SSEc and SSEr
Sadly, your commonly held belief is wrong (imho) -- p values/statistical significance are not a legitimate decision criteria for model "appropriateness," especially scientific appropriateness. That requires more careful consideration of a relevant "utility function" (to use Frank Harrell's phrase), effect sizes, power, etc., a more detailed discussion of which belongs elsewhere, not here, as this has nothing to do with R. For that reason, anyone with a contrary opinion on this -- there may be many who disagree -- should reply personally offlist. Cheers, Bert
On Thu, Jul 26, 2012 at 9:14 AM, suman kumar <sumprain at gmail.com> wrote:
You can make different lm objects by adding all predictors and compare them with anova(lm1,lm2,lm3...). See if p value is not significant, the more complex model is not appropriate. Dr Suman Kumar -- View this message in context: http://r.789695.n4.nabble.com/SSEc-and-SSEr-tp4637855p4637963.html Sent from the R help mailing list archive at Nabble.com.
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Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm