Statistics for Environmental Biology and Toxicology

By A. John Bailer, Walter. Piegorsch

Series Editors: Byron J.T. Morgan, Niels Keiding, Peter Van der Heijden

© 1997 – Chapman and Hall/CRC

584 pages

Purchasing Options:
Hardback: 9780412047312
pub: 1997-07-01
US Dollars$154.95

About the Book

Statistics for Environmental Biology and Toxicology presents and illustrates statistical methods appropriate for the analysis of environmental data obtained in biological or toxicological experiments. Beginning with basic probability and statistical inferences, this text progresses through non-linear and generalized linear models, trend testing, time-to-event data and analysis of cross-classified tabular and categorical data. For the more complex analyses, extensive examples including SAS and S-PLUS programming code are provided to assist the reader when implementing the methods in practice.


"Teachers of statistics to students from other disciplines could will find this book useful, both for its condensed summaries of some of the more sophisticated techniques-in particular that for generalized linear models is helpful and also for the copious exercises and worked examples."

-Short Book Reviews of the ISI

"…I strongly recommend this text and believe it to be an excellent supplement for any biostatistics course or courses in related disciplines."

-Australian & New Zealand Journal of Statistics

"This book is an excellent source for information about the relevant statistical methods with a strong emphasis on applications. This is one of the best books that I have seen recently."

-Technometrics, Vol. 40, No.3

"The many examples are well selected and illustrate the use of the methods introduced on relevant data."… the book introduces many statistical concepts, it is concise but careful and it has many good examples…. could be used as a basic statistical textbook for researchers in environmental biology."

-Statistics in Medicine, Vol. 20: 1143-1152, 2001

Table of Contents

Basic Probability and Statistical Distributions

Introductory Concepts in Probability

Families of Discrete Distributions

Families of Continuous Distributions

The Exponential Class

Families of Multivariate Distributions



Fundamentals of Statistical Inference

Introductory Concepts in Statistical Estimation

Nature and Properties of Estimators

Techniques for Constructing Statistical Estimators

Statistical Inference - Testing Hypotheses

Statistical Inference - Confidence Intervals

Confidence Intervals for Some Special Distributions

Semi-Parametric Inference



Fundamental Issues in Experiment Design

Basic Terminology in Experiment Design

The Experimental Unit

Random Sampling and Randomization

Sample Sizes and Optimal Animal Allocation

Dose Selection



Data Analysis of Treatment versus Control Differences

Two-Sample Comparisons - Testing Hypotheses

Two-Sample Comparisons - Confidence Intervals



Treatment-versus-Control Multiple Comparisons

Comparing More than Two Populations

Multiple Comparisons via Bonferroni's Inequality

Multiple Comparisons among a Control - Normal Sampling

Multiple Comparisons among Binomial Populations

Multiple Comparisons with a Control - Poisson Samling

All-Pairwise Multiple Comparisons



Trend Testing

Simple Linear Regression for Normal Data

William's Test for Normal Data

Trend Tests for Proportions

Cochran-Armitage Trend Test for Counts

Overdispersed Discrete Data

Distribution-Free Trend Testing

Nonparametric Tests for Nonmonotone ("Umbrella") Trends



Dose-Response Modeling and Analysis

Dose-Response Models on a Continuous Scale

Dose-Response Models on a Discrete Scale

Potency Estimation for Dose-Response Data

Comparing Dose-Response Curves



Introduction to Generalized Linear Models


Review of Classical Linear Models

Generalizing the Classical Linear Model

Generalized Linear Models

Examples and Illustrations



Analysis of Cross-Classified Tabular/Categorical Data

RxC Contingency Tables

Statistical Distributions for Categorical Data

Statistical Tests of Independence in RxC Tables

Log-Linear Models and Relationships to GLiMs

Tables of Proportions



Incorporating Historical Control Information

Guidelines for Using Historical Control Data

Two-Sample Hypothesis Testing - Normal Distribution Sampling

Two-Sample Hypothesis Testing - Binomial Sampling

Trend Testing with Historical Controls



Survival Data Analysis

Survival Data

Lifetime Distributions

Estimating the Survivor Function

Nonparametric Methods for Comparing Survival Curves

Regression Models for Survival Data





About the Series

Chapman & Hall/CRC Interdisciplinary Statistics

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Toxicology
SCIENCE / Environmental Science