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Books on the topic 'Aggregated data'

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Published: 4 June 2021
Last updated: 28 January 2023

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1

Ferguson, Walter L. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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2

Ferguson, Walter L. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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3

United States. Dept. of Agriculture. Economic Research Service., ed. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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4

Ferguson, Walter L. Pesticide use on selected crops: Aggregated data, 1977-80. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, 1985.

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5

Hildén, Jonatan, and Laura Koivunen-Niemi. Learn to Create a Visualization of Aggregated Time Data in Python With Data From ACEA (2020). 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605211.

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6

Adamson, Christopher. Mastering Data Warehouse Aggregates. New York: John Wiley & Sons, Ltd., 2006.

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7

Bagnoli, Luca, and Massimo Cini, eds. La cooperazione sociale nell'area metropolitana fiorentina. Florence: Firenze University Press, 2009. http://dx.doi.org/10.36253/978-88-8453-415-6.

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This study proposes a reading of the financial statements of co-operatives operating within the Florentine metropolitan area, with a view to underscoring business aspects of a quantitative and monetary nature. The entrepreneurial nature of the co-operatives is taken as read, focusing as with any other corporate entity on assets and economic results as indicators of success. The financial statements were collected from co-operatives with their registered offices in the provinces of Florence, Prato and Pistoia for the financial years 2004-2007, and the accounts were then aggregated, reclassified and analysed. The observations on the resulting data are then broken down by province and by type of co-operative (A or B).
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8

Bayoumi, Tamim A. Estimating trade equations from aggregate bilateral data. [Washington, D.C.]: International Monetary Fund, Asia and Pacific Department, 1999.

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9

Vasconcellos, Klaus Leite Pinto. Aspects of forecasting aggregate and discrete data. [s.l.]: typescript, 1992.

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10

FGBOU, VO. Digital analytics and financial security control of socially significant organizations. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1863937.

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The monograph is devoted to the formation of the concept of digital financial security analytics. The use of the digital environment and big data analysis tools in the system of monitoring sectoral risks and monitoring the activities of socially significant organizations from the position of the ESG strategy is disclosed. At the same time, financial security is considered as an aggregated result of the action of economic, environmental and social factors in a rapidly changing economy. It covers several key areas that make it possible to digitalize and improve the effectiveness of monitoring the activities of socially significant organizations in a complex: the development of the conceptual apparatus of socially significant business; analytical tools for assessing and forecasting financial security risks based on the concept of sustainable development; standardization of risk management. For students, postgraduates, teachers, as well as for the professional development of managerial personnel in business and government structures.
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11

Chen, Jianzhong. Probabilistic relational data mining with aggregates and hierarchies. [S.l: The author], 2004.

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12

Petrin, Amil. Measuring aggregate productivity growth using plant-level data. Cambridge, Mass: National Bureau of Economic Research, 2005.

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13

Páircéir, Rónán. Knowledge discovery from distributed aggregate data in data warehouses and statistical databases. [s.l: The Author], 2002.

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14

Virginia. Department of Rehabilitative Services. Report of the Commonwealth Neurotrauma Initiative Advisory Board on aggregated data on the operations and funds of the Commonwealth Neurotrauma Initiative Trust Fund: Report of the Virginia Department of Rehabilitative Services to the Governor and the General Assembly of Virginia. Richmond: Commonwealth of Virginia, 2003.

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15

National Postsecondary Education Cooperative. Working Group on Unit Record Data Versus Aggregate Data. Unit record versus aggregate data: Perspectives on postsecondary education data collection, retention, and release : final report of the National Postsecondary Education Cooperative Working Group on Unit Record Data Versus Aggregate Data. Washington, D.C: U.S. Dept. of Education, National Center for Education Statistics, 1998.

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16

Beaudry, Paul. The "news" view of economic fluctuations: Evidence from aggregate Japanese data and sectoral U.S. data. Cambridge, MA: National Bureau of Economic Research, 2005.

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17

Marti, Jed. Cooperative autonomous behavior of aggregate units over large scale terrain. Santa Monica, Calif: Rand, 1990.

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18

Hernández-Murillo, Rubén. The information content of regional employment data for forecasting aggregate conditions. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2004.

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19

Hawkins, D. L. Estimating change over time from aggregate samples plus partial transition data. Arlington, Tex: University of Texas at Arlington, Dept. of Mathematics, 2000.

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20

Facione, Noreen C. Critical thinking assessment in nursing education programs: An aggregate data analysis. Millbrae, CA: California Academic Press, 1997.

