Note: Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- List of Figures -- List of Tables -- I: Basic Concepts -- 1: Introduction -- 1.1 Tool for Inductive Reasoning -- 1.2 The Everglades Example -- 1.2.1 Statistical Issues -- 1.3 Effects of Urbanization on Stream Ecosystems -- 1.3.1 Statistical Issues -- 1.4 PCB in Fish from Lake Michigan -- 1.4.1 Statistical Issues -- 1.5 Measuring Harmful Algal Bloom Toxin -- 1.6 Bibliography Notes -- 1.7 Exercise -- 2: A Crash Course on R -- 2.1 What is R? -- 2.2 Getting Started with R -- 2.2.1 R Commands and Scripts -- 2.2.2 R Packages -- 2.2.3 R Working Directory -- 2.2.4 Data Types -- 2.2.5 R Functions -- 2.3 Getting Data into R -- 2.3.1 Functions for Creating Data -- 2.3.2 A Simulation Example -- 2.4 Data Preparation -- 2.4.1 Data Cleaning -- 2.4.1.1 Missing Values -- 2.4.2 Subsetting and Combining Data -- 2.4.3 Data Transformation -- 2.4.4 Data Aggregation and Reshaping -- 2.4.5 Dates -- 2.5 Exercises -- 3: Statistical Assumptions -- 3.1 The Normality Assumption -- 3.2 The Independence Assumption -- 3.3 The Constant Variance Assumption -- 3.4 Exploratory Data Analysis -- 3.4.1 Graphs for Displaying Distributions -- 3.4.2 Graphs for Comparing Distributions -- 3.4.3 Graphs for Exploring Dependency among Variables -- 3.5 From Graphs to Statistical Thinking -- 3.6 Bibliography Notes -- 3.7 Exercises -- 4: Statistical Inference -- 4.1 Introduction -- 4.2 Estimation of Population Mean and Confidence Interval -- 4.2.1 Bootstrap Method for Estimating Standard Error -- 4.3 Hypothesis Testing -- 4.3.1 t-Test -- 4.3.2 Two-Sided Alternatives -- 4.3.3 Hypothesis Testing Using the Confidence Interval -- 4.4 A General Procedure -- 4.5 Nonparametric Methods for Hypothesis Testing -- 4.5.1 Rank Transformation -- 4.5.2 Wilcoxon Signed Rank Test -- 4.5.3 Wilcoxon Rank Sum Test. , 4.5.4 A Comment on Distribution-Free Methods -- 4.6 Significance Level α, Power 1 - β, and p-Value -- 4.7 One-Way Analysis of Variance -- 4.7.1 Analysis of Variance -- 4.7.2 Statistical Inference -- 4.7.3 Multiple Comparisons -- 4.8 Examples -- 4.8.1 The Everglades Example -- 4.8.2 Kemp's Ridley Turtles -- 4.8.3 Assessing Water Quality Standard Compliance -- 4.8.4 Interaction between Red Mangrove and Sponges -- 4.9 Bibliography Notes -- 4.10 Exercises -- II: Statistical Modeling -- 5: Linear Models -- 5.1 Introduction -- 5.2 From t-test to Linear Models -- 5.3 Simple and Multiple Linear Regression Models -- 5.3.1 The Least Squares -- 5.3.2 Regression with One Predictor -- 5.3.3 Multiple Regression -- 5.3.4 Interaction -- 5.3.5 Residuals and Model Assessment -- 5.3.6 Categorical Predictors -- 5.3.7 Collinearity and the Finnish Lakes Example -- 5.4 General Considerations in Building a Predictive Model -- 5.5 Uncertainty in Model Predictions -- 5.5.1 Example: Uncertainty in Water Quality Measurements -- 5.6 Two-Way ANOVA -- 5.6.1 ANOVA as a Linear Model -- 5.6.2 More Than One Categorical Predictor -- 5.6.3 Interaction -- 5.7 Bibliography Notes -- 5.8 Exercises -- 6: Nonlinear Models -- 6.1 Nonlinear Regression -- 6.1.1 Piecewise Linear Models -- 6.1.2 Example: U.S. Lilac First Bloom Dates -- 6.1.3 Selecting Starting Values -- 6.2 Smoothing -- 6.2.1 Scatter Plot Smoothing -- 6.2.2 Fitting a Local Regression Model -- 6.3 Smoothing and Additive Models -- 6.3.1 Additive Models -- 6.3.2 Fitting an Additive Model -- 6.3.3 Example: The North American Wetlands Database -- 6.3.4 Discussion: The Role of Nonparametric Regression Models in Science -- 6.3.5 Seasonal Decomposition of Time Series -- 6.3.5.1 The Neuse River Example -- 6.4 Bibliographic Notes -- 6.5 Exercises -- 7: Classification and Regression Tree -- 7.1 The Willamette River Example. , 7.2 Statistical Methods -- 7.2.1 Growing and Pruning a Regression Tree -- 7.2.2 Growing and Pruning a Classification Tree -- 7.2.3 Plotting Options -- 7.3 Comments -- 7.3.1 CART as a Model Building Tool -- 7.3.2 Deviance and Probabilistic Assumptions -- 7.3.3 CART and Ecological Threshold -- 7.4 Bibliography Notes -- 7.5 Exercises -- 8: Generalized Linear Model -- 8.1 Logistic Regression -- 8.1.1 Example: Evaluating the Effectiveness of UV as a Drinking Water Disinfectant -- 8.1.2 Statistical Issues -- 8.1.3 Fitting the Model in R -- 8.2 Model Interpretation -- 8.2.1 Logit Transformation -- 8.2.2 Intercept -- 8.2.3 Slope -- 8.2.4 Additional Predictors -- 8.2.5 Interaction -- 8.2.6 Comments on the Crypto Example -- 8.3 Diagnostics -- 8.3.1 Binned Residuals Plot -- 8.3.2 Overdispersion -- 8.3.3 Seed Predation by Rodents: A Second Example of Logistic Regression -- 8.4 Poisson Regression Model -- 8.4.1 Arsenic Data from Southwestern Taiwan -- 8.4.2 Poisson Regression -- 8.4.3 Exposure and Offset -- 8.4.4 Overdispersion -- 8.4.5 Interactions -- 8.4.6 Negative Binomial -- 8.5 Multinomial Regression -- 8.5.1 Fitting a Multinomial Regression Model in R -- 8.5.2 Model Evaluation -- 8.6 The Poisson-Multinomial Connection -- 8.7 Generalized Additive Models -- 8.7.1 Example: Whales in the Western Antarctic Peninsula -- 8.7.1.1 The Data -- 8.7.1.2 Variable Selection Using CART -- 8.7.1.3 Fitting GAM -- 8.7.1.4 Summary -- 8.8 Bibliography Notes -- 8.9 Exercises -- III: Advanced Statistical Modeling -- 9: Simulation for Model Checking and Statistical Inference -- 9.1 Simulation -- 9.2 Summarizing Regression Models Using Simulation -- 9.2.1 An Introductory Example -- 9.2.2 Summarizing a Linear Regression Model -- 9.2.2.1 Re-transformation Bias -- 9.2.3 Simulation for Model Evaluation -- 9.2.4 Predictive Uncertainty -- 9.3 Simulation Based on Re-sampling. , 9.3.1 Bootstrap Aggregation -- 9.3.2 Example: Confidence Interval of the CART-Based Threshold -- 9.4 Bibliography Notes -- 9.5 Exercises -- 10: Multilevel Regression -- 10.1 From Stein's Paradox to Multilevel Models -- 10.2 Multilevel Structure and Exchangeability -- 10.3 Multilevel ANOVA -- 10.3.1 Intertidal Seaweed Grazers -- 10.3.2 Background N2O Emission from Agriculture Fields -- 10.3.3 When to Use the Multilevel Model? -- 10.4 Multilevel Linear Regression -- 10.4.1 Nonnested Groups -- 10.4.2 Multiple Regression Problems -- 10.4.3 The ELISA Example-An Unintended Multilevel Modeling Problem -- 10.5 Nonlinear Multilevel Models -- 10.6 Generalized Multilevel Models -- 10.6.1 Exploited Plant Monitoring-Galax -- 10.6.1.1 A Multilevel Poisson Model -- 10.6.1.2 A Multilevel Logistic Regression Model -- 10.6.2 Cryptosporidium in U.S. Drinking Water-A Poisson Regression Example -- 10.6.3 Model Checking Using Simulation -- 10.7 Concluding Remarks -- 10.8 Bibliography Notes -- 10.9 Exercises -- 11: Evaluating Models Based on Statistical Signicance Testing -- 11.1 Introduction -- 11.2 Evaluating TITAN -- 11.2.1 A Brief Description of TITAN -- 11.2.2 Hypothesis Testing in TITAN -- 11.2.3 Type I Error Probability -- 11.2.4 Statistical Power -- 11.2.5 Bootstrapping -- 11.2.6 Community Threshold -- 11.2.7 Conclusions -- 11.3 Exercises -- Bibliography -- Index.

Note: Literaturverzeichnis: Seiten 515-528