Test of the Randomness of Residuals and Detection of Potential Outliers for the Modified Gompertz Model Used in the Fitting of the Growth of Shigella flexneri

Abstract

The formulation of hypotheses and the recommendation of experiments as the subsequent stages of the research process are both brought about as a result of the utilization of complicated computer models that make it possible to represent intricate biological processes. Because these systems rely on random data, this is a necessity for all parametric statistical assessment procedures. When the diagnostic tests reveal that the residuals make up a pattern, there are a few different treatment choices available to choose from. Two of these alternatives include running a nonparametric analysis or switching to a new model. In this study, we use the Wald-Wolfowitz runs test as a statistical diagnosis tool to determine whether or not the randomization conditions have been met. The runs test found that there were 5 total runs, although the randomness assumption predicted 7.46 runs. The null hypothesis is not rejected since the p-value is greater than 0.05; this suggests that there is no convincing evidence of the residuals' non-randomness; rather, the residuals represent noise. In addition, the Grubb’s outlier test shows no indication of an outlier, further corroborate the scenario of the adequacy of the modified Gompertz model used in the fitting of the growth of Shigella flexneri.