Hello, I have a simple theorical question about regresion...
Let's suppose I have this: Model 1: Y = B0 + B1*X1 + B2*X2 + B3*X3 and Model 2: Y = B0 + B2*X2 + B3*X3 I.E. Model1 = lm(Y~X1+X2+X3) Model2 = lm(Y~X2+X3) The Ajusted R-Square for Model1 is 0.9 and the Ajusted R-Square for Model2 is 0.99, among many other significant improvements. And I want to do the anova test to choose the best one: H0: B1 = 0 H1: B1 != 0 Test = Anova(Model2,Model1) How do I know what model wins? (I'm using a confidence level of 0.1)... My guess is that: If p-value of summary(Test) is greater than 0.1 then I don't reject H0 so Model2 is better and otherwise I reject H0 so Model1 is better? My teacher once said: "If p-value is greater than 0,5 we choose the short model and otherwise we choose the long model", but she never said how the p-value and the significance level were related in this test... Actually she never talked about significance level... In short: Should I consider the significance level or always use 0.05 for this kind of test? Thanks a lot! Hector Guilarte Enviado desde mi dispositivo movil BlackBerry® de Digitel. ______________________________________________ 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.
