However, it is important to note that linear regression assumes a linear relationship between the variables, which may not always be the case. In addition, it is sensitive to outliers and can be affected by multicollinearity, which is when two or more independent variables are highly correlated ...
Questions about Multicollinearity Partager One of the main challenges in building an effective regression model is what we refer to as multicollinearity. Multicollinearity arises when two or more independent variables in a model are highly correlated, leading to unreliable statistical inferences. This can...
Identification and prevention of multicollinearity in MGWR In an MGWR model, multicollinearity can occur in various situations: One of the explanatory variables is spatially clustered. To prevent this, map each explanatory variable and identify the variables that have very few possi...
Resilience has been found to have positive impacts on college students’ well-being and mental health. However, we still lack knowledge on how and und
The variance inflation factor indices of both quality and structural parameters were below 1.5 (Table 1), indicating that the analysis was not affected by the multicollinearity. The least squares regression analysis revealed that both mineral density and AGEs content were important quality parameters acc...
Identification and prevention of multicollinearity in MGWR In an MGWR model, multicollinearity can occur in various situations including the following: One of the explanatory variables is strongly spatially clustered. As MGWR fits local regression models, when a feature and all of ...
The correlations between constructs ranged from 0.293 to 0.499, indicating significant linear relationships while also demonstrating a lack of potential multicollinearity risk [57]. Table 8. Results of Fornell–Larcker criterion. Simultaneously, we calculated the heterotrait to monotrait ratio (HTMT) ...