ENBIS: European Network for Business and Industrial Statistics
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ENBIS7 in Dortmund
24 – 26 September 2007The following abstracts have been accepted for this event:

Taut String as an Alternative to Empirical Distribution Estimators for System Loads in Logistics
Authors: Sonja Kuhnt (1) and Christoph Schürmann(2)
Primary area of focus / application:
Submitted at 24Jun2007 09:02 by
Accepted
conclusions can only be derived if the simulation model is a good image of reality. System loads such as arrival
times of customers or order quantities are often generated from stochastic distributions. The empirical distribution
function is the straightforward choice to derive such a distribution from an observed reallife data set if no information
concerning the underlying model structure is given. As an alternative we consider a distribution estimator based on taut
string methods. The resulting distribution function generally has fewer knots than the empirical distribution function,
leading to a reduced simulation effort. We conduct a simulation study to compare the behaviour of empirical distributions
and taut string estimates with estimates of normal distributions and of distributions from the true family. Distribution
families relevant for system loads as exponential, normal, uniform and tdistribution are treated. The distance between
the estimated and true distribution is measured in terms of KolmogorovSmirnov and quantile distances.
Affiliations:
(1) Department of Mathematics and Computer Science
Technical University Eindhoven
Eindhoven, The Netherlands
(2) Department of Statistics
University of Dortmund
Dortmund, Germany
Specifics: poster presentation 
Statistical Issues in Search Engine Marketing
Authors: Steinberg, D.M. (1), Matas, A. (2), Schamroth, Y. (2)
Primary area of focus / application:
Submitted at 24Jun2007 09:03 by
Accepted
sector. Effective use of SEM requires analytical methodologies that will support decision making. We present here methods
we have applied and created specifically for this zone of eCommerce, that allow for effective planning of advertising
campaigns, based on the results from previous adverts. These methods include modeling the effect of a particular bid
on CostperClick, Position, Clickthroughrate, Impressions, Conversions and ultimately Profit. Once modeled, these
relationships are used to optimize profit at the keyword level. We describe challenges that arise in these efforts,
particularly those stemming from the dynamically changing environment of competitor behavior, and the corresponding
solutions we have developed. We apply multiple inference techniques to quantify estimated error allowing for accurate
decision making per keyword. Finally, we present operations research solutions allowing a particular campaign to be
globally optimized.
Affiliations:
(1) Department of Statistics & OR, Tel Aviv University
(2) Media Boost, LTD, Israel 
Phase I statistical control of key indicators in health care
Authors: Stefano Barone (1), Arturo Caranna (2), Valeria Fonti (3)
Primary area of focus / application:
Submitted at 24Jun2007 13:57 by
Accepted
constantly measure, monitor and control the quality of their services and related costs. The identification of
quantitative indicators for the key processes is a first necessary step. In a preceding work, the authors presented
an approach to define a set of indicators and its successful implementation at ''IRCCS Oasi Maria SS.'', a noprofit
organization for care and research on mental retardation and brain ageing.
In this article the first results of the statistical analysis of indicators data is presented. Collected data concern
twoyears of activity. They were analyzed by the perspective of statistical process control. Particularly, since they
were the first analyzed data, they were used to set up the statistical control framework (phase I).
The main output is a restricted set of control charts, giving top management the possibility to monitor on a
continuous basis the vital processes of the organization, having effect on the quality of provided care, related
services and customer (patient/family) satisfaction.
Keywords:
Performance indicators in heath care, phase I statistical process control, statistical monitoring, key indicators.
Affiliations:
(1) Assistant Professor, University of Palermo, stbarone@dtpm.unipa.it,
Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale,
Viale delle Scienze, Palermo  Italy
(2) Dr., IRCCS Oasi Maria SS., acaranna@oasi.en.it, Via Conte Ruggero, Troina  Italy
(3) PhD. Student, University of Palermo, vfonti@dtpm.unipa.it,
Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale,
Viale delle Scienze, Palermo  Italy

Spiralling in BTA DeepHoleDrilling: Combining Statistical and Physical Models
Authors: Nils Raabe, Dirk Enk, Oliver Webber, Claus Weihs, and Dirk Biermann (University of Dortmund, Dortmund, Germany)
Primary area of focus / application:
Submitted at 24Jun2007 13:59 by
Accepted
disturbance called spiralling. Spiralling can be explained by the
convergence of bending eigenfrequencies with multiples of the rotational
frequency. In former work we therefore proposed a combination of
statistical and physical models describing the course of the
eigenfrequencies.
In our current work we discuss how this model can be used to predict
the occurrence of spiralling. As recent experiments showed the
crossing of multiples of the rotational frequency and bending
eigenfrequencies yields a resonance effect on the specific multiple.
By connecting the amplitude of this resonance to quality
measurements of the workpiece this amplitude can be used as a
quantification of spiralling. This transforms the binary problem of
the decision between 'spiralling' and 'no spiralling' into a
continuous problem and opens up the opportunity of a more explicit
investigation of spiralling and its impact on the workpiece. 
Statistical Modelling of Springback Simulation
Authors: Hilke Kracker (1), Marco Gösling (2)
Primary area of focus / application:
Submitted at 24Jun2007 14:02 by
Accepted
many
engineering applications physical processes can be simulated by complex
computer models also called computer experiments. Deep drawing is such
an
example, where forming and springback can be simulated using finite
element
analysis.
Such simulations are usually very complex: they are based on many
input parameters and calculation of a response can be
timeconsuming. Hence, even with a simulation at hand we do not
entirely understand the input – output relationship at once.
An approach to gain more insight into the input – output
relationship is to build a statistical model based on a restricted
number of evaluations of the computer code. In this talk we will
compare the adequacy of different statistical methods for
approximating the springback simulation for a class of workpiece
geometries. We discuss the adequacy of the statistical models in
terms of their approximation accuracy and interpretability.
Affiliations:
(1) University of Dortmund,
Department of Statistics, 44221 Dortmund,
Germany
(2) University of Dortmund,
Institute of Forming Technology and Lightweight Construction, 44221
Dortmund,
Germany

Residual Analysis in Experimental Design
Authors: Janet Godolphin (University of Surrey, Guildford, Great Britain)
Primary area of focus / application:
Submitted at 24Jun2007 14:15 by
Accepted
Despite the success of these diagnostic procedures, however, it is sometimes difficult to obtain exact tests of critical model assumptions because, unlike the true errors, least squares residuals are correlated and have unequal variances. In this presentation it is shown how to define and use a set of uncorrelated residuals with a common variance to examine these assumptions.
Such techniques are demonstrated to be of particular value in industrial experimentation when the nature of the experimental runs means that observations are indexed by time. It is suggested that the resulting test procedures should complement the usual methods. The construction of this set of uncorrelated residuals is established and shown to be straightforward for practical use when checking for model adequacy.
Specifics: Author prefers to give a talk.