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Stochastic finite element model error for unreinforced masonry walls subjected to one way vertical bending under out-of-plane loading
Published in Jan Kubica, Arkadiusz Kwiecień, Łukasz Bednarz, Brick and Block Masonry - From Historical to Sustainable Masonry, 2020
A.C. Isfeld, M.G. Stewart, M.J. Masia
Hypothesis testing was conducted to determine if the test results could be considered part of the same samples as the stochastic FEA. The null hypothesis was not rejected at a 90% confidence interval using the Student T test for equal variances. The hypotheses that the variances are equal were tested using an F test for equality of variances. The hypothesis that the variances are equal was not rejected. Based on this, the stochastic FEA can be considered as most likely coming from the same population as the wall test results.
Evaluating surface visualization methods in semi-transparent volume rendering in virtual reality
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
Gloria Zörnack, Jakob Weiss, Georg Schummers, Ulrich Eck, Nassir Navab
H3) Inter-observer Variability in Valve Prosthesis Sizes In T2, we measured the valve implant size selected for each dataset/visualisation pair. After subtracting the mean for each data set, we compared the inter-observer variability of the transparent visualisations to OPAQUE as a measure of precision (Figure 5(b)). The Kolmogorov-Smirnov (K-S) test showed that the mean-subtracted results for the combined non-opaque visualisations () as well as opaque () do not significantly depart from a normal distribution. This warrants use of an F-test for equality of variances which showed a significantly lower variability of the transparent visualisations in contrast to OPAQUE ().
Software developers need help too! Developing a methodology to analyse cognitive dimension-based feedback on usability
Published in Behaviour & Information Technology, 2021
Chamila Wijayarathna, Marthie Grobler, Nalin A. G. Arachchilage
After the data collection step, the number of issues that are identified by participants and the number of incorrect issues that are reported by participants (false positives) were compared between the control group and the experimental group. The statistical power analysis (Cohen 1969) performed at the beginning of the study proved the appropriateness of the used participant sample and the reference issue set in order to determine the differences in the probability of identifying an issue and the validity of an identified issues. By conducting a Shapiro-Wilk test (Shapiro and Wilk 1965), the aforementioned two numerical data sets of both the experimental group and the control group are found to be normally distributed. Furthermore, the mean of variances are found to be equal among the two probability data sets as well as among the two validity data sets according to the F-test of equality of variances (Snedecor and Cochran 1989; Wohlin et al. 2012).