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| 16. |  | BESERRA, P.; COUTURIER, G.; GARCIA, M. V. B. On the biology of three new pollinating species (Coleoptera: Curculionidae: Derelomini) of caiaué Elaeis oleifera (Kunth) Cortés and of the F1 hybrid (E. guineensis x E. oleifera) in the State of Amazonas, Brazil. In: INTERNATIONAL CONGRESS OF ENTOMOLOGY, 21.; BRAZILIAN CONGRESS OF ENTOMOLOGY, 18., 2000, Foz do Iguassu. Abstracts... Londrina: Embrapa Soja, 2000. v. 1 p. 227. (Embrapa Soja. Documentos, 143).| Biblioteca(s): Embrapa Amazônia Ocidental. |
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| Registros recuperados : 167 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
11/02/2003 |
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Data da última atualização: |
01/10/2018 |
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Autoria: |
ARAÚJO, A. P. |
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Afiliação: |
Adelson Paulo Araújo, Universidade Federal Rural do Rio de Janeiro - UFRRJ/Departamento de Solos. |
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Título: |
Analysis of variance of primary data on plant growth analysis. |
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Ano de publicação: |
2003 |
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Fonte/Imprenta: |
Pesquisa Agropecuária Brasileira, Brasília, DF, v. 38, n. 1, p. 1-10, jan. 2003 |
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Idioma: |
Inglês |
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Notas: |
Título em português: Análise de variância dos dados primários na análise de crescimento vegetal. |
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Conteúdo: |
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. |
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Palavras-Chave: |
Análise de variância; ANOVA model; common bean; Modelo ANOVA; modelo de ANOVA; statistic. |
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Thesagro: |
Arroz; Crescimento; Estatística; Feijão; Fósforo; Lycopersicon Esculentum; Tomate. |
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Thesaurus NAL: |
phosphorus; rice. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/AI-SEDE/24345/1/001.pdf
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Marc: |
LEADER 02308naa a2200313 a 4500
001 1085678
005 2018-10-01
008 2003 bl uuuu u00u1 u #d
100 1 $aARAÚJO, A. P.
245 $aAnalysis of variance of primary data on plant growth analysis.
260 $c2003
500 $aTítulo em português: Análise de variância dos dados primários na análise de crescimento vegetal.
520 $aPlant 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.
650 $aphosphorus
650 $arice
650 $aArroz
650 $aCrescimento
650 $aEstatística
650 $aFeijão
650 $aFósforo
650 $aLycopersicon Esculentum
650 $aTomate
653 $aAnálise de variância
653 $aANOVA model
653 $acommon bean
653 $aModelo ANOVA
653 $amodelo de ANOVA
653 $astatistic
773 $tPesquisa Agropecuária Brasileira, Brasília, DF$gv. 38, n. 1, p. 1-10, jan. 2003
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Embrapa Unidades Centrais (AI-SEDE) |
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