Hi wizards,
I have the following model:
x<-c(20.79, 22.40, 23.15, 23.89, 24.02, 25.14, 28.49, 29.04, 29.88, 30.06)
y <- c(194.5, 197.9, 199.4, 200.9, 201.4, 203.6, 209.5, 210.7, 211.9, 212.2)
model1 <- lm( y ~ x )
anova(model1)
Df Sum Sq Mean Sq F value Pr(>F)
x 1 368.87 368.87 4384.6 3.011e-12 ***
Residuals 8 0.67 0.08
But, I have realized the following transformation:
lnx <- log(x)
lny <- log(y)
model2 <- lm( lny ~ lnx )
anova(model2)
Response: lny
Df Sum Sq Mean Sq F value Pr(>F)
lnx 1 0.0088620 0.0088620 27234 2.034e-15 ***
Residuals 8 0.0000026 0.0000003
The second model has a Sum of square Residuals very small
I have analyzed the following graph:
plot( model1$fitted.values, model1$residuals)
plot( model2$fitted.values, model2$residuals)
I have observed that maybe the first model has a specification error.
is that correct? Which model is the best?
I was trying to get information about it, but I did not found anything.
Thanks in advance
--
http://ricardorios.wordpress.com/
______________________________________________
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.
