Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.
Dataset of all the data supplied by each local authority and imputed figures used for national estimates.
This file is no longer being updated to include any late revisions local authorities may have reported to the department. Please use instead the Local authority housing statistics open data file for the latest data.
MS Excel Spreadsheet, 1.26 MB
This file may not be suitable for users of assistive technology.
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This is the latest statistical publication of linked HES (Hospital Episode Statistics) and DID (Diagnostic Imaging Dataset) data held by the Health and Social Care Information Centre. The HES-DID linkage provides the ability to undertake national (within England) analysis along acute patient pathways to understand typical imaging requirements for given procedures, and/or the outcomes after particular imaging has been undertaken, thereby enabling a much deeper understanding of outcomes of imaging and to allow assessment of variation in practice. This publication aims to highlight to users the availability of this updated linkage and provide users of the data with some standard information to assess their analysis approach against. The two data sets have been linked using specific patient identifiers collected in HES and DID. The linkage allows the data sets to be linked from April 2012 when the DID data was first collected; however this report focuses on patients who were present in either data set for the period April 2015-February 2016 only. For DID this is provisional 2015/16 data. For HES this is provisional 2015/16 data. The linkage used for this publication was created on 06 June 2016 and released together with this publication on 07 July 2016.
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Context
The dataset tabulates the population of New Point by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Point. The dataset can be utilized to understand the population distribution of New Point by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Point. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for New Point.
Key observations
Largest age group (population): Male # 60-64 years (26) | Female # 40-44 years (16). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for New Point Population by Gender. You can refer the same here
The SCA’s comprehensive capital planning process includes developing and analyzing quality data, creating and updating the Department of Education’s Five-Year Capital Plans, and monitoring projects through completion. The SCA prioritizes capital projects to best meet the capacity and building improvements needs throughout the City. Additionally, the SCA assures that the Capital Plan aligns with New York State and City Department of Education mandates, academic initiatives, and budgetary resources. This is one of the most current published reports.
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BackgroundWe have previously identified four distinct groups of asthma patients in Korean cohorts using cluster analysis: (A) smoking asthma, (B) severe obstructive asthma, (C) early-onset atopic asthma, and (D) late-onset mild asthma.Methods and ResultsA longitudinal analysis of each cluster in a Korean adult asthma cohort was performed to investigate the clinical significance of asthma clusters over 12 months.Cluster A showed relatively high asthma control test (ACT) scores but relatively low FEV1 scores, despite a high percentage of systemic corticosteroid use. Cluster B had the lowest mean FEV1, ACT, and the quality of life questionnaire for adult Korean asthmatics (QLQAKA) scores throughout the year, even though the percentage of systemic corticosteroid use was the highest among the four clusters. Cluster C was ranked second in terms of FEV1, with the second lowest percentage of systemic corticosteroid use, and showed a marked improvement in subjective symptoms over time. Cluster D consistently showed the highest FEV1, the lowest systemic corticosteroid use, and had high ACT and QLQAKA scores.ConclusionOur asthma clusters had clinical significance with consistency among clusters over 12 months. These distinctive phenotypes may be useful in classifying asthma in real practice.
Public Expenditure Statistical Analyses (PESA) is the yearly publication of information on government spending. It brings together recent outturn data, estimates for the latest year, and spending plans for the rest of the current spending review period.
PESA is based on data from departmental budgets and total expenditure on services (TES). The budgeting framework deals with spending within central government department budgets, which is how the government plans and controls spending. TES represents the spending required to deliver services – what is known as the current and capital expenditure of the public sector.
A user survey gathering feedback on the outturn data presented in the Public Spending Statistics National Statistics releases, has been launched this year. This data also feeds into the PESA outturn statistics. Please note, this also includes a brief guide on some of the statistics within these publications, and some examples of their presentation. If you would like to access the survey to assist with user feedback and share your views, please follow this link:
https://www.smartsurvey.co.uk/s/UF50ES/" class="govuk-link">Public Spending Statistics user survey
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BackgroundThe 5-year survival rate of cancer patients is the most commonly used statistic to reflect improvements in the war against cancer. This idea, however, was refuted based on an analysis showing that changes in 5-year survival over time bear no relationship with changes in cancer mortality.MethodsHere we show that progress in the fight against cancer can be evaluated by analyzing the association between 5-year survival rates and mortality rates normalized by the incidence (mortality over incidence, MOI). Changes in mortality rates are caused by improved clinical management as well as changing incidence rates, and since the latter can mask the effects of the former, it can also mask the correlation between survival and mortality rates. However, MOI is a more robust quantity and reflects improvements in cancer outcomes by overcoming the masking effect of changing incidence rates. Using population-based statistics for the US and the European Nordic countries, we determined the association of changes in 5-year survival rates and MOI.ResultsWe observed a strong correlation between changes in 5-year survival rates of cancer patients and changes in the MOI for all the countries tested. This finding demonstrates that there is no reason to assume that the improvements in 5-year survival rates are artificial. We obtained consistent results when examining the subset of cancer types whose incidence did not increase, suggesting that over-diagnosis does not obscure the results.ConclusionsWe have demonstrated, via the negative correlation between changes in 5-year survival rates and changes in MOI, that increases in 5-year survival rates reflect real improvements over time made in the clinical management of cancer. Furthermore, we found that increases in 5-year survival rates are not predominantly artificial byproducts of lead-time bias, as implied in the literature. The survival measure alone can therefore be used for a rough approximation of the amount of progress in the clinical management of cancer, but should ideally be used with other measures.
