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I have a regression model with $R^2$ value. I am trying to use this model on a different data set to see how well the model predicts this data.

So far I have used the model with the 'new' data to make predictions and then I have used a fitted line plot to asses how well the new predictions compare with the real data.

I would like a way of including the original $R^2$ value in this new analysis.

E.g one model has r sq 95.5%, when I plot the predicted values (using this model) against the real data the r sq is 74%.

Is there a better way I can do this analysis rather than just reporting the new $R^2$ value?

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  • $\begingroup$ What do you mean by "including the original rsq value in this new analysis"? $\endgroup$ – mkt - Reinstate Monica Aug 2 '19 at 11:38
  • $\begingroup$ So my original model is very good at explaining the variability in the data set that was used to create the model (r sq 95%). But I have now used that model on a new data set and I'm just trying to understand how I can account for the 5% of the variability my model cant explain in these new results. $\endgroup$ – user252775 Aug 2 '19 at 11:51
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    $\begingroup$ If your model cannot explain that 5%, I'm puzzled by the idea of accounting for it. $\endgroup$ – mkt - Reinstate Monica Aug 2 '19 at 11:53
  • $\begingroup$ Many sources of error cannot be accounted for by a model, such as: transistor amplifier noise, manufacturing variability of equipment, manual recording and transcription errors, and so forth. $\endgroup$ – James Phillips Aug 2 '19 at 12:15
  • $\begingroup$ If you haven't already, you'll want to look into the terms shrinkage and the bias-variance tradeoff. $\endgroup$ – rolando2 Aug 3 '19 at 0:54
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If I understand correctly, your objective is to test the out-of-sample performance of a regression model estimated in a certain training set. And you are looking at the decay of the R2. Since the R2 is computed from the variance of residuals, I think it’s the correct way.. just to give you alternative ideas, if your residuals are close to a normal distribution, you could also use the analogy between MLE and OLS to gauge your model based on some metrics that are typically used in MLE contexts, but why over-complicating? I think it’s fine. The simpler, the clearer, the better.

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    $\begingroup$ Yes, thanks I am looking at the decay of the rsq value and that a really nice way to put it :) thanks again for your help! $\endgroup$ – user252775 Aug 2 '19 at 13:24
  • $\begingroup$ You’re welcome! $\endgroup$ – Fr1 Aug 2 '19 at 13:45

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