What is mulitcollinearity What problems does it cause How do

What is mulitcollinearity? What problems does it cause? How do you avoid it? How do you detect it?

Solution

Multicollinearity is problem that you can run into when you’re fitting a regression model, or other linear model. It refers to predictors that are correlated with other predictors in the model. Unfortunately, the effects of multicollinearity can feel murky and intangible, which makes it unclear whether it’s important to fix.

Multicollinearity is a problem because it can increase the variance of the coefficient estimates and make the estimates very sensitive to minor changes in the model. The result is that the coefficient estimates are unstable and difficult to interpret. Multicollinearity saps the statistical power of the analysis, can cause the coefficients to switch signs, and makes it more difficult to specify the correct model.

Some of the common methods used for detecting multicollinearity include:

One way of reducing data-based multicollinearity is to remove one or more of the violating predictors from the regression model. Another way is to collect additional data under different experimental or observational conditions.