5. Detecting Multicollinearity in Regression using VIF

[@SachinKumar-db2ji]

normally I never comment on videos but it's one of the best explanations of a stat topic, I am not from math background but you made me understand this topic in such an easy way thankyou

[@easyvazquez]

Thank you for providing the short cut for checking VIF in SAS, and you also did an excellent job in explaining the critical VIF factor.

[@bhanurekha6510]

thanks for an explanation .. Please don't stop posting

[@kweweli7821]

Excellence explanation on the table, I now fully understand VIF.

[@akramhossain9576]

Excellent!

[@bhanurekha6510]

nice explanation

[@mohammedtaher3717]

amazing explanation, sir if you could make a video on calculating the correlation coefficient - R^2 of more than three independent variables it would be helpful. thank you again

[@nitindamle5380]

It's very good sir Kindly make video on ridge and lasso regression

[@AnTweeedz]

Nice

[@214vinod]

Hi, While removing variables due to high VIF's , my P value for intercept is increasing and reached around 0.60. how to deal with this situation, when removing variables on the basis of P value. Learning a lot. Thanks for videos.

[@LearnanalyticsIn]

P values of intercept can be ignored, concentrate on the estimates (of independent variables) only

[@110Turab]

Thanks you

[@sajjadhussain5113]

Thanks alot

[@FantasmaAzulDark]

Thanks! Excellent!

[@sumitasarma6187]

How to check for multicollinearity with interaction terms in a regression?

[@LearnanalyticsIn]

Sumita Sarma Interaction terms are considered derived variables. Collinearity will be checked as usual using correlations or VIF. Typically if we use a derived or an interaction variables, we might drop the original variables to prevent collinearity.