Statistical procedure for the analysis of experiments with repeated measures over time in the agricultural and livestock field
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Abstract
The objective of this study was the proposal of a statistical analysis methodology that will guide the researcher by making repeated measurements over time in the same experimental unit, through a case study with legumes as a substrate in the production of in vitro gas in the agricultural and livestock field. The variable in vitro gas production was analyzed. Pearson correlation matrix was calculated, values superior to 0.82 were obtained and the existence of association among sampling days was determined. The sphericity criterion was confirmed by means of the Mauchly statistic and, in front of its failure to fulfill, the fit of the degrees of freedom was made. In the same way, normality assumption was verified
(P <0.0100) and when it was not fulfilled, a mixed generalized linear model was used for analyzing the variants of Poisson, Gamma, Binomial, Normal and Normal Log, to determine the distribution that followed the data, which in this case was Gamma. Toeplitz variance-covariance structure was selected as the one that best fits the model based on the lower values of information criteria. The verification of theoretical assumptions necessary for repeated measures defined the model to be used. The use of a mixed generalized linear model increased the accuracy of results by
properly estimating the variance-covariance structures and allowed to analyze unbalanced data. A work methodology is proposed for data processing with repeated measures over time.
Keywords: information criteria, covariance structures, correlation
matrix
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