This study establishes sufficient conditions for observing instances of Simpson's (data aggregation) Paradox under rank sum scoring (RSS), as used, e.g., in the Wilcoxon-Mann-Whitney (WMW) rank sum test. The WMW test is a primary nonparametric statistical test in FDA drug product evaluation and other prominent medical settings. Using computational nonparametric statistical methods, we also establish the relative frequency with which paradox-generating Simpson Reversals occur under RSS when an initial data sequence is pooled with its ordinal replicate. For each 2-sample, n-element per sample or 2 x n case of RSS considered, strict Reversals occurred for between 0% and 1.74% of data poolings across the whole sample space, roughly similar to that observed for 2 x 2 x 2 contingency tables and considerably less than that observed for path models. The Reversal rate conditional on observed initial sequence is highly variable. Despite a mode at 0%, this rate exceeds 20% for some initial sequences. Our empirical application identifies clusters of Simpson Reversal susceptibility for publicly-released mobile phone radiofrequency exposure data. Simpson Reversals under RSS are not simply a theoretical concern but can reverse nonparametric or parametric biostatistical results even in vitally important public health settings. Conceptually, Paradox incidence can be viewed as a robustness check on a given WMW statistical test result. When an instance of Paradox occurs, results constituting this instance are found to be data-scale dependent. Given that the rate of Reversal can vary substantially by initial sequence, the practice of calculating this rate conditional on observed initial sequence represents a potentially important robustness check upon a result.

The Uniform Appraisal Dataset (UAD) Aggregate Statistics Data File and Dashboards are the nation’s first publicly available datasets of aggregate statistics on appraisal records, giving the public new access to a broad set of data points and trends found in appraisal reports. The UAD Aggregate Statistics for Enterprise Single-Family, Enterprise Condominium, and Federal Housing Administration (FHA) Single-Family appraisals may be grouped by neighborhood characteristics, property characteristics and different geographic levels.DocumentationOverview (10/28/2024)Data Dictionary (10/28/2024)Data File Version History and Suppression Rates (12/18/2024)Dashboard Guide (2/3/2025)UAD Aggregate Statistics DashboardsThe UAD Aggregate Statistics Dashboards are the visual front end of the UAD Aggregate Statistics Data File. The Dashboards are designed to provide easy access to customized maps and charts for all levels of users. Access the UAD Aggregate Statistics Dashboards here.UAD Aggregate Statistics DatasetsNotes:Some of the data files are relatively large in size and will not open correctly in certain software packages, such as Microsoft Excel. All the files can be opened and used in data analytics software such as SAS, Python, or R.All CSV files are zipped.

Rapid growth in scientific output requires methods for quantitative synthesis of prior research, yet current meta-analysis methods limit aggregation to studies with similar designs. Here we describe and validate Generalized Model Aggregation (GMA), which allows researchers to combine prior estimated models of a phenomenon into a quantitative meta-model, while imposing few restrictions on the structure of prior models or on the meta-model. In an empirical validation, building on 27 published equations from 16 studies, GMA provides a predictive equation for Basal Metabolic Rate that outperforms existing models, identifies novel nonlinearities, and estimates biases in various measurement methods. Additional numerical examples demonstrate the ability of GMA to obtain unbiased estimates from potentially mis-specified prior studies. Thus, in various domains, GMA can leverage previous findings to compare alternative theories, advance new models, and assess the reliability of prior studies, extending meta-analysis toolbox to many new problems.

Identifies the type of aggregation used to combine related categories, usually within a common branch of a hierarchy, to provide information at a broader level than the level at which detailed observations are taken. (From: The OECD Glossary of Statistical Terms)

Staff records with attributes (status, rank/title, gender).

Simulated Citizens Broadband Radio Service device deployments, calculated federal incumbent protection move lists, and calculated aggregate interference statistics. This data is associated with publication, "3.5 GHz Federal Incumbent Protection Algorithms," M. R. Souryal, T. T. Nguyen, and N. J. LaSorte, in Proc. IEEE DySPAN 2018, Oct. 2018.

This table contains 4500 series, with data for years 1961 - 2008 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Inputs-outputs (2 items: Inputs; Outputs ...) North American Industry Classification System (NAICS) (64 items: Total industries; Fishing; hunting and trapping; Crop and animal production; Forestry and logging ...) Commodity (114 items: Total commodities; Grains; Other agricultural products; Live animals ...).

The macroecological pattern known as Taylor's power law (TPL) represents the pervasive tendency of the variance in population density to increase as a power function of the mean. Despite empirical illustrations in systems ranging from viruses to vertebrates, the biological significance of this relationship continues to be debated. Here we combined collection of a unique dataset involving 11 987 amphibian hosts and 332 684 trematode parasites with experimental measurements of core epidemiological outcomes to explicitly test the contributions of hypothesized biological processes in driving aggregation. After using feasible set theory to account for mechanisms acting indirectly on aggregation and statistical constraints inherent to the data, we detected strongly consistent influences of host and parasite species identity over 7 years of sampling. Incorporation of field-based measurements of host body size, its variance and spatial heterogeneity in host density accounted for host identity effects, while experimental quantification of infection competence (and especially virulence from the 20 most common host–parasite combinations) revealed the role of species-by-environment interactions. By uniting constraint-based theory, controlled experiments and community-based field surveys, we illustrate the joint influences of biological and statistical processes on parasite aggregation and emphasize their importance for understanding population regulation and ecological stability across a range of systems, both infectious and free-living.

This table contains 5907 series, with data for years 1961 - 2008 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Final demand categories (126 items: Total; final demand: final expenditure on gross domestic product (GDP); Personal expenditure; food and non-alcoholic beverages; Personal expenditure; tobacco products; Personal expenditure; alcoholic beverages bought in stores ...) Commodity (453 items: Total commodities; Cattle and calves; Hogs; Poultry ...).

