Synopses & Reviews
Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation.
Synopsis
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.
Synopsis
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.
Synopsis
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.
About the Author
Babak Shahbaba is Assistant Professor at the University of California, Irvine. His research focuses on developing Bayesian methods and applying them to real-world problems. He is currently conducting research in three areas: (1) incorporating appropriate priors into statistical models in order to improve their performance, (2) developing new nonlinear models that are sufficiently flexible and provide interpretable results, and (3) applying novel statistical methods to solve research questions in genetics, genomics, proteomics, and cancer studies.
Table of Contents
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.