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Notably, classical RL models only explain the ascending, and classical HR models only explain the descending pattern.
The specified models only explain a share of the variation in health care utilization as indicated by relatively low R-squared values (not shown).
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Compared to the small size classes, the RF performance was subordinate for megafauna, invertebrates, and fishes, in which the models only explained 63% to 68% of the observed biomass variance.
While our regression modeling indicates that dairy consumption and produce perchlorate levels are directly related to estimated perchlorate dose, these models only explained a relatively small amount of the total variance in estimated perchlorate dose in the study population (13 14%).
The models only explained 3%, 2 % and <1 of DA, SAA and SA.
With an adjusted R2 of approximately 18%, our models only explained a moderate proportion of variance.
Previous studies describe that use of highly effective predictive regression models only explained between 3% and 22% of HRQOL variance among chronic renal disease patients [ 37, 38].
However, our models only explained 22% of the variation for breast cancer and 29% for ovarian cancer which would support it.
The regional model only explained 13% of the variation in pine seedling density.
The introduction of built environment characteristics in the final regression model only explained an additional 4% of the variance in driving to school.
In other words the linear model only explains 29% of the load variability.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com