LUR models explained significant variability as a function of secular trends, traffic density within 500 m, distance from major roads, and block group population density (R = 0.83; Table 3).
Clutch size, however, did not enter in the best models explaining significant proportion of variance of nestling conspicuousness.
Multivariate regression analyses produced models explaining significant portions of the variance in SF-36 subscales, especially physical functioning (R2 = 42%), and also showed that diabetes-related indicators were more important disease predictors, compared to sociodemographic variables.
In the fledgling model, brood identity explained significant proportion of the variance of digit ratios (Table 1).
Covariates were retained if: 1) there was evidence of confounding (estimates of treatment effect differed markedly between adjusted versus unadjusted models); 2) they explained significant variation in the outcome; 3) they improved the precision of the estimates of treatment effect.
Potential confounding characteristics of homes and residents were included as cluster-level or individual-level fixed or time-varying covariates and retained if evidence of confounding occurred (ie, if estimates of treatment effect differed substantially in the adjusted vs unadjusted models, or they explained significant variation in the outcomes).
The MRM model explained a significant proportion of the variability in total phylogenetic community structure, with most of the environmental parameters in both land-use types (Table 3).
We examine this result more closely with a cumulative quantitative synthesis in Appendix B. The reading model explained a significant amount of effect-size heterogeneity even given a large degree of uncertainty (QM 2) = 7.8, p <.05).
This model explained a significant proportion of the observed variance ([formula: y = 8.464 x + 541.709] R2 = 0.85; P = 6.9 × 10−; fig. 1).
As a whole, the multivariate model explained a significant amount of the proportion of variance in growth scores, R 2 adj.
