ENBIS: European Network for Business and Industrial Statistics
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ENBIS11 in Coimbra
4 – 8 September 2011 Abstract submission: 1 January – 25 June 2011The following abstracts have been accepted for this event:

Applied Statistics as Competitive Advantage
Authors: Igor Barahona, Alex Riba
Affiliation: Department of Statistics and Operations Research at the Technical University of Catalonia
Primary area of focus / application: Business
Keywords: Applied statistics , Scale to rank companies , Key drivers , Competitive advantage
Submitted at 6May2011 11:39 by IGOR BARAHONA
Accepted
Although having better technological tools, Applied Statistics in business generally has a local impact; because it is only used to make decisions at departmental level. Furthermore, neither it has an impact at strategic level, nor it is an important part of company’s competitive advantage.
How is Applied Statistics expanded within the company? The first step is to find out its actual degree of use. Considering this, the present lecture suggests to design a scale to rank the companies according to the use of statistical methods. The lowest value at the scale is for companies that don´t use any statistical technique and the highest is for those that make statistical practice a distinctive competitive advantage. At the middle are developing companies, who are improving on daily basis.
Additionally, 5 key drivers to raise statistic’s use are introduced: Identification of competitive advantages, senior management support, systematic thinking, the use of data, and inventories of statistical techniques. Any company can ascend in the scale by improving its performance in each of these 5 key drivers.
Finally, results obtained with a sample of Catalan Companies are presented and discussed, and the weight of each driver is calculated. Based on the two with the highest weight, an action plan is proposed. 
Using Simulation for Measurement Uncertainty and other Industrial Applications
Authors: Birger Stjernholm Madsen
Affiliation: Novozymes
Primary area of focus / application: Metrology & measurement systems analysis
Keywords: Simulation , Normal Distribution , Skewness , Kurtosis , Measurement Uncertainty , GUM
Submitted at 6May2011 14:25 by Birger Madsen
Accepted
In this presentation, I will illustrate some examples of simulation, e.g.:
1. Illustrating the shape of the normal distribution for different sample sizes.
The normal distribution assumption is crucial in many contexts, e.g. when calculating process capability indices.
Many people are surprised, when an “obvious nonnormal distribution” is tested for normality and accepted, when the sample size is small... On the other hand, many people are surprised, when an “obvious normal distribution” is tested for normality and rejected, when the sample size is large...
2. Calculating limits for skewness and kurtosis, when testing for normality.
A useful tool is calculation of the skewness and kurtosis. These descriptive statistics also provide information on how to “fix” a nonnormal distribution, e.g. by a logtransformation (large skewness) or removing outliers (large kurtosis).
The big question is: How large deviations from 0 are acceptable? Simulation can be used to indicate, when the asymptotic results are valid and to calculate maximum acceptable deviation from 0 for both skewness and kurtosis for small sample sizes.
3. Using simulation in calculation of measurement uncertainty.
Measurement uncertainty is often calculated in an uncertainty budget using the “Bottom up” (or GUM) approach. The assumptions of linearity and normal distributed output in are crucial. If these assumptions are dubious, the reported uncertainty may be of little value.
I suggest an alternative approach, in which simulation replaces calculation of the combined and expanded standard uncertainty. This gives an overview of the actual distribution and a supplement to the traditional approach! 
Estimation of a Normal Process Variance from Measurements with Large RoundOff Errors
Authors: BensonKarhi Diamanta and Schechtman Edna.
Affiliation: the Open University of Israel and BenGurion University of the Negev, Israel
Primary area of focus / application: Metrology & measurement systems analysis
Keywords: calibration curve , discrete scale , MLE , Sheppard correction , SPC , systematic error
Submitted at 8May2011 14:04 by Diamanta BensonKarhi
Accepted
roundingoff may safely be ignored for purposes of statistical inference. The
importance of roundoff is determined by the ratio between the standard deviation σ
and the instrument's scale step h, given by δ =σ / h . In this study we estimate σ
when δ is small (δ < 0.5 ) using a calibration model. The calibration estimators are
compared with a naïve estimator, Sheppard's correction and the maximum likelihood
estimator (MLE), using simulation. We find that the calibration estimator can
significantly improve the estimation in terms of MSE and bias, especially under
circumstances where the other methods are not accurate or cannot provide a
solution. 
Acceptance Sampling Plans by NonGaussian variables with Simultaneous Specification Limits
Authors: Elisabete Carolino, Isabel Barão
Affiliation: ESTeSL, IPL, Portugal / DEIO, FCUL, Portugal
Primary area of focus / application: Process
Keywords: acceptance sampling , nonGaussian random variables , asymmetry , simultaneous specification limit , quality control
Submitted at 9May2011 12:24 by Elisabete Carolino
Accepted
AS is used to inspect either the output process – final product – or the input – initial product. The purpose of AS is to determine a course of action, not to estimate lot quality. AS prescribes a procedure that, if applied to a series of lots, will give a specified risk of accepting lots of given quality. In other words, AS yields quality assurance. An AS plan merely accepts and rejects lots, considering sampling information.
