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.

• Preface ix1 Role of Statistics and Data Analysis 11.1 Introduction 11.2 Case Studies 11.3 Data 21.4 Samples Versus the Population: Some Notation 31.5 Vector and Matrix Notation 41.6 Frequency Distributions and Histograms 51.7 Distribution as a Model 61.8 Sample Moments 91.9 Normal (Gaussian) Distribution 121.10 Exploratory Data Analysis 131.11 Estimation 171.12 Bias 181.13 Causes of Variance 211.14 About Data 211.15 Reasons to Conduct Statistically Based Studies 241.16 Data Mining 251.17 Modeling 251.18 Transformations 271.19 Statistical Concepts 281.20 Statistics Paradigms 301.21 Summary 33Exercises 342 Modeling Concepts 372.1 Introduction 372.2 Why Construct a Model? 372.3 What Does a Statistical Model Do? 382.4 Steps in Modeling 392.5 Is a Model a Unique Solution to a Problem? 442.6 Model Assumptions 452.7 Designed Experiments 472.8 Replication 492.9 Summary 49Exercises 493 Estimation and Hypothesis Testing on Means and Other Statistics 513.1 Introduction 513.2 Independence of Observations 513.3 Central Limit Theorem 523.4 Sampling Distributions 533.5 Confidence Interval Estimate on a Mean 593.6 Confidence Interval on the Difference Between Means 643.7 Hypothesis Testing on Means 703.8 Bayesian Hypothesis Testing 793.9 Nonparametric Hypothesis Testing 823.10 Bootstrap Hypothesis Testing on Means 833.11 Testing Multiple Means via Analysis of Variance 853.12 Multiple Comparisons of Means 873.13 Nonparametric ANOVA 903.14 Paired Data 913.15 Kolmogorov–Smirnov Goodness-of-Fit Test 923.16 Comments on Hypothesis Testing 943.17 Summary 95Exercises 974 Regression 994.1 Introduction 994.2 Pittsburgh Coal Quality Case Study 994.3 Correlation and Covariance 1004.4 Simple Linear Regression 1054.5 Multiple Regression 1254.6 Other Regression Procedures 1394.7 Nonlinear Models 1434.8 Summary 146Exercises 1475 Time Series 1515.1 Introduction 1515.2 Time Domain 1525.3 Frequency Domain 1815.4 Wavelets 189Contents vii5.5 Summary 189Exercises 1906 Spatial Statistics 1936.1 Introduction 1936.2 Data 1936.3 Three-Dimensional Data Visualization 1966.4 Spatial Association 1996.5 Effect of Trend 2086.6 Semivariogram Models 2106.7 Kriging 2186.8 Space–Time Models 2376.9 Summary 239Exercises 2407 Multivariate Analysis 2437.1 Introduction 2437.2 Multivariate Graphics 2447.3 Principal Components Analysis 2467.4 Factor Analysis 2577.5 Cluster Analysis 2637.6 Multidimensional Scaling 2767.7 Discriminant Analysis 2767.8 Tree-Based Modeling 2867.9 Summary 289Exercises 2908 Discrete Data Analysis and Point Processes 2938.1 Introduction 2938.2 Discrete Process and Distributions 2938.3 Point Processes 3018.4 Lattice Data and Models 3088.5 Proportions 3098.6 Contingency Tables 3128.7 Generalized Linear Models 3188.8 Summary 329Exercises 3309 Design of Experiments 3359.1 Introduction 3359.2 Sampling Designs 3359.3 Design of Experiments 3479.4 Comments on Field Studies and Design 3649.5 Missing Data 3669.6 Summary 367Exercises 36810 Directional Data 37110.1 Introduction 37110.2 Circular Data 37110.3 Spherical Data 37910.4 Summary 386Exercises 387References 389Index 399

“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 statisticianswho 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)

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.

• Preface ix1 Role of Statistics and Data Analysis 11.1 Introduction 11.2 Case Studies 11.3 Data 21.4 Samples Versus the Population: Some Notation 31.5 Vector and Matrix Notation 41.6 Frequency Distributions and Histograms 51.7 Distribution as a Model 61.8 Sample Moments 91.9 Normal (Gaussian) Distribution 121.10 Exploratory Data Analysis 131.11 Estimation 171.12 Bias 181.13 Causes of Variance 211.14 About Data 211.15 Reasons to Conduct Statistically Based Studies 241.16 Data Mining 251.17 Modeling 251.18 Transformations 271.19 Statistical Concepts 281.20 Statistics Paradigms 301.21 Summary 33Exercises 342 Modeling Concepts 372.1 Introduction 372.2 Why Construct a Model? 372.3 What Does a Statistical Model Do? 382.4 Steps in Modeling 392.5 Is a Model a Unique Solution to a Problem? 442.6 Model Assumptions 452.7 Designed Experiments 472.8 Replication 492.9 Summary 49Exercises 493 Estimation and Hypothesis Testing on Means and Other Statistics 513.1 Introduction 513.2 Independence of Observations 513.3 Central Limit Theorem 523.4 Sampling Distributions 533.5 Confidence Interval Estimate on a Mean 593.6 Confidence Interval on the Difference Between Means 643.7 Hypothesis Testing on Means 703.8 Bayesian Hypothesis Testing 793.9 Nonparametric Hypothesis Testing 823.10 Bootstrap Hypothesis Testing on Means 833.11 Testing Multiple Means via Analysis of Variance 853.12 Multiple Comparisons of Means 873.13 Nonparametric ANOVA 903.14 Paired Data 913.15 Kolmogorov–Smirnov Goodness-of-Fit Test 923.16 Comments on Hypothesis Testing 943.17 Summary 95Exercises 974 Regression 994.1 Introduction 994.2 Pittsburgh Coal Quality Case Study 994.3 Correlation and Covariance 1004.4 Simple Linear Regression 1054.5 Multiple Regression 1254.6 Other Regression Procedures 1394.7 Nonlinear Models 1434.8 Summary 146Exercises 1475 Time Series 1515.1 Introduction 1515.2 Time Domain 1525.3 Frequency Domain 1815.4 Wavelets 189Contents vii5.5 Summary 189Exercises 1906 Spatial Statistics 1936.1 Introduction 1936.2 Data 1936.3 Three-Dimensional Data Visualization 1966.4 Spatial Association 1996.5 Effect of Trend 2086.6 Semivariogram Models 2106.7 Kriging 2186.8 Space–Time Models 2376.9 Summary 239Exercises 2407 Multivariate Analysis 2437.1 Introduction 2437.2 Multivariate Graphics 2447.3 Principal Components Analysis 2467.4 Factor Analysis 2577.5 Cluster Analysis 2637.6 Multidimensional Scaling 2767.7 Discriminant Analysis 2767.8 Tree-Based Modeling 2867.9 Summary 289Exercises 2908 Discrete Data Analysis and Point Processes 2938.1 Introduction 2938.2 Discrete Process and Distributions 2938.3 Point Processes 3018.4 Lattice Data and Models 3088.5 Proportions 3098.6 Contingency Tables 3128.7 Generalized Linear Models 3188.8 Summary 329Exercises 3309 Design of Experiments 3359.1 Introduction 3359.2 Sampling Designs 3359.3 Design of Experiments 3479.4 Comments on Field Studies and Design 3649.5 Missing Data 3669.6 Summary 367Exercises 36810 Directional Data 37110.1 Introduction 37110.2 Circular Data 37110.3 Spherical Data 37910.4 Summary 386Exercises 387References 389Index 399

