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.onkel...@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-boun...@r-project.org > [mailto:r-help-boun...@r-project.org] Namens Vittorio Colagrande > Verzonden: dinsdag 12 oktober 2010 15:01 > Aan: r-help@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]] > > ______________________________________________ > 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. > ______________________________________________ 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.
