Based on a course which the author has taught for many years, this book provides an introduction to intermediate and advanced methods for environmental data analysis. This book is supported by a Website featuring solutions to exercises, extra computer code, and additional material.

The field of environmental statistics is growing rapidly due to the explosion in automated data collection systems, computing power, interactive, linkable software, public and ecological health concerns, and the continuing need for analysis to support environmental policy-making and regulation. This book provides a coherent introduction to intermediate and advanced methods for environmental data analysis and is based on a course which the author has taught for many years, and prepares students for careers in environmental analysis centered on statistics and allied quantitative methods of evaluation. The text also takes a data-oriented approach to describing the various methods. Each method described is illustrated with real-world examples. It features extensive exercises, enabling use as a course text. It also includes examples of SAS computer code for implementation of the methodology. It is supported by a Website featuring solutions to exercises, extra computer code, and additional material.

Preface.
1 Linear regression.
1.1 Simple linear regression.
1.2 Multiple linear regression.
1.3 Qualitative predictors: ANOVA and ANCOVA models.
1.4 Random--effects models.
1.5 Polynomial regression.
Exercises.
2 Nonlinear regression.
2.1 Estimation and testing.
2.2 Piecewise regression models.
2.3 Exponential regression models.
2.4 Growth curves.
2.5 Rational polynomials.
2.6 Multiple nonlinear regression.
Exercises.
3 Generalized linear models.
3.1 Generalizing the classical linear model.
3.2 Theory of generalized linear models.
3.3 Specific forms of generalized linear models.
Exercises.
4 Quantitative risk assessment with stimulus--response data.
4.1 Potency estimation for stimulus--response data.
4.2 Risk estimation.
4.3 Benchmark analysis.
4.4 Uncertainty analysis.
4.5 Sensitivity analysis.
4.6 Additional topics.
Exercises.
5 Temporal data and autoregressive modeling.
5.1 Time series.
5.2 Harmonic regression.
5.3 Autocorrelation.
5.4 Autocorrelated regression models.
5.5 Simple trend and intervention analysis.
5.6 Growth curves revisited.
Exercises.
6 Spatially correlated data.
6.1 Spatial correlation.
6.2 Spatial point patterns and complete spatial randomness.
6.3 Spatial measurement.
6.4 Spatial prediction.
Exercises.
7 Combining environmental information.
7.1 Combining P--values.
7.2 Effect size estimation.
7.3 Meta--analysis.
7.4 Historical control information.
Exercises.
8 Fundamentals of environmental sampling.
8.1 Sampling populations -- simple random sampling.
8.2 Designs to extend simple random sampling.
8.3 Specialized techniques for environmental sampling.
Exercises.
A Review of probability and statistical inference.
A.1 Probability functions.
A.2 Families of distributions.
A.3 Random sampling.
A.4 Parameter estimation.
A.5 Statistical inference.
A.6 The delta method.
B Tables.
References.
Author index.
Subject index.