Introduction to Data Analysis and Graphical Presentation in Biostatistics with R [electronic resource] : Statistics in the Large /
MacFarland, Thomas W.author.
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.
Description based upon print version of record.
Includes bibliographical references.
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.
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.
Statistics.
R (Computer program language).
Statistics for Life Sciences, Medicine, Health Sciences.
Statistics and Computing/Statistics Programs.