{'fraction_non_alphanumeric': 0.018467220683287166, 'fraction_numerical': 0.007386888273314866, 'mean_word_length': 5.089887640449438, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Grubbâ€™s Test for the Presence of Outlier in the Four-parameter Logistic Model Used in Obtaining the IC50 Value for Salvia officinalis Extract Against Aeromonas hydrophila

The current trend in treating fish infection is to reduce the dependency to antibiotics. Active compounds from plants are being intensively studied as potential antibacterial to treat or to prevent infections caused by fish pathogens. A nonlinear regression exercise using the four-parameter dose response variable slope of the inhibition curve of the bacterium Aeromonas hydrophila using solvent extracts from the plant Salvia officinalis is checked for the presence of an outlier [at 95 or 99% of confidence). A potential outlier in a nonlinear regression is actually an extreme data point that is most certainly too extreme. This is normally done using the Grubb's test, which is the focus of this study. Grubbâ€™s statistical tests for the residuals indicated that the nonlinear regression model i.e. the four-parameter logistic model is adequate in finding the IC50 value as there was no outlier present.