ENBIS: European Network for Business and Industrial Statistics
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ENBIS16 in Sheffield
11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016The following abstracts have been accepted for this event:

Modelling Bubbles and Crashes: Applications to Bitcoin and to Trading
Authors: John Fry (Sheffield Business School)
Primary area of focus / application: Other: Applied Statistics Meets Practical Statistics
Secondary area of focus / application: Finance
Keywords: Speculative bubbles, Econophysics, Bitcoin, Trading strategies

Assessing and Enhancing Conceptual Understanding: a Statistical and Educational Challenge
Authors: Germana Trinchero (Ministry of Public Education, Turin), Ferdinando Arzarello (Department of Mathematic, University of Turin), Ron S. Kenett (Department of Mathematic, University of Turin), Ornella Robutti (Department of Mathematic, University of Turin), Paola Carante (Department of Mathematic, University of Turin)
Primary area of focus / application: Education & Thinking
Keywords: MERLO, Formative assessment, Teachers professional development, Mathematics education, Statistic education
Submitted at 6Apr2016 13:41 by Germana Trinchero
Accepted
What we call ‘MERLO pedagogy’ is composed of structured activities covering specific concepts within a discipline, through multisemiotic representations in multiple sign systems as elements of items to be solved by the students, along with specific teaching practices and methodologies to be applied by the teacher with the students (Arzarello et alii, 2015). Each MERLO item includes five different statement representations that can share or not share the same meaning, being similar only in appearance. The innovation in MERLO consists of directly challenging students in discovering deep relations among different representations, and not in simply stating if they are true or false, or relate each other because they are similar in appearance (Arzarello et alii, in preparation). The teaching innovation is the design of these items by teacher educators, researchers, and teachers, according to the MERLO pedagogy.
The talk will present an example based on MERLO student scores with results that can be generalized in the general context of statistical education. We will show how to compare performance of different classes, the advantage of group exercises and how one can identify the level of conceptual understanding of different concepts. This study shows how to meet the statistical challenge of measuring the level of understanding of students with implications on how Statistics can be taught. 
Understanding the Inductance Part of the Lévy Generator: Some Applications
Authors: Chris McCollin (Nottingham Trent University), Rainer Göb (University of Würzburg)
Primary area of focus / application: Reliability
Secondary area of focus / application: Process
Keywords: Lévy process, Drainpipe theory, Japanese control chart, Langevin equation

Likelihoodfree Optimum Experimental Design: ABCD
Authors: Werner G. Mueller (Johannes Kepler University)
Primary area of focus / application: Other: ISBIS session
Secondary area of focus / application: Design and analysis of experiments
Keywords: Approximate Bayesian computation, Simulationbased design, Design criterion, Spatial extremes
The objective of optimum experimental design is to find the best possible configuration of factor settings with respect to a welldefined criterion or measure of information for a specific statistical model. In Bayesian experimental design, a prior distribution is attached to the parameters of the statistical model. This prior distribution reflects prior knowledge about the parameters of the model. In the Bayesian setting it is natural to average a criterion over the parameter values with respect to the prior distribution. In a decisiontheoretic approach to experimental design the criterion of interest is computed for the posterior distribution of the parameters and then averaged over the marginal distribution of the data. The information criterion on the posterior distribution reflects some notion of learning from the observations.
The computation of the expected criterion value can be a challenging task. Usually this involves the evaluation of integrals or sums. If the integrals are analytically intractable and numerical integration routines do not work, Monte Carlo simulation strategies can be applied in a framework of stochastic optimization. Some of our proposed methods will be based on simulationbased optimal design algorithms which utilize Markov chain Monte Carlo (MCMC) methods, but we intend to go beyond that class. Simulationbased methods make it possible to efficiently solve a wider range of problems for which standard methods cannot provide tractable solutions. In this presentation we outline potentials and limitations of ABC for design purposes, hence ABCD (D for design). Furthermore we will report details on an application for dealing with spatial extremes. 
The Parameter Diagram as a DoE Planning Tool
Authors: Matthew Barsalou (BorgWarner Turbo Systems Engineering GmbH)
Primary area of focus / application: Design and analysis of experiments
Keywords: Parameter diagram, Design of experiments, System understanding, Noise factors
Submitted at 10Apr2016 14:13 by Matthew Barsalou
Accepted
A pdiagram is a graphical depiction of the inputs and outputs of a system. The inputs may be the main inputs such as raw material or a control signal in the form of voltage. There are also control factors, which are directly controlled such as when they are tested before hand to ensure they conform to requirements. Noise factors also influence a system, but these are uncontrolled such as weather or wear over time. Ideal functions and error states are also listed, with the ideal function being the desired output of the system and the errors states representing ways in which things could go wrong. Potential DoE factors can be drawn from the input and control factors while noise factors can be useful for identifying potential blocks. The response variable would either be the ideal function when it must be optimized or the error state when there is a condition that should be avoided. A pdiagram can be created in a presentation program or even on a whiteboard. However, SMEs are needed to provide the pdiagram inputs. These inputs are needed for a proper DoE and use of the pdiagram helps to ensure they are captured during DoE planning.
This talk will describe the pdiagram and its application in DoE. Examples will be presented using actual DoEs from the literature. These case studies are the identification of the AA battery configuration with the longest life, improving the quality of a molded part, increasing the life of a molded tank deterrent device, and the optimization of a silver powder production process. After attending this talk, participants will be able to use a pdiagram for DoE planning. 
Supersaturated SplitPlot Screening Experiments
Authors: Emily Matthews (University of Southampton)
Primary area of focus / application: Design and analysis of experiments
Keywords: Screening, Design of Experiments, Splitplot, Supersaturated