How can correlations between distributions be integrated and how can SPC (process capability) indicators be calculated for a simulation model?

An Excel sheet containing both the data and the results for use in this tutorial can be downloaded by clicking here.

Simulation models

Simulation models allow to obtain information, such as mean or median or confidence intervals, on variables that do not have an exact value, but for which we either know or assume a distribution. If some "result" variables depend on these "distributed" variables by the way of known or assumed formulae, then the "result" variables will also have a distribution. XLSTAT-Sim allows you to define the distributions, and then to obtain, through simulations, an empirical distribution of the input and output variables as well as the corresponding statistics.

Simulation models are used in many areas such as finance and insurance, medicine, oil and gas prospecting, accounting, or sales prediction.

Four elements are involved in the construction of a simulation model:

- Distributions are associated to random variables. XLSTAT gives a choice of more than 30 distributions to describe the uncertainty on the values that a variable can take. For example, you can choose a triangular distribution if you have a quantity for which you know it can vary between two bounds, but with a value that is more likely (a mode). At each iteration of the computation of the simulation model, a random draw is performed for each distribution that has been defined.

- Scenario variables allow to include in the simulation model a quantity that is fixed in the model, except during the tornado analysis where it can vary between two bounds.

- Result variables correspond to outputs of the model. They depend either directly or indirectly, through one or more Excel formulae, on the random variables to which distributions have been associated and, if available, on the scenario variables. The goal of computing the simulation model is to obtain the distribution of the result variables.

- Statistics allow to track a given statistic for a result variable. For example, we might want to monitor the standard deviation of a result variable.

A correct model should comprise at least one distribution and one result variable. Models can contain any number of these four elements. A model can be limited to a single Excel sheet or can use a whole Excel folder.

In this tutorial we add to the model of the first tutorial a correlation matrix and a SPC analysis.

Our simulation model is based on sales and costs of a shop. The benefit is simply the difference between sales and costs in this simple case. Based on historical data for costs and sales that were analyzed with the tool "distribution fitting" we found out that the costs follow a normal distribution (mu=120, sigma=10) and the sales a normal distribution (mu=80, sigma=20) (see tutorial on distribution fitting for more details).

We suppose that the costs and the sales correlate and have a spearman correlation of 0.8. This is shown in our correlation matrix. The lower triangle is sufficient. It is important that the rows and column have names in the header that are the same than the names of the distribution variables and that were defined during the definition of the distribution variables.

Additionally a SPC analysis for the three model variables is carried out. During the planning for the running year we defined upper and lower specification limits and a target value that is identical to the static model.
Remark: The SPC analysis is only available if a valid SPC license is present.


This model can be found in the sheet Model.

To start the simulation run, select the XLSTAT/ XLSTAT-SIM /Simulation - Run command, or click on the corresponding button of the "XLSTAT-SIM".

The simulation - run dialog box will appear. Then select the data on the Excel sheet. Set the number of simulations to 1000. Activate the option proximity matrix and select the correlation matrix including the row and column description.


In the "Options" tab, enter the parameters of the tornado and spider analysis. Select the standard cell value as default value. Choose 10 data points in the interval from -10% up to +10% of the value deviation:


In the "SPC" tab, if a valid SPC license is present, we activate the "calculate process capabilities". Choose for the selection field LSL the three Excel cells below the cell LSL, because the column label is not necessary. Fill out USL in the same way. In the field Name choose the 3 cells with the names of the model elements for that the SPC analysis should be carried out: sales, costs and benefit situated on the left of the model elements itself. At last, activate the option "target" to calculate SPC values that need a target value.


The computations begin once you have clicked on "OK".

Interpreting the results of a simulation model integrating correlations between distributions and SPC indicators

You are asked to confirm the number of rows and columns (this message can be bypassed by un-selecting the "Ask for selections confirmation" in the XLSTAT options panel).

The first results are the same like in the first tutorial Sim1. Additionally in the model summary the proximity matrix is displayed:


In the following tables that contain the details of the distribution and result variables additional results of the SPC analysis are displayed:


Finally the correlation matrix of the different distribution and result variables is displayed. We see that the costs and the sales are like selected earlier correlated at about 0.8. If the number of iterations during the simulation will is bigger, then this correlation will be even closer to 0.8.


Click here for other tutorials.

About KCS

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

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