For archiving reasons: 1. "Practical Regression and Anova using R" by Faraway 2. Possible reason: multi-collinearity in predictor variables.
Thanks everybody! On Thu, Aug 8, 2013 at 1:43 PM, Stathis Kamperis <ekamp...@gmail.com> wrote: > Hi everyone, > > I have a response variable 'y' and several predictor variables 'x_i'. > I start with a linear model: > > m1 <- lm(y ~ x1); summary(m1) > > and I get a statistically significant estimate for 'x1'. Then, I > modify my model as: > > m2 <- lm(y ~ x1 + x2); summary(m2) > > At this moment, the estimate for x1 might become non-significant while > the estimate of x2 significant. > > As I add more predictor variables (or interaction terms), the > estimates for which I get a statistically significant result vary. So > sometimes x1, x2, x6 are significant, while others, x2, x4, x5 are. > > It seems to me that I could tweak my model in such a way (by > adding/removing predictor variables or "suitable" interaction terms) > that I could "prove" whatever I'd like to prove. > > What is the proper methodology involved here ? What do you people do > in such cases ? I can provide the data if anyone cares and would like > to have a look at them. > > Best regards, > Stathis Kamperis ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.