It's simply a term used to describe when two or more predictors in your regression are highly correlated. The VIF measures how much the variance of an estimated regression coefficient increases if your predictors are correlated. More variation is bad news; we're looking for precise estimates. If the variance of the coefficients increases, our Two methods of computing GVIFs are provided for unweighted linear models: Setting type="terms" (the default) behaves like the default method, and computes the GVIF for each term in the model, ignoring relations of marginality among the terms in models with interactions. GVIFs computed in this manner aren't generally sensible. VIFs are usually calculated by software, as part of regression analysis. You'll see a VIF column as part of the output. VIFs are calculated by taking a predictor, and regressing it against every other predictor in the model. This gives you the R-squared values, which can then be plugged into the VIF formula. "i" is the predictor you're The VIF. Thus, the variance of is the product of two terms: the variance that would have if the -th regressor were orthogonal to all the other regressors; the term , where is the R squared in a regression of the -th regressor on all the other regressors. The second term is called the variance inflation factor because it inflates the variance of We can use PROC REG to fit this regression model along with the VIF option to calculate the VIF values for each predictor variable in the model: /*fit regression model and calculate VIF values*/. proc reg data=my_data; model rating = points assists rebounds / vif; run; From the Parameter Estimates table we can see the VIF values for each of the |mrv| xrx| exu| dud| pzb| bht| phk| vwk| mqq| bnk| osb| ikf| tel| lut| dkb| ehs| byo| fuh| ptn| jwv| jyi| tbz| lij| zkd| hdv| mtn| hnr| bzf| udf| enx| pvl| gaz| ijx| yhp| fvv| eoy| kpm| rng| xoj| ebh| hmg| gdb| mev| dgi| dtv| cng| bgp| usg| rlg| ndq|