search for: iv5

...ed in years), previous performance (continuous IV2), country (categorical IV3 - six countries), the percentage of PhD graduates in each country (continuous IV4 - country level data - apparently only six different percentages since we have only six countries) and population of country (continuous IV5 - country level data - again only six numbers here, one for each country population). My own opinion is that the lm function cannot be used with country level data as IVs (for example IV4 and IV5 cannot be entered into the model because they are country level data). If IV4 and IV5 are included...

...sured in years), previous performance (continuous IV2), country (categorical IV3 - six countries), the percentage of PhD graduates in each country (continuous IV4 - country level data - apparently only six different percentages since we have only six countries) and population of country (continuous IV5 - country level data - again only six numbers here, one for each country population). My own opinion is that the lm function cannot be used with country level data as IVs (for example IV4 and IV5 cannot be entered into the model because they are country level data). If IV4 and IV5 are included in...

...5, label %for.cond.cleanup, label %for.body.4.lr.ph.preheader for.body.4.lr.ph.preheader: ; preds = %entry %0 = shl i32 %Size, 2 br label %for.body.4.lr.ph for.body.4.lr.ph: ; preds = %for.body.4.lr.ph.preheader, %for.cond.cleanup.3 %lsr.iv5 = phi i32* [ %Src, %for.body.4.lr.ph.preheader ], [ %2, %for.cond.cleanup.3 ] %lsr.iv1 = phi i32* [ %Dst, %for.body.4.lr.ph.preheader ], [ %1, %for.cond.cleanup.3 ] %Outer.026 = phi i32 [ %inc10, %for.cond.cleanup.3 ], [ 0, %for.body.4.lr.ph.preheader ] %lsr.iv56 = bitcast i32* %lsr.iv5 to i1...

...mation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1", "dv2", "dv3", "iv1", "iv2", "iv3", "iv4", "iv5", "intv1", "intv2") names(my.df) <- var.list # I have some are DVs: dvs <- c("dv1", "dv2", "dv3") # some IVs: ivs <- c("iv1", "iv2", "iv3", "iv4", "iv5") # and some binary interactio...

...gt; for.body.4.lr.ph.preheader: ; preds = %entry > > %0 = shl i32 %Size, 2 > > br label %for.body.4.lr.ph > > > > for.body.4.lr.ph: ; preds = > > %for.body.4.lr.ph.preheader, %for.cond.cleanup.3 > > %lsr.iv5 = phi i32* [ %Src, %for.body.4.lr.ph.preheader ], [ %2, > > %for.cond.cleanup.3 ] > > %lsr.iv1 = phi i32* [ %Dst, %for.body.4.lr.ph.preheader ], [ %1, > > %for.cond.cleanup.3 ] > > %Outer.026 = phi i32 [ %inc10, %for.cond.cleanup.3 ], [ 0, > > %for.body.4.lr.ph.pre...

On Mon, Aug 31, 2015 at 5:52 PM, Jake VanAdrighem <jvanadrighem at gmail.com> wrote: > Do you have some specific performance measurements? Averaging 4 runs of 10000 iterations each of Coremark on my X86_64 desktop showed: -O2 performance: +2.9% faster with the L.E.V. pass -Os size: 1.5% smaller with the L.E.V. pass In the case of Coremark, the benefit comes mainly from the matrix

...ession. What I am interested is to compare the coefficients of the same IVs across three regressions and across three groups.  Some basic information about my model/design: My model includes 2 fixed factors (task, group) and 5 more IVs of interest. Choice ~ Task * Group + IV1 + IV2 + IV3 + IV4 + IV5 I established that the Task * Group interaction is significant (by running two models of a regression, one comparing a model with and one without the interaction term) and I am interested to explore group differences within each task in a separate logistic regression. Task has 3 levels, therefor...

Hello list. I'm trying to rebuild the 2.6.9.67.0.20.EL kernel, but it fails even without modifications. How did I try it? Created a (non-root) build environment (not a mock ) Installed the kernel.scr.rpm and did a rpmbuild -ba --target=`uname -m` kernel-2.6.spec 2> prep-err.log | tee prep-out.log The build failed at the end: Processing files: kernel-xenU-devel-2.6.9-67.0.20.EL Checking