## What to do if there is multicollinearity in multiple regression?

How to Deal with Multicollinearity

- Remove some of the highly correlated independent variables.
- Linearly combine the independent variables, such as adding them together.
- Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.

**What is the problem with multicollinearity in a multiple regression model?**

Multicollinearity exists whenever an independent variable is highly correlated with one or more of the other independent variables in a multiple regression equation. Multicollinearity is a problem because it undermines the statistical significance of an independent variable.

**How is multicollinearity detected and removed?**

How do we detect and remove multicollinearity? The best way to identify the multicollinearity is to calculate the Variance Inflation Factor (VIF) corresponding to every independent Variable in the Dataset. VIF tells us about how well an independent variable is predictable using the other independent variables.

### What remedial measures can be taken to alleviate the problem of multicollinearity?

One of the most common ways of eliminating the problem of multicollinearity is to first identify collinear independent variables and then remove all but one. It is also possible to eliminate multicollinearity by combining two or more collinear variables into a single variable.

**What happens if multicollinearity exists?**

Multicollinearity occurs when two or more independent variables are highly correlated with one another in a regression model. This means that an independent variable can be predicted from another independent variable in a regression model.

**What are the remedial measures for the problem of multicollinearity?**

## What is the main problem with multicollinearity?

Multicollinearity is a problem because it undermines the statistical significance of an independent variable. Other things being equal, the larger the standard error of a regression coefficient, the less likely it is that this coefficient will be statistically significant.

**How do you deal with highly correlated features?**

The easiest way is to delete or eliminate one of the perfectly correlated features. Another way is to use a dimension reduction algorithm such as Principle Component Analysis (PCA).

**Which one of the following is not a plausible remedy for near multicollinearity?**

Which one of the following is NOT a plausible remedy for near multicollinearity? Correct! Principal components analysis (PCA) is a plausible response to a finding of near multicollinearity.

### How is multicollinearity diagnosed?

A simple method to detect multicollinearity in a model is by using something called the variance inflation factor or the VIF for each predicting variable.