CH14

A ___________ uses repeated random sampling to represent uncertainty in a model representing a real system and that computes the values of model outputs. a. Monte Carlo simulation b. what-if analysis c. deterministic model d. discrete event simulation

A

For a given mean and standard deviation, the ____________ function in Excel is used to generate a value for the random variable characterized by a normal distribution. a. NORM.INV b. RAND c. VLOOKUP d. FREQUENCY

A

In a(n) ___________ relationship between two quantities, either one quantity never increases as the other increases, or one quantity never decreases as the other increases. a. monotonic b. random c. classified d. independent

A

In simulation analysis, the ___________ of random variables can be adjusted to determine the impact of the assumptions about the shape of the uncertainty on the results. a. probability distributions b. ranges c. relative frequencies d. manual generations

A

The Excel add-in __________ is used to design a spreadsheet simulation model. a. Analytic Solver Platform b. Goal Seek c. Solver d. GPSS

A

The process of evaluating a decision in the face of uncertainty by quantifying the likelihood and magnitude of an undesirable outcome is known as a. risk analysis. b. regression analysis. c. data mining. d. decision tree analysis.

A

Which of the following is true of verification? a. It is largely a debugging task. b. It requires an agreement among analysts and managers. c. It deals with the accurate modeling of real system operations. d. It is performed prior to the development of the computer procedure for simulation.

A

Which of the following parameters is required to convert a computer-generated random variable into a uniform random variable? a. Range of the distribution b. Mean of the distribution c. Variance of the distribution d. Moments of the distribution

A

A ___________ analysis involves considering alternative values for the random variables and computing the resulting value for the output. a. random b. what-if c. risk d. cluster

B

A description of the range and relative likelihood of possible values of an uncertain variable is known as a a. risk analysis. b. probability distribution. c. base-case scenario. d. simulation optimization.

B

A distribution of a random variable for which values extremely larger or smaller than the mean are increasingly unlikely can possibly be modeled as a(n) _____________ probability distribution. a. binomial b. normal c. exponential d. gamma

B

According to the ___________, the sum of independent random variables can be approximated by a normal probability distribution. a. what-if analysis b. central limit theorem c. simulation optimization approach d. discrete-event simulation method

B

The Excel function __________ generates integer values between lower and upper bounds. a. RAND b. RANDBETWEEN c. LOWER d. UPPER

B

The __________ function is used to generate a pseudorandom number in Excel. a. FREQUENCY() b. RAND() c. NORM.INV() d. ROUND()

B

The outcome of a simulation experiment is a(n) a. objective function. b. probability distribution for one or more output measures. c. single number. d. what-if scenario.

B

The process of determining that a computer program implements a simulation model as it is intended is known as a. validation. b. verification. c. correlation. d. optimization.

B

The values for random variables in a Monte Carlo simulation are a. selected manually. b. generated randomly from probability distributions. c. taken from forecasting analysis. d. derived secondarily using formulas.

B

Which of the following is a disadvantage of using simulation? a. Experimenting directly with a simulation model is often not feasible. b. Each simulation run provides only a sample of how the real system will operate. c. The simulation models are used to describe systems without requiring the assumptions that are required by mathematical models. d. Simulation models warn against poor decision strategies by projecting disastrous outcomes such as system failures, large financial losses, and so on.

B

A disadvantage of the simple what-if analyses is that a. there are errors induced as a result of rounding. b. the optimal solutions are not guaranteed. c. there is no indication of the likelihood of various output values. d. it cannot compute alternate optimal solutions.

C

A set of values for the random variables is called a(n) a. event. b. permutation. c. trial. d. combination.

C

A simulation model extends spreadsheet modeling by a. extending the range of parameters for which solutions are computed. b. using real-time values for parameters from the application to formulate solutions. c. replacing the use of single values for parameters with a range of possible values. d. using historical data to make predictions about future values and expected trends.

C

A(n) ___________ is an input to a simulation model whose value is uncertain and described by a probability distribution. a. identifier b. constraint c. random variable d. decision variable

C

All the values of computer-generated random numbers are a. Poisson distributed. b. lognormally distributed. c. uniformly distributed. d. normally distributed.

C

In a simulation process, the error of the estimates of the output can be reduced by a. increasing the range of possible values for the random variables. b. increasing the number of random variables. c. increasing the number of trials per simulation. d. conducting regression analysis to forecast values.

C

The __________ function in Excel is used to compute the statistics required to create a histogram. a. NORM.INV b. RAND c. FREQUENCY d. STDEV.S

C

The weekly demand for an item in a retail store follows a uniform distribution over the range 70 to 83. What would be the weekly demand if its corresponding computer-generated value is 0.5? a. 90.1 b. 83 c. 76.5 d. 50.85

C

Which of the following is true of simulation optimization using Analytic Solver Platform (ASP)? a. It is computationally simple and cheap. b. It can be executed in a very short time. c. It is an iterative process. d. It guarantees an optimal solution.

C

________ is a measure of dependence between two random variables. a. Approximation b. Verification c. Correlation d. Validation

C

An input to a simulation model that is selected by the decision maker is known as a a. random variable. b. nonnegativity constraint. c. probable input. d. controllable input.

D

Analytic Solver Platform (ASP) cannot guarantee an optimal solution to a simulation optimization model because a. the optimization problem will have only one local optimal solution due to the linear relationship. b. of sampling error resulting from nonlinear relationships. c. it is not possible to compare solutions across runs. d. of sampling error resulting from the presence of the random variables.

D

In Excel, the expression LN(RAND())*(-m) would generate a(n) __________ random variable with mean m. a. lognormal b. logarithmic c. normal d. exponential

D

In a ___________, a random variable can take any value in a specified range. a. discrete probability distribution b. cumulative distribution c. relative frequency distribution d. continuous probability distribution

D

In a base-case scenario, the output is determined by assuming a. worst values that can be expected for the random variables of a model. b. the mean trial values for the random variables of a model. c. best values that can be expected for the random variables of a model. d. the most likely values for the random variables of a model.

D

The choice of the probability distribution for a random variable can be guided by a. an objective function. b. likelihood factors. c. forecasting. d. historical data.

D

The random variables corresponding to the interarrival times of customers and the service times of customers are commonly described by a(n) __________ distribution. a. Poisson b. Rayleigh c. lognormal d. exponential

D

The range of computer-generated random numbers is a. [-8, 8]. b. [-8, 0). c. [1, 8]. d. [0, 1).

D

___________ is the process of determining that a simulation model provides an accurate representation of a real system. a. Regression b. Verification c. Consideration d. Validation

D