We show that the expected value of the largest order statistic in Gaussian samples can be accurately approximated as (0.2069 ln (ln (n))+0.942)4, where n∈[2,108] is the sample size, while the standard deviation of the largest order statistic can be approximated as −0.4205arctan(0.5556[ln(ln (n))−0.9148])+0.5675. We also provide an approximation of the probability density function of the largest order statistic which in turn can be used to approximate its higher order moments. The proposed approximations are computationally efficient, and improve previous approximations of the mean and standard deviation given by Chen and Tyler (1999).

About this dataset

Context

The dataset presents median household incomes for various household sizes in State Line City, IN, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, State Line City did not include 4, 5, 6, or 7-person households. Across the different household sizes in State Line City the mean income is $64,968, and the standard deviation is $33,244. The coefficient of variation (CV) is 51.17%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $36,144. It then further increased to $101,336 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/state-line-city-in-median-household-income-by-household-size.jpeg" alt="State Line City, IN median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for State Line City median household income. You can refer the same here

Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval.

Increases in the production rate of cosmogenic radionuclides associated with geomagnetic excursions have been used as global tie-points for correlation between records of past climate from marine and terrestrial archives. We have investigated the relative timing of variations in 10Be production rate and the corresponding palaeomagnetic signal during one of the largest Pleistocene excursions, the Iceland Basin (IB) event (ca. 190 kyr), as recorded in two marine sediment cores (ODP Sites 1063 and 983) with high sedimentation rates. Variations in 10Be production rate during the excursion were estimated by use of 230Thxs normalized 10Be deposition rates and authigenic 10Be/9Be. Resulting 10Be production rates are compared with high-resolution records of geomagnetic field behaviour acquired from the same discrete samples. We find no evidence for a significant lock-in depth of the palaeomagnetic signal in these high sedimentation-rate cores. Apparent lock-in depths in other cores may sometimes be the result of lower sample resolution. Our results also indicate that the period of increased 10Be production during the IB excursion lasted longer and, most likely, started earlier than the corresponding palaeomagnetic anomaly, in accordance with previous observations that polarity transitions occur after periods of reduced geomagnetic field intensity prior to the transition. The lack of evidence in this study for a significant palaeomagnetic lock-in depth suggests that there is no systematic offset between the 10Be signal and palaeomagnetic anomalies associated with excursions and reversals, with significance for the global correlation of climate records from different archives.

Using the simple anomaly method (modifying a historical baseline with differences or ratios projected by General Circulation Models), scientists from the California Academy of Sciences downscaled monthly average temperature and monthly total precipitation from 16 different global circulation models (GCMs). The GCMs were described in the latest Intergovernmental Panel for Climate Change (IPCC 2007) and archived at the WCRP PCMDI (http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php). Monthly maximum temperature and monthly minimum temperatures were downscaled from the only 6 GCMs that archived these particular variables. Scientists used Worldclim v.1.4 (Hijmans et al 2005) at 5 arc-minute (~10km) spatial grain as the current climate baseline averaged over the period 1950-2000. They assessed future change simulated by each GCM by calculating the difference (or ratio) between historical and future conditions projected by the climate models thus creating anomalies. Each monthly climate variable was averaged for 20 years of GCM simulation, supporting time series analyses from 2000 through 2099. Seasonal climate variables were generated by averaging values for three months (ex. winter conditions correspond to the average climate for December, January and February). Two sets of future climate projections are available, corresponding to either the regional economic A2 or the global environmental and equitable B1 greenhouse gas emissions scenarios. Every spatial climate data layer has a corresponding layer representing the standard deviation across time and GCMs, which gives an estimate of the variability of the climate during the 20 years and across the various GCMs (16 or 6) used to calculate the average. The final dataset includes 128 spatial climate layers, and their corresponding standard deviations (another 128 files) at a 10km spatial grain for the terrestrial fraction of the globe.

