Nominal Median and Nominal Mean Income Measures by National Income Definition, Year and Statistic

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The food dollar series measures annual expenditures by U.S. consumers on domestically produced food. This data series is composed of three primary series—the marketing bill series, the industry group series, and the primary factor series—that shed light on different aspects of the food supply chain. The three series show three different ways to split up the same food dollar. Nominal DataThe FoodDollarDataNominal.xls file and the NominalData.csv file include statistics reported in current year dollars. In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2015UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)Real Data The FoodDollarDataReal.xls file and the FoodDollarDataReal.csv file include statistics reported in constant year 2009 dollars. Since the March 30, 2016 update, 2006 data in cents per domestic real food dollar units have been added to the real food dollar series.In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2014UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). Integrated Global Radiosonde Archive is a digital data set archived at the former National Climatic Data Center (NCDC), now National Centers for Environmental Information (NCEI). This dataset contains monthly means of geopotential height, temperature, zonal wind, and meridional wind derived from the Integrated Global Radiosonde Archive (IGRA). IGRA consists of radiosonde and pilot balloon observations at over 1500 globally distributed stations, and monthly means are available for the surface and mandatory levels at many of these stations. The period of record varies from station to station, with many extending from 1970 to 2016. Monthly means are computed separately for the nominal times of 0000 and 1200 UTC, considering data within two hours of each nominal time. A mean is provided, along with the number of values used to calculate it, whenever there are at least 10 values for a particular station, month, nominal time, and level.

SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format.The data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.This product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).

Data Set Overview The Mars Express (MEX) Planetary Fourier Spectrometer (PFS) Data Archive is a collection of raw data collected during the MEX Mission to Mars. For more information on the investigations proposed see the PFS documentations in the DOCUMENT/ folder. This data set was collected during the MEX Mission phases: First Extension Mission Phase Mission Phase Definition It should be noted that the Mars Express (MEX) Planetary Fourier Spectrometer (PFS) group uses mission phases which deviate from the ones defined in the MISSION.CAT files given by ESA in order to keep the keywords and abbreviations consistent for Mars Express, Venus Express and Rosetta. Those mission phase abbreviations are also used in the data description field of the dataset_id. MaRS mission name | abbreviation | time span Near Earth Verification | NEV | 20030602 20030731 Interplanetary Cruise | IC | 20030801 20031225 Nominal Mission | Nominal | 20031226 20051130 First Extension Mission | EXT1 | 20060101 20070930 Second Extension Mission| EXT2 | 20071001 20091231 Data files Data files are: The tracking files from Deep Space Network (DSN) and from the Intermediate Frequency Modulation System (IFMS) used by the ESA ground station New Norcia. Level 1b data are archived. The Geometry files All Level binary data files will have the file name extension eee .DAT Data levels It should be noted that these data levels which are also used in the file names and data directories are PSA dat truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]

SAMSN7L3ZMTG is the Nimbus-7 Stratospheric and Mesospheric Sounder (SAMS) Level 3 Zonal Means Composition Data Product. The Earth's surface is divided into 2.5-deg latitudinal zones that extend from 50 deg South to 67.5 deg North. Retrieved mixing ratios of nitrous oxide (N2O) and methane (CH4) are averaged over day and night, along with errors, at 31 pressure levels between 50 and 0.125 mbar. Because the N2O and CH4 channels cannot function simultaneously, only one type of measurement is made for any nominal day. The data were recovered from the original magnetic tapes, and are now stored online as one file in its original proprietary binary format.The data for this product are available from 1 January 1979 through 30 December 1981. The principal investigators for the SAMS experiment were Prof. John T. Houghton and Dr. Fredric W. Taylor from Oxford University.This product was previously available from the NSSDC with the identifier ESAD-00180 (old ID 78-098A-02C).

The North American Roads dataset was compiled on October 27, 2020 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). On March 31, 2025, the errant records with a value of 2 in the "NHS" field were corrected to have a value of 7 (Other NHS). This dataset contains geospatial information regarding major roadways in North America. The data set covers the 48 contiguous United States plus the District of Columbia, Alaska, Hawaii, Canada and Mexico. The nominal scale of the data set is 1:100,000. The data within the North American Roads layer is a compilation of data from Natural Resources Canada, USDOT’s Federal Highway Administration, and the Mexican Transportation Institute. North American Roads is a digital single-line representation of major roads and highways for Canada, the United States, and Mexico with consistent definitions by road class, jurisdiction, lane counts, speed limits and surface type. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529071

Sources:

