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.

Statistics Analysis of Geographical Data: An Introduction provides a comprehensive and accessible introduction to the theory and practice of statistical analysis in geography. It covers a wide range of topics including graphical and numerical description of datasets, probability, calculation of confidence intervals, hypothesis testing, collection and analysis of data using analysis of variance and linear regression. Taking a clear and logical approach, this book examines real problems with real data from the geographical literature in order to illustrate the important role that statistics play in geographical investigations. Presented in a clear and accessible manner the book includes recent, relevant examples, designed to enhance the reader’s understanding.

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.

Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustain

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.

It is increasingly important for the earth science student to appreciate that the acquisition of skills in statistics and computerised data analysis is as much part of modern geography as work in the field, laboratory or library. In this respect, Geographical Data Analysis aims to link the use of statistical techniques by means of computer software, to the acquisition of geographical-knowledge and the scientific method of enquiry. The book has three objectives: to explain basic statistical techniques and demonstrate their application to quantitative geography; to equip students with the knowledge and skills necessary for carrying out research projects; and to make the link between statistical analysis and the substantive topics taught as part of a geography course. An important innovative feature of the book is its project-orientated approach, which utilises exemplar projects drawn from human and physical geography. Each exemplar project shows the progress from the conception of the initial research through to the formulation of tentative hypotheses and the subsequent statistical analysis. The projects exemplify both primary and secondary methods for collecting geographical data, with the computer-based application of a wide range of statistical techniques. Thus, these projects allow discussion of sample design, data collection and computerisation, and a selection of appropriate statistical techniques. As such, Geographical Data Analysis integrates quantitative and geographical methodologies and provides a thorough understanding of basic statistical techniques for the undergraduate geography student; it will be of use from first year through to final degree dissertations.