This further supports our findings which suggest that the overweight/obese variable classification may be influential in assessing the sex-specific overweight/obese and asthma association as different classifications may yield varying results.
Also, random forests is a nonparametric approach that supports multivariate and nonlinear associations whereas PLS regression models linear dependencies only, which may yield different variable importance measures especially in the case where highly nonlinear associations between predictors of interest are present.
In addition, systematically examining, how other factors, particularly attitudinal and motivational variables that influence food choice, may be changed in order to achieve healthier food choices in different target population groups, may yield valuable information.
The presence of a discrete confounding variable may yield spurious correlations if the marginal distributions vary across the groups implied by the confounder.
Therefore, this suggests that deregressed EBV as the response variable may yield higher reliabilities of genomic breeding values than EBV.
These customary scalar/vector approaches, which violate the tensorial nature of stress, together with other quasi-tensorial applications found in the literature that consider the tensor components as statistically independent variables, may yield biased results.
However, currently, the customary scalar/vector approaches to the generation of random stress tensors, which violate the tensorial nature of stress, together with other existing quasi‑tensorial applications that consider the tensor components as statistically independent variables, may yield biased results.
Evaluation of a larger number of bones may yield more age-related variables.
These variables may yield meaningful individual differences when compared before and after therapeutic interventions, as has been shown to be true for diastolic myocardial velocity quantified by pulsed tissue Doppler imaging and reported to be suitable for supporting therapeutic decision-making [ 47, 53, 54, 100].
Step 1 ensures that the covariates chosen are possible confounders in the traditional sense of the word; if the covariates are descendants of X, then the statistical model adjusting for these variables may yield a biased estimate for the total causal effect of X on the outcome and a different set of covariates needs to be chosen.
