This article describes how to compute the *variance inflation factors* (VIF) of linear models and *generalized variance-inflation factors* (GVIF) for generalized linear models to diagnose multicollinearity.

## Requirements

- A Regression output for one of the following types of regressions:
- Linear
- Binary Logit
- Ordered Logit
- Poisson
- Quasi-Poisson
- NBD

Note: This feature is not compatible with multinomial logit regressions

## Method

- Select the Regression output.
- Go to the
**object inspector > Inputs > DIAGNOSTICS >Multicollinearity Table (VIF)**, or go to**Anything**>**Advanced Analysis > Regression > Diagnostics >Multicollinearity Table (VIF)**.

## Next

How to Run Binary Logit Regression

How to Run a Generalized Linear Model

How to Run a Multinomial Logit Regression

How to Run NBD Regression in Displayr

How to Run Ordered Logit Regression

How to Run Quasi-Poisson Regression

How to Run a Stepwise Regression

How to Create a Prediction-Accuracy Table

How to Create a Goodness-of-Fit Plot

How to Test Residual Heteroscedasticity of Regression Models

How to Save Predicted Values of Regression Models

How to Save Fitted Values of Regression Models

How to Save Probabilities of Each Response of Regression Models

How to Test Residual Normality (Shapiro-Wilk) of Regression Models

How to Test Residual Serial Correlation (Durbin-Watson) of Regression Models