Environmental and Ecological Statistics with R / Edition 1

Environmental and Ecological Statistics with R / Edition 1

by Song S. Qian
     
 

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

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Overview

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.

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Product Details

ISBN-13:
9781420062069
Publisher:
Taylor & Francis
Publication date:
08/10/2009
Series:
Chapman & Hall/CRC Applied Environmental Statistics Series
Pages:
440
Product dimensions:
5.90(w) x 9.20(h) x 0.90(d)

Table of 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

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