## 7.3 Multicollinearity

```
library(car)
vif(regOut3)
## hardwork basicsmarts curiosity
## 1.059952 1.072746 1.041906
```

The variance inflation factor (VIF) is a measure of the amount of multicollinearity in a set of multiple regression variables. The VIF for a variable is 1 if there is no multicollinearity in the model, and greater than 1 if there is multicollinearity. The VIF for a variable is the square of the correlation between that variable and all other variables in the model.

- A value of 1 indicates there is no correlation between a given predictor variable and any other predictor variables in the model.
- A value between 1 and 5 indicates moderate correlation between a given predictor variable and other predictor variables in the model, but this is often not severe enough to require attention.
- A value greater than 5 indicates potentially severe correlation between a given predictor variable and other predictor variables in the model. In this case, the coefficient estimates and p-values in the regression output are likely unreliable.