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Title: Growth performance of shorea seedlings at kalutara district in Sri Lanka
Authors: Jahufer, A
Keywords: Principal Component Analysis
Eigen values
Eigen vectors
Tukey's Studentized Mean Comparison
Issue Date:  10
Publisher: Faculty of management and commerce South eastern university of Sri Lanka
Citation: Journal of Management. Volume III. No. 1. pp 58-62. October 2005.
Abstract: Principal Component Analysis (PCA) is one of the oldest and most widely used multivariate statistical techniques. The original purpose of PCA was to reduce a large number (p) of variables to a much small number (m) of principal components whilst retaining as much as possible of the variation in the p-original variables. The technique is especially useful if m«<p, and if the m principal components can be readily interpreted. The main objective of this research study is to find the growth performance of seedlings of seven rain forest canopy, dominant Shorea species, in Kalutara district within the humid zone of Sri Lanka for a period of 24 months. A research was carried out by Prof LA. U.N. Gunetilleka and Prof C. VS. Gunetilleka (1989) to collect the data from 336 Shorea seedlings which were transplanted at Kalutara district in 125m altitude elevations. At the end of the experiment period only 112 seedlings were chosen at random for the analysis. The measurements of constituent parts such as stem weight, leaf weight, tap root weight, fine root weight, leaf number and total dry mass of those randomly selected seedlings were recorded in the process. The above two researchers analyzed the recorded data using the ANOVA and GLM procedure of SAS in one-way ANOVA models for each of the attributes of these constituent parts, to be measured separately to compare the performance of species grown at this district. In this study, all the constituent parts of one species is combined using the principal component analysis, and used this new combined new variable to compare the performance of seven species grown at this district. To analyze this combined and reduced response variable (data) ANOVA procedure of SAS was used. Tu key's studentized range method was used for multiple comparison of means among species at the 5% level of significance.
ISSN: 11391-8230
Appears in Collections:Volume 3. Issue.1

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