ENBIS: European Network for Business and Industrial Statistics
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ENBIS15 in Prague
6 – 10 September 2015; Prague, Czech Republic Abstract submission: 1 February – 3 July 2015The following abstracts have been accepted for this event:

Data Visualisation Books and Blogs: An Overview
Authors: Gaj Vidmar (University Rehabilitation Institute, Republic of Slovenia)
Primary area of focus / application: Other: Graphics and Graphical Models for Illuminating Data
Secondary area of focus / application: Education & Thinking
Keywords: Data visualisation, Statistical graphics, Books, Blogs, Reviews, Communication, Education
Submitted at 16May2015 11:20 by Gaj Vidmar
Accepted

Simulating and Analyzing Experiments in the Tennessee Eastman Process Simulator
Authors: Francesca Capaci (Luleå University of Technology), Bjarne Bergquist (Luleå University of Technology), Erik Vanhatalo (Luleå University of Technology), Murat Kulahci (Technical University of Denmark)
Primary area of focus / application: Design and analysis of experiments
Keywords: Experimental design, Tennessee Eastman process simulator, Autocorrelated data, Continuous process
Submitted at 19May2015 15:53 by Francesca Capaci
Accepted
However real production processes are not suitable for studying new experimental methodologies, partly because unknown disturbances/experimental settings may lead to erroneous conclusions. Moreover large scale experimentation in production processes is frowned upon due to consequent disturbances and production delays. Hence realistic simulation of such processes offers an excellent opportunity for experimentation and methodological development.
One commonly used process simulator is the Tennessee Eastman (TE) challenge chemical process simulator. The process produces two products from four reactants, containing 41 measured variables and 12 manipulated variables. In addition to the process description, the problem statement defines process constraints, 20 types of process disturbances, and six operating modes corresponding to different production rates and mass ratios in the product stream.
The purpose of this paper is to illustrate the use of the TE process with an appropriate feedback control as a testbed for the methodological developments of new experimental design and analysis techniques.
The paper illustrates how twolevel experimental designs can be used to identify how the input factors affect the outputs in a chemical process.
Simulations using Matlab/Simulink software are used to study the impact of e.g. process disturbances, closed loop control and autocorrelated data on different experimental arrangements.
The experiments are analysed using a time series analysis approach to identify inputoutput relationships in a process operating in closedloop with multivariate responses. The dynamics of the process are explored and the necessary run lengths for stable effect estimates are discussed. 
Projection Properties of Mixed Level Designs in Fewer Runs
Authors: John Sølve Tyssedal (Department of Mathematical Sciences, The Norwegian University of Science and Technology), Muhammad Azam Chaudhry (Department of Mathematical Sciences, The Norwegian University of Science and Technology)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Design and analysis of experiments
Keywords: Projectivity, Mixed level designs, Twolevel factor, Threelevel factor, Projection properties, Minimum run design
In this paper we checked the projection properties of mixed designs with 2level and 3level to see the output in 12 runs. Our recommended design has the property to estimate all main effects, all two factor interactions and quadratic effects when projected into three factors. 
A Picture is Worth a Thousand Words. A Video is Worth a Thousand Pictures. Applications in Statistics.
Authors: Bernard Francq (Université Catholique de Louvain, University of Glasgow)
Primary area of focus / application: Education & Thinking
Keywords: Teaching statistics, Learning statistics, Animated graph, Videos
Submitted at 20May2015 12:22 by Bernard Francq
Accepted
This presentation will illustrate, with a variety of videos and animated graphs, several tools that may prove useful in the challenge of teaching at different levels and for a wide diversity of students (undergraduate, (post)graduate, CPD, clients in consultancy or multinational conference delegates – to name but a few). Videos and animations are attractive tools to heighten participant engagement with the subject, and can lead to a better understanding of the material and dissemination of knowledge.
During this presentation, the incorporation of videos and animations in teaching will be discussed with several examples:
 Screencast, which may be useful to demonstrate the use of specialised software
 Podcast, to illustrate practical solving of problems
 Animation, to explain a statistical concept or to provide the ‘visual proof’ of a theorem
The following concepts will be discussed:
 Which tools are best suited to which audience.
 How to incorporate these tools into your teaching.
 Is there a risk that these new methods may replace the instructor?
To conclude, it will be shown that ‘if a picture is worth a thousand words, then a video is worth a million’. In tough and abstract fields especially, the use of multimedia can facilitate the visualization of problems from new and entertaining angles. 
The Bayes Factor for Computer Model Validation
Authors: Guillaume Damblin (EDF R&D), Merlin Keller (EDF R&D), Pierre Barbillon (AgroParisTech), Alberto Pasanisi (EIFER), Eric Parent (AgroParisTech)
Primary area of focus / application: Modelling
Secondary area of focus / application: Quality
Keywords: Code validation, Hypothesis testing, Model selection, Intrinsic Bayes factor, Fractional Bayes factor
When the code depends on uncertain parameters, the null hypothesis becomes the composite one that there exists a, socalled “true”, value of the vector of parameters, which yields a perfect adjustment of code predictions to the physical system. The alternative hypothesis is then that each distinct value of the parameter vector defines a nonzero model error function between code predictions and the actual physical system.
Such a test can be performed in a frequentist fashion, by deriving the null distribution of a certain test statistic, such as the sum of squares of the differences between code predictions and physical measurements. Though popular, this approach suffers from several known limitations. In particular, because statistical tests typically control the type I (false rejection) error rate, it is never possible to accept the null hypothesis (i.e., validate the computer model) with a given level of confidence.
To overcome such limitations, we propose to recast this statistical test in the more general framework of model selection, performed in a Bayesian fashion, i.e. by computing the Bayes factor between the null and alternative hypotheses. The Bayes factor can be interpreted as their posterior odds, given equal prior probabilities. Hence, it can be used to effectively “validate” the computer model when it takes large enough values, according for instance to Jeffreys’ scale of evidence.
Under the assumption that the code outputs depend linearly on the uncertain parameters, we show how to compute the Bayes factor. However, the choice of a prior distribution under each hypothesis remains a difficult challenge, as it influences substantially the ensuing value of the Bayes factor. This is especially true for small datasets and vague prior information, a typically setting in the field of computer experiments.
We compare several solutions to this problem, such as the intrinsic and the fractional Bayes factor. Both approaches use a fraction of the dataset to update an initially vague or improper prior into a proper informative posterior distribution. The latter is then used as a prior to compute the Bayes factor, based on the remaining data. These methods are tested on synthetic datasets, and then applied to an industrial test case, dealing with a computer model simulating the electricity produced by a power plant. 
Application of Random Forests to Create TaskBased Control for a Parallel Hybrid Forklift – A Case Study
Authors: Koen Rutten (Flanders Make), Beau Piccart (Flanders Make), Catalin Stefan Teodorescu (Flanders Make), Bruno Depraetere (Flanders Make)
Primary area of focus / application: Mining
Secondary area of focus / application: Process
Keywords: Classification, Control, Vehicle, Casestudy
Submitted at 26May2015 17:38 by Koen Rutten
Accepted