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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.

How to prevent corporate scandals and fix good companies that do wrong The news media is replete with stories of corporate scandal, corruption and misdeeds. The need for effective crisis management and corporate governance strategies has never been greater. Broken Business explains why corporate scandals happen, what to do when scandals arise in your company, and how to prevent their future occurrence. Offering real-world anecdotes and solutions, this book details how corporations can mitigate the risk of scandal, reform corporate image and install structures to create a more ethical and profitable company. This insightful resource dispels common misconceptions of corporate misconduct and its causes through fascinating research into human nature, and compelling storytelling that demonstrates fundamental flaws in corporate culture. Author José Hernandez draws on decades of experience working with high-profile global corporations to present seven essential steps for transforming a company, including building a better culture, more effective compliance systems and re-focusing the strategy. This book allows you to: Examine current and highly publicized cases of corporate scandal and their impact on corporate credibility Employ practical methods to rehabilitate your corporation’s public image Implement managerial frameworks to quickly address cases of misconduct Promote a culture of compliance and integrity to encourage good conduct in your corporate environment At its core, this book is a simple, engaging “how to” guide that offers practical advice on institutionalizing integrity in any organization. Broken Business: Seven Steps to Reform Good Companies Gone Bad is an essential text for leaders seeking a concise review at how things can go wrong, how to deal with scandal fallout and how to ultimately become a better company.

Contains an overview of several technical topics of Quantile Regression Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to visually introduce several issues, and to illustrate each method. All the topics are treated theoretically and using real data examples. Designed as a practical resource, the book is thorough without getting too technical about the statistical background. The authors cover a wide range of QR models useful in several fields. The software commands in R and Stata are available in the appendixes and featured on the accompanying website. The text: Provides an overview of several technical topics such as robustness of quantile regressions, bootstrap and elemental sets, treatment effect estimators Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles Offers a general introduction to linear programming focusing on the simplex method as solving method for the quantile regression problem Considers time-series issues like non-stationarity, spurious regressions, cointegration, conditional heteroskedasticity via quantile regression Offers an analysis that is both theoretically and practical Presents real data examples and graphical representations to explain the technical issues Written for researchers and students in the fields of statistics, economics, econometrics, social and environmental science, this text offers guide to the theory and application of quantile regression models.

The emergence of social networks, OpenCourseWare, Massive Open Online Courses, informal remote learning and connectivist approaches to learning has made the analysis and evaluation of Digital Learning Environments more complex. Modeling these complex systems makes it possible to transcribe the phenomena observed and facilitates the study of these processes with the aid of specific tools. Once this essential step is taken, it then becomes possible to develop plausible scenarios from the observation of emerging phenomena and dominant trends. This book highlights the contribution of complex systems theory in the study of next generation Digital Learning Environments. It describes a realistic approach and proposes a range of effective management tools to achieve it.