ENBIS: European Network for Business and Industrial Statistics
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ENBIS14 in Linz
21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014The following abstracts have been accepted for this event:

IntegerValued Autoregressive Models for Counts Showing Underdispersion
Authors: Christian Weiß (Department of Mathematics and Statistics, Helmut Schmidt University Hamburg)
Primary area of focus / application: Modelling
Keywords: Count data time series, Good distribution, INAR(1) model, Lerch distribution, Underdispersion, Weighted Poisson distribution
Submitted at 23Jan2014 20:31 by Christian Weiß
Accepted
Weiß, C.H.: Integervalued Autoregressive Models for Counts Showing Underdispersion.
Journal of Applied Statistics 40(9), pp. 19311948, 2013. 
Perceiving the World through Statistics: Some Dimensional Thoughts
Authors: Michel Lutz (Lutz Industries Research & Consulting, OCTO Technology), Rodolphe Le Riche (CNRS, ENSMSE)
Primary area of focus / application: Education & Thinking
Keywords: Epistemology, Dimensions reduction, Statistical thinking, Knowledge generation
Submitted at 24Jan2014 15:24 by Michel Lutz
Accepted

Statistics in Context: Grounding Quantitative DecisionAid in Business Needs
Authors: Michel Lutz (Lutz Industries Research & Consulting, OCTO Technology), Xavier Boucher (Ecole des Mines de SaintEtienne)
Primary area of focus / application: Business
Keywords: Decisionaid process, Contextualization, Quantitative and qualitative modelling, Semiconductor industry, Applied statistics
Submitted at 25Jan2014 19:24 by Michel Lutz
Accepted

Quickly Communicating DoE to Subject Matter Experts
Authors: Matthew Barsalou (BorgWarner Turbo Systems Engineering GmbH)
Primary area of focus / application: Design and analysis of experiments
Keywords: Design of Experiments, Fractional factorial, Terminology, Basics
There may be problems when discussing a DoE with a subject matter expert who lacks a basic understanding of DoE. A subject matter expert may not need to know the word “response variable”; however, if they don’t understand the concept, they can’t help to identify the correct response variable for a DoE.
This talk will present a simple concept for teaching engineers and other subject matter experts the basic concepts needed for them to contribute to a DoE. It will include explanations for factors, response variable, levels, experimental runs, blocking, randomization and resolution that are easy to communicate to those without a background in DoE and will also explain how to give a one minute DoE “elevator speech” using a paper helicopter. 
Mosaic Plots for Visualizing Confounding Properties of Factorial Designs
Authors: Ulrike Grömping (Beuth Hochschule für Technik Berlin)
Primary area of focus / application: Design and analysis of experiments
Keywords: Factorial experiment, Confounding, Design of Experiments, Mosaic plot
Mosaic plots are a tool for visualizing the structure of multidimensional frequency tables. They are usually attributed to Hartigan and Kleiner (1981 and 1984) and have met with increased attention lately (e.g. Hofmann 2003, Wickham and Hofmann 2011). A frequency table of several factors in a factorial design captures the confounding structure among these factors. This talk proposes to use mosaic plots of such frequency tables for visualizing the degree of confounding. Mosaic plots are particularly useful for design and analysis of orthogonal main effects plans, which are usually of resolution III only.
The plots are available in open source software: the R package DoE.base (Grömping 2013) creates them, based on the Rpackage vcd (Meyer and Hornik 2006).
References
Grömping, U. (2013). DoE.base: Full factorials, orthogonal arrays and base utilities for DoE packages. R package version 0.252. In R Core Team (2013). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
Hartigan, J. A., and Kleiner, B. (1981). Mosaics for contingency tables. In W. F. Eddy (Ed.), Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface. New York: SpringerVerlag.
Hartigan, J. A., and Kleiner, B. (1984). A mosaic of television ratings. The American Statistician 38, 3235.
Hofmann, H. (2003). Constructing and Reading Mosaicplots. Computational Statistics and Data Analysis 43, 565–580.
Meyer, D., Zeileis, A. and Hornik, K. (2006). The Strucplot Framework: Visualizing Multiway Contingency Tables with vcd. Journal of Statistical Software 17, Issue 3, 1:48.
Wickham, H. and Hofman, H. (2011). Product Plots. IEEE Transactions on Visualization and Computational Graphics 17, 22232230. 
Half Normal Effects Plots in the Presence of a Few Error Degrees of Freedom
Authors: Ulrike Grömping (Beuth Hochschule für Technik Berlin)
Primary area of focus / application: Design and analysis of experiments
Keywords: Half normal effects plot, Lenth’s method, (Almost) unreplicated experiment, Orthogonalization of error space, Augmented half normal effects plot
References
Daniel, C. (1959). Use of Halfnormal effects plots in Interpreting Two Level Experiments. Technometrics 1, 311–340.
Daniel, C. (1976). Application of Statistics to Industrial Experimentation. Wiley, New York.
Edwards, D. J. and Mee, R. W. (2008). Empirically Determined pValues for Lenth tStatistics. Journal of Quality Technology 40, 368–380.
Grömping, U. (2013). DoE.base: Full factorials, orthogonal arrays and base utilities for DoE packages. R package version 0.252. In R Core Team (2013). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
Larntz, K. and Whitcomb, P. (1998). Use of replication in almost unreplicated factorials. Manuscript of a presentation given at the 42nd ASQ Fall Technical conference in Corning, New York. Downloaded 4/26/2013 at http://www.statease.com/pubs/useofrep.pdf.
Lenth, R.V. (1989). Quick and easy analysis of unreplicated factorials. Technometrics 31, 469–473.