ENBIS: European Network for Business and Industrial Statistics
Forgotten your password?
Not yet a member? Please register
ENBIS12 in Ljubljana
9 – 13 September 2012 Abstract submission: 15 January – 10 May 2012The following abstracts have been accepted for this event:

Interpretation of Shewhart Control Charts: New Opportunities
Authors: Yuri Adler (VEI), Olga Maksimova (VEI), Vladimir Shper (VEI)
Primary area of focus / application: Education & Thinking
Keywords: Shewhart control chart, special cause, interpretation rules, statistical thinking
This work shows that within the framework of such approach the interpretation of signals on Shewhart charts can be different from commonly used. For example, the lack of points beyond 3σ level can mean not the problems of rational subgroupping as it is usually being explained in literature but can be caused by a change of the distribution function (DF). Or, another example: a point beyong the control limits may signal not about the change of average or standard deviation but about the DF's change again. As a result the technicalities in using SCC decrease and the gap between our models and reality diminish as well. This means that SCC may loose some characteristics of exact mathematical models but can gain some new operational opportunities. 
A Cointegration Approach to Monitoring Nonstationary Multivariate Processes
Authors: Bart De Ketelaere (Katholieke Universiteit Leuven)
Primary area of focus / application: Process
Keywords: statistical process control, nonstationarity, multivariate, cointegration
Submitted at 15Apr2012 21:46 by Bart De Ketelaere
Accepted

When the Surgeon Does Not Cut Straight
Authors: Bernard Francq (Université Catholique de Louvain)
Primary area of focus / application: Consulting
Keywords: accuracy resecting bone tumor, mixed model, parametric bootstrap, delta method
Through this presentation, I will explain and focus on the statistical details by answering questions such as: How to measure the "quality" of a resecting bone tumors? How to analyze the results using a mixed model with fixed, random and nested effects? And the most important issue for surgeons: what is the probability (+ CI around the estimated probability) to get an error greater than X millimeter in a resecting bone tumor for each technology (navigated or nonnavigated) in comparison to the ‘target’ cut? This question is very simple to understand but less to resolve. We will then see the usefulness of the variancecovariance matrix of the variances of random effects, the delta method and the parametric bootstrap in a mixed model. 
How to Accept the Equivalence of Two Measurement Methods?
Authors: Bernard Francq (Université Catholique de Louvain), Bernadette Govaerts (Université Catholique de Louvain)
Primary area of focus / application: Metrology & measurement systems analysis
Keywords: measurement methods comparison, (correlated)errorsinvariablesregressions, Bland and Altman's plot, tolerance intervals
Submitted at 15Apr2012 23:00 by Bernard Francq
Accepted
To compare two measurement methods, a certain characteristic of a sample can be measured by the two methods in the experimental domain of interest. Firstly, to statistically test the equivalence of measurement methods, the measures given by the reference method and the alternative one can be modelled by an errorsinvariables regression (a straight line). The estimated parameters are very useful to test the equivalence. Indeed, an intercept significantly different from zero indicates a systematic analytical bias between the two methods and a slope significantly different from one indicates a proportional bias. A joint confidence interval can also be used to test the equivalence. Secondly, the differences between paired measures can be plotted against their averages and analyzed to assess the degree of agreement between the two measurement methods by computing the limits of agreement. This is the very well known and widely used Bland and Altman’s approach.
We review and compare the two methodologies, the Bland and Altman’s approach and the errorsinvariables regression, with simulations and real data. Then, we’ll propose some improvements like the tolerance interval on the Bland and Altman’s approach and how to accept the equivalence by specifying a practical difference threshold on the results given by two measurement methods in the approach of errorsinvariables regression. We’ll conclude by explaining whether it’s more suitable to regress in a XY plot or to regress the differences with their averages like in a Bland and Altman plot to assess the equivalence of measurement methods. 
Classification and Regression Trees in the Process Industry
Authors: Stefanie Feiler (AICOS Technologies AG), Philippe Solot (AICOS Technologies AG)
Primary area of focus / application: Mining
Keywords: data mining, process industry, CART (Classification and Regression Trees), consulting experience, software usage
Submitted at 15Apr2012 23:54 by Stefanie Feiler
Accepted
We here want to share our experience in this field. For instance, we have used Classification and Regression Trees for comparing the performance of different reactors used in parallel in a chemical plant, or for improving a pharmaceutical production. The related technique of Random Forests has been valuable for assessing parameter importance. Our examples show that the tool is indeed well suited also for analysing process industry data. As software tools, we either work with R, or use the commercial CART/SPM of Salford Systems (the original developers of the method). We therefore shortly present both tools, discuss their differences, and address some important practical aspects (e.g. dealing with missing values / high level categorical parameters). 
How to Market Statistics
Authors: Winfried Theis (Shell Global Solutions BV)
Primary area of focus / application: Consulting
Keywords: marketing, communication, consultancy, promoting statistics