For example, if you want to predict age in the big class data set from height and weight and then decide if you should add the height*weight term, you would add all three terms in the model launch dialog box. Using fit model add all of the possible terms in every group as model effects. Instead of adding variables in blocks, you can add them individually in any order and just stop after adding each block. The effect summary section of the least squares report has options to add/remove terms, you can compare your summary statistics just as is described in the stepwise section. Note that for ensemble modeling you can extend this beyond just linear regression, you could decide whether to include a partition after a liner regression. adding/removing terms in the standard leas squares report,.That could be because there are fairly simple alternatives (at least based on my understanding of Hierarchical Linear Regression): If you refer to Hierarchical Linear Regression then I don't think this a separate platform in JMP.
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