Using the simple anomaly method (modifying a historical baseline with differences or ratios projected by General Circulation Models), scientists from the California Academy of Sciences downscaled monthly average temperature and monthly total precipitation from 16 different global circulation models (GCMs). The GCMs were described in the latest Intergovernmental Panel for Climate Change (IPCC 2007) and archived at the WCRP PCMDI (http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php). Monthly maximum temperature and monthly minimum temperatures were downscaled from the only 6 GCMs that archived these particular variables. Scientists used Worldclim v.1.4 (Hijmans et al 2005) at 5 arc-minute (~10km) spatial grain as the current climate baseline averaged over the period 1950-2000. They assessed future change simulated by each GCM by calculating the difference (or ratio) between historical and future conditions projected by the climate models thus creating anomalies. Each monthly climate variable was averaged for 20 years of GCM simulation, supporting time series analyses from 2000 through 2099. Seasonal climate variables were generated by averaging values for three months (ex. winter conditions correspond to the average climate for December, January and February). Two sets of future climate projections are available, corresponding to either the regional economic A2 or the global environmental and equitable B1 greenhouse gas emissions scenarios. Every spatial climate data layer has a corresponding layer representing the standard deviation across time and GCMs, which gives an estimate of the variability of the climate during the 20 years and across the various GCMs (16 or 6) used to calculate the average. The final dataset includes 128 spatial climate layers, and their corresponding standard deviations (another 128 files) at a 10km spatial grain for the terrestrial fraction of the globe.

This table provides 2021 data on the mean and estimated standard deviation of health-related quality of life as measured by the EVA rate in the population aged 16 and over. The information is disaggregated territorially at the level of large regions of the Canary Islands.

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the standard deviation of maximum daily wave power (kW/m) from 2000-2013. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The standard deviation of the long-term mean wave power was calculated by taking the standard deviation of the maximum daily time series of wave power data from 2000-2013 for each 500-m grid cell.

Using the simple anomaly method (modifying a historical baseline with differences or ratios projected by General Circulation Models), scientists from the California Academy of Sciences downscaled monthly average temperature and monthly total precipitation from 16 different global circulation models (GCMs). The GCMs were described in the latest Intergovernmental Panel for Climate Change (IPCC 2007) and archived at the WCRP PCMDI (http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php). Monthly maximum temperature and monthly minimum temperatures were downscaled from the only 6 GCMs that archived these particular variables. Scientists used Worldclim v.1.4 (Hijmans et al 2005) at 5 arc-minute (~10km) spatial grain as the current climate baseline averaged over the period 1950-2000. They assessed future change simulated by each GCM by calculating the difference (or ratio) between historical and future conditions projected by the climate models thus creating anomalies. Each monthly climate variable was averaged for 20 years of GCM simulation, supporting time series analyses from 2000 through 2099. Seasonal climate variables were generated by averaging values for three months (ex. winter conditions correspond to the average climate for December, January and February). Two sets of future climate projections are available, corresponding to either the regional economic A2 or the global environmental and equitable B1 greenhouse gas emissions scenarios. Every spatial climate data layer has a corresponding layer representing the standard deviation across time and GCMs, which gives an estimate of the variability of the climate during the 20 years and across the various GCMs (16 or 6) used to calculate the average. The final dataset includes 128 spatial climate layers, and their corresponding standard deviations (another 128 files) at a 10km spatial grain for the terrestrial fraction of the globe.

About this dataset

Context

The dataset presents median household incomes for various household sizes in South Range, MI, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, South Range did not include 4, 5, 6, or 7-person households. Across the different household sizes in South Range the mean income is $51,844, and the standard deviation is $18,238. The coefficient of variation (CV) is 35.18%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,226. It then further increased to $65,869 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/south-range-mi-median-household-income-by-household-size.jpeg" alt="South Range, MI median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income. You can refer the same here

This table provides 2021 data on the mean and estimated standard deviation of perceived functional social support in the population aged 16 and over. The information is disaggregated territorially at the level of large regions of the Canary Islands.

