[R] Question about linear models

[David Winsemius]

You can always inflate the SS by using smaller units, which is what  
your log transformation is doing. What is important for inference  is  
the ratios of those sums of squares. The rest of your homework is  
something you will need to complete yourself.

http://www.ugr.es/~falvarez/relaMetodos2.pdf    ..... see Question 9
http://www.uclm.es/profesorado/jesuslopezfidalgo/MODELOS.pdf  ......  
see Question 47

-- 
David Winsemius, MD
Heritage Labs


On Nov 18, 2008, at 11:44 PM, Ricardo Ríos wrote:

> 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/
>
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