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Foundational and Applied Statistics for Biologists Using R

Ken A. Aho
Publication Date:
December 17, 2013
Content Details:
618 pages

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List Price:   $76.95

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  • About the Book

    Book 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.


      • 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


      "The book is written in an accessible style for undergraduate students and is built in a lecture-style format. Each chapter commences with a brief description of its contents in the ‘how to read this chapter’ section and finishes with a summary and exercises to help the students practice using the notions that they discovered within the chapter. The book also contains an extensive appendix of mathematical concepts and has the advantage of a large collection of references, which can be accessed for further studies."
      Zentralblatt MATH 1306

      "The book contains a host of examples to illustrate various methods of analysis and statistical concepts … The statistical concepts described are illustrated with some terrific interactive GUIs and sliders; code to implement these is provided in the R package asbio, which accompanies the text. More than a plaything for the distracted statistician, these are great resources for teaching students and conveying statistical ideas to the non-statistically trained. … does this book offer anything new, not available elsewhere in the crowded market of statistics books using R? Yes it does. The approach, focusing on statistical foundations first, building upwards from basic philosophical concepts, before progressing to implementation and real world applications, is certainly novel. I would, without a doubt, recommend this book to those statisticians working with biologists …"
      Statistical Methods in Medical Research, 2015

      "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

  • 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

    One-Way ANOVA
    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.