Most statistical software has the ability to compute VIF for a regression model. Utilizing the Variance Inflation Factor (VIF) The most common way to detect multicollinearity is by using the variance inflation factor (VIF), which measures the correlation and strength of correlation between the predictor variables in a regression model. This makes it difficult to determine which predictor variables are actually statistically significant. The precision of the coefficient estimates are reduced, which makes the p-values unreliable.The coefficient estimates of the model (and even the signs of the coefficients) can fluctuate significantly based on which other predictor variables are included in the model.In general, multicollinearity causes two types of problems: This makes it difficult for the regression model to estimate the relationship between each predictor variable and the response variable independently because the predictor variables tend to change in unison. However, when two or more predictor variables are highly correlated, it becomes difficult to change one variable without changing another. This means we assume that we’re able to change the values of a given predictor variable without changing the values of the other predictor variables. In particular, when we run a regression analysis, we interpret each regression coefficient as the mean change in the response variable, assuming all of the other predictor variables in the model are held constant. One of the main goals of regression analysis is to isolate the relationship between each predictor variable and the response variable. This tutorial explains why multicollinearity is a problem, how to detect it, and how to resolve it. This means that multicollinearity is likely to be a problem in this regression. In this case, height and shoe size are likely to be highly correlated with each other since taller people tend to have larger shoe sizes. If the degree of correlation is high enough between variables, it can cause problems when fitting and interpreting the regression model.įor example, suppose you run a regression analysis using the response variable max vertical jump and the following predictor variables: Multicollinearity in regression analysis occurs when two or more predictor variables are highly correlated to each other, such that they do not provide unique or independent information in the regression model.
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