Quantitative and Statistical Approaches to Geography: A Practical Manual is a practical introduction to some quantitative and statistical techniques of use to geographers and related scientists. This book is composed of 15 chapters, each begins with an outline of the purpose and necessary mechanics of a technique or group of techniques and is concluded with exercises and the particular approach adopted. These exercises aim to enhance student's ability to use the techniques as part of the process by which sound judgments are made according to scientific standards while tackling complex problems. After a brief introduction to the principles of quantitative and statistical geography, this book goes on dealing with the topics of measures of central tendency; probability statements and maps; the problem of time-dependence, time-series analysis, non-normality, and data transformations; and the elements of sampling methodology. Other chapters cover the confidence intervals and estimation from samples, statistical hypothesis testing, analysis of contingency tests, and non-parametric tests for independent and dependent samples. The final chapters consider the evaluation of correlation coefficients, regression prediction, and choice and limitations of statistical techniques. This book is of value to undergraduate geography students.

The second edition of this widely-used undergraduate textbook is shorter than its predecessor, but now contains a thorough treatment of computational methods. It aims to cover degree students' needs in statistical methods from their first to final years. Assuming no more than basic secondary or high school mathematics, the authors provide coverage of a range of techniques, from simple descriptive to parametric and non-parametric methods in bivariate and multivariate settings. Almost invariably each technique is introduced 'manually', followed up by the appropriate application using either the SPSS or MINITAB software packages - the two statistical packages most widely used by geographers. Topics are introduced in an orderly sequence, reinforced by worked examples, so that the book can be equally valuable as part of a teaching scheme, as an aid in distance learning or as an accessible reference source on the different techniques.

Statistical Methods for Geography is a systematic introduction to the essential techniques that all students must understand to complete a module in statistical analysis. It provides an integrated overview of spatial data and spatial data analysis. Key features include: - An applied focus on methods for geographers: the text presents a detailed discussion of regression analysis and autocorrelation - the principal methods of statistical inference used in geography. - An introduction to the principles of spatial analysis and spatial patterns, including probability and probability models; hypothesis testing and sampling; analysis of variance; point patterns; and patterns in areal data.

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.

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.

How do beginning students of statistics for geography learn to fully understand the key concepts and apply the principal techniques? This text, now in its Fourth Edition, provides exactly that resource. Accessibly written, and focussed on student learning, it’s a statistics 101 that includes definitions, examples, and exercise throughout. Now fully integrated with online self-assessment exercises and video navigation, it explains everything required to get full credits for any undergraduate statistics module: Descriptive statistics, probability, inferential statistics, hypothesis testing and sampling, variance, correlation, regression analysis, spatial patterns, spatial data reduction using factor analysis and cluster analysis. Exercises in the text are complemented with online exercise and prompts that test the understanding of concepts and techniques, additional online exercises review understanding of the entire chapter, relating concepts and techniques. Completely revised and updated for accessibility, including new material (on measures of distance, statistical power, sample size selection, and basic probability) with related exercises and downloadable datasets. It is the only text required for undergraduate modules in statistical analysis, statistical methods, and quantitative geography.

A textbook for advanced undergraduate/first year graduate level courses in statistical methods in geography. Presents methods useful in research design, hypothesis testing, and analyzing spatial and functional relationships. Introduces basic statistical terms and techniques for displaying and describing distributions, and covers a range of working methods including probability and sampling, simple linear regression, extensions of the simple linear model to multiple regression and its assumptions, stepwise logit regression, and canonical and discriminant analysis.