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Only stage and grade show a higher coefficient of regression (beta) than their standard error, thereby attributing them an independent prognostic significance.* low/up) 95% confidence interval.
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A higher coefficient of the OLS regression model indicates a higher average inequality brought about by the corresponding group of individuals.
The statistically significant fitted models were selected based on the higher coefficient of determination (r2) and lower regression standard error (RSE).
The optimal design to estimate the highest coefficient of a polynomial regression of fixed degree k−1 (ek-optimal design) in the class of designs with LOF-efficiency of at least r has the same mass points as the classical ek-optimal design if r is small enough.
The experimental data were fitted to quadratic response surface models using multiple regression analysis with high coefficient of determination value (R) of 0.9906.
The obtained experimental data were fitted to a second-order polynomial equation using multiple regression analysis with high coefficient of determination value (R2) of 0.994.
This regression exhibits a very high coefficient of determination (R2 = 0.988), and a mean PPE of 25.6%.
Analysis of variance (ANOVA) showed a high coefficient of determination value (R2 = 0.9658) and satisfactory prediction regression model was derived.
The relatively high coefficient of determination obtained for both RR and YR-based regression models was surprising.
Analysis of variance (ANOVA) showed a high coefficient of determination value (R2 = 0.978) and satisfactory prediction second-order regression.
Analysis showed a high coefficient of determination value (R2 = 0.8547) and satisfactory prediction for second-order regression model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com