Foundational and Applied Statistics for Biologists Using R
 Edition:
 1st
 Author(s):
 Ken A. Aho
 ISBN:
 9781439873380
 Format:
 Hardback
 Publication Date:
 December 17, 2013
 Content Details:
 618 pages
 Language:
 English
Also of Interest

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 graduatelevel 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 commandline 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.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
Reviews
"The book is written in an accessible style for undergraduate students and is built in a lecturestyle 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 nonstatistically 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 intermediatelevel 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 duallevel book. … I would strongly recommend the book for mature students … I look forward to using it with my upperlevel undergrads and the Masters and PhD students I continue to work with."
—MAA Reviews, September 2014 
Contents
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 BiologyIntroduction to Probability
Introduction: Models for Random Variables
Classical Probability
Conditional Probability
Odds
Combinatorial Analysis
Bayes RuleProbability Density Functions
Introduction
Introductory Examples of pdfs
Other Important Distributions
Which pdf to Use?
Reference TablesParameters and Statistics
Introduction
Parameters
Statistics
OLS and ML Estimators
Linear Transformations
Bayesian ApplicationsInterval Estimation: Sampling Distributions, Resampling Distributions, and Simulation Distributions
Introduction
Sampling Distributions
Confidence Intervals
Resampling Distributions
Bayesian Applications: Simulation DistributionsHypothesis 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 TestingSampling Design and Experimental Design
Introduction
Some Terminology
The Question Is: What Is the Question?
Two Important Tenets: Randomization and Replication
Sampling Design
Experimental DesignAPPLICATIONS
Correlation
Introduction
Pearson’s Correlation
Robust Correlation
Comparisons of Correlation ProceduresRegression
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 YIntercept
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 RegressionANOVA
Introduction
OneWay ANOVA
Inferences for Factor Levels
ANOVA as a General Linear Model
Random Effects
Power, Sample Size, and Effect Size
ANOVA Diagnostics and Assumptions
TwoWay Factorial Design
Randomized Block Design
Nested Design
SplitPlot Design
Repeated Measures Design
ANCOVA
Unbalanced Designs
Robust ANOVA
Bayesian Approaches to ANOVATabular Analyses
Introduction
Probability Distributions for Tabular Analyses
OneWay Formats
Confidence Intervals for p
Contingency Tables
TwoWay Tables
Ordinal Variables
Power, Sample Size, and Effect Size
ThreeWay Tables
Generalized Linear ModelsAppendix
References
Index
A Summary and Exercises appear at the end of each chapter.