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Variance Inflation Factor Calculator

Detect multicollinearity between predictors using the variance inflation factor.

About Variance Inflation Factor Calculator

A VIF of 10 corresponds to an R-squared of 0.9 between one predictor and the rest, and inflates that coefficient’s standard error more than threefold. Coefficients become unstable long before predictions do. Multicollinearity does not harm predictive accuracy but makes individual coefficients uninterpretable, which matters whenever the goal is explanation rather than prediction.

Formula

Variance Inflation Factor

VIF = 1 ÷ (1 − R²ⱼ); standard errors inflate by √VIF

VIF
How much a coefficient’s variance is inflated by correlation with other predictors
tolerance
The reciprocal of VIF

Worked example

With the default values below, Variance Inflation Factor Calculator returns Variance Inflation Factor of 4.00000. Change any input above and every figure here is recalculated in your browser.

Inputs used

R-squared regressing this predictor on the othersaccepts 0–1
0.75
Number of predictorsaccepts 2–50
5
Standard error without collinearityminimum 0
0.2

Results

Variance Inflation Factor
4.00000
Tolerance
0.250000
Standard Error Inflation Factor
2.00000
Inflated Standard Error
0.40000
Verdict
Mild multicollinearity

Step by step

  1. 1Values used
  2. 2Variance Inflation Factor
  3. 3Variance Inflation Factor= 4.00000
  4. 4Tolerance= 0.250000
  5. 5Standard Error Inflation Factor= 2.00000
  6. 6Inflated Standard Error= 0.40000
  7. 7Verdict= Mild multicollinearity

Assumptions and limits

  • The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 6 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

How is Variance Inflation Factor calculated?

VIF = 1 ÷ (1 − R²ⱼ); standard errors inflate by √VIF A VIF of 10 corresponds to an R-squared of 0.9 between one predictor and the rest, and inflates that coefficient’s standard error more than threefold. Coefficients become unstable long before predictions do.

Why does Variance Inflation Factor matter?

Multicollinearity does not harm predictive accuracy but makes individual coefficients uninterpretable, which matters whenever the goal is explanation rather than prediction.

What values do I need to enter?

This calculator takes 3 inputs: R-squared regressing this predictor on the others, Number of predictors, Standard error without collinearity. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.

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