Thanks a lot for this detailled answer. This is much new input for my study and fortunately Harrell's book is available in our campus library;)
Bastian Marc Schwartz <[EMAIL PROTECTED]> wrote in message news:<[EMAIL PROTECTED]>... > On Thu, 18 Sep 2003 13:57:14 -0700, 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 > > > Some of the questions you need to ask: > > 1. Do the 'non-significant' (NS) variables contribute to improving the > model fit? Compare model fit metrics with and without the NS variables. > > 2. Do the NS variables contribute to the interpretation of > the model for the target audience/users or detract from it? > > 3. If you remove the NS variables from the model, are there other > variables in your dataset that might be considered? Do the 15 constitute > all of your available data or only a subset? > > 4. Would the inclusion of the NS variables result in over-fitting of the > model? > > 5. Are there are any transformations of the NS variables that might > increase their power in the model? For example, if you used log(var) or > var^2 for continuous variables. If so, how might this impact the other > variables and model fit? > > 6. On a univariate basis how, if at all, are the NS variables correlated > to the independent variable? Does the correlation make sense within the > context of your data? > > 7. As with 6, within the multivariable model, do the regression model > parameters for the NS variables make sense? > > 8. If you drop the NS variables, how does that impact the remaining > variables (which goes back to number 1 above) and the interpretation of > the model? > > 9. Is there any pre-cursor work in the domain of your data that can offer > some guidance? This may offer some insight into what others have done and > possibly any domain specific community standards that might be applicable. > > 10. What is the intended purpose of the model? Are you doing exploratory > reviews or trying to create a prediction model? > > Also, one thought to keep in mind: > > Non-sigificant does not mean irrelevant. > > > A good book to review would be: > > Regression Modeling Strategies > by Frank E. Harrell > http://www.amazon.com/exec/obidos/ASIN/0387952322/ > > While some would suggest that Frank's book is targeted to more advance > users, many of the fundamental design concepts in his book can be used by > anyone. You might want to see if a copy is available in your library for > review. > > You are in an area that can at times be more art than science, so you may > want to get some support from folks with more experience who can look over > your shoulder and offer advice. > > HTH, > > Marc Schwartz . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
