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Karl Dinkelmann, Peter Granda and Michael Shove

.1002/9781119041702.ch17 . De Waal, T. 2013. “Selective Editing: A Quest for Efficiency and Data Quality.” Journal of Official Statistics 29(4): 473–488. Doi: https://doi.org/10.2478/jos-2013-0036 . Fellegi, I.P. and D. Holt. 1976. “A Systematic Approach to Automatic Edit and Imputation.” Journal of the American Statistical Association 71: 17–35. Doi: https://doi.org/10.1080/01621459.1976.10481472 . Groves, R.M. and L. Lyberg. 2010. “Total Survey Error: Past, Present, and Future.” Public Opinion Quarterly 74(5): 849–879. Doi: https://doi.org/10.1093/poq

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Matthias Schnetzer, Franz Astleithner, Predrag Cetkovic, Stefan Humer, Manuela Lenk and Mathias Moser

. Scannapieco. 2006. Data Quality: Concepts, Methodologies and Techniques. New York: Springer. Berka, C., S. Humer, M. Lenk, M. Moser, H. Rechta, and E. Schwerer. 2010. “A Quality Framework for Statistics based on Administrative Data Sources using the Example of the Austrian Census 2011.” Austrian Journal of Statistics 39: 299-308. Berka, C., S. Humer, M. Lenk, M. Moser, H. Rechta, and E. Schwerer. 2012. “Combination of Evidence from Multiple Administrative Data Sources: Quality Assessment of the Austrian Register-Based Census 2011.” Statistica

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Brian Foley, Ian Shuttleworth and David Martin

. Fotheringham, P. Rees, P. Boyle, and J. Stillwell. 1998. The Determinants of Migration Flows in England: A Review of Existing Data and Evidence. Report for the Department of the Environment, Transport and the Regions, UK. Available at: http://www.geog.leeds.ac.uk/publications/DeterminantsOfMigration/report.pdf (accessed August 2017). Duke-Williams, O. 2009. “The Geographies of Student Migration in the UK.” Environment and Planning A 41(8): 1826-1848. Doi: http://dx.doi.org/10.1068/a4198. Eurostat. 2003. Quality Assessment of Administrative

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Piotr O. Czechowski, Artur J. Badyda, Grzegorz Majewski, Aneta Oniszczuk-Jastrząbek, Andrzej K. Kraszewski, Wioletta Rogula-Kozłowska and Tomasz Owczarek

Pollutants on Lung Function: Warsaw Study. Adv. Exp. Med. Biol. 788, 229–235. BADYDA A., GRELLIER J., DĄBROWIECKI P. 2016: Ambient PM2.5 Exposure and Mortality Due to Lung Cancer and Cardiopulmonary Diseases in Polish Cities. Adv. Exp. Med. Biol. doi. 10.1007/5584_2016_55. CALORI G., FINZI G., TONEZZER C. 1994: A decision support system for air quality network design. Environ. Monit. Assess. 33, 101–104. CZECHOWSKI P.O. 2013: New methods and models of data measurements quality in air pollution monitoring networks assessment. Gdynia Maritime

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References [1] GKÚ 2010. Cenník produktov. Bratislava: GKÚ, 2010, [cit. 4/2013][online] Dostupné na internete: [2] http://www.gku.sk/docs/cennik2010+dod_1-9.pdf [3] INSPIRE Thematic Working Group ELEVATION, 2011. Data Specification on Elevation - Draft Guidelines [online]. 2013, version 3.0rc3 [4] ISO/TS 19138:2006, Geographic information -- Data quality measures. [5] ISO/TS 19157:2012 Geographic information - Data quality(DRAFT) [6] Smernica Európskeho parlamentu

