Biostatistics with R is designed to mimic the interaction between theory and application in statistics. Topics include data exploration, estimation, and clustering with two appendices on installing and running R and R-commander.

Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis.

This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.

Discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools Topics are motivated by real examples Intended for a wide range of readers Includes supplementary material: sn.pub/extras

Autorentext
Babak Shahbaba is Assistant Professor of Biostatistics at the University of California, Irvine, where he has been teaching undergraduate courses such as Introduction to Biostatistics, and graduate courses such as Advanced Statistical Methods and Bayesian Analysis. His research interest is related to developing novel statistical methods to answer research questions in genomics, proteomics, and cancer studies.

Inhalt

Introduction.- Data Exploration.- Exploring Relationships.- Probability.- Random Variables and Probability Distribtions.- Estimation.- Hypothesis Testing.- Testing a Hypothesis on the Relatinoship Between Two Variables.- Analysis of Variance.- Analysis of Categorial Variables.- Regression Analysis.- Clustering.- Bayesian Analysis.