Synopses & Reviews
Review
"For more than 15 years, I've used prior editions of this text to teach statistics, because it explains difficult but crucial concepts--such as the central limit theorem--clearly and in depth. I also like the geographical examples and the inclusion of descriptive spatial and temporal statistics, such as moving averages and location quotients. Now, with David Rigby on board and a full-fledged treatment of multiple regression, spatial autocorrelation, and spatial regression, the best book just got even better."--Michael Kuby, School of Geographical Sciences, Arizona State University "A comprehensive introduction to statistical techniques and their appropriate use and application in geographic research. The book is unique in its treatment of both spatial and temporal data-analysis issues, and its methods are grounded in interesting research settings. Statistical concepts are presented in a clear and effective manner, with attention given to the theories and assumptions underpinning the techniques. Instructors will appreciate the structured exercises appearing at the end of each chapter, many of which make use of downloadable datasets.
This appealing book is especially well suited as a text for senior undergraduate and beginning graduate geography courses in statistical analysis."--Mark W. Horner, Department of Geography, Florida State University
"It is hard to imagine a more comprehensive introductory treatment of geographic statistics. Elementary Statistics for Geographers has an excellent mix of quantitative material, problem-solving techniques, and examples. The examples, both numerical and graphical, clearly demonstrate the utility and limitations of the methods presented. Early chapters on the nature of geographic data, exploratory data analysis, and bivariate statistical relationships provide a strong foundation for the rigorous chapters that follow. The range and depth of the third edition are outstanding, with new sections on ANOVA, spatial statistics, and nonparametric statistics. This book will be equally valuable as a text for advanced undergraduates and beginning graduate students and as a general reference."--Scott M. Robeson, Chair, Department of Geography, Indiana University
Review
"For more than 15 years, I've used prior editions of this text to teach statistics, because it explains difficult but crucial concepts--such as the central limit theorem--clearly and in depth. I also like the geographical examples and the inclusion of descriptive spatial and temporal statistics, such as moving averages and location quotients. Now, with David Rigby on board and a full-fledged treatment of multiple regression, spatial autocorrelation, and spatial regression, the best book just got even better."--Michael Kuby, School of Geographical Sciences, Arizona State University "A comprehensive introduction to statistical techniques and their appropriate use and application in geographic research. The book is unique in its treatment of both spatial and temporal data-analysis issues, and its methods are grounded in interesting research settings. Statistical concepts are presented in a clear and effective manner, with attention given to the theories and assumptions underpinning the techniques. Instructors will appreciate the structured exercises appearing at the end of each chapter, many of which make use of downloadable datasets.
This appealing book is especially well suited as a text for senior undergraduate and beginning graduate geography courses in statistical analysis."--Mark W. Horner, Department of Geography, Florida State University
"It is hard to imagine a more comprehensive introductory treatment of geographic statistics. Elementary Statistics for Geographers has an excellent mix of quantitative material, problem-solving techniques, and examples. The examples, both numerical and graphical, clearly demonstrate the utility and limitations of the methods presented. Early chapters on the nature of geographic data, exploratory data analysis, and bivariate statistical relationships provide a strong foundation for the rigorous chapters that follow. The range and depth of the third edition are outstanding, with new sections on ANOVA, spatial statistics, and nonparametric statistics. This book will be equally valuable as a text for advanced undergraduates and beginning graduate students and as a general reference."--Scott M. Robeson, Chair, Department of Geography, Indiana University
Synopsis
Widely adopted, this uniquely comprehensive text introduces the techniques and concepts of statistics in human and physical geography. Unlike other texts that gloss over the conceptual foundations and focus solely on method, the book explains not only how to apply quantitative tools but also why and how they work. Students gain important skills for utilizing both conventional and spatial statistics in their own research, as well as for critically evaluating the work of others. Most chapters are self-contained in order to provide maximum flexibility in course design. Requiring no math beyond algebra, the book is well suited for undergraduate and beginning graduate-level courses. Helpful features include chapter summaries, suggestions for further reading, and practice problems at the end of each chapter.
New to This Edition
*Restructured and updated to reflect current developments in the field.
*Five entirely new chapters cover graphical methods, spatial relationships, analysis of variance, extending regression analysis, and spatial analysis.
*Features even more worked examples, many with accompanying graphics.
*The companion website offers datasets and solutions to selected end-of-chapter exercises.
About the Author
James E. Burt is Professor and former chair of Geography at the University of Wisconsin-Madison. His current research focuses on development of expert system and statistical approaches for quantitative prediction of soils information. Gerald M. Barber is Associate Professor of Geography and teaches introductory and advanced courses in statistics at Queens University in Kingston, Ontario, Canada. In addition, he is the director of the program in Geographic Information Science and runs the GISLAB. His principal interests are in the application of statistical and optimization models within GIS.
David L. Rigby is Professor of Geography and Statistics at the University of California, Los Angeles. His research interests include regional growth, technological change, evolutionary economic dynamics, and the impacts of globalization and trade on wage inequality.
Table of Contents
I. Introduction
1. Statistics and Geography
II. Descriptive Statistics
2. Displaying and Interpreting Data
3. Describing Data with Statistics
4. Statistical Relationships
III. Inferential Statistics
5. Random Variables and Probability Distributions
6. Sampling
7. Point and Interval Estimation
8. One-Sample Hypothesis Testing
9. Two-Sample Hypothesis Testing
10. Nonparametric Methods
11. Analysis of Variance
12. Inferential Aspects of Linear Regression
13. Extending Regression Analysis
IV. Patterns in Space and Time
14. Spatial Patterns and Relationships
15. Time Series Analysis
Appendix: Statistical Tables