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基本説明
The text explains how to conduct data analysis, discusses simulation for model checking, and presents multilevel regression models.
Full Description
Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The author uses many examples to illustrate the statistical models and presents R implementations of the models. The book first builds a foundation for conducting a simple data analysis task, such as exploratory data analysis and fitting linear regression models. It then focuses on statistical modeling, including linear and nonlinear models, classification and regression tree, and the generalized linear model. The text also discusses the use of simulation for model checking, provides tools for a critical assessment of the developed model, and explores multilevel regression models, which are a class of models that can have a broad impact in environmental and ecological data analysis. Based on courses taught by the author at Duke University, this book focuses on statistical modeling and data analysis for environmental and ecological problems.By guiding readers through the processes of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.
Contents
BASIC CONCEPTS Introduction The Everglades Example Statistical Issues R What Is R? Getting Started with R The R Commander Statistical Assumptions The Normality Assumption The Independence Assumption The Constant Variance Assumption Exploratory Data Analysis From Graphs to Statistical Thinking Statistical Inference Estimation of Population Mean and Confidence Interval Hypothesis Testing A General Procedure Nonparametric Methods for Hypothesis Testing Significance Level alpha, Power 1 - beta, and p-Value One-Way Analysis of Variance Examples STATISTICAL MODELING Linear Models ANOVA as a Linear Model Simple and Multiple Linear Regression Models General Considerations in Building a Predictive Model Uncertainty in Model Predictions Two-Way ANOVA Nonlinear Models Nonlinear Regression Smoothing Smoothing and Additive Models Classification and Regression Tree The Willamette River Example Statistical Methods Comments Generalized Linear Model Logistic Regression Model Interpretation Diagnostics Seed Predation by Rodents: A Second Example of Logistic Regression Poisson Regression Model Generalized Additive Models ADVANCED STATISTICAL MODELING Simulation for Model Checking and Statistical Inference Simulation Summarizing Linear and Nonlinear Regression Using Simulation Simulation Based on Resampling Multilevel Regression Multilevel Structure and Exchangeability Multilevel ANOVA Multilevel Linear Regression Generalized Multilevel Models References Index
