# How can i account for the error in a model when using that model to predict data?

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?

• What do you mean by "including the original rsq value in this new analysis"? – mkt - Reinstate Monica Aug 2 '19 at 11:38
• 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. – user252775 Aug 2 '19 at 11:51
• If your model cannot explain that 5%, I'm puzzled by the idea of accounting for it. – mkt - Reinstate Monica Aug 2 '19 at 11:53
• 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. – James Phillips Aug 2 '19 at 12:15
• If you haven't already, you'll want to look into the terms shrinkage and the bias-variance tradeoff. – rolando2 Aug 3 '19 at 0:54