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Methods are presented for detecting differential expression using statistical hypothesis testing methods including analysis of variance (ANOVA). Practicalities of experimental design, power, and sample size are discussed. Methods for multiple testing correction and their application are described. Instructions for running typical analyses are given in the R programming environment. R code and the sample data set used to generate the examples are available at http://microarray.cpmc.columbia.edu/pavlidis/pub/aovmethods/.
m, n = 2, 1 tearing mode onset empirical probability and machine learning analyses of a multiscenario DIII-D database of over 14 000 H- mode discharges show that the normalized plasma beta, the rotation profile, and the magnetic equilibrium shape have the strongest impact on the 2,1 tearing mode stability, in qualitative agreement with neoclassical tearing modes (m and n are the poloidal and toroidal mode numbers, respectively). In addition, 2,1 tearing modes are most likely to destabilize when n > 1 tearing modes are already present in the core plasma. The covariance matrix of tearing sensitive plasma parameters takes a nearly block-diagonal form, with the blocks incorporating thermodynamic, current and safety factor profile, separatrix shape, and plasma flow parameters, respectively. This suggests a number of paths to improved stability at fixed pressure and edge safety factor primarily by preserving a minimum of 1 kHz differential rotation, increasing the minimum safety factor above unity, using upper single null magnetic configuration, and reducing the core impurity radiation. In addition, lower triangularity, lower elongation, and lower pedestal pressure may also help to improve stability. The electron and ion temperature, collisionality, resistivity, internal inductance, and the parallel current gradient appear to only weakly correlate with the 2,1 tearing mode onsets in this database.
Monthly data on federally administered Supplemental Security Income payments.
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Employment: NF: PW: FA: Lessors of Non Residential Building data was reported at 108.400 Person th in May 2018. This records an increase from the previous number of 106.700 Person th for Apr 2018. Employment: NF: PW: FA: Lessors of Non Residential Building data is updated monthly, averaging 116.600 Person th from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 130.300 Person th in Aug 1997 and a record low of 100.100 Person th in Jan 2013. Employment: NF: PW: FA: Lessors of Non Residential Building data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G030: Current Employment Statistics Survey: Employment: Production Worker: Non Farm.
This dataset collection comprises a set of data tables that are closely connected. These tables are sourced from the 'Tilastokeskus' (Statistics Finland) website based in Finland. All the data tables are structured in a manner that organizes related data in the form of rows and columns, making them easy to read and understand. The dataset is primarily focused on the service interface of the Statistics Finland, providing valuable insights into various statistical parameters. It's important to note that the dataset is updated periodically to reflect the most recent data available. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).
https://data.gov.tw/licensehttps://data.gov.tw/license
Hsinchu City - Statistical Survey of Urban Planning Land Use Development Status
Census Tracts are small, relatively permanent statistical subdivisions of a county or statistically equivalent entity delineated by local participants as part of the U.S. Census Bureau's Participant Statistical Areas Program. The primary purpose of Census Tracts is to provide a stable set of geographic units for the presentation of decennial census data. In 1980 the New Orleans City Planning Commission, for planning and decision-making purposes, divided the city into Census Tract based 'neighborhoods'. Additional neighborhoods were created after the 1990 and 2000 Censuses. Following Hurricane Katrina the Greater New Orleans Community Data Center (GNOCDC) settled on these boundaries to facilitate the use of local data in decision-making. These neighborhoods underwent further change during the 2010 Census due to modifications (consolidation and/or splitting) of Census Tracts, the resulting boundaries were renamed as 'Neighborhood Statistical Areas' to reflect their actual function.
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This is the latest monthly (February 2014) statistical publication in relation to the linked HES (Hospital Episode Statistics) and MHMDS (Mental Health Minimum Data Set) data. The two data sets have been linked using specific patient identifiers collected in HES and MHMDS. The linkage allows the data sets to be linked in this manner from 2006-07; however, this report focuses on patients who were present in the two data sets in the period April 2013 to February 2014 only. The bridging file used for this publication was also released on 13 June 2014; it utilises the latest published Provisional (Monthly) HES data and year-to-date MHMDS data relating to the period April 2013 to February 2014. The HES-MHMDS linkage provides the ability to undertake national (within England) analysis along acute patient pathways for mental health service users' interactions with acute secondary care.
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Graph and download economic data for Unemployed Persons in San Antonio-New Braunfels, TX (MSA) (LAUMT484170000000004) from Jan 1990 to Jul 2025 about San Antonio, household survey, persons, TX, unemployment, and USA.
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United States AHE: sa: PW: PB: Packaging & Labeling Services data was reported at 20.630 USD in Nov 2022. This records an increase from the previous number of 20.430 USD for Oct 2022. United States AHE: sa: PW: PB: Packaging & Labeling Services data is updated monthly, averaging 13.020 USD from Jan 1990 (Median) to Nov 2022, with 395 observations. The data reached an all-time high of 20.630 USD in Nov 2022 and a record low of 7.360 USD in Jan 1990. United States AHE: sa: PW: PB: Packaging & Labeling Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.
Statistical Data on Local Companies Incorporated
The Annual Statistical Supplement, 2022 includes the most comprehensive data available on the Social Security and Supplemental Security Income programs. More than 250 statistical tables convey a wide range of information about those programs from beneficiary counts and benefit amounts to the status of the trust funds. The tables also contain data on Medicare, Medicaid, veterans' benefits, and other related income security programs. The Supplement also includes summaries of the history of the major programs and of current legislative developments and a glossary of terms used in explaining the programs and data.
Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.