Should highly correlated variables be removed?
In a more general situation, when you have two independent variables that are very highly correlated, you definitely should remove one of them because you run into the multicollinearity conundrum and your regression model’s regression coefficients related to the two highly correlated variables will be unreliable.
How do you know if a variable is highly correlated?
It is a measure of multicollinearity in the set of multiple regression variables. The higher the value of VIF the higher correlation between this variable and the rest. If the VIF value is higher than 10, it is usually considered to have a high correlation with other independent variables.
How to remove multicollinearity and variance inflation factor?
As per pairwise correlation analysis, Weight is highly correlated with BSA (r > 0.8) and Pulse (r > 0.6). To remove the multicollinearity, drop the variables BSA and Pulse, and reanalyze the regression model.
How is variance inflation factor used in regression?
Variance inflation factor (VIF) measures the degree of multicollinearity or collinearity in the regression model. VIF, tolerance indices (TIs), and correlation coefficients are useful metrics for multicollinearity detection.
How is the variance of a predictor inflated?
As you can see, three of the variance inflation factors — 8.42, 5.33, and 4.41 — are fairly large. The VIF for the predictor Weight, for example, tells us that the variance of the estimated coefficient of Weight is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.
Why is it important to remove correlated variables when..?
For the model to be stable enough, the above variance should be low. If the variance of the weights is high, it means that the model is very sensitive to data. The weights differ largely with training data if the variance is high. It means that the model might not perform well with test data.