Anderson (U. of Pittsburgh) offers a textbook aimed at students who have had some exposure to (and have an interest in) statistics but are not planning to become statisticians. It is based on an introductory biostatistics II course he taught for more than ten years at the University of Pittsburg. Using the statistical tools R and SAS, Anderson explains current techniques for the analysis of large data sets, as well as classical methods. A sampling of topics includes a review of probability, uses of simulation techniques, the central limit theorem, analysis of time to event data, and nanoparmetric methods. Chapters include exercises and brief tutorials for R and SAS are appended. Familiarization with the basic concepts of algebra is assumed. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)
The emergence of high-speed computing has facilitated the development of many exciting statistical and mathematical methods in the last 25 years, broadening the landscape of available tools in statistical investigations of complex data. Biostatistics: A Computing Approach focuses on visualization and computational approaches associated with both modern and classical techniques. Furthermore, it promotes computing as a tool for performing both analyses and simulations that can facilitate such understanding.
As a practical matter, programs in R and SAS are presented throughout the text. In addition to these programs, appendices describing the basic use of SAS and R are provided. Teaching by example, this book emphasizes the importance of simulation and numerical exploration in a modern-day statistical investigation. A few statistical methods that can be implemented with simple calculations are also worked into the text to build insight about how the methods really work.
Suitable for students who have an interest in the application of statistical methods but do not necessarily intend to become statisticians, this book has been developed from Introduction to Biostatistics II, which the author taught for more than a decade at the University of Pittsburgh.
Recenzijas
"The book presents important topics in biostatistics alongside examples provided in the programming languages SAS and R. The book covers many relevant topics every student should know in a way that it makes it easy to follow each chapter provides exercises encouraging the reader to deepen her/his understanding. I really like that the theory is presented in a clear manner without interruptions of example programs. Instead, the programs are always presented at the end of a section. this book can serve as a good start for the more statistics inclined students who havent yet recognized that in order to become a good biostatistician, you need to be able to write your own code. I can recommend to all serious students who want to get a thorough start into this field." Frank Emmert-Streib, Queens University Belfast, CHANCE, August 2013
Review of Topics in Probability and Statistics. Use of Simulation
Techniques. The Central Limit Theorem. Correlation and Regression. Analysis
of Variance. Discrete Measures of Risk. Multivariate Analysis. Analysis of
Repeated Measures Data. Nonparametric Methods. Analysis of Time to Event
Data. Sample Size and Power Calculations. Appendices. References. Index.
University of Pittsburgh, Pittsburgh, PA University of Bath, UK University of Minnesota, Minneapolis, USA Northwestern University, Evanston, Illinois, USA University of British Columbia, Vancouver, Canada
