EX 1 CH 12 QUIZ YOURSELF

c. Available in Excel, after ASP has been installed.

A listing of all the available Analytic Solver Platform (ASP) RNG functions is: a. Available in ASP. b. Not available. c. Available in Excel, after ASP has been installed. d. Available in Excel by default.

a. Variable whose value cannot be predicted or set with certainty.

A random variable is a: a. Variable whose value cannot be predicted or set with certainty. b. Constant with an unknown mean. c. Number with an unknown variance. d. Parameter whose numerical value is unknown.

b. Replication.

Recalculating the spreadsheet several hundred or several thousand times and recording the resulting values generated for the output cell(s), or bottom-line performance measure(s) is called: a. Automation. b. Replication. c. Estimation. d. Model fitting.

d. All of the above answers are correct.

Simulation is: a. A technique that measures various characteristics of the model bottom-line performance measure. b. A technique that describes various characteristics of the bottom-line performance measure. c. A technique that is useful when one or more values for the independent variables are uncertain. d. All of the above answers are correct.

d. Maximize or minimize the average value of (or some other statistic describing) the cell representing the objective or bottom-line performance measure.

When attempting to optimize a simulation model (also known as simulation optimization) we typically want to: a. Minimize the simulation time and cost. b. Account for multiple sources of variability in the model. c. Maximize the usefulness of simulation results to a decision maker. d. Maximize or minimize the average value of (or some other statistic describing) the cell representing the objective or bottom-line performance measure.

d. All of the above answers are correct.

When faced with uncertainty, people do the following: a. React with paralysis. b. Do exhaustive research. c. Avoid making a decision. d. All of the above answers are correct.

d. Whether the variable can assume discrete or continuous values.

When selecting an RNG for an uncertain variable in a model, it is important to consider: a. Whether the simulation model accurately reflects reality. b. The distribution variance. c. The upper and lower bounds on the decisions. d. Whether the variable can assume discrete or continuous values.

b. Risk.

When such a decision is made, some chance exists that the decision will not produce the intended results. This chance, or uncertainty, represents: a. Bad luck. b. Risk. c. A residual. d. Good luck.

d. Automatically identify probability distributions that fit your historical data reasonably well.

Analytic Solver Platform (ASP) has the ability to: a. Automate the simulation runs. b. Simulate human decision-making. c. Generate historical process data. d. Automatically identify probability distributions that fit your historical data reasonably well.

c. Create the RNGs required for simulating a model.

Analytic Solver Platform (ASP) provides several "Psi" functions that can be used to: a. Interpret the simulation results. b. Complete a PivotTable. c. Create the RNGs required for simulating a model. d. Store the simulation results for replication.

c. Decreases.

As the number of replications (n) increases, the width of the confidence interval: a. There is no impact of (n) on the width of the confidence interval. b. Changes the level of confidence. c. Decreases. d. Increases.

b. Launches the Fit Options dialog.

Clicking the "Fit" icon found within the "Tools" group on the Analytic Solver Platform (ASP) tab: a. Performs a goodness of fit test for the required distribution. b. Launches the Fit Options dialog. c. Fits a normal distribution to the data and estimates its mean and variance. d. Estimates the model parameters.

d. The base-case scenario.

If we don't know what value a particular cell in a spreadsheet will assume and enter a number that we think is the most likely value for the uncertain cell, we can calculate the most likely value of the bottom-line performance measure. This is called: a. The random scenario. b. The pessimistic scenario. c. The optimistic scenario. d. The base-case scenario.

a. "Add Output" button on the Analytic Solver Platform (ASP) menu.

In order to indicate the output cell (or cells) that we want Analytic Solver Platform (ASP) to track during the simulation we can use the: a. "Add Output" button on the Analytic Solver Platform (ASP) menu. b. GenerateOutput(.) function in ASP menu. c. AddPsiChart(.) function. d. AddGraph(.) function.

a. A manager changes the values of the uncertain input variables to see what happens to the bottom-line performance measure.

In what-if analysis: a. A manager changes the values of the uncertain input variables to see what happens to the bottom-line performance measure. b. All variables become parameters. c. Uncertain variables are removed from the decision-making process. d. All variables are assigned random values.

c. You may be interested in examining how sensitive the simulation output results are to various uncertain input cells in the model.

Sensitivity analysis is useful when: a. There are more than one input cells in the model. b. There is a lot of variability in the model. c. You may be interested in examining how sensitive the simulation output results are to various uncertain input cells in the model. d. There is one input cell in the model.

d. All of the above answers are correct.

The benefit(s) of simulation include(s): a. The results of simulation do give us greater insight into the problem. b. It gives a decision maker some idea of the best- and worst-case total outcomes for the problem. c. It provides an idea of the distribution and variability of the possible outcomes. d. All of the above answers are correct.

b. 10,000.

The educational version of Analytic Solver Platform (ASP) allows for ______ trials per simulation. a. 2,500. b. 10,000. c. 20,000. d. 5,000.

c. Placing a random number generator (RNG) formula in each cell that represents a random, or uncertain, independent variable.

The first step in performing a simulation in a spreadsheet is: a. Calculating the expected values of the random variables. b. Fitting a bell-shaped curve to the variable of interest. c. Placing a random number generator (RNG) formula in each cell that represents a random, or uncertain, independent variable. d. Calculating the values of all dependent variables in the model.

b. Provide the most likely value for the bottom-line performance measure (Y).

The manager hopes that using the expected, or most likely, values for all the uncertain variables will: a. Be the right guess. b. Provide the most likely value for the bottom-line performance measure (Y). c. Be supported by the manager's team. d. Guarantee that an optimal decision will be made.