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.
