A Panorama Of Statistics

Dieses Buch nimmt den Leser mit auf eine anregende Reise rund um die Welt der Statistik. Auf eine ganz andere Art werden Theorie und Praxis Dozenten, Studenten und Praktikern nahe gebracht. Auf jeder Etappe dieser Reise untersuchen die Autoren ungewöhnliche und skurille Aspekte der Statistik, stellen historische, biographische und philosophische Dimensionen heraus. Die einzelnen Kapitel beginnen mit einem Ausblick auf das Thema, oftmals aus unterschiedlichen Blickwinkeln. Darauf folgen fünf Fragen, die zum Nachdenken anregen. Ziel ist es, die Kenntnisse der Leser zu erweitern und zu vertiefen. Zu den Fragen gibt es auch immer wieder unterhaltsame Rätsel, mit denen spannende Paradoxa aufgelöst werden. Die Leser können ihre eigenen Entdeckungen in der Welt der Statistik mit den ausführlichen Antworten der Autoren auf die jeweiligen Fragen vergleichen.

A glorious period of Hungarian mathematics started in 1900 when Lipót Fejér discovered the summability of Fourier series.This was followed by the discoveries of his disciples in Fourier analysis and in the theory of analytic functions. At the same time Frederic (Frigyes) Riesz created functional analysis and Alfred Haar gave the first example of wavelets. Later the topics investigated by Hungarian mathematicians broadened considerably, and included topology, operator theory, differential equations, probability, etc. The present volume, the first of two, presents some of the most remarkable results achieved in the twentieth century by Hungarians in analysis, geometry and stochastics. The book is accessible to anyone with a minimum knowledge of mathematics. It is supplemented with an essay on the history of Hungary in the twentieth century and biographies of those mathematicians who are no longer active. A list of all persons referred to in the chapters concludes the volume.

A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Presents readers with a user-friendly, non-technical introductionto statistics and the principles of plant and crop experimentation.Avoiding mathematical jargon, it explains how to plan and design anexperiment, analyse results, interpret computer output and presentfindings. Using specific crop and plant case studies, this guidepresents: * The reasoning behind each statistical method is explained beforegiving relevant, practical examples * Step-by-step calculations with examples linked to three computerpackages (MINITAB, GENSTAT and SAS) * Exercises at the end of many chapters * Advice on presenting results and report writing Written by experienced lecturers, this text will be invaluable toundergraduate and postgraduate students studying plant sciences,including plant and crop physiology, biotechnology, plant pathologyand agronomy, plus ecology and environmental science students andthose wanting a refresher or reference book in statistics.

In the 1970s, scientists claimed that farm animal breeding was finally evolving from an art into a science. In their view, the switch to scientific breeding was as inevitable as the ongoing process of agricultural modernization. However, the art-to-science scenario is too simplistic to do justice to the complex dynamic that characterized the transformation of the field. The livestock breeds that take centre stage in this book – dairy cattle, chickens, pigs, sheep, and horses – were products of the twentieth century. The methods used by breeders to improve their animals, however, were much older. Tracing the history of practical stockbreeding, the role of Mendelism in scientific breeding, and the emergence of quantitative genetics, Beauty or Statistics shows that the story of the scientific modernization of livestock breeding can be more fruitfully analysed from the perspective of changing cultures of breeding, taking practical, commercial, normative, and aesthetic considerations into account.

To achieve the complex task of interpreting what we see, our brains rely on statistical regularities and patterns in visual data. Knowledge of these regularities can also be considerably useful in visual computing disciplines, such as computer vision, computer graphics, and image processing. The field of natural image statistics studies the regularities to exploit their potential and better understand human vision. With numerous color figures throughout, Image Statistics in Visual Computing covers all aspects of natural image statistics, from data collection to analysis to applications in computer graphics, computational photography, image processing, and art. The authors keep the material accessible, providing mathematical definitions where appropriate to help readers understand the transforms that highlight statistical regularities present in images. The book also describes patterns that arise once the images are transformed and gives examples of applications that have successfully used statistical regularities. Numerous references enable readers to easily look up more information about a specific concept or application. A supporting website also offers additional information, including descriptions of various image databases suitable for statistics. Collecting state-of-the-art, interdisciplinary knowledge in one source, this book explores the relation of natural image statistics to human vision and shows how natural image statistics can be applied to visual computing. It encourages readers in both academic and industrial settings to develop novel insights and applications in all disciplines that relate to visual computing.

Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/

This book deals with two main topics. The first is a theory that aims to unify the many interpretations of probability presented in the literature. The second uses this comprehensive theory of probability to answer the questions of quantum mechanics that have long been debated. The entire book proposes original solutions that several experimental cases substantiate.