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The common way to estimate the regression model coefficients is the ordinary least squares method.
Using these data matrices an underlying regression model (coefficients) is learned during the training phase.
While the original version of MMC is mathematically elegant, it is conceptually complex and evaluation of model coefficients is difficult.
Due to the very distinct behaviour of slack, moderately slack and taught moorings, these are analysed separately and the variation in the model coefficients is justified.
(1) The variability associated with estimation of the plot-ALS model coefficients is significant and should be included in the overall estimate of biomass density variance.
As in every correlation coefficient, a scaling of the gravity field model coefficients is not relevant, but the way in which they change with order is.
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The principal h-d models also vary on how the model coefficients are being interpreted, which is especially important if they are then modelled as smooth functions of predictors.
Many of the VAR model coefficients were not statistically significant.
The MNL model coefficients are difficult to interpret.
The related model coefficients are determined with measurable parameters.
Model coefficients are developed using ordinary least squares (OLS) regression.
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