(untitled) (Journal of Environmental Bioremediation and Toxicology, 2022)
Lots of studies do not perform statistical diagnostics on the nonlinear model that was used, therefore the data may not be random. Because these systems rely on random data, this is a necessity for all parametric statistical assessment procedures. When the diagnostic tests show that the residuals constitute a pattern, there are several options for treatment, including switching to a new model or performing a nonparametric analysis. These treatments, as well as others like them, should solve the issue. We employ the Wald-Wolfowitz runs test as a statistical diagnosis tool to determine whether or not the randomization conditions have been met. Because it was critical to examine the randomness of the residual for the Buchanan-3-phase model used in the fitting of the effect of nZVI/Pd and SiO2-nZVI/Pd nanoparticles on the growth of Pseudomonas putida, it was decided to conduct this study using the Wald-Wolfowitz runs test. The run test indicated that there were 7 total runs, whereas 7.15 runs were predicted based on the randomization assumption. This shows that the residual series contains suitable runs. Because the p-value was greater than 0.05, the null hypothesis was not rejected; this means that there is no persuasive evidence of the residuals’ non-randomness; rather, the residuals represent noise. This implies that no extra effort is required to discover potential outliers. Because there was no outlier, the data did not need to be reanalyzed as a result of Grubb’s test results. Overall, the residual analysis indicates that the Buchanan-3-phase model used in the fitting of the effect of nZVI/Pd and SiO2-nZVI/Pd nanoparticles on the growth of P. putida was adequate.