Statistics for Environmental Biology and Toxicology

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.

"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

• 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