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.
