ENBIS: European Network for Business and Industrial Statistics
Forgotten your password?
Not yet a member? Please register
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:

Resilience MultiParameter Estimation
Authors: Alistair Forbes (National Physical Laboratory)
Primary area of focus / application: Metrology & measurement systems analysis
Secondary area of focus / application: Other: IMEKO TC21 Session organised by Franco Pavese
Keywords: Estimation, Metrology, Resilience, Uncertainty
Submitted at 30Apr2015 13:17 by Alistair Forbes
Accepted

Development of an Open Application for Teaching Statistics: Conception and Results
Authors: Lluis MarcoAlmagro (UPC Universitat Politécnica de Catalunya, Barcelona Tech), Eduard Serrahima (UPC Universitat Politécnica de Catalunya, Barcelona Tech), Xavier TortMartorell (UPC Universitat Politécnica de Catalunya, Barcelona Tech), Pere Grima (UPC Universitat Politécnica de Catalunya, Barcelona Tech), Lourdes Rodero (UPC Universitat Politécnica de Catalunya, Barcelona Tech)
Primary area of focus / application: Education & Thinking
Keywords: Statistics teaching, Data analysis, Data visualization, Free software, Projectbased learning
The whole development cycle will be explained in the presentation: first list of user requirements, design decisions, implementation and testing. The application is developed in R language and uses the shiny package for the user interface. Although the application obviously allows the analysis of real data through graphs and statistical methods, its main objective is facilitating the acquisition of statistical concepts. To accomplish this, menus, configuration options and results are presented in a way that fosters reflection on basic statistical ideas. The application has a special emphasis on industrial statistics.
Initial feedback from users testing the application will be exposed, and ways to freely access and use the application will be presented. 
Fostering Diversity in Measurement Science
Authors: Franco Pavese (formerly INRIM, Torino)
Primary area of focus / application: Metrology & measurement systems analysis
Secondary area of focus / application: Consulting
Keywords: Truth, Certainty, Uncertainty, Probability, Risk, Single thought, Diversity
Submitted at 30Apr2015 15:22 by Franco Pavese
Accepted
POSTER 
MOOC and Teaching: An Inevitable Evolution for Lifelong Training?
Authors: François Husson (Agrocampus Ouest), Magalie HouéeBigot (Agrocampus Ouest)
Primary area of focus / application: Other: Realization of MOOCs  technology, content and funding opportunities
Keywords: MOOC, Teaching, Food industry, Lifelong training, Sensometry
In this presentation, we will take the example of a MOOC in sensometry, ie on the statistical treatment of sensory data, to see how we imagine using MOOCs for lifelong training in food industry.
Sensometrics is used in companies such as Nestlé, Danone, Coca Cola, Renault, EDF, etc. but also in smaller companies. This discipline uses well known statistical methods (design of experiments, analysis of variance, principal component analysis, regression, etc.) but on specific data which imposes to adapt the methodology (inclusion of carryover effect in the experimental design, confidence ellipses in PCA, special use of regression in preference mapping, etc.). Data collections evolve which imply to propose new methods or methodologies. It is therefore necessary to have a lifelong training.
We will show in this presentation how the MOOC was built and why it is adapted for learners in food industry as a lifelong training. We will also discuss some ways to adapt a MOOC at lower cost so that companies can have their specific training. Some activities (videos, quizzes, exercises, discussion forum) may be common to everybody, it would be the current MOOC, and others may be restricted to learners groups of a company.
The MOOC promotes healthy competition between employees of a same company and, moreover, one of the main interest is that learners can return to the MOOC when they need it. This individual training is a guarantee of success because the willingness to learn is present. How many people who followed a training session did not apply what they learned immediately and forgot it when they needed it? Thanks to a MOOC, it is possible to come back to courses already followed, as if we had a remake of a training session done 2 years ago. It would be embarrassing to ask his boss to follow the same session training twice. With a MOOC, useless to ask, everything is available! 