Notes.*Response categories: (1) excellent, (2) good, (3) fair, and (4) poor.$ Response categories: (1) yes, without alterations, (2) yes, after minor alterations, (3) yes, after major alterations, (4) no.

This table provides 2021 data on the mean and estimated standard deviation of health-related quality of life in the population aged 8 and 15 years. The information is disaggregated territorially at the level of large regions of the Canary Islands.

No description is available. Visit https://dataone.org/datasets/744ff537687258f5f815b64c1891afd0 for complete metadata about this dataset.

Standard deviation of responses for 'Happy Yesterday' in the First ONS Annual Experimental Subjective Wellbeing survey.

The Office for National Statistics has included the four subjective well-being questions below on the Annual Population Survey (APS), the largest of their household surveys.

• Overall, how satisfied are you with your life nowadays?
• Overall, to what extent do you feel the things you do in your life are worthwhile?
• Overall, how happy did you feel yesterday?
• Overall, how anxious did you feel yesterday?

This dataset presents results from the third of these questions, "Overall, how happy did you feel yesterday?". Respondents answer these questions on an 11 point scale from 0 to 10 where 0 is ‘not at all’ and 10 is ‘completely’. The well-being questions were asked of adults aged 16 and older.

Well-being estimates for each unitary authority or county are derived using data from those respondents who live in that place. Responses are weighted to the estimated population of adults (aged 16 and older) as at end of September 2011.

The data cabinet also makes available the proportion of people in each county and unitary authority that answer with ‘low wellbeing’ values. For the ‘happy yesterday’ question answers in the range 0-6 are taken to be low wellbeing.

This dataset contains the standard deviation of the responses, alongside the corresponding sample size.

The ONS survey covers the whole of the UK, but this dataset only includes results for counties and unitary authorities in England, for consistency with other statistics available at this website.

At this stage the estimates are considered ‘experimental statistics’, published at an early stage to involve users in their development and to allow feedback. Feedback can be provided to the ONS via this email address.

The APS is a continuous household survey administered by the Office for National Statistics. It covers the UK, with the chief aim of providing between-census estimates of key social and labour market variables at a local area level. Apart from employment and unemployment, the topics covered in the survey include housing, ethnicity, religion, health and education. When a household is surveyed all adults (aged 16+) are asked the four subjective well-being questions.

The 12 month Subjective Well-being APS dataset is a sub-set of the general APS as the well-being questions are only asked of persons aged 16 and above, who gave a personal interview and proxy answers are not accepted. This reduces the size of the achieved sample to approximately 120,000 adult respondents in England.

The original data is available from the ONS website.

Detailed information on the APS and the Subjective Wellbeing dataset is available here.

As well as collecting data on well-being, the Office for National Statistics has published widely on the topic of wellbeing. Papers and further information can be found here.

Trace element concentrations of altered basaltic glass shards (layer silicates) and zeolites in volcaniclastic sediments drilled in the volcanic apron northeast of Gran Canaria during Ocean Drilling Program (ODP) leg 157 document variable element mobilities during low-temperature alteration processes in a marine environment. Clay minerals (saponite, montmorillonite, smectite) replacing volcanic glass particles are enriched in transition metals and rare earth elements (REE). The degree of retention of REE within the alteration products of the basaltic glass is correlated with the field strength of the cations. The high field-strength elements are preferentially retained or enriched in the alteration products by sorption through clay minerals. Most trace elements are enriched in a boundary layer close to the interface mineral-altered glass. This boundary layer has a key function for the physico-chemical conditions of the subsequent alteration process by providing a large reactive surface and by lowering the fluid permeability. The release of most elements is buffered by incorporation into secondary precipitates (sodium-rich zeolites, phillipsite, Fe- and Mn-oxides) as shown by calculated distribution coefficients between altered glasses and authigenic minerals. Chemical fluxes change from an open to a closed system behavior during prograde low-temperature alteration of volcaniclastic sediments with no significant trace metal flux from the sediment to the water column.

