Multicollinearity and VIF (theory + R code)

Multicollinearity is essentially a problem of matrix inversion. It affects inference of the linear-dependent covariates, but not so much prediction. One possible way of detecting it is using the Variance Inflation Factor (VIF). In this video we will explore these concepts in depth.
Paper on VIF when using dummy variables: bit.ly/3XoJgUs
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Intro/Outro Music: Dreamer - by Johny Grimes
• Johny Grimes - Dreamer

[@MeerkatStatistics]

Correction, at 9:00 - the right hand side should also be inverted (taken to the power of -1).
Also, note that in the code simulations - the linear dependence changes the estimates such that they give the same basic results. E.g., estimating beta1 for x1 to be 0.5 and beta3 for x3 to be 1.25 makes sense, since x1+x3 = x1+2x1 = 3x1, but this is also equal to 0.5x1 + 2*1.25 x1

[@justsomegirlwithoutamustac5837]

This is awesome!!