- 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.

**FOUNDATIONSPhilosophical 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 ProbabilityOdds

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**

IntroductionSampling 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

**APPLICATIONSCorrelation**

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

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

Resource | OS Platform | Updated | Description | Instructions |
---|---|---|---|---|

Cross Platform | August 12, 2013 | Author created web site | click on http://www.isu.edu/~ahoken/book/ |