Textbook of Medical Statistics: For Medical Students

Author:   Xiuhua Guo ,  Fuzhong Xue
Publisher:   Springer Verlag, Singapore
Edition:   1st ed. 2024
ISBN:  

9789819973897


Pages:   220
Publication Date:   29 May 2024
Format:   Paperback
Availability:   Not yet available   Availability explained
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Textbook of Medical Statistics: For Medical Students


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Overview

This book introduces basic concepts, principle, and methods of medical statistics systematically and practically, especially in the statistical design of the experiment in terms of the specific problems, adequate use of statistical methods based on actual data and the reasonable explanation for statistical results. This textbook combines statistical methods with the common application of SPSS software, which is flexible, convenient, and user-friendly; thus, students can focus on the deep understanding of statistics. The authors emphasize the application and generalization of statistical methods, and combine these methods with the modern statistical theory, such as sequential contingency table and multivariate statistical modelling, etc. This book is a useful textbook for graduate and undergraduate students in medical schools, including MBBS (Bachelor of Medicine and Bachelor of Surgery) student.

Full Product Details

Author:   Xiuhua Guo ,  Fuzhong Xue
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   1st ed. 2024
ISBN:  

9789819973897


ISBN 10:   9819973899
Pages:   220
Publication Date:   29 May 2024
Audience:   College/higher education ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

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Editor Xiuhua Guo is a doctor and professor of Epidemiology and Health Statistics, Capital Medical University, Beijing, China. The main research areas focus on the hierarchical data model or data mining model to handle big data of high dimensional environmental and genetic factors in major infectious diseases, cardiovascular diseases and metabolic disorders, thus to provide scientific proof for the causes of diseases and intervention strategies.

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