# [R] Question about linear models

David Winsemius dwinsemius at comcast.net
Wed Nov 19 05:55:30 CET 2008

```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
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?
>
> anything.
>
>
>
> --
> http://ricardorios.wordpress.com/
>
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