Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/13072
Título: Transformação de dados como alternativa a análise de variância univariada
Palavras-chave: Função discriminante linear de Fisher
Análise de variância multivariada
Mudas - Qualidade
Transformação de dados
Fisher’s linear discriminant function
Multivariate analysis of variance
Seedling quality
Data transformation
Data do documento: 2013
Editor: Universidade Federal de Alfenas
Citação: CAMPOS, K. A.; PAIXÃO, C. A.; MORAES, A. R. de. Transformação de dados como alternativa a análise de variância univariada. Sigmae, Alfenas, v. 2, n. 3, p. 57-64. 2013.
Resumo: In experiments, it is common to obtain various response variables that are subject to individual statistical analysis, leading to results for each characteristic. In order to propose an alternative analysis to deal with several characteristics at the same time, Fisher’s Discriminant Analysis was used in this work. Through this analysis, multivariate data of various characteristics are transformed into a new univariate variable without information loss. To illustrate the technique, we used data from an experiment of producing coffee seedlings in tubes, which evaluated the effect of two commercial substrates (A and B), and five substitution proportions (0, 20, 40, 60 and 80%) of the substrate for an organic compound. Seven quality characteristics of the seedlings were evaluated, and a new variable was obtained through the transformation of the original variables using Fisher’s Linear Discriminant function. The variance analysis of quality characteristics of individual seedlings detected significant differences only in the replacing proportion of the substrate for organic fertilizer, and optimal proportions of 19 to 29% were estimated depending on the characteristic. On the other hand, the variance analysis of the transformed data detected significant differences in substrate interaction × percentage replacement. These results show that using Fisher’s Discriminant Function to transform multivariate data into a new unidimensional variable can be considered a viable technique for evaluating experiments with various characteristics.
URI: http://repositorio.ufla.br/jspui/handle/1/13072
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