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...
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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 Environmental statistics is a rapidly growing field, supported by advances in digital computing power, automated data collection systems, and interactive, linkable Internet software. Concerns over public and ecological health and the continuing need to support environmental policy-making and regulation have driven a concurrent explosion in environmental data analysis. This textbook is designed to address the need for trained professionals in this area. The book is based on a course which the authors have taught for many years, and prepares students for careers in environmental analysis centere
2.6 Multiple nonlinear regressionExercises; 3 Generalized linear models; 3.1 Generalizing the classical linear model; 3.1.1 Non-normal data and the exponential class; 3.1.2 Linking the mean response to the predictor variables; 3.2 Theory of generalized linear models; 3.2.1 Estimation via maximum likelihood; 3.2.2 Deviance function; 3.2.3 Residuals; 3.2.4 Inference and model assessment; 3.2.5 Estimation via maximum quasi-likelihood; 3.2.6 Generalized estimating equations; 3.3 Specific forms of generalized linear models; 3.3.1 Continuous/homogeneous-variance data GLiMs
3.3.2 Binary data GLiMs (including logistic regression)3.3.3 Overdispersion: extra-binomial variability; 3.3.4 Count data GLiMs; 3.3.5 Overdispersion: extra-Poisson variability; 3.3.6 Continuous/constant-CV data GLiMs; Exercises; 4 Quantitative risk assessment with stimulus-response data; 4.1 Potency estimation for stimulus-response data; 4.1.1 Median effective dose; 4.1.2 Other levels of effective dose; 4.1.3 Other potency measures; 4.2 Risk estimation; 4.2.1 Additional risk and extra risk; 4.2.2 Risk at low doses; 4.3 Benchmark analysis; 4.3.1 Benchmark dose estimation
4.3.2 Confidence limits on benchmark dose4.4 Uncertainty analysis; 4.4.1 Uncertainty factors; 4.4.2 Monte Carlo methods; 4.5 Sensitivity analysis; 4.5.1 Identifying sensitivity to input variables; 4.5.2 Correlation ratios; 4.5.3 Identifying sensitivity to model assumptions; 4.6 Additional topics; Exercises; 5 Temporal data and autoregressive modeling; 5.1 Time series; 5.2 Harmonic regression; 5.2.1 Simple harmonic regression; 5.2.2 Multiple harmonic regression; 5.2.3 Identifying harmonics: Fourier analysis; 5.3 Autocorrelation; 5.3.1 Testing for autocorrelation
5.3.2 The autocorrelation function5.4 Autocorrelated regression models; 5.4.1 AR models; 5.4.2 Extensions: MA, ARMA, and ARIMA; 5.5 Simple trend and intervention analysis; 5.5.1 Simple linear trend; 5.5.2 Trend with seasonality; 5.5.3 Simple intervention at a known time; 5.5.4 Change in trend at a known time; 5.5.5 Jump and change in trend at a known time; 5.6 Growth curves revisited; 5.6.1 Longitudinal growth data; 5.6.2 Mixed models for growth curves; Exercises; 6 Spatially correlated data; 6.1 Spatial correlation; 6.2 Spatial point patterns and complete spatial randomness
6.2.1 Chi-square tests
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