Multicollinearity: Dangers and Remedies

This lecture discusses the dangers of multicollinearity in linear models, where adding variables can lead to huge variances, flipped signs, and insignificant coefficients. Diagnostic methods and remedies, such as variable deletion and orthogonal basis selection, are explored using examples like the Body Fat Data. Techniques like backward elimination and eigenvector rotation are presented to address multicollinearity issues.