A MOOC for Everyone ... Including the Business World!
Authors: Magalie HouéeBigot (Agrocampus Ouest), François Husson (Agrocampus Ouest)
Primary area of focus / application: Other: Presentation session on MOOCs
Keywords: MOOC, Teaching, Exploratory multivariate data analysis, Elearning
Submitted at 30Apr2015 15:58 by Magalie HouéeBigot
Accepted
Learners work in research institutes (INRA, INSERM, IRD, etc.), universities (French but also Tunisian, Turkish, Zairian, etc.) but also in different industrial and business companies. Instead of organizing an educational session on exploratory data analysis, some companies invited their employees to enroll in our MOOC. Therefore the MOOC was used as a continuous training by some companies. A satisfaction survey highlighted that learners enjoyed the MOOC, the most enthusiastic learners being the oldest.
During the presentation, we will provide answers to the following question: Why can a MOOC with a diverse audience, satisfy all the learners (students, teachers, industrialists)? Because the MOOC was thought to be attended in different ways according to the available time, because the mathematical formalism was reduced, because methods were explained and illustrated with numerous examples , etc. Because we also took care of the form: the videos, the sound were good, the scripts were available, the software was free and relevant, our presence in the discussion forum was permanent: in short, we took care of the learners.
Finally, we will answer a second question: Does the diversity of the audience enrich a MOOC? 
Impact of Autocorrelation on Principal Component Analysis
Authors: Erik Vanhatalo (Luleå University of Technology), Murat Kulahci (Technical University of Denmark and Luleå University of Technology)
Primary area of focus / application: Process
Keywords: Principal Component Analysis (PCA), Autocorrelation, Statistical Process Control (SPC), Simulation
Submitted at 30Apr2015 16:33 by Erik Vanhatalo
Accepted
One concern with automated data collection schemes is the increased frequency of sampling which inevitably introduces serial dependence (autocorrelation) in the collected data. Jollife (2002, p. 299) states that “when the main objective of PCA is descriptive, not inferential, complications such as nonindependence does not seriously affect this objective.” If PCA is used within SPC, i.e. for inferential purposes and the scores on principal components are monitored, serial dependence in the original variables is expected to affect monitoring performance. This can be explained by the fact that the principal components are linear combinations of autocorrelated variables and hence also the principal component scores will be autocorrelated.
Traditional SPC techniques assume independent data in time. However, this assumption is becoming increasingly unrealistic in today’s applications. The issue of autocorrelation in univariate SPC charts has been discussed at length in the literature. Clearly less research has been reported on the effects of and remedies for autocorrelation in multivariate SPC (MSPC) charts. Nevertheless, the potential solutions that emerge in the literature are: [1] to adjust the control limits of the charts by estimating the “true” process standard deviation, and [2] to use a residuals approach where a univariate or multivariate time series model is fitted to the data and univariate or MSPC control charts are applied to the residuals. A specific solution suggested for PCA is “dynamic PCA”: to apply PCA to a data matrix including timelagged versions of the original variables, see Ku et al. (1995).
Although potential solutions of the autocorrelation problem in MSPC and for PCA have been previously presented, it seems to us as though the impact of autocorrelation on PCAbased SPC is not well documented.
The purpose of this paper is to investigate and illustrate the impact of autocorrelation on the descriptive ability of PCA as well as on the shift detection ability using PCAbased SPC. We illustrate the impact of autocorrelation on the descriptive ability of PCA by visualizing and discussing simulations of a bivariate case from a vector autoregressive model. Through further simulations we also show that the false alarm rate and shift detection ability for PCABased SPC may be substantially affected by autocorrelation.
References:
Jolliffe, IT. (2002). Principal Component Analysis (2nd ed.), SpringerVerlag: New York, NY.
Ku, W, Storer, RH, Georgakis, C. (1995). Disturbance detection and Isolation by Dynamic Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems 30: 179196.