Open access

N.Sz. Suba and Şt. Suba

variables. Part 1: Specification for single sampling plans indexed by acceptance quality limit (AQL) for lot-by-lot inspection for a single quality characteristic and a single AQL. Jakobsson, A. Giversen, J., 2009. Guidelines for Implementing the ISO 19100 Geographic Information Quality Standards in National Mapping and Cadastral Agencies. http://www.eurogeographics.org/documents/Guidelines_ISO19100_Quality.pdf (view at 28 feb. 2015). Joos, G., 2006. Data quality standards. XXIII FIG Congress, Munich, Germany, october 8-13, 2006. https

Open access

Ewa Dudek and Michał Kozłowski

Abstract

The paper presents the concept of a method ensuring quality of aeronautical data. European Union (among others UE 73/2010) as well as international (among others ICAO Annex 15) regulations introduce a number of requirements regarding the quality and safety of aeronautical data. Those directives set up a complementary regulations system. However with their objective and scope they determine mainly the specifications and requirements that are to be implemented and compatible. Mentioned regulations also refer to selected international standards (e.g. ISO 19157), focused on quality and safety of geographic data and information. Nevertheless within the scope of considered regulations and norms no algorithms and methods of ensuring required quality in the process of aeronautical data collection and processing were determined. Taking into account the identified needs, authors proposed the application of statistical method for process quality management – six-sigma.

Open access

Piotr Czechowski, Artur Badyda and Grzegorz Majewski

., Pruska, K., & Wagner, W. (1998). Wnioskowanie przy nieklasycznych założeniach. Wydawnictwo Uniwersytetu Łódzkiego, Łódź 1998. [10] Domański, C., & Pruska, K. (2000). Nieklasyczne metody statystyczne; PWE; Warszawa 2000. [11] EPA, Data Quality Assessment: A Reviewer’s Guide, EPA QA/G-9R 2000 & EPA/240/B-06/002. 2006. [12] EPA.; Guidance for Data Quality Assessment; Practical Methods for Data Analysis; EPA QA/G-9 QA00 Update; EPA/600/R-96/084.2000, 2007. [13] F inzi, G., Calori, G., & Tonezzer, C. (1993). A

Open access

Michela Seghezzi, Sabrina Buoro, Giulia Previtali, Valentina Moioli, Barbara Manenti, Ramon Simon-Lopez, Cosimo Ottomano and Giuseppe Lippi

List of abbreviations B ALG , alignment bias based on intra-individual biological variation B APS , analytical performance specification for bias CV APS , analytical performance specification for imprecision CV I , within-subject biological variation CI, confidence interval CPD, cell population data FSC, forward scatter HA, hematological analyzers HFLC, high fluorescence lymphocytes cell IAF, instrumental alignment factor LY, lymphocytes LY-WX, lymphocyte complexity and width of dispersion of the events measured LY

Open access

Matus Horvath and Edita Vircikova

Abstract

Traditional statistical process control approaches are less effective in dealing with multivariate and autocorrelated processes. With the continual increase in process complexity, this inefficiency is becoming more apparent. A special type of multivariate and autocorrelated process is a process occurring within a heterogeneous production environment (a variety of types of machines, pots, etc. used for the same task). This makes the quality control of such processes more difficult. The approach presented in the paper utilizes time series fitting, cluster analysis and association mining in relation to a single data mining model for the analysis of complex multivariate autocorrelated processes. The aim is to divide the production cells (machines, pots, etc.) into groups exhibiting similar behaviors. This can then be used for more effective quality control of the entire process and afterwards to analyze the reasons for this behavior. This paper includes someof the results obtained from applying the model to an actual multivariate high autocorrelated process, the production of primary aluminum using the Hall-Heroult electrolysis process. The Hall-Heroult electrolysis process is a continual process that is ongoing in several pots simultaneously. The average plant operates 300 pots. Therefore, the quality control of such a complex process faces many issues concerning monitoring and problem diagnosis. The paper describes a method for dividing the pots into control groups exhibiting similar behaviors, which can then be used in the planning phase of the quality control analysis and to make improvements within these groups and thereby within the whole process.