Research Methods in Community Medicine: Surveys, Epidemiological Research, Programme Evaluation, Clinical Trials, Edition 6

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  • A simple and systematic guide to the planning and performance of investigations concerned with health and disease and with health care
  • Offers researchers help in choosing a topic and to think about shaping objectives and ideas and to link these with the appropriate choice of method
  • Fully updated with new sections on the use of the Web and computer programmes freely available in the planning, performance or analysis of studies
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About the author

J.H. Abramson, Emeritus Professor of Social Medicine, The Hebrew University-Hadassah School of Public Health and Community Medicine, Jerusalem.
Author of five previous editions of the formerly titled Survey Methods in Community Medicine and Making Sense of Data with OUP.

Z.H. Abramson, Beit Hakerem Community Clinic (Clalit Health Services) and Department of Family Medicine, The Hebrew University-Hadassah School of Medicine, Jerusalem.

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Additional Information

Publisher
John Wiley & Sons
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Published on
Aug 24, 2011
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Pages
422
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ISBN
9781119964162
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Language
English
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Genres
Mathematics / Probability & Statistics / Stochastic Processes
Medical / Pharmacology
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Content Protection
This content is DRM protected.
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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