Foundational and Applied Statistics for Biologists Using R

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ISBN 9781439873380
Cat# K13403



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ISBN 9781482222128
Cat# KE25228



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  • 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


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.

Table of Contents

Philosophical and Historical Foundations

Nature of Science
Scientific Principles
Scientific Method
Scientific Hypotheses
Variability and Uncertainty in Investigations
Science and Statistics
Statistics and Biology

Introduction to Probability
Introduction: Models for Random Variables
Classical Probability
Conditional Probability
Combinatorial Analysis
Bayes Rule

Probability Density Functions
Introductory Examples of pdfs
Other Important Distributions
Which pdf to Use?
Reference Tables

Parameters and Statistics
OLS and ML Estimators
Linear Transformations
Bayesian Applications

Interval Estimation: Sampling Distributions, Resampling Distributions, and Simulation Distributions
Sampling Distributions
Confidence Intervals
Resampling Distributions
Bayesian Applications: Simulation Distributions

Hypothesis Testing
Parametric Frequentist Null Hypothesis Testing
Type I and Type II Errors
Criticisms of Frequentist Null Hypothesis Testing
Alternatives to Parametric Null Hypothesis Testing
Alternatives to Null Hypothesis Testing

Sampling Design and Experimental Design
Some Terminology
The Question Is: What Is the Question?
Two Important Tenets: Randomization and Replication
Sampling Design
Experimental Design


Pearson’s Correlation
Robust Correlation
Comparisons of Correlation Procedures

Linear Regression Model
General Linear Models
Simple Linear Regression
Multiple Regression
Fitted and Predicted Values
Confidence and Prediction Intervals
Coefficient of Determination and Important Variants
Power, Sample Size, and Effect Size
Assumptions and Diagnostics for Linear Regression
Transformation in the Context of Linear Models
Fixing the Y-Intercept
Weighted Least Squares
Polynomial Regression
Comparing Model Slopes
Likelihood and General Linear Models
Model Selection
Robust Regression
Model II Regression (X Not Fixed)
Generalized Linear Models
Nonlinear Models
Smoother Approaches to Association and Regression
Bayesian Approaches to Regression

Inferences for Factor Levels
ANOVA as a General Linear Model
Random Effects
Power, Sample Size, and Effect Size
ANOVA Diagnostics and Assumptions
Two-Way Factorial Design
Randomized Block Design
Nested Design
Split-Plot Design
Repeated Measures Design
Unbalanced Designs
Robust ANOVA
Bayesian Approaches to ANOVA

Tabular Analyses
Probability Distributions for Tabular Analyses
One-Way Formats
Confidence Intervals for p
Contingency Tables
Two-Way Tables
Ordinal Variables
Power, Sample Size, and Effect Size
Three-Way Tables
Generalized Linear Models




A Summary and Exercises appear at the end of each chapter.

Author Bio(s)

Editorial Reviews

"This is a terrific intermediate-level modern applied statistics text for biologists or anyone else who is interested in data analysis. … a thorough job of introducing and detailing the main concepts, the methods, and the pleasures of modern data analysis. It is a visually pleasing book with good layouts, nice typefaces, and great tables and graphics and the R code to produce them! A great way for a class to really engage with R graphics. … The author has put effort into making the book. A website and a companion R package, asbio, serve two audiences: introductory classes and more advanced classes. He has succeeded nicely in writing a dual-level book. … I would strongly recommend the book for mature students … I look forward to using it with my upper-level undergrads and the Masters and PhD students I continue to work with."
MAA Reviews, September 2014

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