Tutorial: Analyzing a model

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This chapter shows you how to:

• Perform importance analysis
• Perform parametric analysis
• Set up and compare alternative decisions

In this chapter you will analyze the Rent vs. Buy Analysis model, a modified version of the model that you used in Chapter 1, “Using the Rent vs. Buy Model” and Chapter 2, “Exploring the Rent vs. Buy Model”. You will identify its key sources of uncertainty through importance analysis, perform parametric analysis, and compare alternative decisions.

For instructions on how to open a model, see “Opening the Rent vs. Buy model”. In this case, however, open the Rent vs. Buy Analysis model by double-clicking the file labeled Rent vs. Buy Analysis.ana.

Examining the difference between renting and buying

The Rent vs. Buy Analysis model is the module called Model that you explored in Chapter 2, “Exploring the Rent vs. Buy Model,” with the addition of nodes to help you understand the importance of the uncertain inputs to the uncertainty in the output.

In Chapter 1, “Using the Rent vs. Buy Model,” you saw that evaluating Costs of buying and renting produces a graph of two uncertain values. To understand whether it would be financially advantageous to rent or buy, the Rent vs. Buy Analysis model includes the objective node, Difference between buying and renting.

The difference between the two uncertain values is also uncertain. The difference is positive if buying costs less over the time period, and negative if renting costs less over the time period.

Importance analysis

In the Rent vs. Buy Analysis model, as in most complex models, several of the input variables are uncertain.

It is often useful to understand how much each uncertain input contributes to the uncertainty in the output. Typically, a few key uncertain inputs are responsible for the lion’s share of the uncertainty in the output, while the rest of the inputs have little impact.

Analytica’s importance analysis features can help you understand which uncertain inputs con- tribute most to the uncertainty in the output. You can then concentrate on getting more precise estimates or building a more detailed model for the one or two most “important” inputs.

Analytica defines importance as the rank order correlation between the output value and each uncertain input. Each variable’s importance is calculated on a relative scale from 0 to 1. An importance value of 0 indicates that the uncertain input variable has no effect on the uncertainty in the output. A value of 1 implies total correlation, where all of the uncertainty in the output is due to the uncertainty of a single input.

It is clear in the figure above that the input Appreciation Rate is contributing most of the uncertainty in the Difference between buying and renting.

For more information about importance analysis and the steps to create an importance variable in your own model, see “Scatter plots” in the “Sensitivity and Uncertainty Analysis” chapter of the Analytica User Guide.

See Also

Tutorial Chapter 2 <-

Tutorial Chapter 3

-> Tutorial Chapter 4