In the previous post, you saw some common interview questions asked on linear regression. The questions in that segment were mostly related to the essence of linear regression and focused on general concepts related to linear regression. This section extensively covers the common interview questions asked related to the concepts learnt in multiple linear regression.

Q1. What is Multicollinearity? How does it affect the linear regression? How can you deal with it?

Multicollinearity occurs when some of the independent variables are highly correlated (positively or negatively) with each other. This multicollinearity causes a problem as it is against the basic assumption of linear regression. The presence of multicollinearity does not affect the predictive capability of the model. So, if you just want predictions, the presence of multicollinearity does not affect your output. However, if you want to draw some insights from the model and apply them in, let’s say, some business model, it may cause problems.