## How do you calculate VIF in SAS?

The VIF option in the regression procedure can be interpreted in the following ways:

- Mathematically speaking: VIF = 1/(1-R-square)
- Procedurally speaking: The SAS system put each independent variables as the dependent variable e.g.
- Graphically speaking: In a Venn Diagram, VIF is shown by many overlapping circles.

**What is VIF in SAS?**

vif stands for variance inflation factor. As a rule of thumb, a variable whose VIF values is greater than 10 may merit further investigation. Tolerance, defined as 1/VIF, is used by many researchers to check on the degree of collinearity. A tolerance value lower than 0.1 is comparable to a VIF of 10.

### How do you check for multicollinearity in SAS?

There are no such command in PROC LOGISTIC to check multicollinearity . 1) you can use CORRB option to check the correlation between two variables. 2) Change your binary variable Y into 0 1 (yes->1 , no->0) and use PROC REG + VIF/COLLIN .

**What is the formula for VIF?**

For example, we can calculate the VIF for the variable points by performing a multiple linear regression using points as the response variable and assists and rebounds as the explanatory variables. The VIF for points is calculated as 1 / (1 – R Square) = 1 / (1 – . 433099) = 1.76.

#### How do you interpret VIF multicollinearity?

View the code on Gist.

- VIF starts at 1 and has no upper limit.
- VIF = 1, no correlation between the independent variable and the other variables.
- VIF exceeding 5 or 10 indicates high multicollinearity between this independent variable and the others.

**What is multicollinearity PDF?**

Multicollinearity occurs when the multiple linear regression analysis includes several variables that are significantly correlated not only with the dependent variable but also to each other. Multicollinearity makes some of the significant variables under study to be statistically insignificant.

## How do I read VIF results?

In general, a VIF above 10 indicates high correlation and is cause for concern. Some authors suggest a more conservative level of 2.5 or above….A rule of thumb for interpreting the variance inflation factor:

- 1 = not correlated.
- Between 1 and 5 = moderately correlated.
- Greater than 5 = highly correlated.

**What is r2 in VIF?**

Each model produces an R-squared value indicating the percentage of the variance in the individual IV that the set of IVs explains. Consequently, higher R-squared values indicate higher degrees of multicollinearity. VIF calculations use these R-squared values.

### What is a good VIF score?

In general, a VIF above 10 indicates high correlation and is cause for concern. Some authors suggest a more conservative level of 2.5 or above. Sometimes a high VIF is no cause for concern at all.

**What VIF is too high?**

A VIF between 5 and 10 indicates high correlation that may be problematic. And if the VIF goes above 10, you can assume that the regression coefficients are poorly estimated due to multicollinearity. You’ll want to do something about that.