@badp It looks like a good approach. However, neither the question, the tables, nor the reply can be fully understood except by those who play the game (that rules out me and probably the vast majority of participants here). Incidentally, confidence intervals answer the wrong question. You probably want to estimate the probability distributions of the costs that will be incurred (e.g., use tolerance intervals or prediction intervals).
@badp No problem; I'm not complaining: the site is for videogamers, not statisticians! :-)
@AndyW I did some ad hoc empirical research 25 years ago when supervising people to enter large amounts of scientific data into databases. We ended up using highlighters to create these "zebra stripes" so that data wouldn't get mixed up across multiple lines in tables. For big tables I personally stripe every third line rather than every second: it's less intrusive and works fine.
@Tom, what is accuracy? I encourage you to formulate a question on the main page. It does not appear SPSS has anything to do with your question, and so potentially many more people on the forum would be able to provide advice.
I don't know about "automatically" in the regression equations, but you can make a series of interaction variables from the dummy variables pretty easily using DO REPEAT commands and LOOPS if necessary.
That would be a good question for StackOverflow, and such programming questions are more on topic over there than on the stats site.
Alright. I'm not sure if there is a max to the amount of variables I can have though. Eg. if I have 50 variables and I want to test for interaction between all of them that's another 50 * 50 = 2500 variables.
Well, the number of cases you have is typically considered the "max" number. Although I believe there are techniques for when the number of variables is larger than the number of cases.
Perhaps only focusing on variables whose main effects are significant would be a way to limit the number of interaction variables in the model. Although, again this is out of my ken and likely you would get much better advice from formulating a question on the main site.
If the goal is prediction I believe you should always be worried about over-fitting. And when the number of cases is low and the number of variables is high it makes it more difficult as well I believe. I don't how such tests would be done in your situation, but frequently one has a sample in which they estimate the models with a subset of the data, and then apply those models to a hold out sample not used in creating the initial model.