9/25/2023 0 Comments Stata regress if notA general rule of thumb for interpreting VIFs is as follows: This produces a VIF value for each of the explanatory variables in the model. The value for VIF starts at 1 and has no upper limit. Next, we’ll use the vif command to test for multicollinearity: We’ll use the regress command to fit a multiple linear regression model using price as the response variable and weight, length, and mpg as the explanatory variables: Use the following command to load the dataset: Example: Multicollinearity in Stataįor this example we will use the Stata built-in dataset called auto. This tutorial explains how to use VIF to detect multicollinearity in a regression analysis in Stata. This means that multicollinearity is likely to be a problem in this regression.įortunately, it’s possible to detect multicollinearity using a metric known as the variance inflation factor (VIF), which measures the correlation and strength of correlation between the explanatory variables in a regression model. In this case, the explanatory variables shoe size and height are likely to be highly correlated 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 multiple linear regression with the following variables:Įxplanatory variables: shoe size, height, time spent practicing Multicollinearity in regression analysis occurs when two or more explanatory 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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