Who decides how official statistics are produced? Do politicians have control or are key decisions left to statisticians in independent statistical agencies? Interviews with statisticians in Australia, Canada, Sweden, the UK and the USA were conducted to get insider perspectives on the nature of decision making in government statistical administration. While the popular adage suggests there are 'lies, damned lies and statistics', this research shows that official statistics in liberal democracies are far from mistruths; they are consistently insulated from direct political interference. Yet, a range of subtle pressures and tensions exist that governments and statisticians must manage. The power over statistics is distributed differently in different countries, and this book explains why. Differences in decision-making powers across countries are the result of shifting pressures politicians and statisticians face to be credible, and the different national contexts that provide distinctive institutional settings for the production of government numbers.

Die Multimoment-oder Haufigkeitsstudie, in der angelsachsischen Literatur als Activity Sampling oder Work Sampling bezeichnet, hat in letzter Zeit eine steigende Bedeutung fur das Arbeitsstudienwesen erlangt. Das Verfahren wurde ursprunglich zur Bestimmung der Brachzeiten von Webstuhlen entwickelt. Es wird jedoch seit einigen Jahren allgemein verwendet, um die Anteile der ver- schiedenen Zeitarten an der Gesamtzeit, Maschinennutzungsgrade, Lastgrade, Verteilzeitprozentsatze und ahnliche Kenngroessen zu ermitteln. Die Multi- momentstudie wird ferner herangezogen zur Untersuchung von Engpassen im Betrieb und zur Feststellung der Wirksamkeit von Rationalisierungsmassnahmen. Sie leistet Mithilfe bei Verkehrsuntersuchungen und bei der Loesung verschie- dener betrieblicher Probleme. Die Haufigkeitsstudie besitzt grosse wirtschaftliche Vorteile gegenuber der klassischen, fortlaufenden Zeitaufschreibung. Es handelt sich um ein Stichpro- benverfahren, bei dem zu bestimmten Zeitpunkten Augenblicksbeobachtungen des untersuchten Arbeitsplatzes oder Betriebsmittels vorgenommen werden. Dabei wird keine Stoppuhr verwendet, sondern lediglich der qualitative Zustand (z. B. Tatigkeit oder Wartezeit) des untersuchten Prozesses zum Zeitpunkt der Beobachtung festgestellt. Die Grundlagen dieses Verfahrens sind im Jahre 1934 von TIPPETT [1] erstmalig veroeffentlicht worden. TIPPETT fuhrte das Problem auf ein einfaches Bernoul- lisches Experiment zuruck und erhielt so eine Binomialverteilung der beob- achteten Ereignisse. Die Erfolgswahrscheinlichkeit war dabei die Wahrschein- lichkeit, bei einer Beobachtung den Prozess in einem bestimmten Zustand vorzufinden. Auf Grund der sich daraus ergebenden Gesetzmassigkeiten konnten der Fehlerbereich des Ergebnisses und die Zahl der notwendigen Beobachtungen bestimmt werden. An dieser Methode hat sich bis heute grundsatzlich nichts geandert.

This publication provides updated statistics on a comprehensive set of economic, financial, social, and environmental measures as well as select indicators for the Sustainable Development Goals (SDGs). The report covers the 49 regional members of ADB. It discusses trends in development progress and the challenges to achieving inclusive and sustainable economic growth across Asia and the Pacific. This 53rd edition looks at how most economies in the region have bounced back to varying degrees from the COVID-19 pandemic. A gradual recovery of cyclical industries, the release of pent-up consumer demand, and increased confidence levels have contributed to developing Asia's economy. To put into practice the "leave no one behind" principle of the Sustainable Development Goals, detailed and informative data is crucial. The 2022 report features a special supplement, Mapping the Public Voice for Development-Natural Language Processing of Social Media Text Data, which explores how natural language processing techniques can be applied to social media text data to map public sentiment and inform development research and policy making.

This title is part of UC Press's Voices Revived program, which commemorates University of California Press's mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1953.

