First of all, thank you very much for your help! The 15 variables of my regression analysis represent the result of a factor analysis. So if I understand it right, I better should keep the variables in the equation.
Next thing is, that I probably would like to do a regression analysis on the whole dataset with 67 variables, which of cause correlate with each other. Is there a to do this regression or is it best to do a factor analysis before (as I already did'). Any suggestions for literature on this topic? Thanks very much again! Bastian [EMAIL PROTECTED] (Jay Warner) wrote in message news:<[EMAIL PROTECTED]>... > 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/ . > ================================================================= . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
