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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.
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 in Geography has established itself as the best introductory textbook on the subject: the author makes statistical concepts and techniques intellible and their applications in a wide variety of problems comprehensible, even exciting. The main feature of this much-awaited new edition is a set of 17 computer programs (with sample outputs) that cover nearly all the statistical techniques described. These have been carefully written to be user-friendly in an elementary subset of Basic to make them simple to implement on most micro computers. This means students can be more adventurous in their applications and interpretations of statistical techniques. The author has, at the same time, retained all the worked examples in the book so that the reader can gain insight into the logic of the methds by working through them by hand. These, together with problems of various levels of complexity plus comprehensive answers at the back of the book, provide the student with a clear and thorough understanding of both the methods and their potential applications.
'This book provides students with everything they need to know in both a basic statistics course and also in introductory courses focused on spatial data analysis. It is extremely well-written, well-organised and has plenty of easily understood examples - really the ideal textbook. I recommend it extremely highly' - Stewart Fotheringham, Director, National Centre for Geocomputation National University of Ireland Maynooth The Third Edition of this bestselling student favourite has again been revised and updated to provide an expert introduction to the principal methods and techniques needed to understand a statistics module. Features new to this edition include: - further introductory material - updated exercises and illustrative examples - updated downloadable datasets
Introduces the techniques and concepts of statistics in human and physical geography. This book explains not only how to apply quantitative tools but also why and how they work. It helps students gain important skills for utilizing conventional and spatial statistics in their own research, as well as for critically evaluating the work of others.
The fourth edition of An Introduction to Statistical Problem Solving in Geography continues its standing as the definitive introduction to statistics and quantitative analysis in geography. Assuming no reader background in statistics, the authors lay out the proper role of statistical analysis and methods in human and physical geography. They delve into the calculation of descriptive summaries and graphics to explain geographic patterns and use inferential statistics (parametric and nonparametric) to test for differences (t-tests, ANOVA), relationships (regression and correlation), and spatial statistics (point and area patterns, spatial autocorrelation). This edition introduces more advanced topics, including logistic regression, two-factor ANOVA, and spatial estimation (inverse distance weighting, Kriging). Many chapters also include thought-provoking discussions of statistical concepts as they relate to the COVID-19 pandemic. Maintaining an exploratory and investigative approach throughout, the authors provide readers with real-world geographic issues and more than 50 map examples. Concepts are explained clearly and narratively without oversimplification. Each chapter concludes with a list of major goals and objectives. An epilogue offers over 150 open-ended geographic situations, inviting students to apply their new statistical skills to solve problems currently affecting our world.
From Book's Preface: Contains summary population totals for the United States, Puerto Rico, and the Island areas and for major race groups and an overview of political, statistical, and technological context in which the census took place. Describes preparations for the census, including lessons learned from the 1990 census, consultations with governmental and other data users, recommendations from the National Academy of Sciences and other advisory groups, and the plans for and results of census tests conducted between 1992 and 1998. Summarizes the history of each question on the short and long forms, the response categories, data uses, and any associated editing, allocation, and coding instructions. Reviews evaluations and recommendations from the 1990 program, the decision to use paid advertising in Census 2000, developing and implementing an integrated marketing strategy, components of the partnership program, and a series of special initiatives. Describes the organization and distribution of regional census centers and local census offices, the hiring and training of temporary field staff, the hardware and software used to track and assess census progress, and the different components of the enumeration process. Summarizes the decision to hire contractors to conduct data capture and manage the data capture centers, the hardware and software used to capture census data, the headquarters tabulation process, identification and deletion of duplicates, editing and imputation, intermediate data files, and the creation of the 100 percent and sample detail files. Covers such topics as data collection and tabulation geography, mapping, creating and updating the census address list, data products and their dissemination, the experimental and evaluation programs, legislation, litigation, the debate over sampling, and the census in Puerto Rico and the Island Areas.
This book gives examples of the use of geographical analysis in several real life projects. Each chapter describes a specific problem, the environment existing during its definition and the methodologies used to solve it. There is plenty of information to look for more techniques and a bibliography to supplement the knowledge of every method and situation. This is a unique piece of work relating theoretical concepts to their applications in private, government and research companies.
Geography in America at the Dawn of the 21st Century surveys American geographers' current research in their specialty areas and tracks trends and innovations in the many subfields of geography. As such, it is both a 'state of the discipline' assessment and a topical reference. It includes an introduction by the editors and 47 chapters, each on a specific specialty. The authors of each chapter were chosen by their specialty group of the American Association of Geographers (AAG). Based on a process of review and revision, the chapters in this volume have become truly representative of the recent scholarship of American geographers. While it focuses on work since 1990, it additionally includes related prior work and work by non-American geographers. The initial Geography in America was published in 1989 and has become a benchmark reference of American geographical research during the 1980s. This latest volume is completely new and features a preface written by the eminent geographer, Gilbert White.
The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.