Includes bibliographical references (p. 383-387) and index.

1. Introduction -- 1.1. Introduction -- 1.2. Three case studies -- 1.3. Overview of some solution strategies -- 1.4. Review: principles of project management -- 1.5. Applications -- 1.6. S-Plus and R orientation I: introduction -- 1.7. S-Plus and R orientation II: distributions -- 1.8. S-Plus and R orientation III: Estimation of mean and proportion, sampling error, and confidence intervals -- 1.9. S-Plus and R orientation IV: linear regression -- 1.10. Summary -- 2. Bayesian statistical analysis I: Introduction -- 2.1. Introduction -- 2.2. Three methods for fitting models to datasets -- 2.3. Bayesian paradigm for statistical inference: Bayes theorem -- 2.4. Conjugate priors -- 2.5. Other priors -- 2.6. Summary -- 3. Bayesian statistical inference II: Bayesian hypothesis testing and decision theory -- 3.1. Bayesian hypothesis testing: Bayes factors -- 3.2. Bayesian decision theory -- 3.3. Preview: more advanced methods of Bayesian statiscal analysis -- Markov chain Monte Carlo (MCMC) algorithms and WinBUGS software -- 3.4. Summary -- 4. Bayesian statistical inference III: MCMC algorithms and WinBUGS software applications -- 4.1. Introduction -- 4.2. Markov Chain theory -- 4.3. MCMC algorithms -- 4.4. WinBUGS applications -- 4.5. Summary -- 5. Alternative strategies for model selection and inference using information-theoretic criteria -- 5.1. Alternative strategies for model selection and inference: descriptive and predictive model selection -- 5.2. Descriptive model selection: a posteriori exploratory model selection and inference -- 5.3. Predictive model selection: a priori parsimonious model selection and inference using information-theoretic criteria -- 5.4. Methods of Fit -- 5.5. Evaluation of fit: goodness of fit -- 5.6. Model averaging -- 5.7. Applications: frequentist statistical analysis in S-Plus and R; bayesian statistical analysis in WinBUGS -- 5.8. Summary -- 6. Introduction to generalized linear models: logistic regression models -- 6.1. Introduction to generalized linear models (GLMs) -- 6.2. GLM design -- 6.3. GLM analysis -- 6.4. Logistic regression analysis -- 6.5. Other generalized linear models (GLMs) -- 6.6. S-Plus or R and WinBUGS applications -- 6.7. Summary -- 7. Introduction to mixed-effects modeling -- 7.1. Introduction -- 7.2. Dependent datasets -- 7.3. Linear mixed-effects modeling: frequentist statistical analysis in S-plus and R -- 7.4. Nonlinear mixed-effects modeling: frequentist statistical analysis in S-plus and R -- 7.5. Conclusions: frequentist statistical analysis in S-plus and R -- 7.6. Mixed-effects modeling: bayesian statistical analysis in WinBUGS -- 7.7. Summary -- 8. Summary and conclusions -- 8.1. Summary of solutions to chapter 1 case studies -- 8.2. Appropriate application of statistics in the natural resource sciences -- 8.3. Statistical guidelines for design of sample surveys and experiments -- 8.4. Two strategies for model selection and inference -- 8.5. Contemporary methods for statistical analysis I: generalized linear modeling and mixed-effects modeling -- 8.6. Contemporary methods in statistical analysis II: Bayesian statistical analysis using MCMC methods with WinBUGS software -- 8.7. Concluding remarks: effective use of statistical analysis and inference -- 8.8. Summary -- Appendix A. Review of linear regression and multiple linear regression analysis -- A.1. Introduction -- A.2. Least-squares fit: the linear regression model -- A.3. Linear regression and multiple linear regression statistics -- A.4. Stepwise multiple linear regression methods -- A.5. Best-subsets selection multiple linear regression -- A.6. Goodness of fit -- Appendix B. Answers to problems.