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enter image description hereIm fairly new to stats and regression but trying to learn and I've come across something that doesn't seem right to me. I have used dummy variables to run a multiple regression model to predict the rank of an industries performance. I've noticed the tolerance, VIF and SE are all the same for all variables, this strikes me as odd, but the actual outcomes of the model make sense. can anyone shed some light on why this might be happening?

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Good question.

Correct me if I am wrong, but it looks as though you have only one predictor variable (that is dummy coded). If this is the case, collinearity diagnostics are not needed. This is because collinearity is the presence of a linear relationship between predictor variables, and you only have one.

Also, as you are new to regression, I included several easy-to-digest links below that discuss the collinearity diagnostics in SPSS.

I hope this helps!

References

Collinearity between categorical variables

https://www.statisticssolutions.com/testing-assumptions-of-linear-regression-in-spss/#:~:text=Multicollinearity%20refers%20to%20when%20your,highly%20correlated%20with%20each%20other.&text=If%20you%20are%20performing%20a,inflation%20factor%20(VIF)%20values.

https://www.youtube.com/watch?v=oPXjQCtyoG0

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