Analysis of variance of primary data on plant growth analysis

Pesquisa Agropecuária Brasileira

Adelson Paulo Araújo¹ 

[1] ,Universidade Federal Rural do Rio de Janeiro Dep. de Solos Seropédica RJ ,Brazil

关键词: ANOVA model;    statistic;    phosphorus;    common bean;    rice;    modelo de ANOVA;    estatística;    fósforo;    feijão;    arroz;   

DOI  :  10.1590/S0100-204X2003000100001

来源: SciELO

【 摘 要 】

Plant growth analysis presents difficulties related to statistical comparison of growth rates, and the analysis of variance of primary data could guide the interpretation of results. The objective of this work was to evaluate the analysis of variance of data from distinct harvests of an experiment, focusing especially on the homogeneity of variances and the choice of an adequate ANOVA model. Data from five experiments covering different crops and growth conditions were used. From the total number of variables, 19% were originally homoscedastic, 60% became homoscedastic after logarithmic transformation, and 21% remained heteroscedastic after transformation. Data transformation did not affect the F test in one experiment, whereas in the other experiments transformation modified the F test usually reducing the number of significant effects. Even when transformation has not altered the F test, mean comparisons led to divergent interpretations. The mixed ANOVA model, considering harvest as a random effect, reduced the number of significant effects of every factor which had the F test modified by this model. Examples illustrated that analysis of variance of primary variables provides a tool for identifying significant differences in growth rates. The analysis of variance imposes restrictions to experimental design thereby eliminating some advantages of the functional growth analysis.

【 授权许可】

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