Statistical analytics for health data science with SAS and R:
"This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. However, the models in this book can be used to analyse any kind of data. The data are analysed with the...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, London, New York
CRC Press, Taylor & Francis Group
2023
|
| Ausgabe: | First edition |
| Schriftenreihe: | Chapman & Hall/CRC biostatistics series
|
| Schlagworte: | |
| Online-Zugang: | DE-188 Volltext |
| Zusammenfassung: | "This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. However, the models in this book can be used to analyse any kind of data. The data are analysed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers' learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research"-- |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 Online-Ressource (xxi, 257 Seiten) Illustrationen |
| ISBN: | 9781003315674 9781000848861 1000848868 9781000848823 1000848825 |
| DOI: | 10.1201/9781003315674 |
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| 250 | |a First edition | ||
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| 520 | 3 | |a "This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. However, the models in this book can be used to analyse any kind of data. The data are analysed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers' learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research"-- | |
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Datensatz im Suchindex
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| author | Wilson, Jeffrey R. Chen, Ding-Geng Peace, Karl E. 1941- |
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| id | DE-604.BV049678164 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-20T07:30:36Z |
| institution | BVB |
| isbn | 9781003315674 9781000848861 1000848868 9781000848823 1000848825 |
| language | English |
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| physical | 1 Online-Ressource (xxi, 257 Seiten) Illustrationen |
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| publisher | CRC Press, Taylor & Francis Group |
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| series2 | Chapman & Hall/CRC biostatistics series |
| spelling | Wilson, Jeffrey R. Verfasser (DE-588)170935221 aut Statistical analytics for health data science with SAS and R Jeffrey R. Wilson, Ding-Geng Chen, and Karl E. Peace First edition Boca Raton, London, New York CRC Press, Taylor & Francis Group 2023 1 Online-Ressource (xxi, 257 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Chapman & Hall/CRC biostatistics series Includes bibliographical references and index "This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. However, the models in this book can be used to analyse any kind of data. The data are analysed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers' learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research"-- SAS (Computer file) SAS (Computer file) / (OCoLC)fst01364029 Medical statistics R (Computer program language) Statistics as Topic / methods Public Health Data Interpretation, Statistical Software BUSINESS & ECONOMICS / Statistics MATHEMATICS / Probability & Statistics / General Medical statistics / (OCoLC)fst01014672 R (Computer program language) / (OCoLC)fst01086207 Chen, Ding-Geng Verfasser (DE-588)1048528332 aut Peace, Karl E. 1941- Verfasser (DE-588)1048528782 aut Erscheint auch als Druck-Ausgabe, Hardcover 978-1-032-32562-0 Erscheint auch als Druck-Ausgabe, Paperback 978-1-032-32569-9 https://doi.org/10.1201/9781003315674 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Wilson, Jeffrey R. Chen, Ding-Geng Peace, Karl E. 1941- Statistical analytics for health data science with SAS and R |
| title | Statistical analytics for health data science with SAS and R |
| title_auth | Statistical analytics for health data science with SAS and R |
| title_exact_search | Statistical analytics for health data science with SAS and R |
| title_full | Statistical analytics for health data science with SAS and R Jeffrey R. Wilson, Ding-Geng Chen, and Karl E. Peace |
| title_fullStr | Statistical analytics for health data science with SAS and R Jeffrey R. Wilson, Ding-Geng Chen, and Karl E. Peace |
| title_full_unstemmed | Statistical analytics for health data science with SAS and R Jeffrey R. Wilson, Ding-Geng Chen, and Karl E. Peace |
| title_short | Statistical analytics for health data science with SAS and R |
| title_sort | statistical analytics for health data science with sas and r |
| url | https://doi.org/10.1201/9781003315674 |
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