Business Statistics using Excel offers a comprehensive introduction to the subject of statistics and equips students with the tools and skills that will enable them to approach their course with confidence. The step-by-step methods are accompanied by illustrative Excel screenshots to provide clear and helpful explanations of the techniques you will need when applying Excel skills to business statistics. The text is designed for a typical one semester business statistics course and each chapter is packed with exercises to engage students and encourage self-assessment. This second edition has been fully revised to include an online refresher course in numerical skills and Microsoft Excel to reinforce students' confidence in their mathematical ability, or to check for basic maths knowledge if it has been some time since they studied it. The table of contents has been revised to more accurately map to a typical one semester Business Statistics course. More focus has been given to employability skills and the authors draw parallels between textbook content and employability skills, enabling students to contextualise their learning and identify how these skills can be applied and valued in real business environments. A wealth of pedagogical features have been integrated to ensure plenty of examples are used throughout as well as running 'techniques in practice' exercises at the end of each chapter. This textbook is accompanied by an extensive Online Resource Centre which offers a range of additional resources for both students and lecturers. Online Resource Centre Student resources: Introduction to Microsoft Excel 2010 Self-test Multiple Choice Questions Data from the exercises in the book Key websites Online Glossary Revision tips Visual walkthroughs Numerical skills workbook Lecturer resources: Instructor's Manual: Guide to structuring lectures and seminars Worked-out answers to exercises in the book PowerPoint slides Testbank: 30 Questions per chapter Assignment Questions Examination questions

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]

Patterns of Economic Change by State and Area: Income, Employment, and Gross Domestic Product is a special edition of Business Statistics of the United States. It presents data on personal income, employment, and gross domestic product for the United States as a whole, and by region, state, and metropolitan statistical area (MSA). Data on personal income and employment extends back to 1960 for the states and regions and to 1970 for the MSAs. Patterns of Economic Change complements other Bernan Press titles such as the State and Metropolitan Area Data Book and County and City Extra. In contrast to their predominantly current and detailed cross-section data on states and metropolitan areas, this book contributes historical time-series measurements of key aggregates that show how the economies of regions, states, and metropolitan areas have responded over time to cyclical currents and long-term trends. Statistics at the state level provide a framework for analyzing current economic conditions in each state and can serve as a basis for decision making. For example: Federal government agencies use the statistics as a basis for allocating funds and determining matching grants to states. The statistics are also used in forecasting models to project energy and water use. State governments use the statistics to project tax revenues and the need for public services. Academic regional economists use the statistics for applied research. Businesses, trade associations, and labor organizations use the statistics for market research.

Bernan Press proudly presents the 15th edition of Employment, Hours, and Earnings: States and Areas, 2020. A special addition to Bernan Press Handbook of U.S. Labor Statistics: Employment, Earnings, Prices, Productivity, and Other Labor Data, this reference is a consolidated wealth of employment information, providing monthly and annual data on hours worked and earnings made by industry, including figures and summary information spanning several years. These data are presented for states and metropolitan statistical areas. This edition features: Nearly 300 tables with data on employment for each state, the District of Columbia, and the nation's seventy-five largest metropolitan statistical areas (MSAs) Detailed, non-seasonally adjusted, industry data organized by month and year Hours and earnings data for each state, by industry An introduction for each state and the District of Columbia that denotes salient data and noteworthy trends, including changes in population and the civilian labor force, industry increases and declines, employment and unemployment statistics, and a chart detailing employment percentages, by industry Ranking of the seventy-five largest MSAs, including census population estimates, unemployment rates, and the percent change in total nonfarm employment, Concise technical notes that explain pertinent facts about the data, including sources, definitions, and significant changes; and provides references for further guidance A comprehensive appendix that details the geographical components of the seventy-five largest MSAs The employment, hours, and earnings data in this publication provide a detailed and timely picture of the fifty states, the District of Columbia, and the nation's seventy-five largest MSAs. These data can be used to analyze key factors affecting state and local economies and to compare national cyclical trends to local-level economic activity. This reference is an excellent source of information for analysts in both the public and private sectors. Readers who are involved in public policy can use these data to determine the health of the economy, to clearly identify which sectors are growing and which are declining, and to determine the need for federal assistance. State and local jurisdictions can use the data to determine the need for services, including training and unemployment assistance, and for planning and budgetary purposes. In addition, the data can be used to forecast tax revenue. In private industry, the data can be used by business owners to compare their business to the economy as a whole; and to identify suitable areas when making decisions about plant locations, wholesale and retail trade outlets, and for locating a particular sector base.

This publication sets out a framework for measuring the importance of the digital economy in national and global production processes. Amid the growing interest in the digitalization of socioeconomic activities, there is a lack of consensus on an established framework to estimate the digital economy. This report proposes a definition of the core digital economy and an input-output analytical framework to measure it. Applying this framework to selected economies and years, it finds that the digital economy and digitally dependent industries contribute a significant portion of gross domestic product. It examines key digital economy phenomena and trends in relation to sectoral links, temporal price changes, jobs, global value chains, the COVID-19 pandemic, and Industry 4.0.

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.

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."