LOGISTIC MODEL CONSIDERING DIFFERENT ERROR DISTRIBUTIONS APPLIED IN MAIZE HEIGHT DATA
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Abstract
Maize is one of the main cereals produced in the world due to its wide use in food and feed. Knowledge on plant growth is extremely important to its management. One can obtain this knowledge by the use of growth models to provide information through parameters with biological interpretations summarize the characteristic curve of plant growth. This work aimed to fit the logistic model considering heteroscedasticity and different distributions for error, namely, normal, skew normal and skew t-student applied to plant height data (cm) of the maize transgenic hybrid 30F35 Y (YieldGard ) observed over time (days). The models considered had a good fit to the growth curve of the culture, but the logistic model considering skew normal error was selected as most appropriate for modeling the curve, based on the evaluators used.
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