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Statistics vocabulary · Regression and Correlation

Multicollinearity

Meaning in statistics, with examples

nounAdvanced term

In statistics, multicollinearity means: A situation in regression where two or more predictors are strongly correlated with each other, which makes their separate effects hard to estimate.

In Turkish: çoklu doğrusal bağlantı, çoklu bağlantı

How it is used

It inflates standard errors and can make coefficients unstable or even flip their sign, while the model's overall predictions may still be fine. Usually checked with the variance inflation factor (VIF); values above about 5 or 10 are often treated as a warning sign.

Example sentences

  1. Height and weight are so closely related that including both caused multicollinearity.

  2. Check for multicollinearity before you interpret individual coefficients.

Common collocations

  • check for multicollinearity
  • variance inflation factor
  • severe multicollinearity
  • high VIF values
  • multiple regressionA regression model with two or more predictor variables, used to estimate the effect of each one while holding the others constant.
  • regression coefficientA number in a regression model showing how much the outcome is expected to change when a predictor increases by one unit, with the other predictors held constant.
  • correlationA relationship between two variables in which they tend to change together, either in the same direction (positive) or in opposite directions (negative).
  • standard errorThe standard deviation of an estimate, such as a sample mean, across repeated samples; it shows how precise the estimate is likely to be.

More terms: Regression and Correlation

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