Analyzing Environmental Data

Abstract

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.