Fundamentals of Biostatistics, 8th Edition, 9781305268920
Hardcover
FUNDAMENTALS OF BIOSTATISTICS leads you through the methods, techniques, and computations of statistics necessary for success in the medical field. Every new concept is developed systematically through completely worked out examples from current medical research problems.

Fundamentals of Biostatistics, 8th Edition

$506.43

  • Hardcover

    888 pages

  • Release Date

    2 August 2015

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Summary

FUNDAMENTALS OF BIOSTATISTICS leads you through the methods, techniques, and computations of statistics necessary for success in the medical field. Every new concept is developed systematically through completely worked out examples from current medical research problems.

Book Details

ISBN-13:9781305268920
ISBN-10:130526892X
Author:Bernard Rosner
Publisher:Cengage Learning, Inc
Imprint:Brooks/Cole
Format:Hardcover
Number of Pages:888
Edition:8th
Release Date:2 August 2015
Weight:1.91kg
Dimensions:38mm x 210mm x 254mm
What They're Saying

Critics Review

“The book is very comprehensive and generally explains not only how specific tests are done but also the theoretical underpinning of these tests. I use the Rosner book quite frequently as a reference for myself. It is definitely a great resource for a practicing researcher, and probably a useful book for a graduate student wanting to learn more statistics.”“This revision of a “classic” textbook for introductory biostatistics is, in my opinion, the most comprehensive and thorough book on the market. Yet it is written and illustrated very clearly. Most importantly, it has many worked examples and exercises that apply the methods to actual data.”

About The Author

Bernard Rosner

Bernard Rosner is a professor in the Department of Medicine, Harvard Medical School, and the Department of Biostatistics at the Harvard School of Public Health. Dr. Rosner’s research activities currently include longitudinal data analysis, analysis of clustered continuous, binary and ordinal data, methods for the adjustment of regression models for measurement error and modeling of cancer incidence data.

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