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The second class can be considered as "real" errors of the model.
The relative root mean square errors of the model for growth ranged between 0.09 and 0.31.
A correction method was then applied to the statistical parameters in order to reduce systematic errors of the model.
When the plant encounters extreme temperatures that exceed the optimal temperature of growth during the calculation of plant-accumulated temperature, the prediction errors of the model increase.
The prediction errors of the model (a model with subtracting minimum + 2nd derivative pre-processing) for ethanol contents were 0.1% (w/w).
The root mean square errors of the model substantially decreased with the multi-temporal predictors and were below 2 K for most stations.
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This is because the various errors of the models average out [24].
The prediction errors of the models in the training and testing phases are as presented in Table 5.
This was partly due to the consistent errors of the models predicting the future state of sea surface temperatures in the tropical Pacific.
To compare the qualities of these two models, a PRBNS (Pseudo Random Binary Signal) is applied to each model, and the errors of the models are compared.
Standard errors of the model-based 5-year RS estimates were calculated using the delta method as previously described [ 14].
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