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Author: International Monetary Fund. Statistics Dept. Publisher: International Monetary Fund ISBN: 148434202X Category : Business & Economics Languages : en Pages : 671
Book Description
This quarterly issue of Direction of Trade Statistics (DOTS) provides tables with current reported data (or estimates) on the value of merchandise trade statistics (exports and imports) by partner country for all IMF member states and other nonmember countries. Summary tables are also presented for the world and major areas. DOTS republish monthly, quarterly, and annual trade statistics as submitted by countries to the IMF. Furthermore, reported data are supplemented by estimates whenever such data are not current or are not available on a monthly basis. Monthly estimates are consistent with quarterly and annual reported data, when available. The quarterly issues (DOTSQ) and the yearbook (DOTSY) present figures for the most recent quarters and years, respectively. Estimation occurs if a reporting country does not report trade with its partners for a specific period. Data are estimated for all partners. In the absence of some or all the monthly DOTS, quarterly or annual reported DOTS are used.
Author: International Monetary Fund. Statistics Dept. Publisher: International Monetary Fund ISBN: 148434202X Category : Business & Economics Languages : en Pages : 671
Book Description
This quarterly issue of Direction of Trade Statistics (DOTS) provides tables with current reported data (or estimates) on the value of merchandise trade statistics (exports and imports) by partner country for all IMF member states and other nonmember countries. Summary tables are also presented for the world and major areas. DOTS republish monthly, quarterly, and annual trade statistics as submitted by countries to the IMF. Furthermore, reported data are supplemented by estimates whenever such data are not current or are not available on a monthly basis. Monthly estimates are consistent with quarterly and annual reported data, when available. The quarterly issues (DOTSQ) and the yearbook (DOTSY) present figures for the most recent quarters and years, respectively. Estimation occurs if a reporting country does not report trade with its partners for a specific period. Data are estimated for all partners. In the absence of some or all the monthly DOTS, quarterly or annual reported DOTS are used.
Author: International Monetary Fund. Statistics Dept. Publisher: International Monetary Fund ISBN: 1484331109 Category : Business & Economics Languages : en Pages : 1070
Book Description
This paper discusses that for countries that have introduced new currencies, the rates shown in International Financial Statistics (IFS) for the period before the introduction of the most recent currency may be used as conversion factors—they may be used to convert national currency in IFS to US dollar or SDR. In such cases, the factors are constructed by chain linking the exchange rates of the old and the new currencies. The basis used is the value of the new currency relative to the old currency, as established by the issuing agency at the time the new currency was introduced. Notes on the introduction of new currencies can be found in the Country Notes or in IFS print publication (if recent). Data on members’ IMF accounts are presented in the Fund Position section in the country tables and in four world tables. Terms and concepts of IMF accounts and the time series in the country and world tables are explained below.
Author: Peter Bruce Publisher: "O'Reilly Media, Inc." ISBN: 1491952911 Category : Computers Languages : en Pages : 322
Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Author: World Bank Publisher: World Bank Publications ISBN: 1464813566 Category : Business & Economics Languages : en Pages : 201
Book Description
Work is constantly reshaped by technological progress. New ways of production are adopted, markets expand, and societies evolve. But some changes provoke more attention than others, in part due to the vast uncertainty involved in making predictions about the future. The 2019 World Development Report will study how the nature of work is changing as a result of advances in technology today. Technological progress disrupts existing systems. A new social contract is needed to smooth the transition and guard against rising inequality. Significant investments in human capital throughout a person’s lifecycle are vital to this effort. If workers are to stay competitive against machines they need to train or retool existing skills. A social protection system that includes a minimum basic level of protection for workers and citizens can complement new forms of employment. Improved private sector policies to encourage startup activity and competition can help countries compete in the digital age. Governments also need to ensure that firms pay their fair share of taxes, in part to fund this new social contract. The 2019 World Development Report presents an analysis of these issues based upon the available evidence.
Author: David Semmelroth Publisher: John Wiley & Sons ISBN: 1118615840 Category : Business & Economics Languages : en Pages : 327
Book Description
Embrace data and use it to sell and market your products Data is everywhere and it keeps growing and accumulating. Companies need to embrace big data and make it work harder to help them sell and market their products. Successful data analysis can help marketing professionals spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Data Driven Marketing For Dummies helps companies use all the data at their disposal to make current customers more satisfied, reach new customers, and sell to their most important customer segments more efficiently. Identifying the common characteristics of customers who buy the same products from your company (or who might be likely to leave you) Tips on using data to predict customer purchasing behavior based on past performance Using customer data and marketing analytics to predict when customers will purchase certain items Information on how data collected can help with merchandise planning Breaking down customers into segments for easier market targeting Building a 360 degree view of a customer base Data Driven Marketing For Dummies assists marketing professionals at all levels of business in accelerating sales through analytical insights.
Author: John Paul Mueller Publisher: John Wiley & Sons ISBN: 1118843983 Category : Computers Languages : en Pages : 432
Book Description
Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
Author: International Monetary Fund. Statistics Dept.
Author: International Monetary Fund. Statistics Dept.
Author: International Monetary Fund. Statistics Dept.
Author: Peter Bruce
Author: 
Author: Hillol Kargupta
Author: World Bank
Author: International Monetary Fund
Author: David Semmelroth
Author: Viktor Mayer-Schönberger
Author: John Paul Mueller