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Filtering of genes based on r-square (>0.8) was performed and 'vars' was set at 'each', generating as many significant genes extractions as variables in the general regression model.
This work presents a new approach, regression analysis, and develops a general regression model.
Section 2 gives a brief introduction on how to extend the general regression model to the KRLS one.
This approach is formulated for a general regression model and, then is explored through several regression models.
Finally, based on the apparent heat capacity theory, a general regression model for soil rock media in cold regions is obtained.
Using a large number of designed sub-samples from established VS benchmark datasets, a general regression model was developed that allows the prediction of the estimated performance of VS methods.
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We considered the strength of the relationships between this group of variables and fishing density across all gears using generalized linear and general regression models in STATISTICA.
In general, regression modeling of the radiopacity data indicated that increased loadings of La2O3, TiO2 and ZnO are required at the expense of SiO2 in order to increase the radiopaque nature of the glasses.
Clearly, embedding the beta distribution within a more general regression modelling framework has enhanced its uptake in applied research settings.
The General Regression Models (GRM) were used to assess the effect of MMP-9 on HKD/ADHD symptoms (Table 2).
There are many models which can be used in infectious disease forecasting, such as Markov chain models [ 8], Grey models, general regression models, autoregressive integrated moving average class models (ARIMA) [ 11] and artificial neural network [ 12].
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