The subject of collinearity in regression encompasses a wide range of important elements. What is collinearity and why does it matter? Collinearity, also called multicollinearity, refers to strong linear associations among sets of predictors. In regression models, these associations can inflate standard errors, make parameter estimates unstable, and can reduce model interpretability. In this context, multicollinearity - Wikipedia. It's important to note that, in statistics, multicollinearity or collinearity is a situation where the predictors in a regression model are linearly dependent. Perfect multicollinearity refers to a situation where the predictive variables have an exact linear relationship.
Multicollinearity in Regression Analysis - GeeksforGeeks. Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other.
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