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Why use (supervised) Binning?
The bin variables (that are created) are always categorical in nature, the input variables can be categorical or continuous/metric.
To create the bins a CHAID tree is fitted to the data. The tree then is converted into Statistica’s formulas, that are being used to create the bins.
The CHAID tree is controlled via one parameter alone (the p-value for merging).
Cases with missing data are being excluded during the tree fitting and creation of the bin formulas but each formula is extended by a rule assigning cases with missing data to a separate bin “Missing”. New levels (not previously observed in the data) are assigned to the bin “Unknown”.
The primary output of the node are Statistica formulas. These will calculate the bin-variables when applied to data. The formulas can be easily copied to other places and used where necessary. It is also possible to output the transformed data directly.
This process requires a word template (conveniently stored in Statistica Enterprise or the file system) to start with. At minimum, this template can be an empty word document (.docx).
The interaction with Microsoft Word is handled via Microsoft's own OpenXML library hence resulting in standard compliant word documents.
The most important properties of the items and their placement can be configured via a configuration table. The definition can be provided for individual items and for groups of items via wildcards.
The node and its interface are simple, but do not let it fool you, it is a powerful tool to bring the automation of your reporting to a new level (of convenience and effectiveness).