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OK, I really dread to ask that .... much more that I know some discussion about p-values and if they are relevant for regressions were already on the list. I know to get p-val of regression coefficients - this is not a problem. But unfortunately one editor of a journal where i would like to publish some results insists in giving p-values for the squared deviance i get out from different glm and

I am using the lm function in R to fit several linear models to a fair-sized dataset (~160 collections of ~1000 data points each). My data have intrinsic, systematic uncertainty much greater than the measurement errors on any individual point. My thought is to use the residuals of my linear fits to quantify this intrinsic uncertainty, but I am puzzled over the correct interpretation of R's

As you may have noticed, I have been working on a fast register allocator in the last week. The new allocator is intended to replace the local allocator for debug builds. Both allocators use a local allocation strategy. Each basic block is scanned from top to bottom, and virtual registers are assigned to physical registers as they appear. There are no live registers between blocks. Everything is