Introduction to statistics for biomedical engineers

• Main Author: Ropella, Kristina M. 1963-
• Format: Electronic
• Language: English
• Published: San Rafael, Calif. (1537 Fourth St, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, c2007.
• Series: Synthesis lectures on biomedical engineering, #14.
• Subjects: Medical statistics.; Biomedical engineering > Statistical methods.; Biometry.
• Online Access: Abstract with links to resource
• Item Description: Part of: Synthesis digital library of engineering and computer science.; Title from PDF t.p. (viewed on Nov. 4, 2008).; Series from website.
• Physical Description: 1 electronic text (viii, 93 p. : ill. ) : digital file.; Also available in print.
• Format: Mode of access: World Wide Web.; System requirements: Adobe Acrobat Reader.
• Bibliography: Includes bibliographical references (p. 91).
• ISBN: 1598291971 (electronic bk.); 9781598291971 (electronic bk.); 1598291963 (pbk.); 9781598291964 (pbk.)
• ISSN: 1930-0336
• Access: Abstract freely available; full-text restricted to subscribers or individual document purchasers.

There are many books written about statistics, some brief, some detailed, some humorous, some colorful, and some quite dry. Each of these texts is designed for a specific audience. Too often, texts about statistics have been rather theoretical and intimidating for those not practicing statistical analysis on a routine basis. Thus, many engineers and scientists, who need to use statistics much more frequently than calculus or differential equations, lack sufficient knowledge of the use of statistics. The audience that is addressed in this text is the university-level biomedical engineering student who needs a bare-bones coverage of the most basic statistical analysis frequently used in biomedical engineering practice. The text introduces students to the essential vocabulary and basic concepts of probability and statistics that are required to perform the numerical summary and statistical analysis used in the biomedical field. This text is considered a starting point for important issues to consider when designing experiments, summarizing data, assuming a probability model for the data, testing hypotheses, and drawing conclusions from sampled data.