Annual descriptive price statistics for each calendar year 2005 – 2024 for 462 electoral wards within 11 Local Government Districts.

The statistics include:

• Minimum sale price

• Lower quartile sale price

• Median sale price

• Simple Mean sale price

• Upper Quartile sale price

• Maximum sale price

• Number of verified sales

Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded:

• Non Arms-Length sales

• sales of properties where the habitable space are less than 30m2 or greater than 1000m2

• sales less than £20,000.

Annual median or simple mean prices should not be used to calculate the property price change over time.
The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

Blockchain data query: Apechain: Quartiles for tx fee and tx number

Explore global rankings in the education and health sectors for government, private companies, non-profit organizations, and universities.

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Notes: The SIR reports are not league tables. The ranking parameter –the scientific output of institutions- should be understood as a default rank, not our ranking proposal. The only goal of this report is to characterize research outcomes of organizations so as to provide useful scientometric information to institutions, policymakers and research manager so they are able to analyze, evaluate and improve their research results. If someone uses this report to rank institutions or to build a league table with any purpose, he/she will do it under his/her own responsibility.Output - Total number of documents published in scholarly journals indexed in Scopus (Romo-Fernández, et al., 2011).International Collaboration - Institution's output ratio produced in collaboration with foreign institutions. The values are computed by analyzing an institution's output whose affiliations include more than one country address (Guerrero-Bote, Olmeda-Gómez and Moya-Anegón, 2013; Lancho-Barrantes, Guerrero-Bote and Moya-Anegón, 2013; Lancho-Barrantes, et al., 2013; Chinchilla-Rodríguez, et al., 2012)Normalized Impact - Normalized Impact is computed using the methodology established by the Karolinska Intitutet in Sweden where it is named "Item oriented field normalized citation score average". The normalization of the citation values is done on an individual article level. The values (in %) show the relationship between an institution's average scientific impact and the world average set to a score of 1, --i.e. a NI score of 0.8 means the institution is cited 20% below world average and 1.3 means the institution is cited 30% above average (Rehn and Kronman, 2008; González-Pereira, Guerrero-Bote and Moya- Anegón, 2011).High Quality Publications - Ratio of publications that an institution publishes in the most influential scholarly journals of the world, those ranked in the first quartile (25%) in their categories as ordered by SCImago Journal Rank (SJRII) indicator (Miguel, Chinchilla-Rodríguez and Moya-Anegón, 2011).Specialization Index - The Specialization Index indicates the extent of thematic concentration /dispersion of an institution’s scientific output. Values range between 0 and 1, indicating generalist vs. specialized institutions respectively. This indicator is computed according to the Gini Index used in Economy (Moed, et. al., 2011; López-Illescas, Moya-Anegón and Moed, 2011; Arencibia-Jorge et al., 2012). In this indicator, when the value is 0 it means that the data are not sufficient to calculate.Excellence Rate - Excellence rate indicates the amount (in %) of an institution’s scientific output that is included into the set of the 10% of the most cited papers in their respective scientific fields. It is a measure of high quality output of research institutions (SCImago Lab, 2011; Bornmann, Moya-Anegón and Leydesdorff, 2012; Guerrero-Bote and Moya-Anegón, 2012).Scientific Leadership - Leadership indicates an institution’s “output as main contributor”, that is the number of papers in which the corresponding author belongs to the institution (Moya-Anegón, 2012; Moya-Anegón et. al, 2013; Moya-Anegón, et al., forthcoming) Excellence with Leadership - Excellence with Leadership indicates the amount of documents in the Excellence rate in which the institution is the main contributor (Moya-Anegón, et al., 2013).

Epidemic characteristics at a given school, for a given epidemic year. Performed as individual logistic regression models with subclinical infection as the outcome variable and each epidemic characteristic as a single exposure variable, and incorporating random effects for the individual and the individual's school of attendance.*. Each exposure variable was aggregated across each school and for each year. The median represents the midpoint of these aggregated values.†A one-unit increase for proportions (incidence, proportion DENV-1 etc) was defined as a one-quartile increase in value. Quartiles were calculated based upon the range of values (e.g., if incidence had a range of 0–40%, the upper limit for calculating quartiles of incidence was 40%, not 100%). A one-unit increase in the number of serotypes in circulation compared 2 serotypes in circulation to 1 serotype in circulation, for example.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

s(P)RR denotes soluble (pro)renin receptor, SGA: small for gestational age, OR: odds ratio, CI: confidence interval*All multivariate models were adjusted for maternal age, conception by in vitro fertilization, drinking during current pregnancy, family history of diabetes mellitus, preexisting hypertension, preexisting asthma, gestational age at delivery, gestational weight gain, hypertensive disorders during pregnancy, gestational diabetes, placental weight and neonatal sex

Annual descriptive price statistics for each calendar year 2005 – 2023 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

Administrative disposable income is a third pillar of the income statistics that Statbel publishes, alongside "\2" and poverty indicators based on "\2", and allows answering other types of questions than SILC and tax statistics.