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21

A, Facione Peter, ed. Critical thinking assessment in nursing education programs: An aggregate data analysis. Millbrae, CA: California Academic Press, 1997.

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22

Patterson, K. D. The stationarity of data revisions: A study of some national income aggregates. Reading: University of Reading. Department of Economics, 1989.

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23

G, Mendoza Enrique. An anatomy of credit booms: Evidence from macro aggregates and micro data. Washington, D.C: Federal Reserve Board, 2008.

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24

Mendoza, Enrique G. An anatomy of credit booms: Evidence from macro aggregates and micro data. Cambridge, MA: National Bureau of Economic Research, 2008.

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25

Lin, Kenneth Shang-Kai. Estimating asymmetric information equilibrium labor market models from aggregate time series data. Nankang, Taipei, Republic of China: Institute of Economics, Academia Sinica, 1987.

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26

A solution to the ecological inference problem: Reconstructing individual behavior from aggregate data. Princeton, NJ: Princeton University Press, 1997.

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27

King, Gary. A solution to the ecological inference problem: Reconstructing individual behavior from aggregate data. Princeton, NJ: Princeton University Press, 1997.

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28

Froeschl, Karl. Metadata management in statistical information processing: A unified framework for metadata-based processing of statistical data aggregates. Wien: Springer, 1997.

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29

Salisbury, Philip S. Economic expansion and contraction: Related demographics, aggregate theory, monetary theory, equilibrium relationships, and preliminary data. Springfield, IL: People Tree, 2002.

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30

Galí, Jordi. Technology shocks and aggregate fluctuations: How well does the rbs model fit postwar U.S. data? Cambridge, MA: National Bureau of Economic Research, 2004.

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31

Malley, Jim. The permanent income hypothesis revisited: Reconciling evidence from aggregate data with the representative consumer behaviour. Glasgow: University of Glasgow, Department of Economics, 1997.

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32

Hall, Stephen. An application of the Granger and Engle two-step estimation procedureto UK aggregate wage data. London: National Institute of Economic and Social Research, 1986.

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33

Galí, Jordi. Technology shocks and aggregate fluctuations: How well does the RBC model fit postwar U.S. data? Cambridge, Mass: National Bureau of Economic Research, 2004.

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34

Minai, Keiko. Trends in alcoholic beverage consumption in postwar Japan: An analysis and interpretation of aggregate data. Singapore: Dept. of Japanese Studies, National University of Singapore, 1986.

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35

Division, UN Statistics. National accounts statistics: Main aggregates and detailed tables, 2002-2003. New York: UN, 2004.

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36

Leamer, Edward E. Workday, holiday and calendar adjustment with 21st century data: Monthly aggregates from daily diesel fuel purchases. Cambridge, MA: National Bureau of Economic Research, 2011.

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37

Brander, James A. The role of fertility and population in economic growth: Empirical results from aggregate cross-national data. Cambridge, MA: National Bureau of Economic Research, 1993.

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38

Leung, Danny. The effect of adjustment costs and organizational change on productivity in Canada: Evidence from aggregate data. Ottawa: Bank of Canada, 2004.

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39

Division, UN Statistics. National accounts statistics: Main aggregates and detailed tables, 1996-1997. Part 2. New York: UN, 2001.

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40

Attanasio, Orazio P. Intertemporal substitution, risk aversion and the Euler Equation for consumption: Evidence from aggregate and average cohort data. London: University College, 1987.

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41

Idaho. Bureau of Health Policy and Vital Statistics. Idaho vital statistics health district report: Three-year aggregate data, 1996-1998, 1999-2001 and 2002-2004. [Boise]: Idaho Dept. of Health and Welfare, 2007.

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42

Galí, Jordi. Technology shocks and aggregate fluctuations: How well does the real business cycle model fit postwar U.S. data? [Washington, D.C]: International Monetary Fund, Western Hemisphere Dept., 2004.

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43

Mizen, Paul. Should buffer stock theorists be broad or narrow minded?: Some answers from aggregateUK data: 1966-1989. Loughborough, Leics: Department of Economics, Loughborough University of Technology, 1991.

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44

McGrattan, Ellen R. Comment on Gali and Rabanal's "technology shocks and aggregate fluctuations; how well does the RBC model fit postwar U.S. data?". [Minneapolis, Minn.]: Federal Reserve Bank of Minneapolis, 2004.

