Multicollinearity refers to a situation in which more than two explanatory variables in a multiple regression model are highly linearly related.

Many continuous variables not be correlated with each other, a phenomenon called multicollinearity. Establishing relationships between the numerical variables is a common step to detect and treat multicollinearity.

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