Multicollinearity and VIF (theory + R code)
HTML-код
- Опубликовано: 22 авг 2024
- 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
Become a member!
meerkatstatist...
* 🎉 Special RUclips 60% Discount on Yearly Plan - valid for the 1st 100 subscribers; Voucher code: First100 🎉 *
Why become a member?
* All video content
* Extra material on complete-courses (notebooks)
* Access to code and notes
* Community Discussion
* No Ads
* Support the Creator ❤️
If you’re looking for statistical consultation, work on interesting projects, or training workshop, visit my website meerkatstatist... or contact me directly at david@meerkatstatistics.com
~~~~~ SUPPORT ~~~~~
Paypal me: paypal.me/Meer...
~~~~~~~~~~~~~~~~~
Intro/Outro Music: Dreamer - by Johny Grimes
• Johny Grimes - Dreamer
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
This is awesome!!