Hi all,
I am doing a multiple regression using two independent variable (x1, continuous variable and x2, binary variable) and an interaction term of x1*x2. I also have a host of other control variables too. So in the 1st case I am using model as (Y=x1+ controls) (Y=x2+ controls) and (Y=x1*x2+ controls) I am getting perfect results. But when I am using model (y=x1+x2+x1*x2+ controls) , I am not getting expected results. The results are quiet different from earlier models and multi-colinearity is also high. How to reduce that and get results similar to the individual models? Plz help me out.
I am doing a multiple regression using two independent variable (x1, continuous variable and x2, binary variable) and an interaction term of x1*x2. I also have a host of other control variables too. So in the 1st case I am using model as (Y=x1+ controls) (Y=x2+ controls) and (Y=x1*x2+ controls) I am getting perfect results. But when I am using model (y=x1+x2+x1*x2+ controls) , I am not getting expected results. The results are quiet different from earlier models and multi-colinearity is also high. How to reduce that and get results similar to the individual models? Plz help me out.
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