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We model these relationships with a spatial autoregression (SAR) model, and we control for characteristics of the biotechnology firms and the VC firms that provide their funds as well as site-specific factors.
Connecting the types of local RE projects and the resulting implementation processes and land use impacts that have taken place in these two cases is desired in order to provide us with the necessary preliminary data to simulate and model these relationships.
There is insufficient evidence in the current published literature to allow us to model these relationships.
But we go on to model these relationships directly, using non-linear terms.
Parametric approaches model explicitly the nature of the interaction, whereas the nonparametric approaches do not model these relationships.
The computational approach proposed here can complement existing statistical approaches when behaviours involve numerous complex relationships, and quantitative data is not readily available to model these relationships.
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When total fat mass was entered into the model (model 2), these relationships were strengthened (P < 0.01).
Two challenges in modeling these relationships are selecting the appropriate spatial scale of watersheds for the receiving stream segment, and handling the network or pass-through issues of connected watersheds.
However, upon inclusion of parental effects in mixed-effects models, these relationships remained, albeit with reduced significance (Appendix S4).
These results suggest that, given the proper models, these relationships could be exploited to "impute" epigenetic information across related cellular contexts to enable functional annotation.
In modeling these relationships, our fundamental assumption was that cognitive impairment precedes psychiatric symptoms, and that both precede functional impairment as the illness of schizophrenia evolves in patients.
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