Foundational and Applied Statistics for Biologists Using R

Ken A. Aho

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December 17, 2013 by Chapman and Hall/CRC
Textbook - 618 Pages - 130 B/W Illustrations
ISBN 9781439873380 - CAT# K13403

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Features

  • Covers a wide range of analytical topics, including bootstrapping, Bayesian MCMC procedures, regression, model selection, GLMs, GAMs, nonlinear models, ANOVA, mixed effects models, and permutation approaches
  • Emphasizes the understanding of statistical foundations
  • Provides R code for all analyses and uses R to generate the figures
  • Includes many biological examples throughout and extensive exercises at the end of each chapter
  • Reviews linear algebra applications and additional mathematical reference material in the appendix
  • Offers an introduction to R and R code for each chapter on the author’s website

Figure slides available upon qualifying course adoption

Summary

Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complicated procedures step by step and thus better understand the process of obtaining analysis results. The graphical capabilities of R are used to provide interactive demonstrations of simple to complex statistical concepts.

Assuming only familiarity with algebra and general calculus, the text offers a flexible structure for both introductory and graduate-level biostatistics courses. The first seven chapters address fundamental topics in statistics, such as the philosophy of science, probability, estimation, hypothesis testing, sampling, and experimental design. The remaining four chapters focus on applications involving correlation, regression, ANOVA, and tabular analyses.

Unlike classic biometric texts, this book provides students with an understanding of the underlying statistics involved in the analysis of biological applications. In particular, it shows how a solid statistical foundation leads to the correct application of procedures, a clear understanding of analyses, and valid inferences concerning biological phenomena.

Web Resource
An R package (asbio) developed by the author is available from CRAN. Accessible to those without prior command-line interface experience, this companion library contains hundreds of functions for statistical pedagogy and biological research. The author’s website also includes an overview of R for novices.

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