Statistics for Earth and Environmental Scientists (eBook)

John H. Schuenemeyer, PhD, is President of Southwest Statistical Consulting, LLC and Professor Emeritus of Statistics, Geography, and Geology at the University of Delaware. A Fellow of the American Statistical Association, Dr. Schuenemeyer has more than thirty years of academic and consulting experience and was the recipient of the 2004 John Cedric Griffiths Teaching Award, awarded by the International Association for Mathematical Geosciences. Lawrence J. Drew, PhD, is Research Scientist at the U.S. Geological Survey. Dr. Drew has published more than 200 scientific papers on the role of quantitative methods in petroleum and mineral resource assessment, and he is currently is working on an analysis of environmental data. Dr. Drew is the winner of the 2005 Krumbein Medal, awarded by the International Association for Mathematical Geosciences.

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 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.

"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."
(Zentralblatt MATH, 1 January 2013)
"Summing Up: Recommended. Upper-division undergraduates and
graduate students." (Choice, 1 September 2011)

"Proper collection and analsis 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."
(Breitbart.com: Business Wire, 2 March 2011)