“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 statisticianswho 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)

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.

• Preface ix1 Role of Statistics and Data Analysis 11.1 Introduction 11.2 Case Studies 11.3 Data 21.4 Samples Versus the Population: Some Notation 31.5 Vector and Matrix Notation 41.6 Frequency Distributions and Histograms 51.7 Distribution as a Model 61.8 Sample Moments 91.9 Normal (Gaussian) Distribution 121.10 Exploratory Data Analysis 131.11 Estimation 171.12 Bias 181.13 Causes of Variance 211.14 About Data 211.15 Reasons to Conduct Statistically Based Studies 241.16 Data Mining 251.17 Modeling 251.18 Transformations 271.19 Statistical Concepts 281.20 Statistics Paradigms 301.21 Summary 33Exercises 342 Modeling Concepts 372.1 Introduction 372.2 Why Construct a Model? 372.3 What Does a Statistical Model Do? 382.4 Steps in Modeling 392.5 Is a Model a Unique Solution to a Problem? 442.6 Model Assumptions 452.7 Designed Experiments 472.8 Replication 492.9 Summary 49Exercises 493 Estimation and Hypothesis Testing on Means and Other Statistics 513.1 Introduction 513.2 Independence of Observations 513.3 Central Limit Theorem 523.4 Sampling Distributions 533.5 Confidence Interval Estimate on a Mean 593.6 Confidence Interval on the Difference Between Means 643.7 Hypothesis Testing on Means 703.8 Bayesian Hypothesis Testing 793.9 Nonparametric Hypothesis Testing 823.10 Bootstrap Hypothesis Testing on Means 833.11 Testing Multiple Means via Analysis of Variance 853.12 Multiple Comparisons of Means 873.13 Nonparametric ANOVA 903.14 Paired Data 913.15 Kolmogorov–Smirnov Goodness-of-Fit Test 923.16 Comments on Hypothesis Testing 943.17 Summary 95Exercises 974 Regression 994.1 Introduction 994.2 Pittsburgh Coal Quality Case Study 994.3 Correlation and Covariance 1004.4 Simple Linear Regression 1054.5 Multiple Regression 1254.6 Other Regression Procedures 1394.7 Nonlinear Models 1434.8 Summary 146Exercises 1475 Time Series 1515.1 Introduction 1515.2 Time Domain 1525.3 Frequency Domain 1815.4 Wavelets 189Contents vii5.5 Summary 189Exercises 1906 Spatial Statistics 1936.1 Introduction 1936.2 Data 1936.3 Three-Dimensional Data Visualization 1966.4 Spatial Association 1996.5 Effect of Trend 2086.6 Semivariogram Models 2106.7 Kriging 2186.8 Space–Time Models 2376.9 Summary 239Exercises 2407 Multivariate Analysis 2437.1 Introduction 2437.2 Multivariate Graphics 2447.3 Principal Components Analysis 2467.4 Factor Analysis 2577.5 Cluster Analysis 2637.6 Multidimensional Scaling 2767.7 Discriminant Analysis 2767.8 Tree-Based Modeling 2867.9 Summary 289Exercises 2908 Discrete Data Analysis and Point Processes 2938.1 Introduction 2938.2 Discrete Process and Distributions 2938.3 Point Processes 3018.4 Lattice Data and Models 3088.5 Proportions 3098.6 Contingency Tables 3128.7 Generalized Linear Models 3188.8 Summary 329Exercises 3309 Design of Experiments 3359.1 Introduction 3359.2 Sampling Designs 3359.3 Design of Experiments 3479.4 Comments on Field Studies and Design 3649.5 Missing Data 3669.6 Summary 367Exercises 36810 Directional Data 37110.1 Introduction 37110.2 Circular Data 37110.3 Spherical Data 37910.4 Summary 386Exercises 387References 389Index 399

“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 statisticianswho 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)