Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation

This paper employed the technique of Multiple Regression (MR) in estimating the tree stem volume of Roystonea regia (R. regia) based on two volumetric equations, namely, the Huber’s and Newton’s formulae. Variables considered for data mensuration were stem height (or bole), tree height, diameter at...

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Main Authors: Noraini Abdullah, Zainodin H. J., Amran Ahmed
Format: Article
Language:English
English
Published: Universiti Putra Malaysia 2012
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Online Access:https://eprints.ums.edu.my/id/eprint/30024/1/Comparisons%20between%20huber%E2%80%99s%20and%20newton%E2%80%99s%20multiple%20regression%20models%20for%20stem%20biomass%20estimation.pdf
https://eprints.ums.edu.my/id/eprint/30024/2/Comparisons%20between%20huber%E2%80%99s%20and%20newton%E2%80%99s%20multiple%20regression%20models%20for%20stem%20biomass%20estimation1.pdf
https://eprints.ums.edu.my/id/eprint/30024/
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spelling my.ums.eprints.300242021-07-22T03:49:37Z https://eprints.ums.edu.my/id/eprint/30024/ Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation Noraini Abdullah Zainodin H. J. Amran Ahmed T Technology (General) This paper employed the technique of Multiple Regression (MR) in estimating the tree stem volume of Roystonea regia (R. regia) based on two volumetric equations, namely, the Huber’s and Newton’s formulae. Variables considered for data mensuration were stem height (or bole), tree height, diameter at breast height, diameter at middle and diameter at top of the stem before the crown. Correlation coefficient and normality tests were done to screen and select possible variables with their interactions. Transformations were done for normality and variables with Pearson correlation coefficient values greater than 0.95 were eliminated to reduce multicollinearity. All selected models were examined using parameters tests: Global test, Coefficient test and the Wald test. The Wald test was carried out to justify the elimination of the insignificant variables. The eight criteria model selection (8SC) process was done to obtain the best regression model without effects of multicollinearity and insignificant variables. Major contributors to the best Multiple Regression (MR) model were from tree height and diameter at the middle of the stem, while significant contributions were from the bole (h) and diameters at breast height (Dbh) and the top, Dt. Universiti Putra Malaysia 2012 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/30024/1/Comparisons%20between%20huber%E2%80%99s%20and%20newton%E2%80%99s%20multiple%20regression%20models%20for%20stem%20biomass%20estimation.pdf text en https://eprints.ums.edu.my/id/eprint/30024/2/Comparisons%20between%20huber%E2%80%99s%20and%20newton%E2%80%99s%20multiple%20regression%20models%20for%20stem%20biomass%20estimation1.pdf Noraini Abdullah and Zainodin H. J. and Amran Ahmed (2012) Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation. Malaysian Journal of Mathematical Sciences, 6. pp. 1-28. ISSN 1823-8343 https://einspem.upm.edu.my/journal/fullpaper/vol6no1/1.%20Noraini%20edit%204.1.12.pdf
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Noraini Abdullah
Zainodin H. J.
Amran Ahmed
Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation
description This paper employed the technique of Multiple Regression (MR) in estimating the tree stem volume of Roystonea regia (R. regia) based on two volumetric equations, namely, the Huber’s and Newton’s formulae. Variables considered for data mensuration were stem height (or bole), tree height, diameter at breast height, diameter at middle and diameter at top of the stem before the crown. Correlation coefficient and normality tests were done to screen and select possible variables with their interactions. Transformations were done for normality and variables with Pearson correlation coefficient values greater than 0.95 were eliminated to reduce multicollinearity. All selected models were examined using parameters tests: Global test, Coefficient test and the Wald test. The Wald test was carried out to justify the elimination of the insignificant variables. The eight criteria model selection (8SC) process was done to obtain the best regression model without effects of multicollinearity and insignificant variables. Major contributors to the best Multiple Regression (MR) model were from tree height and diameter at the middle of the stem, while significant contributions were from the bole (h) and diameters at breast height (Dbh) and the top, Dt.
format Article
author Noraini Abdullah
Zainodin H. J.
Amran Ahmed
author_facet Noraini Abdullah
Zainodin H. J.
Amran Ahmed
author_sort Noraini Abdullah
title Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation
title_short Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation
title_full Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation
title_fullStr Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation
title_full_unstemmed Comparisons between huber’s and newton’s multiple regression models for stem biomass estimation
title_sort comparisons between huber’s and newton’s multiple regression models for stem biomass estimation
publisher Universiti Putra Malaysia
publishDate 2012
url https://eprints.ums.edu.my/id/eprint/30024/1/Comparisons%20between%20huber%E2%80%99s%20and%20newton%E2%80%99s%20multiple%20regression%20models%20for%20stem%20biomass%20estimation.pdf
https://eprints.ums.edu.my/id/eprint/30024/2/Comparisons%20between%20huber%E2%80%99s%20and%20newton%E2%80%99s%20multiple%20regression%20models%20for%20stem%20biomass%20estimation1.pdf
https://eprints.ums.edu.my/id/eprint/30024/
https://einspem.upm.edu.my/journal/fullpaper/vol6no1/1.%20Noraini%20edit%204.1.12.pdf
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