Bivalve shells can provide excellent archives of past environmental change but have not been used to interpret ocean acidification events. We investigated carbon, oxygen and trace element records from different shell layers in the mussels Mytilus galloprovincialis combined with detailed investigations of the shell ultrastructure. Mussels from the harbour of Ischia (Mediterranean, Italy) were transplanted and grown in water with mean pHT 7.3 and mean pHT 8.1 near CO2 vents on the east coast of the island. Most prominently, the shells recorded the shock of transplantation, both in their shell ultrastructure, textural and geochemical record. Shell calcite, precipitated subsequently under acidified seawater responded to the pH gradient by an in part disturbed ultrastructure. Geochemical data from all test sites show a strong metabolic effect that exceeds the influence of the low-pH environment. These field experiments showed that care is needed when interpreting potential ocean acidification signals because various parameters affect shell chemistry and ultrastructure. Besides metabolic processes, seawater pH, factors such as salinity, water temperature, food availability and population density all affect the biogenic carbonate shell archive.

About this dataset

Context

The dataset presents median household incomes for various household sizes in United States, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in United States. Across the different household sizes in United States the mean income is $94,149, and the standard deviation is $26,829. The coefficient of variation (CV) is 28.50%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $38,463. It then further increased to $114,329 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/united-states-median-household-income-by-household-size.jpeg" alt="United States median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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.

Inspiration

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/.

Recommended for further research

This dataset is a part of the main dataset for United States median household income. You can refer the same here

Sediment depth is given in mbsf. All 14C errors are reported at 1 sigma. Reservoir age uncertainty is estimated to be ±100 years. The reported uncertainty (anal. uncert.) and reservoir age uncertainty (res. corr. uncert.) were added in quadrature to obtain a total 14C uncertainty for each date (total uncert.). The 1 sigma total 14C uncertainty has been added and subtracted from the reservoir-corrected 14C ages to provide 'Cariaco+' and 'Cariaco-', respectively. To match a sample 14C date, first add and subtract 1 sigma uncertainties (including reservoir age uncertainty, if applicable) from the sample 14C age, providing 'Sample+' and 'Sample-', respectively. The limits of the 14C age match are given by the shallowest depth at which 'Cariaco+' is greater than 'Sample-', and the deepest depth at which 'Sample+' is greater than 'Cariaco-'. The depths can then be translated to the sediment reflectance record for precise palaeoclimatic context.

Our record of Younger Dryas intermediate-depth seawater D14C from North Atlantic deep-sea corals supports a link between abrupt climate change and intermediate ocean variability. Our data show that northern source intermediate water (~1700 m) was partially replaced by 14C-depleted southern source water at the onset of the event, consistent with a reduction in the rate of North Atlantic Deep Water formation. This transition requires the existence of large, mobile gradients of D14C in the ocean during the Younger Dryas. The D14C water column profile from Keigwin (2004) provides direct evidence for the presence of one such gradient at the beginning of the Younger Dryas (~12.9 ka), with a 100 per mil offset between shallow (<~2400 m) and deep water. Our early Younger Dryas data are consistent with this profile and also show a D14C inversion, with 35 per mil more enriched water at ~2400 m than at ~1700 m. This feature is probably the result of mixing between relatively well 14C ventilated northern source water and more poorly 14C ventilated southern source intermediate water, which is slightly shallower. Over the rest of the Younger Dryas our intermediate water/deepwater coral D14C data gradually increase, while the atmosphere D14C drops. For a very brief interval at ~12.0 ka and at the end of the Younger Dryas (11.5 ka), intermediate water D14C (~1200 m) approached atmospheric D14C. These enriched D14C results suggest an enhanced initial D14C content of the water and demonstrate the presence of large lateral D14C gradients in the intermediate/deep ocean in addition to the sharp vertical shift at ~2500 m. The transient D14C enrichment at ~12.0 ka occurred in the middle of the Younger Dryas and demonstrates that there is at least one time when the intermediate/deep ocean underwent dramatic change but with much smaller effects in other paleoclimatic records.