I have been using a simple Logistic Regression method with Log odds and it works very well for me

This is an example

X yes no total Prob Odds Log Odds

0 4 1 5 0.8 4 1.39

1 5 2 7 0.71 2.5 0.92

2 3 2 5 0.6 1.5 0.41

3 1 2 3 0.33 0.5 -0.69

y1weights x1weights 1slope intercept

0.66 1.3 -0.65 1.50

The intercept is calculated as y1weightsts1-x1weights*1slope

the prediction follows the normal linear regression

intercept+1slope*x

I have had a idea (maybe slighty crazy) and perform some multiple regression

here is the 2nd set

X yes no total Prob Odds Log Odds

0 2 6 8 0.25 0.33 -1.10

1 3 9 12 0.25 0.33 -1.10

2 2 11 13 0.15 0.18 -1.70

3 1 15 16 0.06 0.07 -2.71

y2weights x2weights 2slope

-1.784954575 1.755102041 -0.595625389

If this was normal multiple regression it would fall like this

intercept+1slope*x1+2slope*x2

But how do I calculate the intercept so it takes in the 2nd set that I am looking at

I had thought

(y1weights-x1weights*1slope)-(y2weights-x2weights*2slope)

But I am not sure of if this is right?