[R] Linear Regression

[ONKELINX, Thierry]

Dear Vittorio,

Notice that anova(regress) gives a warning: ANOVA F-tests on an
essentially perfect fit are unreliable

Maybe summary(regress) should give a similar warning in case of a
perfect fit. Allthough you should notice that the residual standard
error displayed by summary() is extremly small. Which indicates that
something might be wrong.

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx op inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
  

> -----Oorspronkelijk bericht-----
> Van: r-help-bounces op r-project.org 
> [mailto:r-help-bounces op r-project.org] Namens Vittorio Colagrande
> Verzonden: dinsdag 12 oktober 2010 15:01
> Aan: r-help op r-project.org
> Onderwerp: [R] Linear Regression
> 
> Dear R-group,
> 
> We have begun to use it for teaching Statistics. In this 
> context we have run into a problem with linear regression
> 
> where we found the results of are confusing.
> 
> Specifically, considering the data:
> 
>  
> 
> x=c(4,5,6,3,7,8,10,14,13,15,6,7,8,10,11,4,5,17,12,11)
> 
> y=c(rep(7,20))
> 
>  
> 
> and settings
> 
>  
> 
> regress=lm(y~x)
> 
>  
> 
> summary(regress) gives the following results:
> 
>  
> 
>              Estimate Std. Error    t value Pr(>|t|)    
> 
> (Intercept)  7.000e+00  8.623e-17  8.118e+16   <2e-16 ***
> 
> x           -1.116e-17  8.956e-18 -1.247e+00    0.229    
> 
> ---
> 
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> 
>  
> 
> Residual standard error: 1.565e-16 on 18 degrees of freedom
> 
> Multiple R-squared: 0.6416,     Adjusted R-squared: 0.6217 
> 
>  
> 
> Other statistical packages respond that the analysis can not 
> be done. We think that the results of R-squared  
> 
> does not seem to express the variability of y explained by x. 
> We would greatly appreciate any clarification you 
> 
> could provide.
> 
> 
> 
> Thanks you and best regards.
> 
> Marta di Nicola e Colagrande Vittorio
> 	[[alternative HTML version deleted]]
> 
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