“…The review of the transparency framework assesses jurisdictions against the terms of reference which focus on five key elements: i) information gathering process, ii) exchange of information, iii) confidentiality of the information received; iv) statistics on the exchanges on rulings; and v) transparency on certain aspects of intellectual property regimes. …”

“…This third edition of the OECD Style Guide is designed to help draft and organise published material so that readers can easily navigate, understand and access OECD analysis, statistics and information. It does so by 1) providing basic guidelines that meet the needs of our mostly online audiences, 2) offering tips on good writing, and 3) setting out the standards and conventions that should be used to ensure coherence and clarity across all OECD information, on all communications platforms. …”

“…The paper provides a (preliminary) analysis of the tax-to-GDP ratio and the tax mix in China as well as the average and marginal tax wedge on labour income, by applying the OECD’s Revenue Statistics and Taxing Wages methodology. Although a country’s culture, traditions and legal system play an important part in shaping its tax regime and how it can be reformed, the paper also reviews the general design issues on how to make the tax system in China more growth-friendly, simple and transparent, less distortive and fairer. …”

“…While the OECD’s annual Development Co-operation Report serves as a key reference for statistics and analysis on the latest trends in international aid, the Multilateral Aid report – as the name implies – takes a specific look at trends in multilateral aid only…”

“…Until recently, governments have measured economic progress by tallying such statistics as GDP, inflation and balance of payments. …”

“…REGPAT allows patent data to be used in connection with other regional data such as GDP or labour force statistics, and other patent-based information such as citations, technical fields and patent holder's characteristics (industry, university, etc.), thus providing researchers with the means to develop a rich set of new indicators and undertake a broad range of analyses to address issues relating to the regional dimension of innovation. …”

“…The increasing importance of licensing for innovation is supported by ample anecdotal evidence. However, statistics on this topic are scarce. The OECD, together with the European Patent Office and the University of Tokyo, carried out a business survey on the licensing-out of patents. …”

“…This updated and expanded edition of the 1992 Glossaire de l’économie anglais-français contains some 30 000 terms and expressions, covering a wide range of subject areas: general economics, statistics, finance and banking, trade, management, accounting, insurance, manpower and employment, development and tax. …”

“…These internationally comparative statistics are based on exchange rates (US, volume and price indices) and purchasing power parities. …”

“…Current responses to these problems are inadequate. Statistics and analytical tools are lacking, national approaches are highly differentiated, and analyses and concepts are rudimentary and overly sectoral. …”

“…Although trauma victims constitute around one-tenth to one-eighth of the total patient volume in hospital emergency departments, the burden of trauma on humankind is beyond these statistics. The twenty-first century is witnessing a growing threat on human beings imposed by many sources, namely natural disasters, terrorism and other conflicts, warfare, and transportation accidents; all of which ignite the rise of major trauma incidents worldwide. …”

“…It supplies a massive amount of cargo theft statistics and provides solutions and best practices to supply chain security. …”

“…What You Will Learn Explore the basic functions in R and familiarize yourself with common data structures Work with data in R using basic functions of statistics, data mining, data visualization, root solving, and optimization Get acquainted with R’s evaluation model with environments and meta-programming techniques with symbol, call, formula, and expression Get to grips with object-oriented programming in R: including the S3, S4, RC, and R6 systems Access relational databases such as SQLite and non-relational databases such as MongoDB and Redis Get to know high performance computing techniques such as parallel computing and Rcpp Use web scraping techniques to extract information Create RMarkdown, an interactive app with Shiny, DiagramR, interactive charts, ggvis, and more In Detail R is a high-level functional language and one of the must-know tools for data science and statistics. …”

“…The reader is expected to have a background in Python programming and good knowledge of statistics. What You Will Learn Get familiar with the fundamental concepts and some of the jargons used in the machine learning community Use tools and techniques to mine data from websites Grasp the core concepts of Django framework Get to know the most useful clustering and classification techniques and implement them in Python Acquire all the necessary knowledge to build a web application with Django Successfully build and deploy a movie recommendation system application using the Django framework in Python In Detail Python is a general purpose and also a comparatively easy to learn programming language. …”

“…It also establishes links between solutions proposed by different communities that studied 3D shape, such as mathematics and statistics, medical imaging, computer vision, and computer graphics. …”

“…Excel Data Analysis For Dummies, 2nd Edition eliminates the need for advanced statistics or analysis courses by allowing you to harness the full power of Excel to do the heavy lifting for you. …”

“…Make sense of your data and predict the unpredictable About This Book A unique book that centers around develop six key practical skills needed to develop and implement predictive analytics Apply the principles and techniques of predictive analytics to effectively interpret big data Solve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domains Who This Book Is For This book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues. …”

“…Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key Features Master machine learning, deep learning, and predictive modeling concepts in R 3.5 Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains Implement smart cognitive models with helpful tips and best practices Book Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. …”

“…It covers the analysis of statistics for social network analysis such as degree distribution, centrality, clustering coefficient, diameter, and path length. …”