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Example using the next splits panel

Example next splits

The PWE algorithm specified below creates splits by separating into separate nodes, values which are above/below the Mean PWE for the variable. This essentially isolates values for which the Analysis % is high, i.e. containing more Swedish holiday makers, from those where it is low.

For more details on the Algorithm Options see the section How do I set the Algorithm Options?

The following display illustrates how the algorithm is applied:

The codes grouped together in branch 1 all have a PWE score greater than the Mean PWE (of -0.67).

  • The branch column is coloured red for branch 1, since the overall analysis % for this branch is higher than that in the root node.

The codes grouped together in branch 2 all have a PWE score less than the Mean PWE (of -0.67).

  • The branch column is coloured blue for branch 2, since the overall analysis % for this branch is less than that in the root node.

Sorting the table based on the Code column provides a useful insight:

The categories are now in ascending order of Income.

  • It is clear that the middle income bands are the ones that have the most people in the analysis selection, since they are coloured red.
  • The unclassified category has an analysis % lower than the root node (the row is coloured blue), but it has been assigned to branch (1) which on average has an analysis % higher than the root node (branch cell is coloured red). You might want to try manually altering this split to group the unclassified in the other branch. In this case it does not make a significant difference to the final model (increases the power by 0.02).