looking for some further help and to further my understanding of regression topic area.
So I am trying to compare regression models with two different data sets A and B.
Using dataset A, I fitted the regression model y= mx1+ nx2 + c this gave rqs =95%
I want to know how well this model works for dataset B. I have tried to understand this using two methods.
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Using the same variables x1 and x2, fit regression model for dataset B which gives me y=ox1+px2=c (different coefficients and intercept) with r sq = 75%
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I have used the regression eq obtained in original fit y= mx1+ nx2 + c and input the x1 and x2 from dataset B to obtain predicted values for y. I have then plotted the real y values against the predicted y values and assessed the r sq value.
Is any of these methods valid? Or is there a better method for comparing regression models? I have some initial limitations with using method 2 as the model is fitted the real values to a predicted value from a prediction. Can anyone help my understanding of this?
Best Answer
https://en.wikipedia.org/wiki/Chow_test discusses the CHOW TEST which can be used to test the hypothesis of a common set of parameters over k groups ( 2 in your case ). I routinely use this in a time series context to DETECT the point in time when the model parameters change significantly