- 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.
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.