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.
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.
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.
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.
5 Temporal data and autoregressive modeling.
5.1 Time series.
5.2 Harmonic regression.
5.4 Autocorrelated regression models.
5.5 Simple trend and intervention analysis.
5.6 Growth curves revisited.
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.
7 Combining environmental information.
7.1 Combining P-values.
7.2 Effect size estimation.
7.4 Historical control information.
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.
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.
"Some of the unique aspects of Piegorsch and Bailer’s treatment are benchmark dose estimation for toxicants, statistical issues in risk assessment, the assessment of trend and step changes in temporal data, and the discussion of sampling." (Journal of the American Statistical Association, June 2008)
"I enjoyed reading this book and I recommend it to those readers interested in the field of environmental statistics." (Journal of Applied Statistics, January 2009)
"This highly recommended book will provide the background for the proper application of statistical methods. These will make an invaluable contribution to the realistic assessment of the damage to the environment to be expected as a result of global warming. The subject and author indexes are both excellent." (Journal of Chemical Technology and Biotechnology, August 2006)
"This highly recommended book will provide the background for the proper application of statistical methods. These will make an invaluable contribution to the realistic assessment of the damage to the environment to be expected as a result of global warming. The subject and author indexes are both excellent." (Journal of Chemical Technology and Biotechnology, Aug 2008)
"...This is a substantial and thorough book...a handy reference book for any statistician's bookshelf..." (International Statistical Institute, January 2006)
- Provides a coherent introduction to intermediate and advanced methods for modeling and analyzing environmental data.
- Takes a data-oriented approach to describing the various methods.
- Illustrates the methods with real-world examples
- Features extensive exercises, enabling use as a course text.
- Includes examples of SAS computer code for implementation of the statistical methods.
- Connects to a Web site featuring solutions to exercises, extra computer code, and additional material.
- Serves as an overview of methods for analyzing environmental data, enabling use as a reference text for environmental science professionals.