Multivariate regression model for partitioning tree volume of white oak into round-product classes

Daniel Yaussy; David L. Sonderman

doi:10.2737/NE-RN-317

Treesearch

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Informally Refereed

Authors:

Daniel Yaussy, David L. Sonderman

Year:

1984

Type:

Research Note

Station:

Northern Research Station

DOI:

https://doi.org/10.2737/NE-RN-317

Source:

Research Note NE-317. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 4p.

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Download (PDF 491 KB): https://research.fs.usda.gov/download/treesearch/19219.pdf

Abstract

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on tree-quality characteristics.

Citation

Yaussy, Daniel A.; Sonderman, David L. 1984. Multivariate regression model for partitioning tree volume of white oak into round-product classes. Research Note NE-317. Broomall, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 4p.