Primer of Biostatistics, 7th Edition

Primer of biostatistics 7By

  • Stanton A. Glantz, PhD, Professor of Medicine, American Legacy Foundation Distinguished Professor in Tobacco Control, Director, Center for Tobacco Control Research and Education, Member, Cardiovascular Research Institute, Member, Philip R. Lee Institute for Health Policy Studies, Member, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA

A concise, engagingly written introduction to understanding statistics as they apply to medicine and the life sciences

CD-ROM performs 30 statistical tests

Don’t be afraid of biostatistics anymore! Primer of Biostatistics,7th Edition demystifies this challenging topic in an interesting and enjoyable manner that assumes no prior knowledge of the subject. Faster than you thought possible, you’ll understand test selection and be able to evaluate biomedical statistics critically, knowledgeably, and confidently.
With Primer of Biostatistics, you’ll start with the basics, including analysis of variance and the t test, then advance to multiple comparison testing, contingency tables, regression, and more. Illustrative examples and challenging problems, culled from the recent biomedical literature, highlight the discussions throughout and help to foster a more intuitive approach to biostatistics.

The companion CD-ROM contains everything you need to run thirty statistical tests of your own data. Review questions and summaries in each chapter facilitate the learning process and help you gauge your comprehension. By combining whimsical studies of Martians and other planetary residents with actual papers from the biomedical literature, the author makes the subject fun and engaging.

Coverage includes:

  • How to summarize data
  • How to test for differences between groups
  • The t test
  • How to analyze rates and proportions
  • What does “not significant” really mean?
  • Confidence intervals
  • How to test for trends
  • Experiments when each subject receives more than one treatment
  • Alternatives to analysis of variance and the t test based on ranks
  • How to analyze survival data

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