Introduction to Data Analysis and Graphical Presentation in Biostatistics with R

Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.

Includes supplementary material: sn.pub/extras

Autorentext

Dr. MacFarland (tommac@nova.edu) is Senior Research Associate (Office of Institutional Effectiveness, http://www.nova.edu/ie/) and Associate Professor (Graduate School of Computer and Information Sciences, http://scis.nova.edu/) at Nova Southeastern University, Fort Lauderdale, Florida, USA. Dr. MacFarland first used S on a UNIX platform in 1988 to teach statistics for face-to-face and online distance education students majoring in the computing sciences and later in the 1990s transitioned to R. Since then Dr. MacFarland has used R for graduate students in non-computing majors, such as allied health, disaster preparedness, dispute resolution, education, and marine biology. An interest in biostatistics was developed when Dr. MacFarland studied agriculture at the baccalaureate and graduate level, but prior to the use of hand-held calculators and personal computers.

Inhalt

Introduction to Biostatistics and R.- Data exploration, descriptive statistics and measures of central tendency.- Student's t-Test for independent samples.- Student's t-Test for matched pairs.- One way ANOVA.- Two way ANOVA.- Correlation and linear regression.- Future Actions and Next Steps.