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Statistics for Earth and Environmental Scientists

ISBN-10: 0470584696
ISBN-13: 9780470584699
Edition: 2011
List price: $110.00
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Description: The goal of this book is to present skills and knowledge of important statistical concepts through data analytic tools and then apply them to real world problems in the environmental sciences. Since there is no single approach that works best in all  More...

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Book details

List price: $110.00
Copyright year: 2011
Publisher: John Wiley & Sons, Limited
Publication date: 1/4/2011
Binding: Hardcover
Pages: 420
Size: 6.00" wide x 10.00" long x 1.25" tall
Weight: 1.716
Language: English

The goal of this book is to present skills and knowledge of important statistical concepts through data analytic tools and then apply them to real world problems in the environmental sciences. Since there is no single approach that works best in all research circumstances, the authors introduce models using the frequentist approach, but also discuss Bayesian, nonparametric, and computer intensive methods. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, sampling all of which are explored in the context of case studies, which use real data from earth science applications. Subsequent chapters focus on general principles of modeling (namely formulation, estimation, and evaluation), interval estimation and hypothesis testing of means; regression; methods for analyzing time series data; spatial statistics; multivariate analysis; discrete distributions; and experimental design. Most statistical models are introduced by concept and application, given as equations, and then accompanied by heuristic justification rather than a formal proof. In addition, graphs throughout the book illustrate concepts, identify unusual observations, and assist in model evaluation. Each chapter concludes with exercises that are designed to elicit understanding of the processes of data analysis, model building, and statistical inference. The authors encourage readers to collect their own data and incorporate them into these chapter problems, facilitating an intricate and hands-on understanding of the presented concepts. Solutions to most problems are available on the bookrs"s related Web site, along with additional data sets and R-code for most graphs and analyses.

Preface
Role of Statistics and Data Analysis
Introduction
Case Studies
Data
Samples Versus the Population: Some Notation
Vector and Matrix Notation
Frequency Distributions and Histograms
Distribution as a Model
Sample Moments
Normal (Gaussian) Distribution
Exploratory Data Analysis
Estimation
Bias
Causes of Variance
About Data
Reasons to Conduct Statistically Based Studies
Data Mining
Modeling
Transformations
Statistical Concepts
Statistics Paradigms
Summary
Exercises
Modeling Concepts
Introduction
Why Construct a Model?
What Does a Statistical Model Do?
Steps in Modeling
Is a Model a Unique Solution to a Problem?
Model Assumptions
Designed Experiments
Replication
Summary
Exercises
Estimation and Hypothesis Testing on Means and Other Statistics
Introduction
Independence of Observations
Central Limit Theorem
Sampling Distributions
Confidence Interval Estimate on a Mean
Confidence Interval on the Difference Between Means
Hypothesis Testing on Means
Bayesian Hypothesis Testing
Nonparametric Hypothesis Testing
Bootstrap Hypothesis Testing on Means
Testing Multiple Means via Analysis of Variance
Multiple Comparisons of Means
Nonparametric ANOVA
Paired Data
Kolmogorov-Smirnov Goodness-of-Fit Test
Comments on Hypothesis Testing
Summary
Exercises
Regression
Introduction
Pittsburgh Coal Quality Case Study
Correlation and Covariance
Simple Linear Regression
Multiple Regression
Other Regression Procedures
Nonlinear Models
Summary
Exercises
Time Series
Introduction
Time Domain
Frequency Domain
Wavelets
Summary
Exercises
Spatial Statistics
Introduction
Data
Three-Dimensional Data Visualization
Spatial Association
Effect of Trend
Semivariogram Models
Kriging
Space-Time Models
Summary
Exercises
Multivariate Analysis
Introduction
Multivariate Graphics
Principal Components Analysis
Factor Analysis
Cluster Analysis
Multidimensional Scaling
Discriminant Analysis
Tree-Based Modeling
Summary
Exercises
Discrete Data Analysis and Point Processes
Introduction
Discrete Process and Distributions
Point Processes
Lattice Data and Models
Proportions
Contingency Tables
Generalized Linear Models
Summary
Exercises
Design of Experiments
Introduction
Sampling Designs
Design of Experiments
Comments on Field Studies and Design
Missing Data
Summary
Exercises
Directional Data
Introduction
Circular Data
Spherical Data
Summary
Exercises
References
Index

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