We can see, for example, that the evolution of cost contributes almost 80% to the evolution of benefit and that the correlation between these two variables is negative. The table below shows the results of this analysis for the outcome variable (Benefit). This is followed by a sensitivity analysis based on the simulation results. The same tables and graphs are displayed to examine the result variable after the simulation.
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How to interpret the results of the sensitivity analysis? A histogram for the variable sales is also shown. The following histogram shows the distribution of the Costs variable. Several statistical indicators such as mean, median, quartiles, variance, standard deviation and skewness coefficients of the distributions of the two random variables (sales and costs) are shown in the following table. It also contains the formula that explains how to compute the result variable. The first result is a summary of the simulation model that contains the default values of the cells and the distributions of the variables. Interpret the results of a simple simulation model Set up the Charts > Sensitivity tab as shown in the figure below. Select the XLSTAT > Monte Carlo Simulations > Run command. The corresponding function call to XLSTAT_SimRes is inserted in cell B4. The Define a result variable dialog box appears. Then click the XLSTAT>Monte Carlo Simulations >Define a result variable command in the XLSTAT menu. Note that the result variable must not be constant, but must depend on at least one of the distribution variables defined above. Select the result cell that contains the value 40 as the result of the formula =B2-B3. How to define the result variable in XLSTAT? Start again in the cell below to generate a normal distribution with mu=80 and sigma=20. A formula calling the XLSTAT_Sim function is created in cell B2. Choose a normal distribution with mu=120 and sigma=10. Select the Variable Name (sales) in cell A2. The Define a distribution dialog box appears. Select cell B2 cell that contains the amount of sales.Ĭlick on XLSTAT > Monte-Carlo simulations>Define a distribution. Create a simple simulation model How to define the distribution variables in XLSTAT? This model can be found on the sheet "Model". Sales also follow a normal distribution (mu=80, sigma=20) - See Fitting a distribution to a sample of data in Excel for more details.īased on this model, the various model variables are created: Based on historical data for costs and sales analyzed with the distribution fitting tool, we found that costs follow a normal distribution (mu=120, sigma=10).
Profit in this simple case is simply the difference between sales and costs. Our simulation model is based on the sales and costs of a business. More tutorials with all 4 model elements and options can be found here. In this tutorial, a very simple simulation model with two distributions and one result is created to explain the basics of simulation modeling. Dataset for creating and running a simple simulation model Simulation models are used in many fields, such as finance, insurance, medicine, oil and gas exploration, accounting or sales forecasting. The Monte Carlo simulations in XLSTAT allow you to define the distributions and then, through simulations, to obtain an empirical distribution of the input and output variables and the corresponding statistics. If some "result" variables depend on these "distributed" variables via known or assumed formulas, then the "result" variables will also have a distribution.
Simulation models allow us to obtain information such as mean, median, or confidence intervals about variables that do not have an exact value, but for which we either know or assume a distribution. This tutorial will help you set up and run a simple simulation model in Excel using the XLSTAT statistical software.
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