German Central Bank (ed.), 1975: Deutsches Geld- und Bankwesen in Zahlen 1876 – 1975. (German monetary system and banking system in numbers 1876 – 1975) German Central Bank (ed.), different years: monthly reports of the German Central Bank, statistical part, interest rates German Central Bank (ed.), different years: Supplementary statistical booklets for the monthly reports of the German Central Bank 1959 – 1992, security statistics Reich Statistical Office (ed.), different years: Statistical yearbook of the German empire Statistical Office (ed.), 1985: Geld und Kredit. Index der Aktienkurse (Money and Credit. Index of share prices) – Lange Reihe; Fachserie 9, Reihe 2. Statistical Office (ed.), 1987: Entwicklung der Nahrungsmittelpreise von 1800 – 1880 in Deutschland. (Development of food prices in Germany 1800 – 1880) Statistical Office (ed.), 1987: Entwicklung der Verbraucherpreise (Development of consumer prices) seit 1881 in Deutschland. (Development of consumer prices since 1881 in Germany) Statistical Office (ed.), different years: Fachserie 17, Reihe 7, Preisindex für die Lebenshaltung (price index for costs of living) Donner, 1934: Kursbildung am Aktienmarkt; Grundlagen zur Konjunkturbeobachtung an den Effektenmärkten. (Prices on the stock market; groundwork for observation of economic cycles on the stock market) Homburger, 1905: Die Entwicklung des Zinsfusses in Deutschland von 1870 – 1903. (Development of the interest flow in Germany, 1870 – 1903) Voye, 1902: Über die Höhe der verschiedenen Zinsarten und ihre wechselseitige Abhängigkeit.(On the values of different types of interests and their interdependence).

Model degrees of freedom (df) is a fundamental concept in statistics because it quantifies the flexibility of a fitting procedure and is indispensable in model selection. To investigate the gap between df and the number of independent variables in the fitting procedure, Tibshirani introduced the search degrees of freedom (sdf) concept to account for the search cost during model selection. However, this definition has two limitations: it does not consider fitting procedures in augmented spaces and does not use the same fitting procedure for sdf and df. We propose a modified search degrees of freedom (msdf) to directly account for the cost of searching in either original or augmented spaces. We check this definition for various fitting procedures, including classical linear regressions, spline methods, adaptive regressions (the best subset and the lasso), regression trees, and multivariate adaptive regression splines (MARS). In many scenarios when sdf is applicable, msdf reduces to sdf. However, for certain procedures like the lasso, msdf offers a fresh perspective on search costs. For some complex procedures like MARS, the df has been pre-determined during model fitting, but the df of the final fitted procedure might differ from the pre-determined one. To investigate this discrepancy, we introduce the concepts of nominal df and actual df, and define the property of self-consistency, which occurs when there is no gap between these two df’s. We propose a correction procedure for MARS to align these two df’s, demonstrating improved fitting performance through extensive simulations and two real data applications. Supplementary materials for this article are available online.

Refer to the current geographies boundaries table for a list of all current geographies and recent updates.

This dataset is the definitive version of the annually released statistical area 3 (SA3) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 929 SA3s, including 4 non-digitised SA3s.

The SA3 geography aims to meet three purposes:

1. approximate suburbs in major, large, and medium urban areas,

2. in predominantly rural areas, provide geographical areas that are larger in area and population size than SA2s but smaller than territorial authorities,

3. minimise data suppression.

SA3s in major, large, and medium urban areas were created by combining SA2s to approximate suburbs as delineated in the Fire and Emergency NZ (FENZ) Localities dataset. Some of the resulting SA3s have very large populations.

Outside of major, large, and medium urban areas, SA3s generally have populations of 5,000–10,000. These SA3s may represent either a single small urban area, a combination of small urban areas and their surrounding rural SA2s, or a combination of rural SA2s.

Zero or nominal population SA3s

To minimise the amount of unsuppressed data that can be provided in multivariate statistical tables, SA2s with fewer than 1,000 residents are combined with other SA2s wherever possible to reach the 1,000 SA3 population target. However, there are still a number of SA3s with zero or nominal populations.

Small population SA2s designed to maintain alignment between territorial authority and regional council geographies are merged with other SA2s to reach the 5,000–10,000 SA3 population target. These merges mean that some SA3s do not align with regional council boundaries but are aligned to territorial authority.

Small population island SA2s are included in their adjacent land-based SA3.

Island SA2s outside territorial authority or region are the same in the SA3 geography.

Inland water SA2s are aggregated and named by territorial authority, as in the urban rural classification.

Inlet SA2s are aggregated and named by territorial authority or regional council where the water area is outside the territorial authority.

Oceanic SA2s translate directly to SA3s as they are already aggregated to regional council.

The 16 non-digitised SA2s are aggregated to the following 4 non-digitised SA3s (SA3 code; SA3 name):

70001; Oceanic outside region, 70002; Oceanic oil rigs, 70003; Islands outside region, 70004; Ross Dependency outside region.

SA3 numbering and naming

Each SA3 is a single geographic entity with a name and a numeric code. The name refers to a suburb, recognised place name, or portion of a territorial authority. In some instances where place names are the same or very similar, the SA3s are differentiated by their territorial authority, for example, Hillcrest (Hamilton City) and Hillcrest (Rotorua District).

