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Author Qian, Song S., author.

Title Environmental and ecological statistics with R / Song S. Qian.

Published Boca Raton, FL : Taylor & Francis Group, [2017]
©2017.

Copies

Location Call No. Status
 UniM Cres  550.2855133 QIAN    AVAILABLE
Edition Second edition.
Physical description xxiii, 535 pages : illustrations ; 24 cm.
Series Chapman & Hall/CRC applied environmental statistics.
Applied environmental statistics.
Bibliography Includes bibliographical references and index.
Contents Machine generated contents note: 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 --.
Contents note continued: 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 --.
Contents note continued: 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 --.
Contents note continued: 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 --.
Contents note continued: 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 --.
Contents note continued: 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 --.
Contents note continued: 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 Significance Testing -- 11.1.Introduction -- 11.2.Evaluating TITAN -- 11.2.1.A Brief Description of TITAN --.
Contents note continued: 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.
Subject Environmental sciences -- Statistical methods.
Ecology -- Statistical methods.
R (Computer program language)
ISBN 9781498728720 (hardback : alkaline paper)