Abstract
Adequately predicting the sampling errors of tabular data can reduce printing costs by eliminating the need to publish separate sampling error tables. Two generalized variance functions (GVFs) found in the literature and three GVFs derived for this study were evaluated for their ability to predict the sampling error of tabular forestry estimates. The recommended GVFs for most tables are either a GVF which incorporated the sampling errors of the row and column totals or a nonlinear GVF when the sampling errors are not published. Tables composed with one sampling intensity and containing data from a multinomial distribution can be represented by a simple linear estimator.
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