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The root mean square error (RMSE) is an error measure for how well the model performs, and is given by the expression (1) R M S E = ∑ n = 1 N (y − y ^ ) 2 N When representing estimation of future prediction error this is called RMSEP.
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where the notation E represents estimation.
Vertical error bars represent estimation errors taking uncertainties in the spectrum estimation and in the least squares fitting into account.
By contrast, forecasts represent estimation of future rates taking into account expected changes in the factors determining the risk, or outcome, of the disease concerned.
These thresholds represent estimations of their throughput capacities.
This ratio hereafter referred to as PER (portion estimation ratio) provides a measure of the departure in the number of estimated vs. reference portions, with values >1 indicating over-estimation, values close to 1 indicating accurate estimation, and values <1 representing under-estimation.
For each size of the context N, we have a different estimation of the information carried by the studied words, with self-information representing the estimation from a context of size 1.
The square root of the resulting random effect estimates represents an estimation of the SD, which, expressed as percentage DNA methylation, is easier to interpret.
Here, we first assume that and for each have been estimated imperfectly; that is, and, where and are the estimation errors of and ( represents the estimation error of ), respectively.
First, the present estimations represent the total cost of the SBV at farm-level and not the avoidable costs.
The mean values of the bootstrapped estimates represent unbiased estimations of the parameters under investigation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com