Causality in statistics and data science education
Author
Abstract
Suggested Citation
DOI: 10.1007/s11943-022-00311-9
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Andrew Gelman & Aki Vehtari, 2021. "What are the Most Important Statistical Ideas of the Past 50 Years?," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 2087-2097, October.
- Jessica Utts, 2021. "Enhancing Data Science Ethics Through Statistical Education and Practice," International Statistical Review, International Statistical Institute, vol. 89(1), pages 1-17, April.
- Jim Ridgway, 2016. "Implications of the Data Revolution for Statistics Education," International Statistical Review, International Statistical Institute, vol. 84(3), pages 528-549, December.
- Daniel Kaplan, 2018. "Teaching Stats for Data Science," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 89-96, January.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Timo Schmid & Markus Zwick, 2022. "Editorial," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 167-170, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Paulo Canas Rodrigues & Elisabetta Carfagna, 2023. "Data science applied to environmental sciences," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
- Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.
- Cimpoeru, Smaranda & Roman, Monica, 2018. "Statistical Literacy and Attitudes Towards Statistics of Romanian Undergraduate Students," MPRA Paper 90452, University Library of Munich, Germany, revised 31 Aug 2018.
More about this item
Keywords
Statistics education research; Data Science; Causality; Bias and Confounding;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:astaws:v:16:y:2022:i:3:d:10.1007_s11943-022-00311-9. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.