An inverse correlation matrix table can be generated, which contains variance inflation factors on the diagonal elements.
What does VIF stand for?
VIF stands for Variance Inflation Factor
This definition appears frequently and is found in the following Acronym Finder categories:
- Science, medicine, engineering, etc.
- Business, finance, etc.
See other definitions of VIF
We have 39 other meanings of VIF in our Acronym Attic
- Vancouver Island Earth Works Society (Canada)
- Vendor Initiative for Enabling Web Services (consortium; library automation)
- Video Imagery Exploitation Workstation
- Video Intelligence Enhanced Workstations Systems
- Visibility Information Exchange Web System (Cooperative Institute for Research in the Atmosphere; Colorado State University; Fort Collins, CO)
- Visual Interactive Environment for Weapons Simulation
- Vålerengens Idrettsforening (Oslo, Norway)
- Value of Inforce Business
- Vanier Institute of the Family (Institut Vanier de la Famille - Canada)
- Variable Investment Fund
- Vehicle Integration Facility
- Venture Investment Fund Limited (New Zealand)
- Verify In Field
- Versicherung, Immobilien, Finanzierung (German: Insurance, Real Estate, Financing)
- Vertical Integration Facility (rocket launching)
- Very Important Freak
- Very Important Friend
- Very Integration Friendly
- Victoria (Amtrak station code; Victoria, British Columbia, Canada)
- Violencia Intrafamiliar (Spanish: Combating Domestic Violence)
Samples in periodicals archive:
We found no multicollinearity problems with Allison's (1999) methodology by estimating the equivalent linear regression model and evaluating the tolerance and the variance inflation factor for each independent variable.
The variance inflation factor also varied between 1 and 2, and it was certainly less than the threshold value of 5 (Van Laar 1991).
Eliminating perception of mentor as a teacher, multicollinearity was not a concern, as indicated by the Variance Inflation Factor (VIF) values.
A variance inflation factor (VIF) exceeding 10 is often an indication that multicollilnearity may be influencing the least squares estimates.