So say I want to predict the next point in the data.(or outside since its prediction) So first, I would include all the variables inside the dataset into the initial model. Then I would use stepwise AIC to iteratively reduce the model down to the final model with minimum AIC. ( I can do this because I do not care about causal explanation, just about prediction) Now, should the large model consist of just sums of the variables, or should it also include all possible combinations of interactions as well? Finally, what do I with the final model? Do I just interpret the estimates and confidence intervals as usual?