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The r-squared value of this model was 0.74; thus, the model explained almost three-quarters of the variation in health spending.
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The linear model explains almost 81% of the variance of the experimental tm (R = 0.90 and s = 4.29) and the LOO press statistics evidenced its predictive ability (q2 = 0.72 and scv = 4.79).
As the two-component models explained almost all the total R, adding more components yields little difference to results, i.e. insubstantial changes to the estimated regression coefficients.
Table 2 shows that adding area deprivation (in Model 2) explained almost three quarters of the area level variance (72.3%).
We found that models that take into account interactions (models 6−9) explained almost two times more variability than those without interaction (models 1−5).
Since the prediction model of mode choice explained almost half of the variability, a significant proportion of the variance remains to be explained.
The model was robust; it explained almost 50% of total cost variability (R2 = 0.4485).
For instance, a model with only age explained almost 9% of the variation with area under ROC 64.3, whereas age and baseline WBC combined explained 19% of the variation with area under ROC 75.7.
The corresponding model by logistic regression shows that 4 symptoms explained almost 5% of the variation of the LOS (model 4 in table 3).
The constructed model could explain almost 13%% of the variance in GP consultation time.
We found that the random effects explained almost 10% of the mixed model variance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com