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You can make multiple instances of HeadlessWorkspace and they will operate independently on separate threads without interfering with each other.

When running headless, there are some restrictions:

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In order to run 3D headless you must make sure that the org.nlogo.is3D property is set, you can either do this by starting Java with the -Dorg.nlogo.is3d=true option, or you can set it from within Java by using System.setProperty as follows:

Note that org.nlogo.is3D must be set before creating the workspace.

This example code shows how to run HubNet headlessly. It opens the Template model, runs both the hubnet-reset and setup commands, and then runs the go command in a loop, forever.

The next example shows not only how to run HubNet headlessly, but allows for commands to be entered while the model is running. Any commands entered by the while the go command is executing will be run immediately after it finishes.

When your program controls NetLogo using the App class, the entire NetLogo application is present, including tabs, menubar, and so forth. This arrangement is suitable for controlling or "scripting" NetLogo, but not ideal for embedding a NetLogo model in a larger application.

We also have a distinct but similar API which allows embedding only the interface tab, not the whole window, in another application. To access this functionality use the org.nlogo.lite.InterfaceComponent class, which extends javax.swing.JPanel . You can use the embedded component much the same way that you use App's static methods. Here is the App example from above converted to use InterfaceComponent:

The embedding API gives you a variety of model control features in addition to those provided in the App class. You can simulate button presses, enable logging, create and hide widgets, and so on. See the NetLogo API Specification for details.

To use the embedded component you must have NetLogo.jar in your classpath. If you want to use logging you must also have the log4j jar from the lib directory in your classpath.

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In general, a degree of freedom d.f. = N - k, where N is the sample size, and k is a small number, equal to the number of"constraints", the number of"bits of information" already"used up". As we will see in the ANOVA section, degree of freedom is an additive quantity; total amounts of it can be "partitioned" into various components. For example, suppose we have a sample of size 13 and calculate its mean, and then the deviations from the mean; only 12 of the deviations are free to vary. Once one has found 12 of the deviations, the thirteenth one is determined.

In bivariate correlation or regression situations, k = 2. The calculation of the sample means of each variable"uses up" two bits of information, leaving N - 2 independent bits of information.

In a one-way analysis of variance (ANOVA) with g groups, there are three ways of using the data to estimate the population variance. If all the data are pooled, the conventional SST/(n-1) would provide an estimate of the population variance.

If the treatment groups are considered separately, the sample means can also be considered as estimates of the population mean, and thus SSb/(g - 1) can be used as an estimate. The remaining ("within-group","error") variance can be estimated from SSw/(n - g). This example demonstrates the partitioning of d.f.: d.f. total = n - 1 = d.f.(between) + d.f.(within) = (g - 1) + (n - g).

Therefore, the simple 'working definition' of d.f. is ‘sample size minus the number of estimated parameters'. A more complete answer would have to explain why there are situations in which the degrees of freedom is not an integer. After we said all this, the best explanation, is mathematical in that we use d.f. to obtain an unbiased estimate .

In summary, the concept of degrees of freedom is used for the following two different purposes:

A problem with almost all statistical textbooks is that they not only do not provide information to understand connections between statistical tables. Students often ask: Why T- table values with d.f.=1 are much larger compared with other d.f. values? Some tables are limited, what should I do when the sample size is too large? How can I get familiarity with tables and their differences. Is there any type of integration among tables? Are there any connections between test of hypotheses and confidence interval under different scenario, for example testing with respect to one, two more than two populations? And so on.

The following Figure demonstrates useful relationships among common statistical tables:

Some widely used applications of the popular statistical tables can be categorized as follows:

T - Table:

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