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I have two different regression models developed from two different datasets as follows:

Y1 = B0*X1+B1 , R2 = 0.16

Y2 = C0*X2+C1 , R2 = 0.34

Y1 and Y2 are representing the same measure but they have different values

X1 and X1 are different features.

How I can compute the importance (contribution) independent variable in each model?

Yazan Alatoom
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    In case X1 and X2 are two different features, I suggest you create a single regression model containing both features X1 and X2. The p-values give you then an indication on the contribution of each feature. If X1 and X2 are the same features, the result of the model is due to the split of the dataset. – Molitoris Jun 23 '21 at 06:58
  • X1 and X2 are different features from different datasets and cannot be entered in the same model. – Yazan Alatoom Jun 23 '21 at 07:04
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  • Think about what you mean by 'importance' and look at other questions in the tag that you have used. 2) Be careful about using p-values to quantify this (I would not use them). 3) Having 2 predictors, models and datasets does not change anything about your question, which is basically "How do I quantify importance in any regression?"
    • – mkt Jun 24 '21 at 06:45
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    e.g. https://stats.stackexchange.com/questions/422769/feature-importance-for-linear-regression – mkt Jun 24 '21 at 06:46
  • @Reinstate Mocnica Thank you – Yazan Alatoom Jun 24 '21 at 10:34