The AS by variables is based on the hypothesis that the observed quality characteristics follow a known distribution, namely the Gaussian distribution (classical case of the AS by variables – treated in classical standards). This is sometimes, however, an abusive assumption, that leads to wrong decisions.
AS for nonGaussian, mainly asymmetrical variables, is thus relevant. When we have a nonGaussian distribution we can build specific AS plans associated with that distribution. In acceptance sampling we can built plans with a single specification limit (upper or lower) or simultaneous specification limits  studied in this work. In the literature there are few studies on simultaneous limits on acceptance sampling.
In this work we will address the problem of determining acceptance sampling plans by variables with simultaneous specification limits for Gamma distributions (Exponential, in particular), the results being compared to the Gaussian case.
References
ANSI/ASQC Z1.92008 2008. Sampling procedures and tables for inspection by variables for percent nonconforming. ASQ, Milwaukee, WI, EUA.
ANSI/ASQC Z1.91980 1980. Sampling procedures and tables for inspection by variables for percent nonconforming. ASQ, Milwaukee, WI, EUA.
Carolino, E., Casquilho, M., Barão, M. 2007. Amostragem de aceitação para uma variável assimétrica: a Exponencial. Actas do XIV Congresso Anual da Sociedade Portuguesa de Estatística, 281292.
Duncan, A. J. 1986. Quality Control and Industrial Statistics. 5.th edition. IRWIN, USA.
Figueiredo, F. O. 2002. Controlo Estatístico da Qualidade e Métodos Robustos. Dissertação para obtenção do grau de Doutor em Estatística e Investigação Operacional, Especialidade em Probabilidades e Estatística. FCUL, Portugal.
Figueiredo e Gomes. 2002. Transformação de Dados em Controlo Estatístico de Qualidade. Novos Rumos em Estatística. Sociedade Portuguesa de Estatística, 235245.
Box, G.E.P. and Cox, D.R. 1964. An Analysis of Transformations. Journal of the Royal Statistical Society, 211252.
Gomes, M. I., Figueiredo, F., Barão, M. I., 2010. Controlo Estatístico da Qualidade, 2ª edição revista e aumentada, Edições SPE.
Grant, E. L. and Leavenworth, R. S. 1996. Statistical Quality Control, 7.th edition. McGrawHill, New York, NY, EUA.
Guenther, William C. 1977. Sampling inspection in statistical quality control. First published. Whitstable Litho Ltd, GB.
ISO 39511:2008. 2008, ISO standard.
Levinson, W. 1997. Watch out for nonnormal distributions of impurities. Chemical Engineering Progress. May, 7076.
Montgomery, D. C. 2004. Introduction to Statistical Quality Control, 5.th edition. John Wiley and Sons, New York, NY, EUA.
Schilling, E. G. and Neubauer. D. V. 2009. Acceptance Sampling in Quality Control. 2nd edition. Chapman & Hall/CRC, USA.
Wetherill, G. B. and Brown, D. W. 1991. Statistical Process Control. Chapman and Hall, London, UK. 
Customer satisfaction and quality of service: analysis of coherence and latent variables for the University ’s canteen service
Authors: Rossella Berni Department of Statistics “G.Parenti”, University of Florence, Italy; Alessandra Durio Department of Statistics and Applied Mathematics “D. de Castro”, University of Torino, Italy
Primary area of focus / application: Business
Keywords: Customer satisfaction; , Confirmatory factorial analysis , Structural equation models , LogLinear models
In order to identify the latent variables and the measurement groups, we resort to a preliminary confirmatory factorial analysis that gives us also some information on the covariance between the latent variables themselves. The models of structural equations are identified; the systems of structural equations are defined using the standard notation stated by the Lisrel methodology.
According to the evaluation expressed by the students interviewed on some crucial aspects of the perceived quality, we ratify the causal connections between the latent variables of the model and, at the same time, we provide for a dimension of quality perceived by the customers.
Furthermore, the nonresponses and the coherence of respondents is analysed through hybrid loglinear models in order to evaluate the burden of missing values and the individual response in two occasions of the same interview: exante and expost. 
Using Automated Adaptive Experimentation to Achieve Constrained Optimisation
Authors: Chris Marley, Dave Woods, Sue Lewis
Affiliation: University of Southampton, UK
Primary area of focus / application: Design and analysis of experiments
Keywords: Designed experiments , Expected improvement , Gaussian process , Latin hypercube
Submitted at 9May2011 17:47 by Chris Marley
Accepted
Recent advances in technology mean that it is sometimes possible to automate a series of chemical reactions to be performed one at a time, without the need for any reprogramming of machinery or further intervention of a chemist. This is known as “continuous flow mode”, and enables these systems to be left running continuously (for instance overnight), hence reducing costs.
To enable such systems to be fully utilised for process optimisation, the reactions to be performed need to chosen adaptively and automatically. We propose a flexible approach for choosing these reactions based on the Expected Improvement criterion, often used for computer experiments. This general framework allows us to adaptively select design points with the goal of optimising an objective function subject to constraints. We present a simple example incorporating the choice of initial design, the adaptive selection of design points, model fitting and sensitivity analysis.