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Estimating weights when fitting linear regression models for tree volume


Canadian Journal of Forest Research 23(8): 1725-1731
Estimating weights when fitting linear regression models for tree volume
The method of weighted least squares can be used to achieve homogeneity of variance with linear regression that has a heterogeneous error structure. A weight function commonly used when constructing regression equations to predict tree volume is (1/D-i-2H-i)-k!1, where k-1 apprxeq 1.0-2.1. This paper examines the weight function (1/D-i-k!2H-i-k!3)-k!1 for modelling the error structure in two loblolly pine (Pinus taeda L.) data sets and one white oak (Quercus alba L.) data set. The weight function (1/D-i-2.3H-i-0.7)-k!1 is recommended for all three data sets, for which the K-1 values ranged from 1.80 to 2.07.

Accession: 002373069

DOI: 10.1139/x93-216

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