How to build multi parameters linear regression in python
I am also a rookie for data analysis and modeling. 
If I faced this sort of problems, I might consider some questions like:

Whether there is really a significant linear or generalized linear relationship between independent and dependent variables? Should I pre-process or transfer them before regression?
Whether it is necessary to involve interactions among predictor variables?
How the quality of the data set used to train the model? Whether it is good enough for the true underlying relationship between Factors and Responses?
Should I select a more suitable method to create the prediction model? For example, we usually choose Partial Least Squares Regression (PLS), other than Ordinary Least Squares Regression (OLS), to solve multicollinearity in my work area.
Hope these could be helpful for you.
I want to ask about a multi parameters linear regression model.
The question is as following:
We have now data of 100 companies, and for each company, I have the data for parameter A,B,C,D for 3 seasons.(we can call it A1,A2,A3,B1,B2,B3..etc)
We assume that there is some relationship(which we do not know yet, and need to find)between A and BCD, and now we need to predict A for season 4 which is A4...
My method is calculate the relation using the formula of Ordinary least squares and get a final formula in the form as A4=x1*B4+x2*C4+x3*D4.
I get B4, C4, D4 by simply do linear regression on B,C,D
But the problem is the A4 I get in this way is worse than just do linear regression for A...
Can someone tell me a better solution for the problem?
Thanks