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This is a actually very basic question, but I can't get my head around it.

I have two datasets for Europe and U.S. that contain the same two variables. These two variables are in a linear relationsship, which I proved by statistical t-testing. Now I want to check if the two regional differentiated data sets can be combined and are in fact based upon one mechanism. Should I simply combine the data, implement a new linear relationsship and t-test the entirety again on statistical signifance? Or is there a other way?

Best!

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I assume you used a simple linear regression model to estimate the association between these two variables. Now technically, you can append data sets from two different regions but you need to be careful. Does it make sense to combine them, theoretically? (e.g., Europe is a continent, U.S. is a country). Let's say it does (we need to know more about your analysis to make a better judgment), then you could create an indicator variable for each data set. We can call this variable region, or something else, and it will have two categories (Europe and the U.S.). You can include this variable in your regression model as a dummy indicator. That way, you could hope to adjust for the region effect in your multiple regression model.

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