Analyzing Environmental Data

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.* Features extensive exercises, enabling use as a course text.* Includes examples of SAS computer code for implementation of the methodology.* Supported by a Website featuring solutions to exercises, extra computer code, and additional material.

Walter W. Piegorsch, University of South Carolina, Columbia, South Carolina, USA
Walter W. Piegorsch earned an M.S. and a Ph.D. Statistics at the Biometrics Unit, Cornell University. He was a Statistician with the U.S. National Institute of Environmental Health Sciences from 1984 to 1993, then moved to the University of South Carolina, Columbia, where he is now Professor and Director of Undergraduate Studies in Statistics. Walter has co-authored or co-edited two books, Statistics for Environmental Biology and Toxicology with A. John Bailer, and Case Studies in Environmental Statistics with Douglas W. Nychka and Lawrence H. Cox. He also serves or has served as a member of the Editorial Board of Environmental and Molecular Mutagenesis and Mutation Research, the Editorial Review Board of Environmental Health Perspectives, and as an Associate Editor for Environmetrics, Environmental and Ecological Statistics, Biometrics, and the Journal of the American Statistical Association. Walter is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has received a Distinguished Achievement Medal from the American Statistical Association Section on Statistics and the Environment. He has served as Vice-Chair of the American Statistical Association Council of Sections Governing Board, as Program Chairman of the Joint Statistical Meetings, and as Secretary of the Eastern North American Region of the International Biometric Society. He has also served and continues to serve on advisory boards and peer review groups for governmental agencies including the U.S. National Toxicology Program, the U.S. Environmental Protection Agency, and the U.S. National Science Foundation.

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.

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Analyzing Environmental Data

Summary

Author Biography

Table of Contents

Supplemental Materials