Please Read Notes: Brand New, International Softcover Edition, Printed in black and white pages, minor self wear on the cover or pages, Sale restriction may be printed on the book, but Book name, contents, and author are exactly same as Hardcover Edition. Fast delivery through DHL/FedEx express.

Indisputably recognized as a classic in its field, Statistics of Extremes was the first book to exclusively evaluate the relevance of maximum and minimum (extreme) values. More than fifty years after publication, it remains relevant and helpful to the contemporary work of statisticians, engineers, and scientists. In this far-sighted and important work, Gumbel explains the applications of statistical extremes and reinforces its main ideas with generalized exercises. Key topics in this book include: Aims and tools Order statistics and their exceedances Exact distribution of extremes Analytical study of extremes First asymptotic distribution Uses of the first, second, and third asymptotes Range To remain relevant to a wide audience of statisticians, Professor Gumbel relies on elementary language to convey highly technical concepts. With 47 tables and 97 graphs, the focus is on graphical procedures rather than calculations. Gumbel's original research in the 1940s was developed to predict natural disasters, but as he further refined his theory and tools of extreme statistics, it became clear it has applications in numerous fields.

This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.

Statistics: An Intuitive Approach by George H. Weinberg is a comprehensive guide to understanding statistics. The book is written in a clear and concise manner, making it accessible to readers with little to no prior knowledge of statistics. The author takes an intuitive approach to teaching statistics, using real-world examples and practical applications to help readers understand complex concepts. The book covers a wide range of topics, including probability, hypothesis testing, correlation, regression, and analysis of variance. Each chapter includes numerous examples and exercises to help readers practice what they have learned. The author also includes a glossary of key terms and a list of recommended readings for further study. Overall, Statistics: An Intuitive Approach is an excellent resource for students, professionals, and anyone looking to gain a better understanding of statistics. The book is well-written, easy to follow, and provides a solid foundation for further study in the field.This scarce antiquarian book is a facsimile reprint of the old original and may contain some imperfections such as library marks and notations. Because we believe this work is culturally important, we have made it available as part of our commitment for protecting, preserving, and promoting the world's literature in affordable, high quality, modern editions, that are true to their original work.

Gini's mean difference (GMD) was first introduced by Corrado Gini in 1912 as an alternative measure of variability. GMD and the parameters which are derived from it (such as the Gini coefficient or the concentration ratio) have been in use in the area of income distribution for almost a century. In practice, the use of GMD as a measure of variability is justified whenever the investigator is not ready to impose, without questioning, the convenient world of normality. This makes the GMD of critical importance in the complex research of statisticians, economists, econometricians, and policy makers. This book focuses on imitating analyses that are based on variance by replacing variance with the GMD and its variants. In this way, the text showcases how almost everything that can be done with the variance as a measure of variability, can be replicated by using Gini. Beyond this, there are marked benefits to utilizing Gini as opposed to other methods. One of the advantages of using Gini methodology is that it provides a unified system that enables the user to learn about various aspects of the underlying distribution. It also provides a systematic method and a unified terminology. Using Gini methodology can reduce the risk of imposing assumptions that are not supported by the data on the model. With these benefits in mind the text uses the covariance-based approach, though applications to other approaches are mentioned as well.