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Jun 7, 2013 at 8:45 comment added IMA I will write a few words about the potential problems you will face generally in a moment, gonna take a couple of minutes.
Mar 10, 2013 at 17:22 comment added gung - Reinstate Monica @DuaneAllman, my position is No. You should read the post that I linked to.
Mar 9, 2013 at 1:17 comment added Duane Allman Hmm, ok. I've just run a new program which goes through every subset and prints the "best" subset for (1-length, 2-length, 3-length...20-length) etc subsets. I'll treat the results with some scepticism, but it should give me some sort of idea of the variables to use, no?
Mar 9, 2013 at 0:45 comment added gung - Reinstate Monica @DuaneAllman, unfortunately no. What you have suggested is a simplified for of model building akin to "best subsets regression". If these comments don't make much sense / you want to understand why, it may help to read my answer here: algorithms-for-automatic-model-selection.
Mar 8, 2013 at 22:42 comment added Duane Allman Ok, I imagine the problems backward stepwise has is that you could easily remove a criteria which looks bad, but is actually good when correlating with something else (or however you say it)? If I did an exhaustive search of all combinations of independent variables (up to order 3), and pick the combination which gave me smallest "fisher f" value, would that be a decent result?
Mar 8, 2013 at 22:20 comment added whuber Duane, although there's a kernel of good advice in this reply, I suggest you not follow it. The first part advocates a simplified form of model building akin to "backward stepwise regression." This is known to have problems. The second part--"playing"--does not seem to provide any actionable advice at all.
Mar 8, 2013 at 22:00 comment added Duane Allman Ah, now what are the p-values, are they to do with partial residual plots? and what makes them significant or not?
Mar 8, 2013 at 21:42 review First posts
Mar 9, 2013 at 0:45
Mar 8, 2013 at 21:25 history answered wlq CC BY-SA 3.0