The phrase "allow for variance in" is correct and usable in written English.
It can be used when discussing the need to accommodate differences or variations in a particular context, such as data, processes, or expectations.
Example: "When analyzing the results, we must allow for variance in the data to ensure accurate conclusions."
Alternatives: "account for differences in" or "make room for variation in".
This problem is handled by using statistical analyses that incorporate information about time until death and allow for variance in group differences over time.
Other methods that incorporate soft thresholding allow for variance in the actual correlation value based on distribution of node degree [ 29]; this method goes on to use topological overlap matrices to identify modules of functional significance with great accuracy.
To account for variation in under-enumeration between cancer registries, the application of random- and fixed-effect regression (using Stata xtreg with 'fe' and 're' options) was tested, to model and allow for variance in NMSC rates within and between registries.
This invokes the same number of new parameters as the F81 model, but allows for variance in asymmetry across sites.
Secondary analyses will be undertaken on the basis of 'as treated' allowing for variance in compliance and allowing for subjects who show substantial changes in HbA1c levels.
Allowing for variance heterogeneity (i.e., model the variance as a function) in only the additive genetic variance would lead to a bias in the relevant parameter estimate because it is possible in theory for there to be heterogeneity in the residual environmental variance as well.
Meuwissen et al. [ 10] have compared a joint least squares estimation of individually significant haplotype effects with best linear unbiased prediction (BLUP) including all haplotypes and two Bayesian approaches similar to BLUP, but allowing for variation in the genetic variance accounted for by individual haplotype effects.
The challenge is to determine which product aspects can be common to all markets and then to allow for required variances in a manageable way.
In brief, the procedure allows for the variance in the explanatory data set to be partitioned into different variable components through the use of covariables (i.e., variables whose influence is partialled out of the analysis).
These models incorporate regression coefficients that associate study characteristics (i.e., moderators) to study outcomes while allowing for unexplained variance in the model (Raudenbush [2009]).
