| Publikationstyp: | Buchbeitrag |
| Art der Begutachtung: | Editorial review |
| Titel: | Statistical modelling |
| Autor/-in: | Dettling, Marcel Ruckstuhl, Andreas |
| et. al: | No |
| DOI: | 10.1007/978-3-030-11821-1_11 |
| Erschienen in: | Applied data science : lessons learned for the data-driven business |
| Herausgeber/-in des übergeordneten Werkes: | Braschler, Martin Stadelmann, Thilo Stockinger, Kurt |
| Seite(n): | 181 |
| Seiten bis: | 203 |
| Erscheinungsdatum: | 2019 |
| Verlag / Hrsg. Institution: | Springer |
| Verlag / Hrsg. Institution: | Cham |
| ISBN: | 978-3-030-11820-4 978-3-030-11821-1 |
| Sprache: | Englisch |
| Schlagwörter: | Data science; Statistical modelling; Regression analysis |
| Fachgebiet (DDC): | 003: Systeme 510: Mathematik |
| Zusammenfassung: | In 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/18174 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
Es gibt keine Dateien zu dieser Ressource.
Zur Langanzeige
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.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.