Get more reliable answers to your most important questions using Monte Carlo simulation techniques
Monte Carlo simulation is an analysis method that helps decision makers understand the impact of risk and uncertainty in financial, project management, cost and other types of predictive models.
Developing predictive models typically involves some element of uncertainty. For example, you can’t calculate precisely the investment return on a portfolio, or the revenue from an advertising campaign. You need to account for that uncertainty by using historical data to estimate your inputs. These estimates can be useful, but also introduce a degree of risk due to the wide variety of potential outcomes.
SPSS Statistics combines the power of predictive analytics with the what-if capabilities of Monte Carlo simulation to help you assess risk and deal with uncertainty in predictive models.
Start with your existing models and data
When you use SPSS Statistics to perform Monte Carlo simulations, you don’t need to specify your entire model. You can use your existing predictive models and data as the starting points for your simulation. The predictive models can be built in SPSS Statistics or SPSS Modeler, and can be used to specify the strength of the inputs. SPSS Statistics can use existing similar data you have to fit the distribution of the inputs and the correction of the inputs automatically.
The software calculates the results over and over, each time using a different set of random values to produce distributions of possible outcome values.
When the simulation is complete, you have a large number of results from the model, each based on random input values.
You can also modify the parameters used to simulate the data and compare outcomes. For example, you may want to simulate various advertising budget amounts and see how that affects total sales. Depending on the outcome of the simulation, you might decide to spend more on advertising to meet your total sales goal.
Assess risk more accurately
Monte Carlo simulation is often used in business for risk and decision analysis, to help organizations make decisions given uncertainties in market trends, fluctuations and other uncertain factors.
This technique can be applied in many industries and business situations, including:
Monte Carlo simulation can be a valuable tool for predicting outcomes, helping you make better decisions quickly, and further reducing risk to your organization.
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Monte Carlo simulation is just one of the powerful new features included in the latest release of SPSS Statistics. See what you've been missing, then contact us to upgrade your software.
Monte Carlo simulation demo
Learn how Monte Carlo simulations performed with IBM SPSS Statistics can help you decrease risk and make decisions with greater certainty.