Centering variables, linear regression

In summary, when working with multiple regression with two independent variables and an interaction between them, it is important to consider centering the variables and including a constant term in the model. This will allow the regression algorithm to determine the best constant value and which variables should be included based on statistical significance. Most statistics packages have a step-wise regression algorithm for this purpose.
  • #1
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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  • #2
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.
 

What is the purpose of centering variables in linear regression?

The purpose of centering variables in linear regression is to remove the potential for multicollinearity between the independent variables, making the model more stable and easier to interpret. It also helps to reduce the impact of outliers on the regression coefficients.

How do you center variables in linear regression?

To center a variable in linear regression, you subtract the mean of the variable from each individual data point. This results in a new variable with a mean of 0 and a similar distribution to the original variable.

What is the difference between centering and standardizing variables in linear regression?

Centering variables in linear regression involves shifting the mean of the variable to 0, while standardizing involves transforming the variable to have a standard deviation of 1. Centering helps to remove multicollinearity, while standardizing can help with comparing the effects of different variables on the outcome.

What happens if you don't center variables in linear regression?

If you don't center variables in linear regression, it can lead to issues with multicollinearity, making the model less stable and less interpretable. It can also result in misleading regression coefficients and predictions.

When should you center variables in linear regression?

It is generally recommended to center variables in linear regression when there is a potential for multicollinearity, such as when the independent variables are highly correlated with each other. It is also useful when interpreting the regression coefficients and comparing the effects of different variables on the outcome.

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