Skip to Main Content
Generalized Variance Function Applications in ForestryAuthor(s): James Alegria; Charles T. Scott; Charles T. Scott
Source: Res. Note NE-345. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 4 p.
Publication Series: Research Note (RN)
Station: Northeastern Research Station
Download Publication (193.55 KB)
DescriptionAdequately 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.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, email@example.com if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationAlegria, James; Scott, Charles T. 1991. Generalized Variance Function Applications in Forestry. Res. Note NE-345. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 4 p.
- Analysis and Reporting Needs for Annual Forest Inventories in the South
- Michigan's Forest Resources, 2007
- Forest Resources of the United States, 2002
XML: View XML