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Tails of the unexpected (2): Outing the outliers
Published in Alan R. Jones, Probability, Statistics and Other Frightening Stuff, 2018
Grubbs’ Test assumes the Null Hypothesis that there is no outlier in the sample. The Alternative Hypothesis is that there is exactly one outlier in the data set. Grubbs’ Test is sometimes referred to as the Maximum Normed Residual Test; (yes, well, I think we’ll stick to Grubbs’Test here.)
Toxicity of binary mixtures of Al2O3 and ZnO nanoparticles toward fibroblast and bronchial epithelium cells
Published in Journal of Toxicology and Environmental Health, Part A, 2020
Jéssica Schveitzer Köerich, Diego José Nogueira, Vitor Pereira Vaz, Carmen Simioni, Marlon Luiz Neves Da Silva, Luciane Cristina Ouriques, Denice Schulz Vicentini, William Gerson Matias
Statistical analysis was performed using software GraphPad Prism® v6.0. ANOVA was carried out and assumptions of normality (Shapiro-Wilk’s test) and homogeneity of variances (Bartlett’s test) were tested. Grubbs Test was applied to detect outliers. Data were analyzed by one or two-way ANOVA followed by the Tukey’s post hoc test for parametric data or the Kruskal–Wallis test, followed by Dunn’s post hoc test for non-parametric data. Results are presented as mean ± standard deviation (SD) and differences were considered statistically significant at p < .05.
The costs of rework: insights from construction and opportunities for learning
Published in Production Planning & Control, 2018
Peter E. D. Love, Jim Smith, Fran Ackermann, Zahir Irani, Pauline Teo
The total of value of the work for the 98 construction projects where complete cost data was provided was approximately AU$8.65 billion. The mean OCV was $240,292,245 and the standard deviation was AU$112,211,435. In addition, the mean and the median margin were 17.89% and 8.05%, respectively. The mean cost difference between the OCV and final contract value was 81.2%, but there were number of outliers (Figure 3). A Grubbs test was used to detect the outliers from a Normal Distribution with the tested data being the minimum and maximum values (Grubbs 1950).