The phrase "relations among variables" is a correct and commonly used phrase in written English.
It is typically used in discussions or analyses of data or research, specifically when examining how different variables may be connected or related to each other. Example: In this study, the researchers found significant positive relations among variables such as income level, education level, and job satisfaction, indicating a potential link between these factors.
Bayesian regression analysis is including development of model or prediction of the relations among variables.
It is advantageous for real-world data analysis and finding relations among variables.
Formal treatments of the foundations of sciences have avoided discussions of causation and spoken only of functional relations among variables.
Descriptive statistics, t-tests, correlation and regression analyses were conducted to present trends and relations among variables.
Experimental and longitudinal studies should be conducted to provide stronger evidence of causal relations among variables examined in this study.
Concerning relations among variables, we found that AVE values were higher with determination coefficients (r 2) among latent variables (i.e., AVE >r 2), which indicates lack of multicollinearity.
A structural equation model (SEM) was used to evaluate the model of relation among variables of the study.
If this model can be accepted, the relations among latent variables and to external variables can be compared.
Simply speaking, a QDE is the conjunction of all its qualitative constraints, which link the qualitative variables and express the relations among these variables.
This research describes a framework for product development practices, and it discusses relations among these variables as well as important performance variables (product innovation, quality, premium pricing, and profitability).
The capacity of SEM to distinguish between indirect and direct relationships between variables and to specify structural relations among latent variables differentiates SEM from other simpler modeling processes.
