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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorDettling, Marcel-
dc.contributor.authorRuckstuhl, Andreas-
dc.date.accessioned2019-09-12T09:07:37Z-
dc.date.available2019-09-12T09:07:37Z-
dc.date.issued2019-
dc.identifier.isbn978-3-030-11820-4de_CH
dc.identifier.isbn978-3-030-11821-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/18174-
dc.description.abstractIn this chapter, we present statistical modelling approaches for predictive tasks in business and science. Most prominent is the ubiquitous multiple linear regression approach where coefficients are estimated using the ordinary least squares algorithm. There are many derivations and generalizations of that technique. In the form of logistic regression, it has been adapted to cope with binary classification problems. Various statistical survival models allow for modelling of time-to-event data. We will detail the many benefits and a few pitfalls of these techniques based on real-world examples. A primary focus will be on pointing out the added value that these statistical modelling tools yield over more black box-type machine-learning algorithms. In our opinion, the added value predominantly stems from the often much easier interpretation of the model, the availability of tools that pin down the influence of the predictor variables in concise form, and finally from the options they provide for variable selection and residual analysis, allowing for user-friendly model development, refinement, and improvement.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofApplied data science : lessons learned for the data-driven businessde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectData sciencede_CH
dc.subjectStatistical modellingde_CH
dc.subjectRegression analysisde_CH
dc.subject.ddc003: Systemede_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleStatistical modellingde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-11821-1_11de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end203de_CH
zhaw.pages.start181de_CH
zhaw.parentwork.editorBraschler, Martin-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.parentwork.editorStockinger, Kurt-
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedPredictive Analyticsde_CH
zhaw.webfeedStatistik und Quantitative Financede_CH
zhaw.author.additionalNode_CH
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Dettling, M., & Ruckstuhl, A. (2019). Statistical modelling. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 181–203). Springer. https://doi.org/10.1007/978-3-030-11821-1_11
Dettling, M. and Ruckstuhl, A. (2019) ‘Statistical modelling’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 181–203. Available at: https://doi.org/10.1007/978-3-030-11821-1_11.
M. Dettling and A. Ruckstuhl, “Statistical modelling,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 181–203. doi: 10.1007/978-3-030-11821-1_11.
DETTLING, Marcel und Andreas RUCKSTUHL, 2019. Statistical modelling. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 181–203. ISBN 978-3-030-11820-4
Dettling, Marcel, and Andreas Ruckstuhl. 2019. “Statistical Modelling.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 181–203. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_11.
Dettling, Marcel, and Andreas Ruckstuhl. “Statistical Modelling.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 181–203, https://doi.org/10.1007/978-3-030-11821-1_11.


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