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.

Walter W. Piegorsch is Professor of Statistics at the University of South Carolina, Columbia, SC, USA. A. John Bailer is Professor of Mathematics and Statistics and Co-director of the Center for Environmental Toxicology and Statistics at Miami University, Oxford, OH, USA.

Basic Probability and Statistical Distributions
Introductory Concepts in Probability
Families of Discrete Distributions
Families of Continuous Distributions
The Exponential Class
Families of Multivariate Distributions
Summary
Exercises
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
Summary
Exercises
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
Summary
Exercises
Data Analysis of Treatment versus Control Differences
Two-Sample Comparisons - Testing Hypotheses
Two-Sample Comparisons - Confidence Intervals
Summary
Exercises
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
Summary
Exercises
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
Summary
Exercises
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
Summary
Exercises
Introduction to Gener