This is a simple introduction to Geography with explanations of methods, not in symbols alone but in words and the methodsa re illustrated from the geographical problems, rather than the fields of economics, sociology, industrial management or pure science.

First published in 1978. For the non-mathematician, however, even the simpler introductory books on statistics often raise considerable problems. In this second edition First, some attention has been given to the problem of the transformation of data in order to reinforce the appreciation of the need for normally-distributed data for the use of so many techniques. Secondly, the use of probability paper, at least in simple terms, has been introduced to illustrate the ways in which the labour of probability assessments can be circumvented. Thirdly, radical changes have been made, plus considerable expansion added, to the theme of non-parametric testing, to provide a more systematic approach to what is a most important group of possible techniques for geographers. Fourthly, change and expansion are also reflected in the sections on correlation and regression, including some simple consideration of curvilinear relationships and the presentation of computational techniques more geared to the use of desk calculators rather than long-hand methods. Finally, the bibliography has also been expanded, to incorporate a wider range of books on techniques and a selection of research papers using such techniques in a geographical (or near-geographical) context.

Statistical Methods for Geography is the essential introduction for geography students looking to fully understand and apply key statistical concepts and techniques. Now in its fifth edition, this text is an accessible statistics ‘101’ focused on student learning, and includes definitions, examples, and exercises throughout. Fully integrated with online self-assessment exercises and video overviews, it explains everything required to get full credits for any undergraduate statistics module. The fifth edition of this bestselling text includes: · Coverage of descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis. · New examples from physical geography and additional real-world examples. · Updated in-text and online exercises along with downloadable datasets. This is the only text you’ll need for undergraduate courses in statistical analysis, statistical methods, and quantitative geography.

This book is aimed directly at students of geography, particularly those who lack confidence in manipulating numbers. The aim is not to teach the mathematics behind statistical tests, but to focus on the logic, so that students can choose the most appropriate tests, apply them in the most convenient way and make sense of the results. Introductory chapters explain how to use statistical methods and then the tests are arranged according to the type of data that they require. Diagrams are used to guide students toward the most appropriate tests. The focus is on nonparametric methods that make very few assumptions and are appropriate for the kinds of data that many students will collect. Parametric methods, including Student’s t-tests, correlation and regression are also covered. Although aimed directly at geography students at senior undergraduate and graduate level, this book provides an accessible introduction to a wide range of statistical methods and will be of value to students and researchers in allied disciplines including Earth and environmental science, and the social sciences.

Written for undergraduate geography majors and entry-level graduate students with limited backgrounds in statistical analysis and methods, McGrew and Monroe provide a comprehensive and understandable introduction to statistical methods in a problem-solving framework. Engaging examples and problems are drawn from a variety of topical areas in both human and physical geography and are fully integrated into the text. Without compromising statistical rigor or oversimplifying, the authors stress the importance of written narratives that explain each statistical technique. After introducing basic statistical concepts and terminology, the authors focus on nonspatial and spatial descriptive statistics. They transition to inferential problem solving, including probability, sampling, and estimation, before delving deeper into inferential statistics for geographic problem solving. The final chapters examine the related techniques of correlation and regression. A list of major goals and objectives is included at the end of each chapter, allowing students to monitor their own progress and mastery of geographic statistical materials. An epilogue, offering over 150 geographic situations, gives students a chance to figure out which statistical technique should be used for a particular situation.