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I asked another question earlier and I think I miswrote that question.

My older question is here Can I merge multiple linear regressions into one regression?

In the older question I was talking about splitting dataset into multiple subsets and putting them in 4 different models. and I got the answer for that one.

My question this time is a bit different

As my dataset is consists of multiple features, Like this

Y     X1      X2      X3      X4
738   83      29      74      44
849   47      27      84      37
820   16      82      83      64
.
.
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This dataset has 48,000 records

I have split this dataset into 4 different models So each model has 48,000 records with different features

enter image description here

Model 1 equation was $a_1 X_1 + b_1$

Model 1 equation was $a_2 X_2 + b_2$

Model 1 equation was $a_3 X_3 + b_3$

Model 1 equation was $a_4 X_4 + b_4$

Now I want to compile all 4 models into 1 model $Y_* = a_* X_{1,2,3,4} + b_*$

How can I achieve that?

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    $\begingroup$ Have you tried doing a multiple linear regression? $\endgroup$
    – Demetri Pananos
    Commented Oct 13, 2021 at 4:16
  • $\begingroup$ Yes this is what I did. I did multiple linear regressions, each model was taking a different subset of features, but how to combine them all together in the end? $\endgroup$
    – asmgx
    Commented Oct 13, 2021 at 11:00
  • $\begingroup$ That's not what a multiple linear regression model is. You are supposed to put all features in one model, not doing a separate model for each feature. $\endgroup$
    – user2974951
    Commented Oct 13, 2021 at 11:10
  • $\begingroup$ @user2974951 if i pulled all features in one model then it is not a multiple linear regression! it is a single one.. am I getting something wrong here?! $\endgroup$
    – asmgx
    Commented Oct 13, 2021 at 12:27
  • $\begingroup$ Yes, you got it wrong. Multiple refers to multiple variables into one model, not to multiple separate models. $\endgroup$
    – user2974951
    Commented Oct 13, 2021 at 12:32

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