# Elementary Output Charts

The Output Plot dialog box is discussed in detail in Running Models. In SIGMA there are five basic output plots and two plots for advanced analysis. The basic charts are:

1. Step plots, which show the values of traced variables during a simulation run.
2. Line plots, which are similar to step plots except straight lines are drawn between successive data points.
3. Array plots, which show values of each element in an array.
4. Scatter plots, which show the relationship between pairs of traced output variables.
5. Histograms, which count the relative frequencies that different values of a variable occur.

More advanced output analysis is possible using the following charts:

6. Autocorrelation functions, which shows dependencies in the output.
7. Standardized time series (STS), which can be used to detect trends.

## Step and Line Plots

Step plots and line plots (also called index plots) are by far the most common form of graphical simulation output. Here we see how one variable changes during the simulation run. The variable of interest is chosen for the vertical axis of the plot and an indexing variable is chosen for the horizontal axis. The indexing variable is often simulated time, but other variables (such as customer identification number) might be used; the indexing variable should not decrease in value during the run for an index plot to make much sense. The next figure shows a line plot of the queue length in our simulated carwash (exponentially distributed service times were used here).

## Array Plots

Array plots show the maximum and current values of each element in an array. This looks like a series of "thermometers". This is useful, say, when you have an array of queues. You can see how each queue size changes and effects the other queues. (See the following figure) A good example is to look at the variable Q in the model, FLOWSHOP.MOD.

## Scatter Plots

When two variables are of equal importance, they can be chosen as the two axes of a scatter plot. A point is plotted for every observed pair of values for these variables. If the points tend to fall along a line with a positive slope, the two variables are likely to be positively correlated. (Small values of one variable are observed along with small values of the other variable and large with large.) Similarly, if the points in a scatter plot tend to fall along a line with a negative slope, negative correlation between the variables should be suspected. The next figure shows a scatter plot of the waiting time of each customer for our simulated bank in Basic Model Enrichments and the number of customers in line when each customer departed; the expected positive correlation is evident.

## Histograms

Histograms show counts of the number of times the observed values of a variable fall within a specified interval. These counts show the relative frequency that values of a variable are observed. The next figure shows a histogram of the number of customers in the carwash model with exponentially distributed interarrival times.