Information on the outcome of applications for compensation from all the schemes managed by the CICA. Data are aggregated for convenience and where possible released at the individual level in other datasets.

The study includes an overall analysis of the global platelet aggregation devices market and evaluates key trends to forecast the output for the coming years from 2020-2034. aggregation of platelets is a technique in which human platelet cells are engaged in repair bleeding, hemostasis, & re-building of vessels.

Financial overview and grant giving statistics of Governmental Aggregation Project Inc

Indonesia Exports: Aggregate Price Average: Total data was reported at 253.500 USD/Ton in Jun 2019. This records a decrease from the previous number of 256.400 USD/Ton for May 2019. Indonesia Exports: Aggregate Price Average: Total data is updated monthly, averaging 300.250 USD/Ton from Jan 2000 (Median) to Jun 2019, with 234 observations. The data reached an all-time high of 843.200 USD/Ton in Nov 2008 and a record low of 157.900 USD/Ton in Apr 2001. Indonesia Exports: Aggregate Price Average: Total data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.JAA001: Trade Statistics.

The National Mortgage Database (NMDB®) is a nationally​​ representative five percent sample of residential mortgages in the United States. Publication ​of aggregate data from NMDB is a step toward implementing the statutory requirements of section 1324(c) of the Federal Housing Enterprises Financial Safety and Soundness Act of 1992, as amended by the Housing and Economic Recovery Act of 2008. The statute requires FHFA to conduct a monthly mortgage market survey to collect data on the characteristics of individual mortgages, both Enterprise and​ non-Enterprise, and to make the data a​vailable to the public while protecting the privacy of the borrowers.​Notes:1) All CSV file headers are now standardized as described in the Data Dictionary and Technical Notes and all CSV files are zipped.2) Alternate wide format CSV files are available. The wide format may be more easily opened by MS Excel.

The National Mortgage Database (NMDB®) is a nationally representative five percent sample of residential mortgages in the United States.

Financial overview and grant giving statistics of Texas CUC Aggregation Project Inc

This data collection contains a revised SMSA (Standard Metropolitan Statistical Area) aggregate version of the FBI's Uniform Crime Reports (UCR) statistics gathered from 1966-1976, in which original UCR agency records are combined to produce several types of crime rates, by SMSA, for eight crimes. The data were prepared by the Hoover Institution for Economic Studies of the Criminal Justice System, at Stanford University. The data in the file are an aggregation of all relevant law enforcement reporting agencies into 291 SMSAs, and corresponding approximate aggregations of crime rates and dispositions. Each record contains crime rates for one SMSA in one specific year, with data including annual statistics of eight index crimes, i.e., murder, manslaughter, rape, robbery, assault, burglary, larceny, and motor vehicle theft. Calculations include offense-based clearance rates (the number of clearances of juvenile clearances per reported offense), clearance-based rates (the number of persons charged per offense cleared by arrest), and charge-based rates (the number of persons whose cases were disposed in a particular manner per person charged). A related study is UNIFORM CRIME REPORTS, 1966-1976 (ICPSR 7676).

Structural business statistics (SBS) describes the structure, conduct and performance of economic activities, down to the most detailed activity level (several hundred economic sectors).

SBS are transmitted annually by the EU Member States on the basis of a legal obligation from 1995 onwards.

SBS covers all activities of the business economy with the exception of agricultural activities and personal services and the data are provided by all EU Member States, Iceland, Norway and Switzerland, some candidate and potential candidate countries. The data are collected by domain of activity (annex) :

• Annex I - Services,
• Annex II - Industry,
• Annex III - Trade, and
• Annex IV- Constructions and by datasets. Each annex contains several datasets as indicated in the SBS Regulation.

The majority of the data is collected by National Statistical Institutes (NSIs) by means of statistical surveys, business registers or from various administrative sources. Regulatory or controlling national offices for financial institutions or central banks often provide the information required for the financial sector (NACE Rev 2 Section K / NACE Rev 1.1 Section J).

Member States apply various statistical methods, according to the data source, such as grossing up, model based estimation or different forms of imputation, to ensure the quality of SBSs produced.

Main characteristics (variables) of the SBS data category:

• Business Demographic variables (e.g. Number of enterprises),
• "Output related" variables (e.g. Turnover, Value added),
• "Input related" variables: labour input (e.g. Employment, Hours worked); goods and services input (e.g. Total of purchases); capital input (e.g. Material investments).

All SBS characteristics are published on Eurostat’s website by tables and an example of the existent tables is presented below:

• Annual enterprise statistics: Characteristics collected are published by country and detailed on NACE Rev 2 and NACE Rev 1.1 class level (4-digits). Some classes or groups in 'services' section have been aggregated.
• Annual enterprise statistics broken down by size classes: Characteristics are published by country and detailed down to NACE Rev 2 and NACE Rev 1.1 group level (3-digits) and employment size class. For trade (NACE Rev 2 and NACE Rev 1.1 Section G) a supplementary breakdown by turnover size class is available.
• Annual regional statistics: Four characteristics are published by NUTS-2 country region and detailed on NACE Rev 2 and NACE Rev 1.1 division level (2-digits) (but to group level (3-digits) for the trade section).

More information on the contents of different tables: the detail level and breakdowns required starting with the reference year 2008 is defined in Commission Regulation N° 251/2009. For previous reference years it is included in Commission Regulations (EC) N° 2701/98 and amended by Commission Regulation N°1614/2002 and Commission Regulation N°1669/2003.

Several important derived indicators are generated in the form of ratios of certain monetary characteristics or per head values. A list with the available derived indicators is available below in the Annex.

Aggregate statistics of distributions across the three sites of answerers answering different number of unique questions.