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There are different methods suggested to detect the existence of multicollinearity problem between the model explanatory variables.
Table 4 Determinants of energy choices using multivariate probit model Explanatory variables Firewood Charcoal Biogas Dried dung Coef.
Combining the biomonitoring metrics with GIS models related to landscape-scale variability in plant water relations resulted in considerable improvement in the ozone exposure model explanatory power (P < 0.0001, R2 = 0.90, RMSE = 1.35) including the parameters VI-Amount, VI-Severity, elevation, slope and topographic curvature.
For each multiple regression model, explanatory variables were sequentially introduced into the model starting with the variable that exhibited the strongest correlation with the dependent variable and subsequently the residuals from the model, until further additions were not significant, or did not improve the model coefficient of determination by more than 0.01.
In a two-part model, explanatory variables often play different roles in the two parts of the model.
A land use regression (LUR) model selection procedure is developed for determining nonlinear model explanatory variables when they are known to be correlated.
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The outcome variation could thus be linked to distinct process patterns, increasing the turnaround models' explanatory power.
First, the models' explanatory power can be increased by adjusting by the recognized biases and using the adjusted collateralization ratio.
To test the models' explanatory power concerning the compatibility relationships found between different Wolbachia strains, we used several sources of empirical data [16] [19].
Tests of significance were based on likelihood-ratio tests and in all models explanatory variables were stepwise removed in the order of statistical relevance.
An early model of explanatory reduction was put forward by William Wimsatt (1976b).
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