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In the multiple weather variables model, all significant variables found in the single weather variable model were used in a forward stepwise multiple linear regression analysis, as shown in Table 3.
Because the results of statin treatments on arthritis induction using the latent variable model were similar to the results using the log-likelihood model, we have chosen to show the results of the latter model.
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However, likewise the FE approach, also the lagged dependent variable model is not free from problems.
A three-stream flamelet/progress variable model is applied to the Sydney piloted premixed jet burner (PPJB).
A physically based state variable model is proposed to simulate precipitate growth encountered in many industry-relevant precipitation processes.
The variable model is essentially different from the "nonhomogeneous" model.
Mathematically, the variable model is an expansion of the constant model.
The Best 3 variable model was then tested on the test dataset.
Accordingly, chicken-zebra finch alignments with ω > 0.5 where the variable model was significantly favoured (p < 0.05) were identified.
Relative deviance reduction of best single variable model is indicated by dashed horizontal line (associated with Figure 7C, E, respectively).
These models were compared using a likelihood ratio test (LRT) to determine if the variable model was significantly better at explaining the data [ 13].
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