0

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
  • 15
  • 5
  • 1
    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
  • 1
    1) 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
  • 1
    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

0 Answers0