Following the elimination of variables with a questionable level of significance through univariable analysis, the next step is to reduce redundancy in the variables. This is also known as collinearity or multicollinearity. Collinearity, within the statistical context, denotes the existence of variables that rival for the explanation of a shared amount of variability in the data. The process of reducing collinearity in variables is also known as dimensional reduction.