There are many potential variables.
There are simply too many potential variables in any sociological analysis.
More recently, we are using machine learning techniques as an unbiased way of discovering novel risk predictors among a very large set of potential variables.
Use of climatic and watershed descriptors requires variable selection to minimize redundancy from a large pool of potential variables.
Second, some significance might be partly attributed to confounding factors, such as BMI and WHR; some other potential variables could exist, although we did adjust for potential confounding factors.
Potential variables that underlie variation in flake size, and in some instances shape, are investigated in a newly designed experiment.
A numerical procedure based on this decomposition is designed, using projection methods for solving the incompressible variables and a backward-Euler method for solving the potential variables.
The governing equations use only four displacement and potential variables, the number of which is independent of the number of layers involved.
This refined theory shows high accuracy, but use only six displacement and potential variables, the number of which is independent of the number of layers involved.
The former describes the relation between the obvious variables and the potential variables, indicating the observable variables of the potential variables [10].
Uber and Lyft are successful because they approach a classic problem – getting from point A to point B – as a mathematical equation with hundreds of potential variables.
