2013
DOI: 10.4018/ijrcm.2013100105
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Abstract: This study discussed the theoretical literature related to developing and probability distributions for estimating uncertainty. A theoretically selected ten-year empirical sample was collected and evaluated for the Albany NY area (N=942). A discrete probability distribution model was developed and applied for part of the sample, to illustrate the likelihood of petroleum spills by industry and day of week. The benefit of this paper for the community of practice was to demonstrate how to select, develop, test an… Show more

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Cited by 13 publications
(3 citation statements)
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“…The use of parametric statistical techniques requires rigorous designs that ensure the prerequisites of the data are satisfied including distribution, population-sample homogeneity, sample group size, data type, and other inferential thresholds including collinearity and variance tolerance (Strang, 2015d). Learning analytics software generally involves nonparametric distribution-free nonlinear techniques utilised in big data analytics (Chatti et al, 2012, p.10;Strang and Sun, 2015;Sun, Strang and Yearwood, 2014;Xing et al, 2015), which include cluster analysis, neural network analysis with Bayes probability theory, nonlinear math programming, correspondence analysis, and genetic nonlinear programming (Nersesian and Strang, 2013;Strang, 2012;Vajjhala, Strang and Sun, 2015;Xing et al, 2015). The strategy for this study is to accept learning analytics as a 'black box' big data summarisation tool by using its output for input into the unit of analysis during hypothesis testing.…”
Section: Empirical Learning Analytics Researchmentioning
confidence: 99%
“…The use of parametric statistical techniques requires rigorous designs that ensure the prerequisites of the data are satisfied including distribution, population-sample homogeneity, sample group size, data type, and other inferential thresholds including collinearity and variance tolerance (Strang, 2015d). Learning analytics software generally involves nonparametric distribution-free nonlinear techniques utilised in big data analytics (Chatti et al, 2012, p.10;Strang and Sun, 2015;Sun, Strang and Yearwood, 2014;Xing et al, 2015), which include cluster analysis, neural network analysis with Bayes probability theory, nonlinear math programming, correspondence analysis, and genetic nonlinear programming (Nersesian and Strang, 2013;Strang, 2012;Vajjhala, Strang and Sun, 2015;Xing et al, 2015). The strategy for this study is to accept learning analytics as a 'black box' big data summarisation tool by using its output for input into the unit of analysis during hypothesis testing.…”
Section: Empirical Learning Analytics Researchmentioning
confidence: 99%
“…, 2014; Wang, 2009). Regarding the single project, Monte Carlo simulation is one effective method to simulate its contingency, escalation or decision trees under risky environment (Hulett, 2016; Nersesian, 2013; Simon and Hillison, 2012). Owning to the complexity associated with risks generated by interactions among different projects at project level and strategic levels, the development trend of the study is to explore novel alternatives such as complex networks, adaptive systems, artificial intelligence, chaos theory to enhance their applications in future complex decision making (Silvius and Marnewick, 2022; Bai et al.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The use of parametric statistical techniques require rigorous designs that ensure the prerequisites of the data are satisfied including distribution, population-sample homogeneity, sample group size, data type, and other inferential thresholds including collinearity and variance tolerance (Strang, 2015d). Learning analytics software generally involve nonparametric distribution-free nonlinear techniques utilized in big data analytics (Chatti et al., 2012; Strang & Sun, 2015; Sun et al., 2014; Xing et al., 2015) which include cluster analysis, neural network analysis with Bayes probability theory, nonlinear math programming, correspondence analysis, and genetic nonlinear programming (Nersesian & Strang, 2013; Strang, 2012; Vajjhala, Strang, & Sun, 2015; Xing et al., 2015). The strategy for this study is to accept learning analytics as a “black box” big data summarization tool by using its output for input into the unit of analysis during hypothesis testing.…”
Section: Literature Reviewmentioning
confidence: 99%