You are doing some kind of exploration, trying to find significant factors (ind. vars.), right? Then you can drop the 'not significant' ones, at an SL you have previously selected, and see what happens.
I suggest you do the stepwise regression in 'proper' form - run them all, drop the weakest factor, run again, drop the next weakest one, etc. Note: You should not drop low order variables when a high order is significant. If you are looking to develop a prediction equation, dropping weak ones is problematical - I do it a lot, but I get criticized for it. I like my prediction equations to be as simple as possible, but not simpler. If you carefully set up the experimental conditions to test for all 15 variables (first question - why 15?), so that the design array is orthogonal, then some people say you should keep the weak ones, on the grounds that you had good technical reason for putting them in in the first place. I say, let's go back to that 'simple' part, OK? If your design array is not near orthogonal, all your interpretations are suspect anyway, and dropping variables may have dramatic effects on the coefficients. How you interpret these changes, and the coefficients, is not for the faint of heart. Cheers, Jay Bastian wrote: > Hello, > > I did a regression analysis with 15 variables and 4 of them were not > significant. I'm not quite sure what's the best solution for this > problem: > > - leaving the regression equation like it is with all variables and > just don't interprete the not signifikant variables > > or > > - making a new regression analysis without the not significant > variables, i.e. with the method "stepwise". > > Any comments on this or literature how to solve this problem right? I > really appreciate every answer and have to admit I'm quite a newbie in > statistics... > > Thanks a lot, > > Bastian > . > . > ================================================================= > Instructions for joining and leaving this list, remarks about the > problem of INAPPROPRIATE MESSAGES, and archives are available at: > . http://jse.stat.ncsu.edu/ . > ================================================================= -- Jay Warner Principal Scientist Warner Consulting, Inc. 4444 North Green Bay Road Racine, WI 53404-1216 USA Ph: (262) 634-9100 FAX: (262) 681-1133 email: [EMAIL PROTECTED] web: http://www.a2q.com The A2Q Method (tm) -- What do you want to improve today? . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
