Springer Introduction to Data Analysis and Graphical Presentation in Biostatistics with R: Statistics in the Large (SpringerBriefs in Statistics)

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. Review From the book reviews: “This text serves as an introduction to the use of R in biostatistics. It has specifically been structured to demonstrate the use of R syntax as opposed to the use of a point-and-select graphical user interface. … Small and easy-to-follow confidence-building examples have been used throughout this text. … This monograph is very useful not only for students in informatics, but especially also for those in medicine and biology related with the courses in biostatistics (medical statistics) and bioinformatics.” (T. Postelnicu, zbMATH 1306.62016, 2015) From the Back Cover 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. About the Author 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.