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Further research is necessary to fully evaluate the effects of the predictors described in this study.
The effects of the predictors were studied through the partial dependence plots of which some are illustrated in Fig. 2.
The effects of the predictors were studied through their contribution to the models and their partial dependence plots.
Model comparisons employed the differences in the likelihood ratio chi-squares between nested models, and Cox proportional hazard models assisted in explaining the effects of the predictors on survival times.
Thus, a random parameter model better explains the stochastic effects of the predictors on the response variable, reducing the related bias in the model estimation (Mannering et al. 2016).
To further investigate the effects of the predictors identified from the DistLM analysis, the continuous predictors were binned.
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Our investigation of the likely effects of the predictor (independent) variables on entrepreneurship outcomes employs the analytical framework of baseline-category logits (BCL).
Generalized estimating equations (GEE) were used to investigate the effects of the predictor variables on the dependent variables over the 10-year period (2004 2013) when respondents were aged between 16/17 and 25/26.
We therefore focused on the earliest effects of the predictor variables we tested.
We use distributed minimum-norm estimation (MNE) to determine the main neuronal sources of the effects of the predictor variables.
The NLMM requires scaling of the regression parameters and this scaling requires fitting additional models to separately estimate direct effects of the predictor, and effects of the primary predictor on the mediator.
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