SILC uses "\2" at the household level as a concept of income, cumulating the incomes of all household members. In the next step, this disposable income is converted into equivalised disposable income to take into account the composition of the household. Based on the SILC, at-risk-of-poverty figures are published up to the provincial level. However, the sample size does not allow for analyses at a more detailed geographical level. However, statistics based on tax revenues are available up to the level of the statistical sector, but are limited to taxable income in the context of personal income tax returns. Non-taxable income is not taken into account and there is also no correction according to the composition of the household.

The variable "administrative equivalised disposable income" responds to a growing demand for income and poverty figures at the communal level. It uses an income concept based on administrative sources that tries to correspond as much as possible to that of SILC. For the population as a whole, both taxable and non-taxable income are taken into account. They are added together for all members of the household in order to obtain an administrative disposable income for the household. After adjusting for the composition of the household, the variable "administrative equivalised disposable income" is established. This can be used to calculate income and poverty figures at the communal level.

Indicators are not disseminated for an entity and a category when there are at least 15% of people whose equivalent administrative disposable income is missing or when there are less than 100 people with a valid income.

More information on the page "\2" of Statbel

Annual descriptive price statistics for each calendar year 2005 – 2024 for 11 Local Government Districts in Northern Ireland. The statistics include: • Minimum sale price • Lower quartile sale price • Median sale price • Simple Mean sale price • Upper Quartile sale price • Maximum sale price • Number of verified sales Prices are available where at least 30 sales were recorded in the area within the calendar year which could be included in the regression model i.e. the following sales are excluded: • Non Arms-Length sales • sales of properties where the habitable space are less than 30m2 or greater than 1000m2 • sales less than £20,000. Annual median or simple mean prices should not be used to calculate the property price change over time. The quality (where quality refers to the combination of all characteristics of a residential property, both physical and locational) of the properties that are sold may differ from one time period to another. For example, sales in one quarter could be disproportionately skewed towards low-quality properties, therefore producing a biased estimate of average price. The median and simple mean prices are not ‘standardised’ and so the varying mix of properties sold in each quarter could give a false impression of the actual change in prices. In order to calculate the pure property price change over time it is necessary to compare like with like, and this can only be achieved if the ‘characteristics-mix’ of properties traded is standardised. To calculate pure property change over time please use the standardised prices in the NI House Price Index Detailed Statistics file.

Abbreviation definitions: FEV1 = forced expiratory volume in 1 second; FEV1%Pred = forced expiratory volume in 1 second percent predicted; FVC = forced vital capacity; FVC %Pred = forced vital capacity percent predicted; CT = computed tomography.Respiratory Related Variables, For All HIV+ Subjects and By Telomere Length Quartiles.

Crude and adjusted means of total CASI scores according to the VSR quartiles.

This dataset presents information from 2016 at the household level; the percentage of households within each Index of Household Advantage and Disadvantage (IHAD) quartile for Local Government Area (LGA) 2017 boundaries.

The IHAD is an experimental analytical index developed by the Australian Bureau of Statistics (ABS) that provides a summary measure of relative socio-economic advantage and disadvantage for households. It utilises information from the 2016 Census of Population and Housing.

IHAD quartiles: All households are ordered from lowest to highest disadvantage, the lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided up into four groups, depending on their score.

This data is ABS data (catalogue number: 4198.0) used with permission from the Australian Bureau of Statistics.

For more information please visit the Australian Bureau of Statistics.

Please note:

• AURIN has generated this dataset through aggregating the original SA1 level data (with calculated number of households/quartile) to LGA level.

• Aggregation was achieved through calculating the centroid for each SA1 and assigning it to the LGA it fell within.

• The number of occupied private dwellings, and number of households in each of the IHAD quartiles were calculated for each LGA by aggregating the peviously assigned SA1 values of each of those specified columns from the SA1 dataset. Percentages of households in each of the IHAD quartiles were calculated for each LGA from these aggregated totals.

• A household is defined as one or more persons, at least one of whom is at least 15 years of age, usually resident in the same private dwelling. All occupants of a dwelling form a household. For Census purposes, the total number of households is equal to the total number of occupied private dwellings (Census of Population and Housing: Census Dictionary, 2016 cat. no. 2901.0).

