According to literature review, in a dataset, multicollinearity is the presence of two or more linked variables that are linearly dependent on one another . Therefore, it is a type of data disorder, and if it exists, statistical inferences formed from the data may not be accurate or trustworthy . A Multicollinearity test can aid with the selection of appropriate factors for hazard mapping, which can improve the model's results . The most common causes of multicollinearity are inaccuracies in using dummy variables, variables of the same kind being repeated, and a high degree of relationship among the variables . The multicollinearity test is performed using the tolerance (TOL) and variance inflation factor (VIF) indices. It is not error-prone if VIF 0.1 because the variables are not multi-collinear. Multi-collinearity in the linear domain is a common feature of every statistical application and should be noted.

The question is Why do authors use TOL and VIF indices simultaneously when VIF = 1 / TOL? See formula :

VIF=[1/Tolerance].