Statistics for Earth and Environmental Scientists

A comprehensive treatment of statistical applications for solving real-world environmental problems A host of complex problems face today's earth science community, such as evaluating the supply of remaining non-renewable energy resources, assessing the impact of people on the environment, understanding climate change, and managing the use of water. Proper collection and analysis of data using statistical techniques contributes significantly toward the solution of these problems. Statistics for Earth and Environmental Scientists presents important statistical concepts through data analytic tools and shows readers how to apply them to real-world problems. The authors present several different statistical approaches to the environmental sciences, including Bayesian and nonparametric methodologies. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, and sampling all of which are explored through case studies that use real data from earth science applications. Subsequent chapters focus on principles of modeling and the key methods and techniques for analyzing scientific data, including: Interval estimation and Methods for analyzinghypothesis testing of means time series data Spatial statistics Multivariate analysis Discrete distributions 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. Data analysis, model building, and statistical inference are stressed throughout, and readers are encouraged to collect their own data to incorporate into the exercises at the end of each chapter. Most data sets, graphs, and analyses are computed using R, but can be worked with using any statistical computing software. A related website features additional data sets, answers to selected exercises, and R code for the book's examples. Statistics for Earth and Environmental Scientists is an excellent book for courses on quantitative methods in geology, geography, natural resources, and environmental sciences at the upper-undergraduate and graduate levels. It is also a valuable reference for earth scientists, geologists, hydrologists, and environmental statisticians who collect and analyze data in their everyday work.

A comprehensive treatment of statistical applications for solvingreal-world environmental problemsA host of complex problems face today's earth science communitysuch as evaluating the supply of remaining non-renewable energyresources, assessing the impact of people on the environmentunderstanding climate change, and managing the use of water. Propercollection and analysis of data using statistical techniquescontributes significantly toward the solution of these problems.Statistics for Earth and Environmental Scientists presentsimportant statistical concepts through data analytic tools andshows readers how to apply them to real-world problems.The authors present several different statistical approaches tothe environmental sciences, including Bayesian and nonparametricmethodologies. The book begins with an introduction to types ofdata, evaluation of data, modeling and estimation, randomvariation, and sampling--all of which are explored throughcase studies that use real data from earth science applications.Subsequent chapters focus on principles of modeling and the keymethods and techniques for analyzing scientific dataincluding:* Interval estimation and Methods for analyzinghypothesis testingof means time series data* Spatial statistics* Multivariate analysis* Discrete distributions* Experimental designMost statistical models are introduced by concept andapplication, given as equations, and then accompanied by heuristicjustification rather than a formal proof. Data analysis, modelbuilding, and statistical inference are stressed throughout, andreaders are encouraged to collect their own data to incorporateinto the exercises at the end of each chapter. Most data setsgraphs, and analyses are computed using R, but can be worked withusing any statistical computing software. A related websitefeatures additional data sets, answers to selected exercises, and Rcode for the book's examples.Statistics for Earth and Environmental Scientists is anexcellent book for courses on quantitative methods in geologygeography, natural resources, and environmental sciences at theupper-undergraduate and graduate levels. It is also a valuablereference for earth scientists, geologists, hydrologists, andenvironmental statisticians who collect and analyze data in theireveryday work.

Chapter 1. Role of statistics and data analysis.1.1 Introduction.1.2 Case studies.1.3 Data.1.4 Samples versus the population, some notation.1.5 Vector and matrix notation.1.6 Frequency distributions and histograms1.7 The distribution as a model.1.8 Sample moments.1.9 Normal (Gaussian) distribution.1.10 Exploratory data analysis.1.11 Estimation.1.12 Bias.1.13 Causes of variance.1.14 About data.1.15 Reasons to conduct statistically based studies.1.16 Data mining.1.17 Modeling.1.18 Transformations.1.19 Statistical concepts.1.20 Statistics paradigms.1.21 Summary.1.22 Exercises.Chapter 2. Modeling concepts.2.1 Introduction.2.2 Why construct a model?2.3 What does a statistical model do?2.4 Steps in modeling.2.5 Is a model a unique solution to a problem?2.6 Model assumptions.2.7 Designed experiments.2.8 Replication.2.9 Summary.2.10 Exercises.Chapter 3. Estimation and hypothesis testing on means andother statistics.3.1 Introduction.3.2 Independence of observations.3.3 The Central Limit Theorem.3.4 Sampling distributions.3.4.1 t-distribution.3.5 Confidence interval estimate on a mean.3.6 Confidence interval on the difference between means.3.7 Hypothesis testing on means.3.8 Bayesian hypothesis testing.3.9 Nonparametric hypothesis testing.3.10 Bootstrap hypothesis testing on means.3.11 Testing multiple means via analysis of variance.3.12 Multiple comparisons of means.3.13 Nonparametric ANOVA.3.14 Paired data.3.15 Kolmogorov-Smirnov goodness-of-fit test.3.16 Comments on hypothesis testing.3.17 Summary.3.18 Exercises.Chapter 4. Regression.4.1 Introduction.4.2 Pittsburgh coal quality case study.4.3 Correlation and covariance.4.4 Simple linear regression.4.5 Multiple regression.4.6 Other regression procedures.4.7 Nonlinear models.4.8 Summary.4.9 Exercises.Chapter 5. Time series.5.1 Introduction.5.2 Time Domain.5.3 Frequency Domain.5.4 Wavelets.5.5 Summary.5.6 Exercises.Chapter 6. Spatial statistics.6.1 Introduction.6.2 Data.6.3 Three-dimensional data visualization.6.4 Spatial association.6.5 The effect of trend.6.6 Semivariogram models.6.7 Kriging.6.8 Space-time models.6.9 Summary.6.10 Exercises.Chapter 7. Multivariate analysis.7.1 Introduction.7.2 Multivariate graphics.7.3 Principal component analysis.7.4 Factor analysis.7.5 Cluster analysis.7.6 Multidimensional scaling.7.7 Discriminant analysis.7.8 Tree based modeling.7.9 Summary.7.10 Exercises.Chapter 8. Discrete data analysis and pointprocesses.8.1 Introduction.8.2 Discrete process and distributions.8.3 Point processes.8.4 Lattice data and models.8.5 Proportions.8.6 Contingency tables.8.7 Generalized linear models.8.8 Summary.8.9 Exercises.Chapter 9 Design of experiments.9.1 Introduction.9.2 Sampling designs.9.3 Design of experiments.9.4 Comments on field studies and design.9.5 Missing data.9.6 Summary.9.7 Exercises.Chapter 10 Directional data.10.1 Introduction.10.2 Circular data.10.3 Spherical data.10.4 Summary.10.5 Exercises.