Say I have 100 people each with a height, weight, and age. I make a regression that predicts age based on height and weight. Now, I would like to update that model when I meet someone new. I don't want to just re-process 101 people though--I want to take the model that I already have and incorporate the new person into it.
For example, say that I found from the first 100 that:
age = .08*height + .06*weight + 7.
Now, I meet someone with age 120 height 56 and weight 34.
I do know the number of cases with which I originally made the regression. So, my initial idea was that I could just assume that they all fit the model age = .08*height + .06*weight + 7 and somehow weight the new case so that it takes the old into account (e.g., assume I have 100 people that fit the model, so create a regression equation based on the 100 identical data points and then just add the 101? That's almost just like running the regression again on the 101 data points, except that nothing needs to be stored then, because you could derive 100 from the equation.
I would like to do something like this on a large scale, and I don't want to be creating an enormous database of cases, I just want to update the model with each new case.