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45

Brace, Paul. Aggregating Survey Data to Estimate Subnational Public Opinion. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.15.

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Public opinion’s role in shaping governmental actions is a central concern of democracy, yet the absence of systematic state-level survey data has inhibited analyses of public opinion at the subnational level. This essay traces the evolution of studies of public opinion at that level, first reviewing studies using surrogates derived from demographic variables. It next considers methodologies that develop state-level opinion from aggregated national samples. Finally, it discusses recent efforts to develop state-level opinion measures using post-sample stratification integrating limited survey data with demographic variables. There is evidence of significant cross-sectional and temporal variation in public opinion and policy across and within the states. Research on subnational public opinion once hinged on assumptions about opinion surrogates, but is now based on abundant and progressively rigorous opinion data. These studies reveal that public opinion plays an enormous role in subnational politics, with effects varying across issues, contexts, and conditions.
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46

Association of Learned and Professional Society Publishers. and John Cox Associates, eds. Aggregators and the primary journal: An ALPSP report on the impact of aggregated databases on primary journals in the academic library market and a review of publisher practice. Worthing, West Sussex: Association of Learned Professional Society Pub., 2004.

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47

Patisaul, Heather B., and Scott M. Belcher. The Path Forward. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780199935734.003.0008.

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This chapter focuses on the contemporary approaches of research being used to understand the actions of EDCs and emerging high-throughput screening approaches to examine new and existing chemicals for endocrine-disrupting activities. Concepts arising from the 2007 NRC report “Toxicity Testing in the 21st Century: A Vision and a Strategy” are delineated and the ongoing development of predictive computational toxicology approaches are addressed. The screening strategies being developed under the Tox21 and Toxicity Forecaster (ToxCast) programs are described, with a review of advantages, challenges, and progress to date. There is a brief overview of the EPA’s Interactive Chemical Safety for Sustainability (iCSS) Dashboard as a portal for accessing the ToxCast data through ToxCastDB, and the EPA’s Aggregated Computational Toxicology data warehouse (ACToR), which contains all publicly available EPA chemical toxicity data. Additional challenges related to the inability of current screening approaches to address complex physiology involved in neuroendocrine disruption are addressed.
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48

Adamson, Christopher. Mastering Data Warehouse Aggregates. Wiley & Sons, Incorporated, John, 2006.

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49

Protocol for Enhanced Isolate-Level Antimicrobial Resistance Surveillance in the Americas. Primary Phase: Bloodstream Infections. Pan American Health Organization, 2021. http://dx.doi.org/10.37774/9789275122686.

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Antimicrobial resistance (AMR) surveillance plays an important role in the early detection of resistant strains of public health importance and prompt response to outbreaks in hospitals and the community. Surveillance findings are needed to inform medical practice, antibiotic stewardship, and policy and interventions to combat AMR. Appropriate use of antimicrobials, informed by surveillance, improves patients’ treatment outcomes and reduces the emergence and spread of AMR. This protocol describes the steps and procedures to establish/enhance AMR surveillance in Latin America and the Caribbean. It provides technical guidance to integrate patient, laboratory, and epidemiological data to monitor AMR emergence, trends, and effects in the population. It also provides the necessary elements to move from aggregated data to isolate-level data surveillance starting with blood isolates. It facilitates uniform data collection processes, methods, and tools to ensure data comparability within the Region of the Americas. Finally, it builds on over a decade of experience of the regional AMR surveillance network—ReLAVRA by its Spanish acronym—and its procedures are aligned with the Global Antimicrobial Resistance Surveillance System (GLASS) methodology, enabling countries to participate in the global GLASS AMR surveillance.
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50

Innes, Martin, and Helen Innes. Signal Crimes, Social Reactions, and the Future of Environmental Criminology. Edited by Gerben J. N. Bruinsma and Shane D. Johnson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190279707.013.11.

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This chapter examines the precepts associated with the signal crimes perspective (SCP). It begins by setting out that a signal is something that transmits messages to an audience. Thinking in terms of signals and “signaling” opens up new ways of seeing crime, disorder, and social control. In particular, it keys into an event-based unit of analysis, as opposed to measuring impacts in an aggregated form. Having laid out the conceptual apparatus of the SCP, the discussion proceeds on to briefly consider how SCP compares with more established criminological frameworks for studying reactions to and consequences of crime. The latter sections of the chapter focus on the ways that changes to the information environment, associated with an era of “big data” and social media, are altering the incidents that signal and how their impacts travel across space and time.
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