Under these conditions, 98% color, 78% COD and 59% TOC removals were experimentally obtained and fitted the model predictions well.
Owing to computational constraints arising from the size of the data set, we fitted the model above to replicate data sets directly subsampled without replacement from the original site-wise data set.
We fitted the model to data of ∼580,000 daughters of ∼5,000 Brown Swiss bulls with suitable observations available (≥10 daughters per bull).
In this study, the difference was only 0.0118 or 1.18%, revealing reasonable agreement and the data fitted the model well.
We fitted the model in (1) and (2) to data on disappearances and homicide for three separate time periods.
This area-based characteristic fitted the model of other area-based initiatives that were a feature of much government policy at that time.
Iteratively, the algorithm fitted the model parameters anew and updated the values until the convergence criterion or the maximum number of iterations was reached [14].
Based on the results in Table 6, the F-statistics (14.859; sig. = < 0.01) revealed that the data statistically fitted the model well.
We fitted the model using the Restricted Maximum Likelihood (REML) with unstructured variance covariance, using the mixed procedure in SAS [55].
We started by changing one parameter, the transient Ca conductance gCaT, and fitted the model again to the original experimental data.
We fitted the model to the data for the constant speed (CS) condition, fitting the x- and y-components of the velocity separately.
