Alternativas e comparações de modelos lineares para estimativa da biomassa verde de Bambusa vulgaris na existência de multicolinearidade

Abstract

The objective of this work was to use univariate and multivariate statistical methods on selection of independent variables, in the mathematical linear models, to estimate the green biomass of the main bamboo rod, bambusa vulgaris, pursuing time and cost reduction without loss of precision. The data came from an experiment carried out for the Agroindustrial Excelsior S. A. (Agrimex) company located in the city of Goiana – PE.Quantified by its green biomass weight,450 bamboo rods were used and 4 independent variables measured in the rod. Initially, the effect of the multicollinearity could be verified through the correlation matrix of the independent variables and the varience inflation factors.To select the independent variables two methods were used: Stepwise and K component retention. The alternatives used were component regression and Ridge regression. In general, in only one situation the variable selection methods behave adequately while multicollinearity is present among the independent variables, that is the multivaried method of retention K=3 component for the covariate matrix, model of Spurr. The estimative of alternative methods showed similar responses, however, the principal component regression yields the best results.