• IHAD output has been confidentialised to meet ABS requirements. In line with standard ABS procedures to minimise the risk of identifying individuals, a technique has been applied to randomly adjust cell values of the output tables. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

This instructional activity introduces students to the application of statistical tools for analyzing biological data, with a focus on measures of center (mean, median, mode) and measures of spread (range, quartiles, standard deviation). Using real-world biological contexts. students learn how to summarize datasets, identify trends, and evaluate variability. The activity integrates the use of MS Excel and TI-84 Plus graphing calculators to calculate descriptive statistics and interpret results. By engaging with authentic biological data, students develop quantitative reasoning skills that enhance their ability to detect patterns, recognize variability, and draw meaningful conclusions about biological systems

This paper examines whether tobacco expenditure leads to the crowding out or crowding in of different expenditure items in South Africa. We apply genetic matching to expenditure quartiles of the 2010/2011 South African Income and Expenditure Survey. Genetic matching is a more appealing approach for dealing with the endogeneity of tobacco expenditure that often plagues studies using systems of demand equations. Further, genetic matching provides transparent measures of covariate balance giving the analyst objective means of assessing match success. We find that the poorest tobacco consuming households in South Africa consistently allocate smaller budget shares towards food items than non-smoking households. Specifically, we find that dairy, fruits, nuts and oils are displaced in favour of tobacco expenditure in the two poorest quartiles. Unsurprisingly, food items are never displaced for households in the top two quartiles, given these households’ greater access to resources. Like other studies in the literature, we find that tobacco expenditure consistently crowds-in alcohol across all quartiles confirming the strong complementarities between the two.

The Index of Household Advantage and Disadvantage (IHAD) provides a summary measure of relative socio-economic advantage and disadvantage for households, based on the characteristics of dwellings and the people living within them, using 2021 Census data.

All in-scope households are ordered from lowest to highest score. A low score indicates relatively greater disadvantage and a lack of advantage in general. A high score indicates a relative lack of disadvantage and greater advantage in general.

This dataset presents IHAD data in quartiles. The lowest 25% of households are given a quartile number of 1, the next lowest 25% of households are given a quartile number of 2 and so on, up to the highest 25% of households which are given a quartile number of 4. This means that households are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of households as it depends on the distribution of the IHAD scores. The data is grouped by Statistical Area Level 2 (SA2 2021). SA2s are defined by the Australian Statistical Geography Standard (ASGS) Edition 3.

Key Attributes:

      Field alias
      Field name
      Description


      Statistical Areas Level 2 2021 code
      SA2_CODE_2021
      2021 Statistical Areas Level 2 (SA2) codes from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


        Statistical Areas Level 2 2021 name
      SA2_NAME_2021
      2021 Statistical Areas Level 2 (SA2) names from the Australian Statistical Geography Standard (ASGS), Edition 3. SA2s are medium-sized general purpose areas built to represent communities that interact together socially and economically. 


      Area in square kilometres
      AREA_ALBERS_SQKM
      The area of a region in square kilometres, based on the Albers equal area conic projection.


      Uniform Resource Identifier
      ASGS_LOCI_URI_2021
      A uniform resource identifier can be used in web linked applications for data integration. 


      IHAD quartile 1
      IHAD_QUARTILE1
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 1, indicating relatively greater disadvantage and a lack of advantage in general.


      IHAD quartile 2
      IHAD_QUARTILE2
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 2.


      IHAD quartile 3
      IHAD_QUARTILE3
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 3.


      IHAD quartile 4
      IHAD_QUARTILE4
      Proportion of in-scope dwellings in the SA2 that fall into IHAD quartile 4, indicating a relative lack of disadvantage and greater advantage in general.


      Occupied private dwellings
      OPD_2021
      Dwellings in-scope of the IHAD i.e. classifiable occupied private dwellings.


      SEIFA IRSAD quartile
      IRSAD_QUARTILE
      Index of Relative Socio-economic Advantage and Disadvantage quartile. All SA2s are ordered from lowest to highest score, the lowest 25% of SA2s are given a quartile number of 1, the next lowest 25% of SA2s are given a quartile number of 2 and so on, up to the highest 25% of SA2s which are given a quartile number of 4. This means that SA2s are divided into four equal sized groups, depending on their score. In practice these groups won’t each be exactly 25% of SA2s as it depends on the distribution of SEIFA scores.


      Usual resident population
      URP_2021
      Population counts in this column are based on place of usual residence as reported on Census Night. These include persons out of scope of the IHAD.


      Dwellings
      DWELLING
      Total dwellings at Census time, including dwellings out of scope of the IHAD e.g. unoccupied private dwellings.

Please note: Proportional totals may equal more than 100% due to rounding and random adjustments made to the data. When calculating proportions, percentages, or ratios from cross-classified or small area tables, the random error introduced can be ignored except when very small cells are involved, in which case the impact on percentages and ratios can be significant. Refer to the Introduced random error / perturbation Census page on the ABS website for more information.

Data and geography references

Source data publication: Index of Household Advantage and Disadvantage Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Index of Household Advantage and Disadvantage methodology, 2021 Source: Australian Bureau of Statistics (ABS)

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Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Baseline characteristics of participants according to the quartile groups of fasting blood glucose levels (N = 19,050).