What do variance inflation factors mean

If we review the pairwise correlations again:. We can choose to remove either predictor from the model. The decision of which one to remove is often a scientific or practical one. For example, if the researchers here are interested in using their final model to predict the blood pressure of future individuals, their choice should be clear.

Which of the two measurements — body surface area or weight — do you think would be easier to obtain?! If indeed weight is an easier measurement to obtain than body surface area, then the researchers would be well-advised to remove BSA from the model and leave Weight in the model. Therefore, the researchers could also consider removing the predictor Pulse from the model. Let's see how the researchers would do.

Aha — the remaining variance inflation factors are quite satisfactory! That is, it appears as if hardly any variance inflation remains. Incidentally, in terms of the adjusted R 2 -value, we did not seem to lose much by dropping the two predictors BSA and Pulse from our model. The adjusted R 2 -value decreased to only Eberly College of Science. The higher the value, the greater the correlation of the variable with other variables. Values of more than 4 or 5 are sometimes regarded as being moderate to high, with values of 10 or more being regarded as very high.

These numbers are just rules of thumb; in some contexts a VIF of 2 could be a great problem e. In the simplest case, two variables will be highly correlated, and each will have the same high VIF. Where a VIF is high, it makes it difficult to disentangle the relative importance of predictors in a model, particularly if the standard errors are regarded as being large.

This is particularly problematic in two scenarios, where:. The higher the VIF, the more the standard error is inflated, and the larger the confidence interval and the smaller the chance that a coefficient is determined to be statistically significant. Sign Up for Displayr. Market research Social research commercial Customer feedback Academic research Polling Employee research I don't have survey data. R in Displayr Visualizations. Keep updated with the latest in data science.

Variance Inflation Factor VIF measures the intercorrelation among independent variables in a multiple regression model. In mathematical terms, the variance inflation factor for a regression model variable would be the ratio of the overall model variance to the variance of the model with a single independent variable.

A high VIF indicates a high correlation between variables. A multiple regression model is used in a situation where a person wants to examine the effect of multiple variables on an outcome. Here, the dependent variable would be the outcome that is tested with the independent variables.

The independent variables would form inputs into the model. The existence of high intercorrelation between variables makes them less independent. Thus, intercorrelation between variables in a multiple regression model creates problems in testing the variables.

It makes it difficult to determine how much the combination of independent variables impacts the dependent variable or the outcome of the regression model. Even small changes in the data or in the structure of the regression model can lead to large and, sometimes, erratic changes in the coefficients of variables. VIF is a statistical tool which helps in testing a regression model for correctness.

It tests how the behaviour of an independent variable is altered due to a correlation with other independent variables. Thus, it helps in identifying the severity of the issues to facilitate adjustment to the model.