simulation model occurs when the true stead state average results have been reached.

T


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