This annual publication, jointly produced by the African Development Bank (AfDB), the African Union Commission (AUC) and the United Nations Economic Commission for Africa (ECA), is a result of the fruitful collaboration that exists among the three pan-African organizations within the field of statistics. This synergistic collaboration has two principal benefits: (1) it minimizes the risk of inconsistent information being produced by the three organizations, and (2) it reduces the reporting burden on member states, who might otherwise be obliged to submit data separately to each institution. The yearbook continues to serve the intended purpose of bringing together, in one volume, data on African countries for policy-makers, researchers and other users. The present edition presents a time series showing how African countries performed on several economic and social indicators over the period 2011-2019

This report presents the results of a feasibility study on high-quality poverty statistics in Thailand using satellite imagery, geospatial data, and advanced algorithmic techniques to complement conventional survey methods. The ""leave no one behind"" principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country's population. This entails detailed, granular data on population groups that extend beyond national trends and averages. ADB collaborated with the National Statistical Office of Thailand and the Word Data Lab for the feasibility study, which aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand.

This text provides a comprehensive overview of Data Science. With the continued advancement in storage and computing technologies, data science has emerged as one of the most desired fields in driving business decisions. Data science employs techniques and methods from many other fields, such as statistics, mathematics, computer science, and information science. Besides the methods and theories drawn from several fields, data science uses visualization techniques using specially designed big data software and statistical programming language, such as R programming, and Python. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI). The book is divided into four different areas divided into different chapters. These chapters explain the core of Data Science. Part I of the book introduces the field of Data Science, different disciplines it comprises of, and the scope with future outlook and career prospects. This section also explains analytics, business analytics, and business intelligence and their similarities and differences with Data Science. Since the data is at the core of Data science, Part II is devoted to explaining the data, big data, and other features of data. One full chapter is devoted to Data Analysis, creating visuals, pivot table, and other applications using Excel with office 365. Part III explains the statistics behind Data Science. It uses several chapters to explain the statistics and its importance, numerical and data visualization tools and methods, probability, and probability distribution applications in Data Science. Other chapters in the Part III are Sampling, Estimation, and Hypothesis Testing. All these are integral part of Data Science applications. Part IV of the book provides the basics of Machine Learning (ML) and R-statistical software. Data Science has wide applications in the areas of Machine Learning (ML) and Artificial Intelligence (AI) and R-statistical software is widely used by data science professionals. The book also outlines a brief history, the body of knowledge, skills and education requirements for Data Scientist and data science professionals. Some statistics on job growth and prospects are also summarized. A career in data science is ranked at the third best job in America for 2020 by Glassdoor, and was ranked the number one best job from 2016-2019.[29]

This report presents the results of a feasibility study on generating high-quality poverty statistics in the Philippines using satellite imagery, geospatial data, and powerful machine-learning algorithms. The Oleave no one behindO principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a countryOs population. Conducted by ADB in collaboration with the Philippine Statistics Authority and the World Data Lab, the study aimed to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in the Philippines.

Intended primarily to prepare first-year graduate students for their ongoing work in econometrics, economic theory, and finance, this innovative book presents the fundamental concepts of theoretical econometrics, from measure-theoretic probability to statistics. A. Ronald Gallant covers these topics at an introductory level and develops the ideas to the point where they can be applied. He thereby provides the reader not only with a basic grasp of the key empirical tools but with sound intuition as well.

In addition to covering the basic tools of empirical work in economics and finance, Gallant devotes particular attention to motivating ideas and presenting them as the solution to practical problems. For example, he presents correlation, regression, and conditional expectation as a means of obtaining the best approximation of one random variable by some function of another. He considers linear, polynomial, and unrestricted functions, and leads the reader to the notion of conditioning on a sigma-algebra as a means for finding the unrestricted solution. The reader thus gains an understanding of the relationships among linear, polynomial, and unrestricted solutions. Proofs of results are presented when the proof itself aids understanding or when the proof technique has practical value.

A major text-treatise by one of the leading scholars in this field," An Introduction to Econometric Theory" will prove valuable not only to graduate students but also to all economists, statisticians, and finance professionals interested in the ideas and implications of theoretical econometrics.

Dieser Buchtitel ist Teil des Digitalisierungsprojekts Springer Book Archives mit Publikationen, die seit den Anfangen des Verlags von 1842 erschienen sind. Der Verlag stellt mit diesem Archiv Quellen fur die historische wie auch die disziplingeschichtliche Forschung zur Verfugung, die jeweils im historischen Kontext betrachtet werden mussen. Dieser Titel erschien in der Zeit vor 1945 und wird daher in seiner zeittypischen politisch-ideologischen Ausrichtung vom Verlag nicht beworben.

This book presents key concepts related to quantitative analysis in business. It is targeted at business students (both undergraduate and graduate) taking an introductory core course. Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts. This second edition adds material on Tableau, a very useful software for business analytics. This supplements the tools from Excel covered in the first edition, to include Data Analysis Toolpak and SOLVER.

Wer berufsmaBig sich mit den Problemen der SozialOkonomie be faBt, der muB sich bewuBt sein, daB es hierbei nicht um den einzelnen Menschen, sondern um die Wirtschaft groBer Gesellschaftsgebilde, ins besondere der Volkswirtschaft, geht; der weiB aber auch, daB, indem er von der V olkswirtschaft spricht, er den einzelnen Menschen, aber als Glied des Ganzen, erfaBt und das Wohl und Wehe dieser Einzelnen im Gedeihen der Gesamtwirtschaft beschlossen liegt. Wer wissenschaftlich arbeitet, weiB, daB allein die Sache an sich ihn etwas angeht und weder das Interesse Einzelner noch das einzelner Wirtschaftsgruppen. Gerade in der nationalOkonomischen Forschung taucht immer wieder die Gefahr auf, daB das Objekt nicht Gegen stand einer Betrachtung von hoherer Warte aus ist, sondern im Lichte dieses oder jenes Interessenstandpunktes gesehen wird. Darunter leidet nicht nur diewissenschaftlicheForschung selbst-es schwindet der Glaube an die Ehrlichkeit und Wahrheitsliebe des Forschers - es leidet ebenso die Entwicklung der menschlichen Wirtschaft, soweit sie auf Ergebnisse der objektiven Forschung angewiesen ist. Die Verfolgung eines Einzel interesses wird nur dann auf lange Sicht erfolgreich sein, wenn sie kon form geht mit derjenigen des Gesellschaftsinteresses und deshalb steht die sozialokonomische, von einzelnen Menschen abstrahierende Forschung an erster Stelle, und nur unter Beachtung ihrer Ergebnisse konnen Mog lichkeiten der wirtschaftlichen Forderung von Einzelnen oder Gruppen des Ganzen wissenschaftlich untersucht werden."