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:
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Interval estimation and Methods for analyzinghypothesis testing of means time series data
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Spatial statistics
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Multivariate analysis
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Discrete distributions
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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.
Table of Contents
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 histograms
1.7 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 and other 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 point processes.
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.
