Математика - скачать книги или читать онлайн. Страница 12

В первой части книги выдающийся математик В. И. Арнольд в полемической форме рассуждает о соотношении чистой и прикладной математики.

Эта брошюра представляет собой записки курса по элементарной теории множеств, который профессор МГУ О. В. Локуциевский читал в 60-е годы прошедшего века ученикам восьмого (в современной нумерации – девятого) класса московской математической школы номер 2.

Первая часть книги «История математики» посвящена периоду до конца XVII века. В ней сначала рассказывается об истории математики Древнего Египта, Вавилона, Древней Греции, Китая, Индии и арабских стран. Затем действие переносится в Западную Европу, и за кратким обзором Средних веков и Возрождения следует подробный рассказ о математике XVII века.

В книге рассматриваются неравенства повышенной сложности. Даны их краткие и подробные решения методом рационализации (методом замены множителей).

Профессор Иэн Стюарт в увлекательной манере и с юмором рассказывает о том, как развивалась математика – с древнейших времен и до наших дней. Он рассматривает наиболее значимые темы и события, обращая особое внимание на их прикладной характер.

Данное универсальное пособие окажет неоценимую услугу не только учащимся начальных классов и учителям, но и родителям, занимающимся со своими детьми самостоятельно. Книга включает в себя большое количество контрольных и проверочных работ по математике, которые помогут учащимся проверить и закрепить все необходимые знания и навыки. Весь материал в книге дается в строгом соответствии с требованиями школьной программы. Контрольные и проверочные работы сгруппированы по разделам, сложность которых увеличивается по мере изучения предмета. Задания в них интересны и разнообразны. Они включают в себя примеры, задачи, уравнения – все то, что необходимо знать учащимся начальной школы.

Стабильность обменного курса национальной валюты имеет большое значение для устойчивого роста экономики. В стране-экспортере нефти валютный курс в значительной степени определяется валютными поступлениями от продажи нефти и, следовательно, ценой нефти на международном рынке. В настоящей работе одномерные GARCH модели и двумерные VAR-BEKK модели используются для анализа зависимости волатильности валютного курса рубля от волатильности цены нефти.

Standardizes the definition and framework of analytics ABOK stands for Analytics Body of Knowledge. Based on the authors’ definition of analytics—which is “a process by which a team of people helps an organization make better decisions (the objective) through the analysis of data (the activity)”– this book from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. The INFORMS ABOK documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics. The INFORMS ABOK offers in-depth insight from peer-reviewed chapters that provide readers with a better understanding of the dynamic field of analytics. Chapters cover: Introduction to Analytics; Getting Started with Analytics; The Analytics Team; The Data; Solution Methodology; Model Building; Machine Learning; Deployment and Life Cycle Management; and The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem. Across industries and academia, readers with various backgrounds in analytics – from novices who are interested in learning more about the basics of analytics to experienced professionals who want a different perspective on some aspect of analytics – will benefit from reading about and implementing the concepts and methods covered by the INFORMS ABOK.

Provides a modern approach to least squares estimation and data analysis for undergraduate land surveying and geomatics programs Rich in theory and concepts, this comprehensive book on least square estimation and data analysis provides examples that are designed to help students extend their knowledge to solving more practical problems. The sample problems are accompanied by suggested solutions, and are challenging, yet easy enough to manually work through using simple computing devices, and chapter objectives provide an overview of the material contained in each section. Understanding Least Squares Estimation and Geomatics Data Analysis begins with an explanation of survey observables, observations, and their stochastic properties. It reviews matrix structure and construction and explains the needs for adjustment. Next, it discusses analysis and error propagation of survey observations, including the application of heuristic rule for covariance propagation. Then, the important elements of statistical distributions commonly used in geomatics are discussed. Main topics of the book include: concepts of datum definitions; the formulation and linearization of parametric, conditional and general model equations involving typical geomatics observables; geomatics problems; least squares adjustments of parametric, conditional and general models; confidence region estimation; problems of network design and pre-analysis; three-dimensional geodetic network adjustment; nuisance parameter elimination and the sequential least squares adjustment; post-adjustment data analysis and reliability; the problems of datum; mathematical filtering and prediction; an introduction to least squares collocation and the kriging methods; and more. Contains ample concepts/theory and content, as well as practical and workable examples Based on the author's manual, which he developed as a complete and comprehensive book for his Adjustment of Surveying Measurements and Special Topics in Adjustments courses Provides geomatics undergraduates and geomatics professionals with required foundational knowledge An excellent companion to Precision Surveying: The Principles and Geomatics Practice Understanding Least Squares Estimation and Geomatics Data Analysis is recommended for undergraduates studying geomatics, and will benefit many readers from a variety of geomatics backgrounds, including practicing surveyors/engineers who are interested in least squares estimation and data analysis, geomatics researchers, and software developers for geomatics.

A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.

Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

A preeminent expert in the field explores new and exciting methodologies in the ever-growing field of robust statistics Used to develop data analytical methods, which are resistant to outlying observations in the data, while capable of detecting outliers, robust statistics is extremely useful for solving an array of common problems, such as estimating location, scale, and regression parameters. Written by an internationally recognized expert in the field of robust statistics, this book addresses a range of well-established techniques while exploring, in depth, new and exciting methodologies. Local robustness and global robustness are discussed, and problems of non-identifiability and adaptive estimation are considered. Rather than attempt an exhaustive investigation of robustness, the author provides readers with a timely review of many of the most important problems in statistical inference involving robust estimation, along with a brief look at confidence intervals for location. Throughout, the author meticulously links research in maximum likelihood estimation with the more general M-estimation methodology. Specific applications and R and some MATLAB subroutines with accompanying data sets—available both in the text and online—are employed wherever appropriate. Providing invaluable insights and guidance, Robustness Theory and Application: Offers a balanced presentation of theory and applications within each topic-specific discussion Features solved examples throughout which help clarify complex and/or difficult concepts Meticulously links research in maximum likelihood type estimation with the more general M-estimation methodology Delves into new methodologies which have been developed over the past decade without stinting on coverage of “tried-and-true” methodologies Includes R and some MATLAB subroutines with accompanying data sets, which help illustrate the power of the methods described Robustness Theory and Application is an important resource for all statisticians interested in the topic of robust statistics. This book encompasses both past and present research, making it a valuable supplemental text for graduate-level courses in robustness.