The proposed model considers variables such as threat and consequence scenarios, probability of adverse events, vulnerability, failure modes, percentages of risk reduction and mitigation costs.
The proposed model considers variables readily available for ionic liquids and that have important effect on heat capacity, according to the literature information.
The blade model considers variable mass and stiffness per unit length.
Our model differs from coarse-resolution models that were previously used to study changing carbon fluxes in the Southern Ocean (Lov07, LM07), as we succeed in modeling silicate limitation in the Southern Ocean, use an active sea-ice model, consider variable phytoplankton stoichiometry, and use a higher resolution (see section 2).
We then constructed a multivariable logistic regression model, first considering variables significantly associated with outcomes in univariate analysis, followed by a search for collinear terms.
Both the LS and QR models are estimated considering variables at student level, hence the multilevel structure of data (i.e. pupils within classes/school) is not taken into account.
To capture long run cointegration among variables, we formulate following ARDL models considering each variable as dependent variable to estimate best fitted model for further analysis (shown in matrix form).
The results of models considering the variables mentioned before are displayed in Table 3 and Table 4.
Based on model 4, model 5 considers variables representing area income level in order to examine whether there are significant effects on health due to the characteristics of the area an individual resides in.
To enter the model, we considered variables with p < 0.20 and in order to be kept in the model, the variables should present p < 0.05.
