# Understanding Multicollinearity in Linear Regression

## What is Multicollinearity?

Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other. In simpler terms, it’s the presence of strong linear relationships between predictors. This correlation can create problems because it becomes challenging to discern the individual effects of each independent variable on the dependent variable.

To illustrate this concept, consider a scenario where you want to predict the distance your golf driver can achieve using two explanatory variables: weight and strength. If weight and strength are highly correlated (which is likely the case since heavier individuals tend to be stronger), it becomes difficult to determine the unique impact of each variable on the golf driver’s distance.

### Why Multicollinearity Matters

Multicollinearity matters because it can lead to several issues in regression analysis:

### Detecting Multicollinearity

Detecting multicollinearity is essential before running a regression analysis. One common method for doing so is by calculating the Variance Inflation Factor (VIF). The VIF measures how much the variance of a coefficient estimate is increased due to multicollinearity.

The formula for calculating VIF is: VIF=11−R2VIF=1−R21​