Applied Statistics with Python: Volume I: Introductory Statistics and Regression: Leon Kaganovskiy, Boca Raton, FL: Chapman & Hall/CRC Press, 2025, x + 309 pp., $126.99(H), ISBN: 978-1-032-75193-1.
The article reviews *Applied Statistics with Python: Volume I: Introductory Statistics and Regression*, a textbook designed to integrate Python programming directly into the teaching of introductory statistics and regression. Targeted at undergraduate students across various disciplines, the book em...
| Publicado en: | American Statistician Vol. 80; no. 2; pp. 329 - 330 |
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| Formato: | Book Review |
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Taylor & Francis Ltd
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193251897&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193251897 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: May2026 vid: 80 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 193251897 10.1080/00031305.2026.2620699 ppf: 329 ppct: 1 formats: tig: atl: Applied Statistics with Python: Volume I: Introductory Statistics and Regression: Leon Kaganovskiy, Boca Raton, FL: Chapman & Hall/CRC Press, 2025, x + 309 pp., $126.99(H), ISBN: 978-1-032-75193-1. aug: au: Iannario, Maria su: Python programming language Regression analysis Computational statistics Statistics Data analysis Computational thinking Textbooks sug: subj: Other printing Python programming language Regression analysis Computational statistics Statistics Data analysis Computational thinking Textbooks ab: The article reviews *Applied Statistics with Python: Volume I: Introductory Statistics and Regression*, a textbook designed to integrate Python programming directly into the teaching of introductory statistics and regression. Targeted at undergraduate students across various disciplines, the book emphasizes computational literacy alongside statistical intuition, using real-world datasets and step-by-step coding examples to bridge theory and application. While it provides a clear and accessible foundation in descriptive statistics, probability, inference, and simple linear regression, the text offers limited coverage of inferential theory and model diagnostics, and focuses exclusively on Python, which may affect its adaptability in diverse instructional settings. Overall, the book serves as a practical resource for applied statistics education that prioritizes coding and interpretation over formal mathematical rigor. pubtype: Review doctype: Book Review src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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