Multicollinearity is a term used in data analytics that describes the occurrence of two exploratory variables in a linear regression model that is found to be correlated through adequate analysis and a predetermined degree of accuracy. The variables are independent and are found to be correlated in some regard.
Multicollinearity is studied in data science and business analytics programs, becoming a critical tool in making data-based decisions. It is considered a data disturbance, and if it is found within the model, it can mean that the entire model and its outcomes may not be reliable.
Multicollinearity occurs when two exploratory variables in a linear regression model are found to be correlated.
It is generally detected to a standard of tolerance.
Multicollinearity comes with many pitfalls that can affect the efficacy of a model and understanding why it can lead to stronger models and a better ability to make decisions.
Degrees of Multicollinearity – Creating a Standard
Multicollinearity is generally detected to a standard of tolerance. The tolerance is usually calculated about the variance inflation factor, and if it is 10 or above, it is considered a problematic relationship between the two variables.
Multicollinearity can result in huge swings based on independent variables within a model and reduces the strength of the coefficients used within a model. The relationship between variables becomes difficult to interpret using the model and may make its results null.
Reasons for Multicollinearity – An Analysis
Below is a list of some of the reason’s multicollinearity can occur when developing a regression model:
A high correlation between variables – one variable could be developed through another variable used in the regression
Poor usage and choice of dummy variables
Multicollinearity in a Regression Model – How to Fix
Once you’ve determined that there’s an issue with multicollinearity in your model, there are several different ways that you can go about trying to fix it so that you can create an accurate regression model. Below are some of the ways to make it possible:
Obtain more data: The more data you obtain for your model, the more precise the measurements can be and the less variance there will be. This is one of the more obvious solutions to multicollinearity.
Removing a variable: Removing a variable can make your model less representative; however, it can sometimes be the only solution to removing and avoiding multicollinearity altogether.
Create a standard set of independent variables.
Utilize a ridge regression or partial squares regression in conjunction with your model.
If all else fails or you decide it’s not worth it to do any additional work on the model, do nothing: Even by not changing a model where you know multicollinearity exists, it still may not affect the efficiency of taking data from the existing model.
The Multicollinearity Phenomena – Understanding its Role in Statistics
When dealing with regression models or building them from the ground up, one must always understand the pitfalls that can negatively affect the reliability and skew the data.
If not understood properly, it can lead to coming up with inferences surrounding the null hypothesis that is not supported by the data and run the risk of leading to inaccurate conclusions and professional decision-making.
Thank you for reading CFI’s guide to Multicollinearity. To keep learning and developing your knowledge base, please explore the additional relevant resources below:
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