Elementary Statistics for Geographers Third Edition

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.

I. Introduction1. Statistics and GeographyII. Descriptive Statistics2. Displaying and Interpreting Data3. Describing Data with Statistics4. Statistical RelationshipsIII. Inferential Statistics5. Random Variables and Probability Distributions6. Sampling7. Point and Interval Estimation8. One-Sample Hypothesis Testing9. Two-Sample Hypothesis Testing10. Nonparametric Methods11. Analysis of Variance12. Inferential Aspects of Linear Regression13. Extending Regression AnalysisIV. Patterns in Space and Time14. Spatial Patterns and Relationships15. Time Series AnalysisAppendix: Statistical Tables

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 Queen’s 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.