Barely Significant

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highly significantp < 0.00010.0× alphaqualifiedgold
1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} Y & = - 1361.23 + 47.44A + 61.81B + 35.33C \\ & \;\;\; + 8.93D - 0.21AB + 0.06AC + 0.057AD \\ & \;\;\; - 0.87BC - 1.82BD - 0.075CD - 8.70A^{2} \\ & \;\;\; - 1.32B^{2} - 0.25C^{2} - 0.03D^{2} \\ \end{aligned}$$\end{document} Variance analysis of the model coefficients (Table 5 ) indicated that the model is highly significant ( p < 0.0001), with a non-significant lack-of-fit term ( p = 0.125), suggesting a good fit between the model and the experimental data.
highly significantp ~ 1e-40.0× alphaqualifiedgold
While MEASSA’s performance advantage is most pronounced and highly significant (p ~ 1e-4) against simpler algorithms like SCA, GWO, and its predecessor ASSA, it also maintains a clear and statistically significant edge (p < 0.05) over its closest competitors, mJS and PSO-ACO, which show the smallest yet still definitive p-values (p ~ 1e-2 to 1e-3).
extremely significantP-value < 0.00010.0× alphaqualified
able 4 : the coefficient of determination R 2 of the model is 0.9742, meaning the model can explain 97.42% of the adsorption rate variation, which is much higher than the fitting requirement of general regression models (usually R 2 ≥0.95 is considered excellent fitting); the synergistic results of extremely significant model P-value < 0.0001 and non-significant lack-of-fit term P = 0.4314 > 0.05 not only confirm that the model equation has extremely high statistical reliability within the experimental range, but also indicate that the model does not miss key influencing factors, which can effectively avoid the in