Applying an intertemporal multivariate probit model, we reveal complex complementarity and substitution effects as well as new insights on the drivers of adopting input-intensive and natural resource management (NRM) practices in rural Ethiopia. First, the latent factor that drives each practice is positively and significantly correlated across time, suggesting persistency in adoption decisions. Second, the significant synergies and tradeoffs between the input-intensive and NRM practices underscore that these practices are highly compatible and, hence the importance of promoting technology packages. Third, the covariates that drive adoption significantly differ between practices, reflecting the heterogeneity in farmer behavior. Farm size was associated with the adoption of several input-intensive and NRM practices while off-farm income has the reverse effect. These findings have significant implications for food security policy in sub-Saharan Africa.

WHOSIS, the WHO Statistical Information System, is an interactive database bringing together core health statistics for the 193 WHO Member States. It comprises more than 100 indicators, which can be accessed by way of a quick search, by major categories, or through user-defined tables. The data can be further filtered, tabulated, charted and downloaded. The data are also published annually in the World Health Statistics Report released in May. The WHO Statistical Information System is the guide to health and health-related epidemiological and statistical information available from the World Health Organization. Most WHO technical programs make statistical information available, and they will be linked from here. Sponsors: WHOSIS is supported by the World Health Organization. Note: The WHO Statistical Information System (WHOSIS) has been incorporated into the Global Health Observatory (GHO) to provide you with more data, more tools, more analysis and more reports.

Statistical summary on number of assessments and rateable values contained in the Valuation List and the Government Rent Roll. The multiple file formats are available for dataset download in API.

List of variables, definitions and descriptive statistics among block groups in the study area (n = 250).

Statistical table of the number of cases by region, age group, and gender since 2017 (disease name: Listeriosis, date type: onset date, case type: confirmed case, source of infection: local, imported).

The validity of empirical research often relies upon the accuracy of self-reported behavior and beliefs. Yet, eliciting truthful answers in surveys is challenging especially when studying sensitive issues such as racial prejudice, corruption, and support for militant groups. List experiments have attracted much attention recently as a potential solution to this measurement problem. Many researchers, however, have used a simple difference-in-means estimator without being able to efficiently examine multivariate relationships between respondents' characteristics and their answers to sensitive items. Moreover, no systematic means exist to investigate role of underlying assumptions. We fill these gaps by developing a set of new statistical methods for list experiments. We identify the commonly invoked assumptions, propose new multivariate regression estimators, and develop methods to detect and adjust for potential violations of key assumptions. For empirical illustrations, we analyze list experiments concerning racial prejudice. Open-source software is made available to implement the proposed methodology.

Maternal and paternal ages are at the time of the respondent's birth, not the time of survey.

This statistic shows the results of a survey conducted in the United States in October 2018. U.S. consumers were asked if they were using the holiday promotions to make a purchase on which they had previously held off. During the survey, ** percent of the respondents said that they planned to use the promotions to purchase apparel or footwear.

The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94VL32J. Granato, G.E., and Cazenas, P.A., 2009, Highway-Runoff Database (HRDB Version 1.0)--A data warehouse and preprocessor for the stochastic empirical loading and dilution model: Washington, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, 57 p. https://pubs.usgs.gov/sir/2009/5269/disc_content_100a_web/FHWA-HEP-09-004.pdf Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the stochastic empirical loading and dilution model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136

List of Top Disciplines of Open Journal of Statistics sorted by citations.

Education and further studies: refers to various learning, education and related information collections.

Split the city's report data into smaller statistical units such as effective duplicates, with fixed rules to locate the data through independent URLs, providing CSV transmission format for different applications or software to collect and integrate data.

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.

List of Top Disciplines of Statistics and Its Interface sorted by citations.

Number of applicants and average waiting time for subsidised community care services for the elderly.