Centering variables, linear regression

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SUMMARY

This discussion focuses on centering independent variables in multiple regression analysis involving two independent variables and their interaction term, expressed as y = b1x1 + b2x2 + b3x1x2. The consensus is that both independent variables should be centered simultaneously to accurately assess their individual significance. Additionally, including a constant term in the regression model is recommended to enhance the model's accuracy. Utilizing a step-wise regression algorithm can further refine variable selection based on residual statistical significance.

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  • Understanding of multiple regression analysis
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Statisticians, data analysts, and researchers involved in regression modeling, particularly those working with multiple independent variables and interaction effects.

monsmatglad
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I am working with multiple regression with two independent variables, and interaction between them.
the expression is: y = b1x1 + b2x2 and b3x1x2
The question is: does one center both independent variables at the same time, when checking for the significance of the effect of the independent variables separately?
Or, should I center one of the IV, and then rerun regression centering the other IV?

Hope this was understandable.

Mons
 
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If you are worried about "centering" the variables, you should probably include a constant term in your model. That will allow the regression algorithm to determine the best constant value.

A step-wise regression algorithm would determine which variables should be included based on the residual statistical significance. Every statistics package that I am familiar with includes such an algorithm.
 

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