SA3 codes have five digits. North Island SA3 codes start with a 5, South Island SA3 codes start with a 6 and non-digitised SA3 codes start with a 7. They are numbered approximately north to south within their respective territorial authorities. When first created in 2025, the last digit of each code was 0. When SA3 boundaries change in future, only the last digit of the code will change to ensure the north-south pattern is maintained.

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High-definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007

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Further information

To download geographic classifications in table formats such as CSV please use Ariā

For more information please refer to the Statistical standard for geographic areas 2023.

Contact: geography@stats.govt.nz

Abstract

The Establishment Census 2011 was conducted as a joint undertaking by the Ministry of Public Service and Labour (MIFOTRA), Ministry of Trade and Industry (MINICOM), Private Sector Federation (PSF) and the National Institute of Statistics of Rwanda (NISR). The Census provides a comprehensive picture of Establishments in Rwanda, both formal and informal, for the first time. It will allow Government, private sector associations, researchers and others to base economic planning, policy design, analysis and beyond upon robust information leading to more effective results and findings. An establishment is defined as an enterprise or (part of) with a constant site that performs one or more economic activities under one administration. The holder of the establishment could be a natural or nominal person, or governmental body.

The 2011 Establishment Census is designed to achieve the following objectives: a) To produce a comprehensive and updated data profile of all economic activities practiced by establishments operating in Rwanda; b) To provide detailed tabulations for the establishments' characteristics, e.g. geographical location, number of employees, registration status, legal status, ownership, sector, manager/owner gender; c) To produce data necessary to classify establishments according to size into Micro, small, middle, large and very large; d) To lay out the data foundation needed to identify formal and informal economic sectors in Rwanda; e) To help establishing a Business Register that can be utilized in carrying out future economic sample surveys and creating comprehensive data base and Geographic Information System (GIS) of the business community in Rwanda.

Geographic coverage

National coverage

Analysis unit

Establishment

Universe

All Rwandan establishments from the nationally sampled area. An establishment is defined as an enterprise or (part of) with a constant site that performs one or more economic activities under one administration. The holder of the establishment could be a natural or nominal person, or governmental body. The definition of an establishment used in the 2011 Census does not include: a) Street Vendors b) Taxis and Motor drivers

Kind of data

Census/enumeration data [cen]

Sampling procedure

With the aim of avoiding omissions and/or duplications the enumerators followed a rigorous approach in enumerating all establishments in a village. A thorough and systematic canvassing of the whole village was performed by the enumerator before completing the Census questionnaires.

Step 1: In the first working day, the enumerator started with identification of the village boundaries and illustration of a sketch map showing these boundaries. This indicated whether one or two banks of a boundary are included in the village.

Step 2 (Boundaries): Boundaries are then allocated a number, with the first being selected in such a way that the whole village is located on the right hand side (B1 on the Illustrative Diagram of village canvassing). Whilst walking along this boundary, the enumerator lists the establishments along the right bank by entering their serial numbers on the sketch map and on the wall right to the entrance as well as in the Establishment Listing Form of serial numbers, establishment names and establishment addresses. If both banks of the boundary lied in the village, the enumerator returned back on the boundary to count the establishments existing on the other bank of the boundary.

Step 3 (Roads): Once establishments along the boundary are listed, the enumerator enters the first road inside the village from the boundary, counting all establishments on the right bank of this boundary followed by the establishments on the left bank (R1 on the Illustrative Diagram). After counting, listing and locating on the sketch map each of the establishments on the road (R1), the enumerator enters the first branch on the right hand side (R2) following the same process, and then carries the same out for all other branches. When all roads and branches associated with the boundary (B1) are finished, the enumerator continues the process from the next boundary (B2). However attention is paid to the possibility that some of the establishments may have already been counted (for example R3 has already been counted as an associated branch of R1). In the case of a market place that include several establishments, the enumerator need not to locate on the sketch map each and every establishment present in the market, instead writing a range of serial numbers and filling in the listing form for each establishment.

Sampling deviation

In total, the Establishment Census 2011 enumerated 127,662 establishments. Despite 127,662 establishments being recorded, the majority of the results presented within this report focus on a slightly reduced sample of 123,526 operating establishments (most of the others were permanently closed).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed according to the objectives specified in Chapter 1 in English, and then translated into Kinyarwanda. In order to minimise potential problems arising in the field, several tests were performed. Feedback was provided by trainees in the central training centre in Kigali, which was then followed by a formal pre-test (see below). After this, revisions were incorporated into the survey with additional feedback being given by trainees at the local training centres around the country. The final version of the questionnaire was developed in Kinyarwanda and translated back into English

Cleaning operations

Data editing was continuously performed during and after the data entry phase in order to detect out-of-range and/or inconsistent data values. Appropriate actions were taken to introduce necessary corrections or deal with incorrect data. In many cases follow up contacts with the establishments were made in order to verify previously reported data. Upon producing the clean data file, statistical tabulations have been generated and are subsequent chapters present these census tabulations.