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The variables in the final regression model significantly predicted HR-AUCg, F = 12.626, p < .001, adj.
The multiple regression model significantly predicted low prevalence accuracy, F 4, 136) = 36.29, p < 0.001, adjusted R 2 = 0.502.
The variables in the final regression model significantly predicted the sAA stress response, F = 8.796, p < .001, and adj.
The multiple regression model significantly predicted the low prevalence target-absent reaction time, F 4, 136) = 19.97, p < 0.001, adjusted R 2 = 0.351.
The model significantly predicted performance on the earth science concept items at post-test, R 2 = .29, F 3, 41) = 5.14, MSE = 2.10, p < .01.01
Multiple and hierarchical regression analyses showed that the TPB model significantly predicted intention to perform both dietary behaviors and intention significantly predicted both behaviors.
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The regression analysis (see Table 6) reveals that the model significantly predicts ACCEPTANCE scores, p < .001.001
The regression model significantly predicts likelihood of SRE expression (χ = 274.52, df = 6, p < 0.005).
Models significantly predicted obesity classification with maximum discriminative ability for morbid-obesity (p = 3.15×10−18).
The models significantly predicted pain and disability at follow-up, but not sick leave.
AUC estimates indicated the models significantly predicted overweight and obesity classification with maximum discriminative ability when employing model 3 to predict class III obesity (AUC = 0.750, 95% CI = [0.702, 0.797]).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com