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If not then where are we supposed to use that? If yes, then my vif values are all below 3, but statistically they are insignificant variables then what should I do next? How do I make myself understand this in easy terms? How do I remove the variables which creates the same effect?

Osro_db40
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  • 1) Exclude very strongly correlated variables. 2) Build your model with a model selection procedure. 3)Check the vif's and other diagnostics to check the quality of your model. – Knarpie Sep 11 '17 at 11:42
  • @Knarpie - Okay, will do that. If some variables are statistically insignificant but VIF factor is lower than 5, does that mean I have good model? – Osro_db40 Sep 11 '17 at 12:48
  • I'm not sure you completely understand the VIF. First eliminate the insignificant variables in your model building process, then check the VIF's of the final model, together with other diagnostics. – Knarpie Sep 11 '17 at 12:54
  • Okay, will learn more about that. – Osro_db40 Sep 11 '17 at 22:59
  • I fear there is some bad advice here. In general, you should not remove insignificant variables (see: [Algorithms for automatic model selection](https://stats.stackexchange.com/a/20856/7290)) that's not what hypothesis tests are for. In addition, what to do w/ highly correlated variables is a substantive judgement, not something to be performed algorithmically (see: [Logistic regression and inclusion of independent and/or correlated variables](https://stats.stackexchange.com/a/298292/7290)). – gung - Reinstate Monica Sep 13 '17 at 16:01
  • I have met many mean people here Mr Gung, It's Okay. Had a plus one to my question and then they gave minus one. What some folks here really don't understand is those who are willing to learn and are equipped with overwhelming resources needs a genuine guidance. There's no such thing as STUPID questions. Coming back to your comment, I will read and thoroughly study about the links you have provided. – Osro_db40 Sep 14 '17 at 01:25

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