- Membership
- Publications
- Meetings
- Competitions
- Community
- Programs
- Students
- High School Teachers
- Faculty and Departments
- Underrepresented Groups
- MAA Awards
- MAA Grants

- News
- About MAA

Publisher:

Chapman & Hall/CRC

Publication Date:

2014

Number of Pages:

596

Format:

Hardcover

Price:

69.95

ISBN:

9781439873380

Category:

Textbook

[Reviewed by , on ]

Michael Sutherland

09/30/2014

This is a terrific intermediate level modern applied statistics text for biologists… or anyone else who is interested in data analysis. It is a big, handsome book of 600 plus pages. That might seem annoyingly large, but it sure does make it easy to do 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 Table of Contents shows 7 foundational chapters followed by 4 applications chapters. The first chapter on philosophical and historical foundations of science and “knowing” is novel, well done and too brief! But I deeply appreciated its presence, its importance for students and the opportunity it provides for making connections between fields. The remaining 6 foundational chapters present the traditional set of concepts: probability, density functions, their parameters and statistics, interval estimation (by sampling, resampling and simulation), hypothesis testing, sampling design and experimental design. The following application chapters are the classic core of statistical models: regression, ANOVA, and contingency counts.

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. Selective skips and deletes will still leave a well written introductory text with engaging worked examples each offering the opportunity to develop one’s R skills. For my more mature students there is ample advanced material, many references to the literature, and still those many worked examples with advanced questions to both exercise their statistical understanding and also develop their R skills in a modern analytic environment.

As an example, the book discusses the advanced concept of sphericity in both introductory and advanced contexts. It then shows how easy it is to find and grab a repeated measures ANOVA procedure from an R package off the internet which addresses the issues of sphericity and how to make the adjustments calculations needed. This is one of those many worked examples I mentioned earlier. It uses readily available R code. It proved to be just what I needed to help convince a colleague to leave her old, expensive statistical computing environment for the freedom of R. How nice to have an introductory book that includes grown-up conversations about advanced ideas!

The book is well produced and I enjoyed the companion website, http://www2.cose.isu.edu/~ahoken/book/. I would strongly recommend the book for mature students, such as those who have engaged and declared a major in bio/enviro/ecosystem studies programs. It is not a book I’d pick for an introductory survey course. I look forward to using it with my upper level undergrads and the Masters and PhD students I continue to work with.

Mike Sutherland is a semi retired statistical consultant who works on interesting academic and business problems. He was a founding faculty member of Hampshire College, then moved to the University of Massachusetts to become the Director of the Statistical Consulting Center.

**FOUNDATIONS
Philosophical and Historical Foundations**

Introduction

Nature of Science

Scientific Principles

Scientific Method

Scientific Hypotheses

Logic

Variability and Uncertainty in Investigations

Science and Statistics

Statistics and Biology

**Introduction to Probability**

Introduction: Models for Random Variables

Classical Probability

Conditional Probability

Odds

Combinatorial Analysis

Bayes Rule

**Probability Density Functions**

Introduction

Introductory Examples of pdfs

Other Important Distributions

Which pdf to Use?

Reference Tables

**Parameters and Statistics**

Introduction

Parameters

Statistics

OLS and ML Estimators

Linear Transformations

Bayesian Applications

**Interval Estimation: Sampling Distributions, Resampling Distributions, and Simulation Distributions**

Introduction

Sampling Distributions

Confidence Intervals

Resampling Distributions

Bayesian Applications: Simulation Distributions

**Hypothesis Testing**

Introduction

Parametric Frequentist Null Hypothesis Testing

Type I and Type II Errors

Power

Criticisms of Frequentist Null Hypothesis Testing

Alternatives to Parametric Null Hypothesis Testing

Alternatives to Null Hypothesis Testing

**Sampling Design and Experimental Design**

Introduction

Some Terminology

The Question Is: What Is the Question?

Two Important Tenets: Randomization and Replication

Sampling Design

Experimental Design

**APPLICATIONS
Correlation**

Introduction

Pearson’s Correlation

Robust Correlation

Comparisons of Correlation Procedures

**Regression**

Introduction

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 (

Generalized Linear Models

Nonlinear Models

Smoother Approaches to Association and Regression

Bayesian Approaches to Regression

**ANOVA**

Introduction

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

ANCOVA

Unbalanced Designs

Robust ANOVA

Bayesian Approaches to ANOVA

**Tabular Analyses**

Introduction

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

**Appendix**

**References**

**Index**

- Log in to post comments